From c3ceff8a33a0ba6c6863d95eab8e1385817cc823 Mon Sep 17 00:00:00 2001 From: NIKIEMA Jean Date: Fri, 8 Dec 2023 15:30:16 +0100 Subject: [PATCH 01/19] first draft of microgrid simulator with h2 and Battery. --- examples/Microgrid_Wind-Solar-H2.ipynb | 1658 +++++++++++++++++ examples/Microgrid_Wind-Solar.ipynb | 315 +++- examples/Microgrid_Wind-Solar_data.jl | 59 +- .../Microgrid_sizingH2_optimization1.ipynb | 1122 +++++++++++ examples/Microgrid_sizing_optimization.ipynb | 100 +- examples/images/microgrid_sizing - h2.png | Bin 0 -> 177607 bytes src/Microgrids.jl | 8 +- src/components.jl | 93 +- src/dispatch.jl | 88 + src/economics.jl | 52 +- src/operation.jl | 189 +- 11 files changed, 3511 insertions(+), 173 deletions(-) create mode 100644 examples/Microgrid_Wind-Solar-H2.ipynb create mode 100644 examples/Microgrid_sizingH2_optimization1.ipynb create mode 100644 examples/images/microgrid_sizing - h2.png diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb new file mode 100644 index 0000000..dc48e23 --- /dev/null +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -0,0 +1,1658 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6edd7377-ce3a-468e-82f0-d3a1f5292a1d", + "metadata": {}, + "source": [ + "# Microgrid performance simulation\n", + "\n", + "Case of Microgrid project with *wind* and *solar* sources,\n", + "plus a *battery* and a *dispatchable generator*:\n", + "\n", + "schematic of a microgrid with wind and H2\n", + "\n", + "Demo of main data structures and functions of [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl).\n", + "Main steps are:\n", + "\n", + "1. Describe the Microgrid project and components (including loading time series data)\n", + "2. Simulation the Microgrid\n", + "3. Analyze simulation results (technical and economic)\n", + "\n", + "After this demonstration, you can go to the next step: [Microgrid sizing optimization](Microgrid_sizing_optimization.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "855af8dd-d21d-4587-84cb-ae2b932deeaa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "using Microgrids\n", + "using Printf, CSV, DataFrames, Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f7cc6435-9bd2-487f-943f-225c452db904", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "using PyPlot # For plotting only. Can be skipped or commented out" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c910f9e4-1c92-44ca-8ce3-8c520ec9d231", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_plots = isdefined(Main, :PyPlot)" + ] + }, + { + "cell_type": "markdown", + "id": "e60c3e6e-75fc-42b5-bfc3-6e47d1092747", + "metadata": { + "tags": [] + }, + "source": [ + "## Load time series data\n", + "\n", + "Read load and solar data:\n", + "- Load: real consumption data at an hourly timestep from the [Ushant island](https://en.wikipedia.org/wiki/Ushant) in 2016\n", + "- Solar and wind data comes from [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html). See [data/SOURCES.md](data/SOURCES.md)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "27688e3f-f2d5-4ee1-b4f5-ed1d451c44d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time series statistics:\n", + "- Load: min 294, mean 773, max 1707 kW\n", + "- PV capacity factor: 11.83 %\n", + "- Wind speed: mean 8.03 m/s\n" + ] + } + ], + "source": [ + "data = DataFrame(CSV.File(\"$(@__DIR__)/data/Ouessant_data_2016.csv\"))\n", + "\n", + "# Simulation steps\n", + "nsteps = length(data.Load)\n", + "@assert(nsteps == 8760) # 1 year at an hourly timestep\n", + "\n", + "# Split load, solar and wind data:\n", + "Pload = data.Load # kW\n", + "Ppv1k = data.Ppv1k ./ 1000; # convert to kW/kWp\n", + "wind_speed = data.Wind; # m/s\n", + "\n", + "# Calibrate wind speed data against a mast measurement \n", + "ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean\n", + "wind_speed = ws_gain*wind_speed\n", + "\n", + "println(\"Time series statistics:\")\n", + "@printf(\"- Load: min %.0f, mean %.0f, max %.0f kW\\n\",\n", + " minimum(Pload), mean(Pload), maximum(Pload))\n", + "@printf(\"- PV capacity factor: %.2f %%\\n\", mean(Ppv1k)*100) # yearly capacity factor of 12% (quite low in 2016 in Ouessant)\n", + "@printf(\"- Wind speed: mean %.2f m/s\\n\", mean(wind_speed))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c24c3df6-4062-450d-ba2d-32d71af7a823", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1453.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload[1]" + ] + }, + { + "cell_type": "markdown", + "id": "9b775b85-27eb-4815-8963-15d24a0e1840", + "metadata": {}, + "source": [ + "Display load time series" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "td = collect((0:nsteps-1)/24) # time in days\n", + "\n", + "\"\"\"helper: plot time series with a temporal zoom\"\"\"\n", + "function plot_ts_zoom(t, ts; zoom, color::String, title::String, xlabel::String, ylabel::String)\n", + " fig, (ax1, ax2) = plt.subplots(1,2, figsize=(7.5,2.7), sharey=true,\n", + " gridspec_kw=Dict(\"width_ratios\" => [2,1]))\n", + " ax1.plot(t, ts, color=color)\n", + " ax1.axvspan(zoom[1], zoom[2], color=\"k\", alpha=0.3)\n", + " ax2.plot(t, ts, color=color)\n", + " ax2.set_xlim(zoom[1], zoom[2])\n", + "\n", + " ax1.grid(true)\n", + " ax2.grid(true)\n", + " fig.suptitle(title)\n", + " ax1.set_xlim(t[1], t[end])\n", + " ax1.set(\n", + " title=\"full data length\",\n", + " xlabel=xlabel,\n", + " ylabel=ylabel\n", + " )\n", + " ax2.set(\n", + " title=\"temporal zoom $(zoom[1])–$(zoom[2])\",\n", + " xlabel=xlabel,\n", + " )\n", + " fig.tight_layout()\n", + " return fig, (ax1, ax2)\n", + "end\n", + "\n", + "if with_plots\n", + " fig, (ax1, ax2) = plot_ts_zoom(\n", + " td, Pload;\n", + " zoom=(60,61), color=\"tab:blue\",\n", + " title=\"Electricity consumption in Ushant island in 2016\",\n", + " xlabel=\"day of the year\", ylabel=\"kW\"\n", + " )\n", + "end;" + ] + }, + { + "cell_type": "markdown", + "id": "40e49a64-5483-4d89-8228-9accaf80a5e0", + "metadata": {}, + "source": [ + "### Generate wind power capacity factor time series from wind speed" + ] + }, + { + "cell_type": "markdown", + "id": "5a4f59f2-2d4d-4ded-bd62-a8f433c372ed", + "metadata": {}, + "source": [ + "We use the generic wind power curve model `capacity_from_wind` to transform wind speed time series into a capacity factor time series (normalized power).\n", + "The main parameters of this power curve are:\n", + "- Rotor swept area (m²), i.e. derived from the rotor diameter with $S=\\pi(D/2)^2$\n", + "- Turbine Specific Power (W/m²), i.e. rated power compared to area" + ] + }, + { + "cell_type": "markdown", + "id": "55a6fc2d-8450-40f1-bc14-900b1a7e5c9b", + "metadata": {}, + "source": [ + "Wind turbine parameters used here are fitted to an [EWT 900 kW DW52](https://ewtdirectwind.com/products/dw52/):" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "S_D52 = pi * (52/2)^2 # rotor swept area m²\n", + "TSP_D52 = 900e3/S_D52 # W/m²\n", + "v_out = 25.0 # m/s\n", + "Cp_D52, α_D52 = 0.521, 3.1; # fitted from actual power curve" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wind power capacity factor: 39.70 %\n" + ] + } + ], + "source": [ + "cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52)\n", + "@printf(\"Wind power capacity factor: %.2f %%\\n\", mean(cf_wind)*100) # (pretty high in Ouessant)" + ] + }, + { + "cell_type": "markdown", + "id": "d6734baf-c018-4130-9c6d-c5d37b237abb", + "metadata": {}, + "source": [ + "## Microgrid description\n", + "\n", + "Describe the Microgrid project and its components using dedicated data structures" + ] + }, + { + "cell_type": "markdown", + "id": "cdfff296-430d-4581-9171-40464d82db2d", + "metadata": {}, + "source": [ + "### Project parameters\n", + "\n", + "Financial parameters like discount rate, as well as technical details like the timestep of input data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dfa81d70-79a5-4e45-88c4-aab803d5d708", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Project(25, 0.05, 1.0, \"€\")" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lifetime = 25 # yr\n", + "discount_rate = 0.05\n", + "timestep = 1. # h\n", + "\n", + "project = Project(lifetime, discount_rate, timestep, \"€\")" + ] + }, + { + "cell_type": "markdown", + "id": "e3103c27-e833-4aa9-8ea2-93b2a1ab93a9", + "metadata": {}, + "source": [ + "Parameters chosen to be common to all Components (but they could differ)\n", + "\n", + "- replacement price relative to initial investment price\n", + "- salvage price relative to initial investment price" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6a9a9819-2f3a-4305-9710-b62d6d21f8f6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "replacement_price_ratio = 1.0\n", + "salvage_price_ratio = 1.0;" + ] + }, + { + "cell_type": "markdown", + "id": "08d88c9c-5eaf-4044-bced-3f914481b061", + "metadata": {}, + "source": [ + "### Dispatchable generator (Diesel)\n", + "\n", + "Used as last recourse, when there is not enough production from renewables and the battery is empty (not used in this example)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "324dd7e0-50e7-4cb6-9017-5b5f8e897773", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_rated_gen = 0. # rated power (kW) → /2 to see some load shedding\n", + "# fuel parameters:\n", + "fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max)\n", + "fuel_slope = 0.240 # fuel curve slope (L/h/kW)\n", + "# prices:\n", + "fuel_price = 1. # fuel price ($/L)\n", + "investment_price_gen = 400. # initial investiment price ($/kW)\n", + "om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation)\n", + "# other technical parameters\n", + "lifetime_gen = 15000. # generator lifetime (h)\n", + "load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1]\n", + "input_unit_gen = \"L\" # fuel quantity unit (used in fuel price and consumption curve parameters)\n", + "output_unit_gen = \"KWh\"\n", + "\n", + "generator = ProductionUnit(power_rated_gen,\n", + " fuel_intercept, fuel_slope, fuel_price,\n", + " investment_price_gen, om_price_gen, lifetime_gen,\n", + " load_ratio_min_gen,\n", + " replacement_price_ratio, salvage_price_ratio, input_unit_gen,output_unit_gen)\n" + ] + }, + { + "cell_type": "markdown", + "id": "1cf5cc14-8109-42be-88c6-d167545bba03", + "metadata": {}, + "source": [ + "### fuel tank " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9079e059-fd4a-4d5e-9358-29758fb06c86", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capacity_rated_ftank = 0. # rated power capacity (L)\n", + "investment_price_ftank = 0.0 # initial investment price ($/L)\n", + "om_price_ftank = 0.# operation and maintenance price ($/L/y)\n", + "lifetime_ftank = 25. # calendar lifetime (y)\n", + "loss_factor_ftank = 0. # hydrogen used on site \n", + "fuel_ini_ratio = 1. # Initial load ratio ∈ [0,1]\n", + "fuel_min_ratio = 0. # minimum load ratio ∈ [0,1]\n", + "fuel_max_ratio = 1. # maximum load ratio ∈ [0,1]\n", + "\n", + "ftank = Tank(capacity_rated_ftank,investment_price_ftank, om_price_ftank,lifetime_ftank,loss_factor_ftank,fuel_ini_ratio,\n", + " fuel_min_ratio, fuel_max_ratio,replacement_price_ratio, salvage_price_ratio)" + ] + }, + { + "cell_type": "markdown", + "id": "67bff58e-0434-4379-a080-7389810de1d3", + "metadata": {}, + "source": [ + "### Fuel cell " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2c1bd7f7-24a0-43f5-822b-d77927ab76c8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_rated_fc = 1800. # rated power capacity (KW)\n", + "cons_intercept_fc=0. #\n", + "cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé)\n", + "cons_price_fc = 0. #\n", + "investment_price_fc = 1000. # initial investment price ($/KW)\n", + "om_price_fc = 10. # operation and maintenance price ($/kW/y)\n", + "lifetime_fc = 45000. # Fuel Cell lifetime (h)\n", + "load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1]\n", + "input_unit_fc= \"Kg\"\n", + "output_unit_fc=\"KWh\"\n", + "\n", + "fuel_cell = ProductionUnit(power_rated_fc,cons_intercept_fc, cons_rate_fc,cons_price_fc,investment_price_fc, om_price_fc,lifetime_fc,\n", + " load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "402b5772-a8b6-4c87-bd97-f397f782382d", + "metadata": {}, + "source": [ + "### H2 Tank" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fad9eb7c-789d-49ab-aa2b-e309df77b3e5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capacity_rated_hytank = 10000. # rated power capacity (kg)\n", + "investment_price_hytank = 0.8 # initial investment price ($/kg)\n", + "om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y)\n", + "lifetime_hytank = 25. # calendar lifetime (y)\n", + "loss_factor_hytank = 0. # hydrogen used on site \n", + "LoH_ini_ratio = 0. # Initial load ratio ∈ [0,1]\n", + "LoH_min_ratio = 0.2 # minimum load ratio ∈ [0,1]\n", + "LoH_max_ratio = 1. # maximum load ratio ∈ [0,1]\n", + "\n", + "hytank = Tank(capacity_rated_hytank,investment_price_hytank, om_price_hytank,lifetime_hytank,loss_factor_hytank,LoH_ini_ratio,\n", + " LoH_min_ratio, LoH_max_ratio,replacement_price_ratio, salvage_price_ratio)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3c2342f8-a300-48f8-b748-fd7280bebb5c", + "metadata": {}, + "source": [ + "### Dispatchable Compound " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1cab4a19-ae90-486d-b6c6-4863fda458f5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dispatchables = DispatchableCompound{Float64}([generator,], [fuel_cell])" + ] + }, + { + "cell_type": "markdown", + "id": "9fee97a8-2b1a-434d-8cc8-1f7ecbab453d", + "metadata": { + "tags": [] + }, + "source": [ + "### Tank compound " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6b00b378-1ee2-4682-8b45-f7e0fc1a012a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tanks = TankCompound{Float64}(ftank,hytank)" + ] + }, + { + "cell_type": "markdown", + "id": "f97c5fc3-81f0-4848-a358-45e404e42031", + "metadata": { + "tags": [] + }, + "source": [ + "### Battery energy storage\n", + "\n", + "Used as a buffer between the renewables production and the consumption" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f744269a-b6bc-4fce-804a-66311843ed76", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energy_rated_sto = 5000. # rated energy capacity (kWh)\n", + "investment_price_sto = 350. # initial investiment price ($/kWh)\n", + "om_price_sto = 10. # operation and maintenance price ($/kWh/y)\n", + "lifetime_sto = 15. # calendar lifetime (y)\n", + "lifetime_cycles = 3000. # maximum number of cycles over life (1)\n", + "# Parameters with default values\n", + "charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1)\n", + "discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1)\n", + "loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1]\n", + "SoC_min = 0. # minimum State of Charge ∈ [0,1]\n", + "SoC_max = 1. # minimum State of Charge ∈ [0,1]\n", + "SoC_ini = 0. # initial State of Charge ∈ [0,1]\n", + "\n", + "battery = Battery(energy_rated_sto,\n", + " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", + " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,SoC_ini,\n", + " replacement_price_ratio, salvage_price_ratio)" + ] + }, + { + "cell_type": "markdown", + "id": "0805e1ba-a6eb-4b1a-aee2-464db2c39d46", + "metadata": { + "tags": [] + }, + "source": [ + "### Electrolyzer\n", + "to convert renewable surproduction into hydrogen" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "059c09d0-4fb5-408a-975c-228c971c9b0f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_rated_elyz = 2000. # rated power capacity (Kw)\n", + "cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2)\n", + "cons_slope_elyz = 56. # consumption rate (KWhé/KgH2)\n", + "cons_price_elyz = 0.#\n", + "investment_price_elyz = 1000. # initial investment price ($/kW)\n", + "om_price_elyz = 10. # operation and maintenance price ($/kW/y)\n", + "lifetime_elyz = 45000. #Electrolyzer lifetime (h)\n", + "load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1]\n", + "\n", + "input_unit_elyz= \"KWh\"\n", + "output_unit_elyz=\"Kg\"\n", + "\n", + "elyz = ProductionUnit(power_rated_elyz,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, investment_price_elyz, om_price_elyz, lifetime_elyz,\n", + " load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8165bd94-05a7-49d0-9d51-7416cf180beb", + "metadata": {}, + "source": [ + "### Photovoltaic (PV) generation\n", + "\n", + "Used in priority to feed the load (along wind power). PV generation is assumed proportional to the irradiance data load in previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "10ce4795-54f9-4e77-bf7f-886851756d84", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_rated_pv = 3000. # rated power (kW)\n", + "irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²)\n", + "investment_price_pv = 1200. # initial investiment price ($/kW)\n", + "om_price_pv = 20.# operation and maintenance price ($/kW/y)\n", + "lifetime_pv = 25. # lifetime (y)\n", + "# Parameters with default values\n", + "derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1]\n", + "\n", + "photovoltaic = Photovoltaic(power_rated_pv, irradiance,\n", + " investment_price_pv, om_price_pv,\n", + " lifetime_pv, derating_factor_pv,\n", + " replacement_price_ratio, salvage_price_ratio)" + ] + }, + { + "cell_type": "markdown", + "id": "69bf0b76-a3b9-4355-9e9e-c86692193b2c", + "metadata": {}, + "source": [ + "Display PV production time series (which is proportional to the rated power of the plant `power_rated_pv`)\n", + "- PV production is computed by calling the `production` method on the `Photovoltaic` instance" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1a047087-37e1-4f3f-a936-c562ebfb6f8d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if with_plots\n", + " fig, (ax1, ax2) = plot_ts_zoom(\n", + " td, production(photovoltaic);\n", + " zoom=(100,103), color=\"tab:orange\",\n", + " title=\"Production of a $power_rated_pv kW PV plant in Ushant\",\n", + " xlabel=\"day of the year\", ylabel=\"kW\"\n", + " )\n", + "end;" + ] + }, + { + "cell_type": "markdown", + "id": "1c5696fe-e207-4f78-819b-6043d850c62b", + "metadata": {}, + "source": [ + "### Wind power generation\n", + "\n", + "Used in priority to feed the load along solar power.\n", + "\n", + "The simple wind power model use the fixed capacity factor data derived from wind speed in the previous section." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fefdc955-8473-4f10-b19d-6674bedf773e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power_rated_wind = 1800. # rated power (kW)\n", + "investment_price_wind = 3500. # initial investiment price ($/kW)\n", + "om_price_wind = 100.# operation and maintenance price ($/kW/y)\n", + "lifetime_wind = 25. # lifetime (y)\n", + "\n", + "windgen = WindPower(power_rated_wind, cf_wind,\n", + " investment_price_wind, om_price_wind,\n", + " lifetime_wind,\n", + " replacement_price_ratio, salvage_price_ratio)" + ] + }, + { + "cell_type": "markdown", + "id": "5e2a34a9-2ea6-49ca-8a6d-1bc6bc86b07a", + "metadata": {}, + "source": [ + "Display wind power time series:\n", + "- Wind production is computed by calling the `production` method on the `WindPower` instance" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ea472eff-4cdd-42aa-8515-a1b72dc66c62", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if with_plots\n", + " fig, (ax1, ax2) = plot_ts_zoom(\n", + " td, production(windgen);\n", + " zoom=(100,103), color=\"tab:purple\",\n", + " title=\"Production of $power_rated_wind kW of wind power in Ushant\",\n", + " xlabel=\"day of the year\", ylabel=\"kW\"\n", + " )\n", + "end;" + ] + }, + { + "cell_type": "markdown", + "id": "a63e4cc3-96cd-4e55-bdc1-33c72f8efacc", + "metadata": {}, + "source": [ + "### Microgrid data structure\n", + "\n", + "the `Microgrid` data structure groups:\n", + "- project parameters\n", + "- load time series\n", + "- all components: energy storage systems, renewable power generators, " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "13483089-225f-4fe4-98c8-656a675ba84d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "microgrid = Microgrid(project, Pload,dispatchables,\n", + " [elyz,],tanks,battery, [\n", + " photovoltaic,\n", + " windgen\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0e43de8d-82fe-4d33-9010-cbd6b620cca9", + "metadata": {}, + "source": [ + "## Simulate the microgrid\n", + "\n", + "launched with the `simulate` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "9038c442-f3bc-4fe7-8e3f-83e77a42d2e7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "oper_traj, oper_stats, mg_costs = simulate(microgrid);" + ] + }, + { + "cell_type": "markdown", + "id": "8ab2808c-d25a-4bac-858b-abd436664730", + "metadata": {}, + "source": [ + "In details, the simulation is done in two stages:\n", + "\n", + "1. simulate the **operation**, in particular the energy flow at an hourly timestep between components: \n", + " - generates operation statistics as `OperationStats` data structure\n", + " - records trajectories of operation variables\n", + "2. evaluate the **economic cost** of the project, based on its description and on the operation statistics: \n", + " - generates cost data as `MicrogridCosts` data structure\n", + "\n", + "These successive stages can be run manually with their dedicated functions (see `simulate` code)." + ] + }, + { + "cell_type": "markdown", + "id": "ddc7b937-ec29-4fa7-be9e-5afd4d167d43", + "metadata": {}, + "source": [ + "Microgrid simulation code is optimized to run in less than 1 ms:\n", + "\n", + "(you can use [Benchmarktools](https://juliaci.github.io/BenchmarkTools.jl/)'s `@btime` for more accurate timing results)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "c9bf9e90-a9dc-43ca-95f8-4a3d76eb0cf1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.073339 seconds (912.97 k allocations: 26.776 MiB)\n" + ] + } + ], + "source": [ + "@time oper_traj, oper_stats, mg_costs = simulate(microgrid);" + ] + }, + { + "cell_type": "markdown", + "id": "c35a1953-57c4-4312-a593-d9d80f874f87", + "metadata": {}, + "source": [ + "## Analyze Microgrid simulation results" + ] + }, + { + "cell_type": "markdown", + "id": "57bf3e9f-04f9-4697-9dcb-5e88c4091336", + "metadata": {}, + "source": [ + "### Technical performance\n", + "\n", + "Operation statistics are in `oper_stats` (`OperationStats` data structure) with fields such as `renew_rate` (fraction of renewables in the energy mix), `shed_rate` (fraction of shed, i.e. unserved load)..." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "3695a442-4e04-4436-9374-70c85036beda", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load shedding rate: 4.8%\n", + "Renewable rate: 100.0%\n" + ] + } + ], + "source": [ + "@printf(\"Load shedding rate: %.1f%%\\n\", oper_stats.shed_rate*100)\n", + "@printf(\"Renewable rate: %.1f%%\\n\", oper_stats.renew_rate*100)" + ] + }, + { + "cell_type": "markdown", + "id": "142b0cdd-a853-49c3-be2b-8a2b53a3990d", + "metadata": {}, + "source": [ + "All available statistics:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "dcdb5710-b0c0-4fb7-b92f-71265b6f2489", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1133.9075685798962" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.Pbatt[3000]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0090bf4d-f684-492b-9583-bd56854329b0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OperationStats with fields:\n", + "- served_energy: 6.4502e6 kWh\n", + "- shed_energy: 324750.0 kWh\n", + "- shed_max: 33396.0 kW\n", + "- shed_hours: 443.0 h\n", + "- shed_duration_max: 56.0 h\n", + "- shed_rate: 0.047934 in [0,1]\n", + "- gen_energy: 0.0 kWh\n", + "- gen_hours: 0.0 h\n", + "- gen_fuel: 0.0 L\n", + "- fc_energy: 1.0862e6 kWh\n", + "- fc_hours: 2535.0 h\n", + "- h2_consumed: 67889.0 kg\n", + "- h2_produced: 69889.0 kg\n", + "- elyz_hours: 4694.0 h\n", + "- elyz_consumed_energy: 3.9138e6 kWh\n", + "- h2_chain_loss: -2.8277e6 kWh\n", + "- storage_cycles: 37.192 \n", + "- storage_char_energy: 195260.0 kWh\n", + "- storage_dis_energy: 176660.0 kWh\n", + "- storage_loss_energy: 18596.0 kWh\n", + "- spilled_energy: 71228.0 kWh\n", + "- spilled_max: 3271.4 kW\n", + "- spilled_rate: 0.0076037 in [0,1]\n", + "- renew_potential_energy: 9.3676e6 kWh\n", + "- renew_energy: 9.2964e6 kWh\n", + "- renew_rate: 1.0 in [0,1]\n" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "59e59883-d545-4de7-9584-ac06b5110005", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10000.0" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.LoH[7000]" + ] + }, + { + "cell_type": "markdown", + "id": "5120f110-3a6b-4073-8be7-d69804dfb1b3", + "metadata": {}, + "source": [ + "### Economic performance\n", + "\n", + "Economic performance indicators are stored in `mg_costs` (as a `MicrogridCosts` data structure). This structure contains the following fields:\n", + "- two highlevel cost indicators: Net present Cost `npc` and Levelized Costs of Electricity `lcoe`\n", + "- a break down of cost factors for the entire project in `system`: initial investment, fuel... (as a `CostFactors` structure)\n", + "- a detailed break down of cost factors for each component in `generator`, `storage`, `nondispatchables[n]`... (also as `CostFactors` structures)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "8b904163-9ef8-4b87-a11c-d06ed53af5ac", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Levelized Cost of Electricity: 0.253 $/kWh\n", + "Net Present Cost: 23.00 M$ (over 25 years at 5% discount rate)\n" + ] + } + ], + "source": [ + "@printf(\"Levelized Cost of Electricity: %.3f \\$/kWh\\n\", mg_costs.lcoe)\n", + "@printf(\"Net Present Cost: %.2f M\\$ (over %d years at %.0f%% discount rate)\\n\",\n", + " mg_costs.npc/1e6, project.lifetime, project.discount_rate*100)" + ] + }, + { + "cell_type": "markdown", + "id": "e3133a9b-c89c-453a-9b8f-9885b713193c", + "metadata": {}, + "source": [ + "Break down of cost factors for the entire project:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "7e5fde52-1cd8-43a1-9f9b-c04d659d4f1a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CostFactors(2.2999237665849324e7, 1.5458e7, 3.636477520274991e6, 4.623166166276827e6, 0.0, -718406.0207024994)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_costs.system" + ] + }, + { + "cell_type": "markdown", + "id": "ca89063d-0123-43ef-8283-d54713a5b346", + "metadata": {}, + "source": [ + "Break down of cost factors for each nondispatchable power sources (i.e. renewables), in the same order as in `microgrid.nondispatchables`:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "82c503dc-02a8-4fcf-86c0-b0e3bc3784dd", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-element Vector{CostFactors}:\n", + " CostFactors(4.445636673962684e6, 3.6e6, 0.0, 845636.6739626848, 0.0, -0.0)\n", + " CostFactors(8.836910021888055e6, 6.3e6, 0.0, 2.5369100218880544e6, 0.0, -0.0)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_costs.nondispatchables" + ] + }, + { + "cell_type": "markdown", + "id": "01744584-246a-4a53-ab67-5eb043547d32", + "metadata": {}, + "source": [ + "Definition of the fields of cost factors (`total` is the sum of all the other ones):" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "82273c01-fa92-414a-af84-46bb793c7988", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(:total, :investment, :replacement, :om, :fuel, :salvage)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fieldnames(CostFactors)" + ] + }, + { + "cell_type": "markdown", + "id": "cb3a7eae-8696-4780-a63e-8c2811908061", + "metadata": {}, + "source": [ + "Remark 1: `npc` is equal to `system.total`\n", + "\n", + "Remark 2: `CostFactors` structures like `mg_costs.system` support arithmetic operations (+,*,/) and rounding:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "d0dc9505-34d3-4657-a40b-2d5aa684a19b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CostFactors(23.0, 15.46, 3.64, 4.62, 0.0, -0.72)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(mg_costs.system/1e6; digits=2) # rounded costs in M$" + ] + }, + { + "cell_type": "markdown", + "id": "ea65446a-8e6b-4559-a98f-d71fe2a5df4f", + "metadata": {}, + "source": [ + "Individual cost factors can be accessed by field names. For examples fuel costs for the dispatchable generator:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "5eb2896b-4e36-4eb8-adcc-fb8cbd67809d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CostFactors(2.5, 1.8, 0.76, 0.25, 0.0, -0.31)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(mg_costs.fuel_cell/1e6; digits=2) # M$" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "106877f9-9008-45de-8f68-05a1cb4bb6fa", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "CostFactors(4.09, 2.0, 2.04, 0.28, 0.0, -0.23)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(mg_costs.electrolyzer/1e6; digits=2) # M$" + ] + }, + { + "cell_type": "markdown", + "id": "cdb834c3-7616-453f-a1f5-89ebfe5fa434", + "metadata": {}, + "source": [ + "### Operation trajectories" + ] + }, + { + "cell_type": "markdown", + "id": "3f2b70c2-08ef-458e-9090-919ff4e3efa6", + "metadata": {}, + "source": [ + "`oper_traj` is of type `OperationTraj` with fields such as `Pgen` (generator power), `Pbatt` (battery power)...\n", + "\n", + "**Graph 1** Zoom to first week of January: high load, wind at maximum, few solar → battery often empty" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "function plot_oper_traj(microgrid, oper_traj)\n", + " fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", + " ax1.plot(td, Pload, label=\"load\")\n", + " ax1.plot(td, oper_traj.Pfc, \"tab:red\", label=\"Pfc\")\n", + " ax1.plot(td, oper_traj.Prenew_pot, \"tab:orange\", label=\"ren\")\n", + " ax1.plot(td, oper_traj.Pelyz, \"tab:gray\", label=\"Pelyz\")\n", + " ax1.plot(td, oper_traj.Pfc, \"tab:pink\", label=\"Pfc\")\n", + " ax3=ax1.twinx()\n", + " ax3.set_ylim([0, 1])\n", + " ax3.plot(td, oper_traj.Ebatt[1:end-1]/5000, \"C2\")\n", + " \n", + " ax1.legend()\n", + " ax1.grid(true)\n", + " ax1.set(\n", + " ylabel=\"kW\"\n", + " )\n", + " \n", + " ax2.grid(true)\n", + " ax2.set(\n", + " title=\"H2 Tank level\",\n", + " xlabel=\"time (d)\",\n", + " ylabel=\"kWh\"\n", + " \n", + " )\n", + " \n", + " ax2.plot(td, oper_traj.LoH[1:end-1]/10000, \"C2\")\n", + " \n", + " fig.tight_layout()\n", + " return fig, (ax1, ax2)\n", + "end\n", + "\n", + "if with_plots\n", + " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", + " ax1.set_ylim(0, 5000)\n", + " pygui(true)\n", + " plt.show()\n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3ac88fac-41bd-48c6-8e43-fe108b5a4109", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1×365 Matrix{Float64}:\n", + " 1310.75 1202.75 1040.17 1018.17 … 1214.92 1354.92 1416.79 1409.08" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean(reshape(Pload, 24, :); dims=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "548cfc6b-3343-4468-85c6-22ab2699842b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "search: \u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1me\u001b[22m\u001b[0m\u001b[1ms\u001b[22m\u001b[0m\u001b[1mh\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1me\u001b[22m p\u001b[0m\u001b[1mr\u001b[22momot\u001b[0m\u001b[1me\u001b[22m_\u001b[0m\u001b[1ms\u001b[22m\u001b[0m\u001b[1mh\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1me\u001b[22m\n", + "\n" + ] + }, + { + "data": { + "text/latex": [ + "\\begin{verbatim}\n", + "reshape(A, dims...) -> AbstractArray\n", + "reshape(A, dims) -> AbstractArray\n", + "\\end{verbatim}\n", + "Return an array with the same data as \\texttt{A}, but with different dimension sizes or number of dimensions. The two arrays share the same underlying data, so that the result is mutable if and only if \\texttt{A} is mutable, and setting elements of one alters the values of the other.\n", + "\n", + "The new dimensions may be specified either as a list of arguments or as a shape tuple. At most one dimension may be specified with a \\texttt{:}, in which case its length is computed such that its product with all the specified dimensions is equal to the length of the original array \\texttt{A}. The total number of elements must not change.\n", + "\n", + "\\section{Examples}\n", + "\\begin{verbatim}\n", + "julia> A = Vector(1:16)\n", + "16-element Vector{Int64}:\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", + " 5\n", + " 6\n", + " 7\n", + " 8\n", + " 9\n", + " 10\n", + " 11\n", + " 12\n", + " 13\n", + " 14\n", + " 15\n", + " 16\n", + "\n", + "julia> reshape(A, (4, 4))\n", + "4×4 Matrix{Int64}:\n", + " 1 5 9 13\n", + " 2 6 10 14\n", + " 3 7 11 15\n", + " 4 8 12 16\n", + "\n", + "julia> reshape(A, 2, :)\n", + "2×8 Matrix{Int64}:\n", + " 1 3 5 7 9 11 13 15\n", + " 2 4 6 8 10 12 14 16\n", + "\n", + "julia> reshape(1:6, 2, 3)\n", + "2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\n", + " 1 3 5\n", + " 2 4 6\n", + "\\end{verbatim}\n" + ], + "text/markdown": [ + "```\n", + "reshape(A, dims...) -> AbstractArray\n", + "reshape(A, dims) -> AbstractArray\n", + "```\n", + "\n", + "Return an array with the same data as `A`, but with different dimension sizes or number of dimensions. The two arrays share the same underlying data, so that the result is mutable if and only if `A` is mutable, and setting elements of one alters the values of the other.\n", + "\n", + "The new dimensions may be specified either as a list of arguments or as a shape tuple. At most one dimension may be specified with a `:`, in which case its length is computed such that its product with all the specified dimensions is equal to the length of the original array `A`. The total number of elements must not change.\n", + "\n", + "# Examples\n", + "\n", + "```jldoctest\n", + "julia> A = Vector(1:16)\n", + "16-element Vector{Int64}:\n", + " 1\n", + " 2\n", + " 3\n", + " 4\n", + " 5\n", + " 6\n", + " 7\n", + " 8\n", + " 9\n", + " 10\n", + " 11\n", + " 12\n", + " 13\n", + " 14\n", + " 15\n", + " 16\n", + "\n", + "julia> reshape(A, (4, 4))\n", + "4×4 Matrix{Int64}:\n", + " 1 5 9 13\n", + " 2 6 10 14\n", + " 3 7 11 15\n", + " 4 8 12 16\n", + "\n", + "julia> reshape(A, 2, :)\n", + "2×8 Matrix{Int64}:\n", + " 1 3 5 7 9 11 13 15\n", + " 2 4 6 8 10 12 14 16\n", + "\n", + "julia> reshape(1:6, 2, 3)\n", + "2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\n", + " 1 3 5\n", + " 2 4 6\n", + "```\n" + ], + "text/plain": [ + "\u001b[36m reshape(A, dims...) -> AbstractArray\u001b[39m\n", + "\u001b[36m reshape(A, dims) -> AbstractArray\u001b[39m\n", + "\n", + " Return an array with the same data as \u001b[36mA\u001b[39m, but with different dimension sizes\n", + " or number of dimensions. The two arrays share the same underlying data, so\n", + " that the result is mutable if and only if \u001b[36mA\u001b[39m is mutable, and setting elements\n", + " of one alters the values of the other.\n", + "\n", + " The new dimensions may be specified either as a list of arguments or as a\n", + " shape tuple. At most one dimension may be specified with a \u001b[36m:\u001b[39m, in which case\n", + " its length is computed such that its product with all the specified\n", + " dimensions is equal to the length of the original array \u001b[36mA\u001b[39m. The total number\n", + " of elements must not change.\n", + "\n", + "\u001b[1m Examples\u001b[22m\n", + "\u001b[1m ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n", + "\n", + "\u001b[36m julia> A = Vector(1:16)\u001b[39m\n", + "\u001b[36m 16-element Vector{Int64}:\u001b[39m\n", + "\u001b[36m 1\u001b[39m\n", + "\u001b[36m 2\u001b[39m\n", + "\u001b[36m 3\u001b[39m\n", + "\u001b[36m 4\u001b[39m\n", + "\u001b[36m 5\u001b[39m\n", + "\u001b[36m 6\u001b[39m\n", + "\u001b[36m 7\u001b[39m\n", + "\u001b[36m 8\u001b[39m\n", + "\u001b[36m 9\u001b[39m\n", + "\u001b[36m 10\u001b[39m\n", + "\u001b[36m 11\u001b[39m\n", + "\u001b[36m 12\u001b[39m\n", + "\u001b[36m 13\u001b[39m\n", + "\u001b[36m 14\u001b[39m\n", + "\u001b[36m 15\u001b[39m\n", + "\u001b[36m 16\u001b[39m\n", + "\u001b[36m \u001b[39m\n", + "\u001b[36m julia> reshape(A, (4, 4))\u001b[39m\n", + "\u001b[36m 4×4 Matrix{Int64}:\u001b[39m\n", + "\u001b[36m 1 5 9 13\u001b[39m\n", + "\u001b[36m 2 6 10 14\u001b[39m\n", + "\u001b[36m 3 7 11 15\u001b[39m\n", + "\u001b[36m 4 8 12 16\u001b[39m\n", + "\u001b[36m \u001b[39m\n", + "\u001b[36m julia> reshape(A, 2, :)\u001b[39m\n", + "\u001b[36m 2×8 Matrix{Int64}:\u001b[39m\n", + "\u001b[36m 1 3 5 7 9 11 13 15\u001b[39m\n", + "\u001b[36m 2 4 6 8 10 12 14 16\u001b[39m\n", + "\u001b[36m \u001b[39m\n", + "\u001b[36m julia> reshape(1:6, 2, 3)\u001b[39m\n", + "\u001b[36m 2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\u001b[39m\n", + "\u001b[36m 1 3 5\u001b[39m\n", + "\u001b[36m 2 4 6\u001b[39m" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "?reshape" + ] + }, + { + "cell_type": "markdown", + "id": "fac01e62-1ae5-425a-95dc-8827bf9fa50e", + "metadata": {}, + "source": [ + "**Graph 2** Zoom to one week in summer: much solar → battery often full → spillage" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "256a0b88-4972-46f3-a29e-fb7016aee700", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if with_plots\n", + " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", + " ax1.set_xlim(150, 151)\n", + "end;" + ] + }, + { + "cell_type": "markdown", + "id": "4570d462-880a-461f-b847-8c23cebe3a08", + "metadata": {}, + "source": [ + "---\n", + "**Next steps**: \n", + "after this demonstration which focused on Microgrid *simulation*, you can go to the [Microgrid sizing optimization](Microgrid_sizing_optimization.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a3792fd-d69c-4ceb-98b5-fee10d08afa3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb index 175bde0..3bf4149 100644 --- a/examples/Microgrid_Wind-Solar.ipynb +++ b/examples/Microgrid_Wind-Solar.ipynb @@ -26,8 +26,18 @@ "cell_type": "code", "execution_count": 1, "id": "855af8dd-d21d-4587-84cb-ae2b932deeaa", - "metadata": {}, - "outputs": [], + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" + ] + } + ], "source": [ "using Microgrids\n", "using Printf, CSV, DataFrames, Statistics" @@ -37,7 +47,9 @@ "cell_type": "code", "execution_count": 2, "id": "f7cc6435-9bd2-487f-943f-225c452db904", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "using PyPlot # For plotting only. Can be skipped or commented out" @@ -47,7 +59,9 @@ "cell_type": "code", "execution_count": 3, "id": "c910f9e4-1c92-44ca-8ce3-8c520ec9d231", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -67,7 +81,9 @@ { "cell_type": "markdown", "id": "e60c3e6e-75fc-42b5-bfc3-6e47d1092747", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "## Load time series data\n", "\n", @@ -80,7 +96,9 @@ "cell_type": "code", "execution_count": 4, "id": "27688e3f-f2d5-4ee1-b4f5-ed1d451c44d6", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -116,6 +134,29 @@ "@printf(\"- Wind speed: mean %.2f m/s\\n\", mean(wind_speed))" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c24c3df6-4062-450d-ba2d-32d71af7a823", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1453.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload[1]" + ] + }, { "cell_type": "markdown", "id": "9b775b85-27eb-4815-8963-15d24a0e1840", @@ -126,13 +167,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "Figure(PyObject
)" ] @@ -209,9 +252,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "S_D52 = pi * (52/2)^2 # rotor swept area m²\n", @@ -222,9 +267,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -261,9 +308,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "dfa81d70-79a5-4e45-88c4-aab803d5d708", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -271,7 +320,7 @@ "Project(25, 0.05, 1.0, \"€\")" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -297,9 +346,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "6a9a9819-2f3a-4305-9710-b62d6d21f8f6", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "replacement_price_ratio = 1.0\n", @@ -318,9 +369,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "324dd7e0-50e7-4cb6-9017-5b5f8e897773", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -328,7 +381,7 @@ "DispatchableGenerator{Float64}(1800.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\")" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -366,9 +419,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "f744269a-b6bc-4fce-804a-66311843ed76", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -376,7 +431,7 @@ "Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0)" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -412,9 +467,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "10ce4795-54f9-4e77-bf7f-886851756d84", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -422,7 +479,7 @@ "Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -453,13 +510,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "1a047087-37e1-4f3f-a936-c562ebfb6f8d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "Figure(PyObject
)" ] @@ -493,9 +552,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "fefdc955-8473-4f10-b19d-6674bedf773e", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -503,7 +564,7 @@ "WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -531,13 +592,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "ea472eff-4cdd-42aa-8515-a1b72dc66c62", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "Figure(PyObject
)" ] @@ -572,9 +635,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "b549561f-3e09-4552-a695-d17723cd80e4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -582,7 +647,7 @@ "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableGenerator{Float64}(1800.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\"), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -596,6 +661,92 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d0742994-6dd3-43d4-9ba3-b38f52d38d62", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "search: \u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22m 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\u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22mCosts\n", + "\n" + ] + }, + { + "data": { + "text/latex": [ + "Microgrid system description\n", + "\n" + ], + "text/markdown": [ + "Microgrid system description\n" + ], + "text/plain": [ + " Microgrid system description" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Microgrid(" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "073acd6d-bf20-4f0f-8c6c-a08dfcbb4397", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-element Vector{NonDispatchableSource}:\n", + " Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)\n", + " WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[photovoltaic, windgen]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "3832020f-6142-4503-8bb3-41bd967a99c2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[Photovoltaic{Float64} <: NonDispatchableSource" + ] + }, { "cell_type": "markdown", "id": "0e43de8d-82fe-4d33-9010-cbd6b620cca9", @@ -608,9 +759,11 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "id": "9038c442-f3bc-4fe7-8e3f-83e77a42d2e7", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "oper_traj, oper_stats, mg_costs = simulate(microgrid);" @@ -644,15 +797,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "id": "c9bf9e90-a9dc-43ca-95f8-4a3d76eb0cf1", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 0.000267 seconds (152 allocations: 829.234 KiB)\n" + " 0.001151 seconds (152 allocations: 829.234 KiB)\n" ] } ], @@ -680,9 +835,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "id": "3695a442-4e04-4436-9374-70c85036beda", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -708,9 +865,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "id": "0090bf4d-f684-492b-9583-bd56854329b0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -737,7 +896,7 @@ "- renew_rate: 0.75308 in [0,1]\n" ] }, - "execution_count": 20, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -761,9 +920,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 27, "id": "8b904163-9ef8-4b87-a11c-d06ed53af5ac", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -790,9 +951,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "id": "7e5fde52-1cd8-43a1-9f9b-c04d659d4f1a", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -800,7 +963,7 @@ "CostFactors(2.189002772908652e7, 9.22e6, 2.7882202252348172e6, 4.498223347698841e6, 5.65860947086071e6, -275025.3147078488)" ] }, - "execution_count": 22, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -819,9 +982,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "id": "82c503dc-02a8-4fcf-86c0-b0e3bc3784dd", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -831,7 +996,7 @@ " CostFactors(4.418455010944027e6, 3.15e6, 0.0, 1.2684550109440272e6, 0.0, -0.0)" ] }, - "execution_count": 23, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -850,9 +1015,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 30, "id": "82273c01-fa92-414a-af84-46bb793c7988", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -860,7 +1027,7 @@ "(:total, :investment, :replacement, :om, :fuel, :salvage)" ] }, - "execution_count": 24, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -881,9 +1048,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 31, "id": "d0dc9505-34d3-4657-a40b-2d5aa684a19b", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -891,7 +1060,7 @@ "CostFactors(21.89, 9.22, 2.79, 4.5, 5.66, -0.28)" ] }, - "execution_count": 25, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -910,9 +1079,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 32, "id": "5eb2896b-4e36-4eb8-adcc-fb8cbd67809d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -920,7 +1091,7 @@ "5.66" ] }, - "execution_count": 26, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -949,13 +1120,15 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "Figure(PyObject
)" ] @@ -1038,7 +1211,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.2", + "display_name": "Julia 1.9.3", "language": "julia", "name": "julia-1.9" }, @@ -1046,7 +1219,7 @@ "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.2" + "version": "1.9.3" } }, "nbformat": 4, diff --git a/examples/Microgrid_Wind-Solar_data.jl b/examples/Microgrid_Wind-Solar_data.jl index 86bfcf3..46ae89f 100644 --- a/examples/Microgrid_Wind-Solar_data.jl +++ b/examples/Microgrid_Wind-Solar_data.jl @@ -44,7 +44,7 @@ replacement_price_ratio = 1.0 salvage_price_ratio = 1.0 # Dispatchable generator (Diesel) -power_rated_gen = 1800. # rated power (kW) → /2 to see some load shedding +power_rated_gen = 2000. # rated power (kW) → /2 to see some load shedding # fuel parameters: fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max) fuel_slope = 0.240 # fuel curve slope (L/h/kW) @@ -54,8 +54,44 @@ investment_price_gen = 400. # initial investiment price ($/kW) om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation) # other technical parameters lifetime_gen = 15000. # generator lifetime (h) -load_ratio_min = 0.0 # minimum load ratio ∈ [0,1] -fuel_unit = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) +load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1] +input_unit_gen = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) +output_unit_gen = "KWh" + +#fuel tank + +capacity_rated_ftank = 0. # rated power capacity (L) +investment_price_ftank = 0.0 # initial investment price ($/L) +om_price_ftank = 0.# operation and maintenance price ($/L/y) +lifetime_ftank = 25. # calendar lifetime (y) +loss_factor_ftank = 0. # hydrogen used on site +fuel_ini_ratio = 0. # Initial load ratio ∈ [0,1] +fuel_min_ratio = 0. # minimum load ratio ∈ [0,1] +fuel_max_ratio = 1. # maximum load ratio ∈ [0,1] + +#H2 Tank +capacity_rated_hytank = 10000. # rated power capacity (kg) +investment_price_hytank = 0.8 # initial investment price ($/kg) +om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y) +lifetime_hytank = 25. # calendar lifetime (y) +loss_factor_hytank = 0. # hydrogen used on site +LoH_ini_ratio = 0.2 # Initial load ratio ∈ [0,1] +LoH_min_ratio = 0.1 # minimum load ratio ∈ [0,1] +LoH_max_ratio = 1. # maximum load ratio ∈ [0,1] + + +#Fuel cell +power_rated_fc = 1800. # rated power capacity (KW) +cons_intercept_fc=0. # +cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé) +cons_price_fc = 0. # +investment_price_fc = 1000. # initial investment price ($/KW) +om_price_fc = 10. # operation and maintenance price ($/kW/y) +lifetime_fc = 45000. # Fuel Cell lifetime (h) +load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] +input_unit_fc= "Kg" +output_unit_fc="KWh" + # Battery energy storage energy_rated_sto = 5000. # rated energy capacity (kWh) @@ -69,6 +105,20 @@ discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1) loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1] SoC_min = 0. # minimum State of Charge ∈ [0,1] SoC_ini = 0. # initial State of Charge ∈ [0,1] +SoC_max = 1. # initial State of Charge ∈ [0,1] + +#electrolyzer +power_rated_elyz = 2000. # rated power capacity (Kw) +cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2) +cons_slope_elyz = 56. # consumption rate (KWhé/KgH2) +cons_price_elyz = 0.# +investment_price_elyz = 1000. # initial investment price ($/kW) +om_price_elyz = 10. # operation and maintenance price ($/kW/y) +lifetime_elyz = 45000. #Electrolyzer lifetime (h) +load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1] + +input_unit_elyz= "KWh" +output_unit_elyz="Kg" # Photovoltaic (PV) generation power_rated_pv = 3000. # rated power (kW) @@ -83,4 +133,5 @@ derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1] power_rated_wind = 900. # rated power (kW) investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) -lifetime_wind = 25. # lifetime (y) \ No newline at end of file +lifetime_wind = 25. # lifetime (y) + diff --git a/examples/Microgrid_sizingH2_optimization1.ipynb b/examples/Microgrid_sizingH2_optimization1.ipynb new file mode 100644 index 0000000..11cf921 --- /dev/null +++ b/examples/Microgrid_sizingH2_optimization1.ipynb @@ -0,0 +1,1122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b14f854f-51e9-4253-8946-d7ca39628ed0", + "metadata": {}, + "source": [ + "# Microgrid sizing optimization\n", + "\n", + "Following the first example about [Microgrid performance simulation](Microgrid_Wind-Solar.ipynb)\n", + "using [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl),\n", + "this notebook delves into **optimal microgrid sizing**.\n", + "This means finding the optimal size (rated power or energy) of each component by solving an optimization problem where the objective function is composed of Key Performance Indicators (KPIs) related to the economic and technical performance of the project.\n", + "\n", + "Like in the previous example, the sizing is done for a microgrid project with *wind* and *solar* sources, plus a *battery* and a *dispatchable generator*.\n", + "\n", + "schematic of a microgrid with a symbolic representation of the notion of sizing\n", + "\n", + "The main steps of the optimal sizing process are:\n", + "1. set up the problem (in particular loading project data like prices and time series)\n", + "2. run the optimization\n", + "3. analyze the results\n", + "\n", + "## About the optimization formulation and process\n", + "\n", + "### Optimization formulation\n", + "\n", + "In this illustrative sizing example, the optimization objective (criterion) is:\n", + "- minimize the Levelized Costs of Electricity `lcoe` (€/kWh)\n", + "- while keeping the load shedding rate below some threshold (ex.: shedding rate ≤ 1%)\n", + "\n", + "but other indicators could be used as well: rate of renewables above some threshold...\n", + "\n", + "The above problem is a constrained optimization problem. Since we'll use unconstrained optimization solvers, we will reformulate it as: objective = lcoe + penalty for excess of shedding.\n", + "\n", + "### Optimization process\n", + "\n", + "This is a “blackbox” type of optimization problem: the optimization algorithm calls iteratively the Microgrid simulator with sizings to be tested until convergence (or reaching the maximum allowed number of iterations):\n", + "\n", + "schematic of microgrid sizing optimization process: iterations call of the simulator by optimizer\n", + "\n", + "The code is meant for using the [NLopt](https://nlopt.readthedocs.io/en/latest/) library of optimization solvers (through its [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl/) wrapper), but it could be adapted to other optimizers with minor changes (see remark below about the modularity of the code)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c2f04250-290c-4c38-93e6-074702ec0700", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" + ] + } + ], + "source": [ + "using Microgrids\n", + "using NLopt # optimization solvers\n", + "using Printf # pretty print results\n", + "using BenchmarkTools # for timing performance, optional" + ] + }, + { + "cell_type": "markdown", + "id": "5e20bad8-81a3-4a6b-8c1e-6848a20c0d55", + "metadata": {}, + "source": [ + "## Load Microgrid project data\n", + "\n", + "Loading parameters and time series for a Microgrid project with *wind* and *solar* sources, plus a *battery* and a *dispatchable generator*. \n", + "Values gathered from the [Microgrid_Wind-Solar.ipynb]((Microgrid_Wind-Solar.ipynb)) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data definition for Microgrid with wind, solar, storage and generator...\n" + ] + } + ], + "source": [ + "include(\"./Microgrid_Wind-Solar_data.jl\");" + ] + }, + { + "cell_type": "markdown", + "id": "9431f37f-4a44-42f7-a017-4935125731f2", + "metadata": {}, + "source": [ + "## Setting up the cost function (criterion) to be optimized\n", + "\n", + "The key coding objective is to **encapsulate** the microgrid simulators (`simulate` function of `Microgrids.jl` package) into an objective function that can be called by the optimization algorithm, that is which respects its expected interface (here NLopt).\n", + "\n", + "To increase the modularity which facilites using optimization solvers others that NLopt's we implement the encapsulation by **3 nested functions**:\n", + "\n", + "1. Simulation of Microgrid project described by a sizing vector `x` (vector input) → returns all simulation statistics\n", + "2. Extract KPIs of interest to build a multi-objective criterion: here lcoe and shedding rate\n", + "3. Combine these KPIs as one mono-objective criterion: here LCOE + penalty if shedding rate > shed_max\n", + " - and match the interface expected by NLopt's optimizers\n", + "\n", + "but if one cares more about compactness, this could be assembled into one single function." + ] + }, + { + "cell_type": "markdown", + "id": "adb0b6bb-54f7-4849-b1b0-b236681be8d9", + "metadata": {}, + "source": [ + "### Wrapper of the Microgrid simulator\n", + "\n", + "accept a sizing vector `x`, then create all `Microgrids.jl` components and call `simulate`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "simulate_microgrid" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", + "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", + "\n", + "Returns stats, costs\n", + "\"\"\"\n", + " function simulate_microgrid(x)\n", + " project = Project(lifetime, discount_rate, timestep, \"€\")\n", + " # Split decision variables (converted MW → kW):\n", + " power_rated_gen = x[1]*1000\n", + " energy_rated_sto = x[2]*1000\n", + " power_rated_pv = x[3]*1000\n", + " power_rated_wind = x[4]*1000\n", + " power_rated_elyz = x[5]*1000\n", + " power_rated_fc = x[6]*1000\n", + " capacity_rated_hytank = x[7]*1000\n", + " \n", + "\n", + " # Create components\n", + " gen = ProductionUnit(power_rated_gen,\n", + " fuel_intercept, fuel_slope, fuel_price,\n", + " investment_price_gen, om_price_gen, lifetime_gen,\n", + " load_ratio_min_gen, replacement_price_ratio,\n", + " salvage_price_ratio, input_unit_gen,output_unit_gen)\n", + " ftank = Tank(capacity_rated_ftank,investment_price_ftank, om_price_ftank,lifetime_ftank,loss_factor_ftank,fuel_ini_ratio,\n", + " fuel_min_ratio, fuel_max_ratio,replacement_price_ratio, salvage_price_ratio)\n", + " fuel_cell = ProductionUnit(power_rated_fc,cons_intercept_fc, cons_rate_fc,cons_price_fc,investment_price_fc, om_price_fc,lifetime_fc,\n", + " load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc)\n", + " hytank = Tank(capacity_rated_hytank,investment_price_hytank, om_price_hytank,lifetime_hytank,loss_factor_hytank,LoH_ini_ratio,\n", + " LoH_min_ratio, LoH_max_ratio,replacement_price_ratio, salvage_price_ratio)\n", + " dispatchables = DispatchableCompound{Float64}([gen,], [fuel_cell])\n", + " tanks = TankCompound{Float64}(ftank,hytank)\n", + " \n", + " elyz = ProductionUnit(power_rated_elyz,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, investment_price_elyz, om_price_elyz, lifetime_elyz,\n", + " load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz)\n", + "\n", + " batt = Battery(energy_rated_sto,\n", + " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", + " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_ini,SoC_max,\n", + " replacement_price_ratio, salvage_price_ratio)\n", + " pv = Photovoltaic(power_rated_pv, irradiance,\n", + " investment_price_pv, om_price_pv,\n", + " lifetime_pv, derating_factor_pv,\n", + " replacement_price_ratio, salvage_price_ratio)\n", + " windgen = WindPower(power_rated_wind, cf_wind,\n", + " investment_price_wind, om_price_wind,\n", + " lifetime_wind,\n", + " replacement_price_ratio, salvage_price_ratio)\n", + " \n", + " mg = Microgrid(project, Pload,dispatchables,\n", + " [elyz,],tanks,batt, [\n", + " pv,\n", + " windgen\n", + " ]\n", + " )\n", + " # Launch simulation:\n", + " traj, stats, costs = simulate(mg)\n", + "\n", + " return mg, traj ,stats, costs\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "df58e986-633b-4d9c-8d25-dd1d6e8a2e9f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capacity_rated_ftank" + ] + }, + { + "cell_type": "markdown", + "id": "e5321132-a5bf-467b-839b-99e36a2e781c", + "metadata": {}, + "source": [ + "Test of the simulator wrapper (on a baseline sizing):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d765dc6a-eeda-4727-b259-479176670dc1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "type OperationStats has no field lcoe", + "output_type": "error", + "traceback": [ + "type OperationStats has no field lcoe", + "", + "Stacktrace:", + " [1] getproperty(x::OperationStats, f::Symbol)", + " @ Base .\\Base.jl:37", + " [2] top-level scope", + " @ In[5]:5" + ] + } + ], + "source": [ + "# Baseline sizing: same as in Microgrid_Wind-Solar.ipynb notebook\n", + "x_base = [power_rated_gen, energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", + "# run simulation:\n", + "traj, stats, costs = simulate_microgrid(x_base)\n", + "x_base, costs.lcoe, costs.npc/1e6" + ] + }, + { + "cell_type": "markdown", + "id": "6e51a19f-0f68-44e4-ae09-ad11806f5747", + "metadata": {}, + "source": [ + "### Build the objective functions (criteria)\n", + "\n", + "- first bi-objective function x ↦ (lcoe, shedding rate)(x)\n", + "- then wrapped into a mono objective x ↦ J(x) by using a penalty for the excess of shedding rate\n", + " - and match the interface expected by NLopt's optimizers" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "470f5630-322c-497f-bac9-beb87f014f71", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "obj_multi" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", + "function obj_multi(x)\n", + " mg, traj,stats, costs = simulate_microgrid(x)\n", + " # Extract KPIs of interest\n", + " lcoe = costs.lcoe # $/kWh\n", + " shed_rate = stats.shed_rate; # in [0,1]\n", + " return lcoe, shed_rate\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6b3eb2d2-1513-424b-baa0-a49ca5247bb4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "obj" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Mono-objective criterion: LCOE + penalty if shedding rate > `shed_max`\n", + "\n", + "with signature adapted to NLopt with `grad` as 2nd argument\n", + "\n", + "load shedding penalty threshold `shed_max` should be in [0,1[\n", + "\"\"\"\n", + "function obj(x, grad, shed_max, w_shed_max=1e5)\n", + " lcoe, shed_rate = obj_multi(x)\n", + " over_shed = shed_rate - shed_max\n", + " if over_shed > 0.0\n", + " penalty = w_shed_max*over_shed\n", + " else\n", + " penalty = 0.0\n", + " end\n", + " J = lcoe + penalty\n", + "end" + ] + }, + { + "cell_type": "markdown", + "id": "c57f7af9-ea2f-40a8-b502-ba98b472410c", + "metadata": {}, + "source": [ + "### Tests the objective functions\n", + "\n", + "Sizing being tested:\n", + "- baseline sizing from the simulation notebook: perfect quality of service (QoS) with zero load shedding\n", + "- baseline modified with a halved generator sizing: very good QoS with a bit of load shedding → not penalized\n", + "- small PV and small wind generators only: low LCOE (i.e. the production-only LCOE of these sources) but but extremely bad QoS → huge penalty" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8de2153d-559f-4a60-880e-e8ea5fa3b810", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Base. multi: (0.21461387127508236, 0.20080291410297912) mono: 19080.506024169186\n", + "Shed. multi: (0.20721897266661307, 0.20080291410297912) mono: 19080.49862927058\n", + "PV. multi: (0.10149685980966677, 0.923547868561659) mono: 91354.8883530257\n", + "Wind. multi: (0.10040264224635914, 0.74395737102815) mono: 73395.83750545724\n" + ] + } + ], + "source": [ + "# Test:\n", + "shed_max = 0.01 # in [0,1]\n", + "\n", + "x_shed = [0, energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", + "x_pv = [0. 0. 500. 0. 0. 0. 0.]/1000.\n", + "x_wind = [0. 0. 0. 500. 0. 0. 0.]/1000.\n", + "\n", + "println(\"Base. multi: \", obj_multi(x_base), \" mono: \", obj(x_base,[], shed_max))\n", + "println(\"Shed. multi: \", obj_multi(x_shed), \" mono: \", obj(x_shed,[], shed_max))\n", + "println(\"PV. multi: \", obj_multi(x_pv), \" mono: \", obj(x_pv,[], shed_max))\n", + "println(\"Wind. multi: \", obj_multi(x_wind), \" mono: \", obj(x_wind,[], shed_max))" + ] + }, + { + "cell_type": "markdown", + "id": "dfbf1ccf-a7df-474a-991c-9437bd83d8ab", + "metadata": {}, + "source": [ + "## Optimization" + ] + }, + { + "cell_type": "markdown", + "id": "d15efd32-c2ab-4d9a-98cf-5918cdf0b9b1", + "metadata": {}, + "source": [ + "### Setting up the optimization problem" + ] + }, + { + "cell_type": "markdown", + "id": "87ccea54-0d83-4b01-8f7e-de37c8dfa0d9", + "metadata": {}, + "source": [ + "bounds of the design space and starting point: derived from maximal load power" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5ac92d7e-5de1-4ddf-a050-6897dbc8af2d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7-element Vector{Float64}:\n", + " 0.0\n", + " 17.07\n", + " 25.605\n", + " 8.535\n", + " 34.14\n", + " 2.0484\n", + " 17.07" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload_max = maximum(Pload)\n", + "\n", + "xmin = [0., 0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", + "x0 = [0., 3.0, 3.0, 2.0, 1.0, 1.0, 5.0] * (Pload_max/1000)\n", + "xmax = [0., 10.0, 15.0, 5.0, 20., 1.2 , 10. ] * (Pload_max/1000)" + ] + }, + { + "cell_type": "markdown", + "id": "6c2bc984-bae8-403f-8130-d50efc8f5717", + "metadata": {}, + "source": [ + "Optionally ban some choices:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9f65e094-4c58-4e9b-b4ee-b626f6504f6c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Solar power forbidden (optional)\n", + "#x0[3] = 1e-3\n", + "#xmax[3] = 1e-3\n", + "# Wind power forbidden (optional)\n", + "#x0[4] = 0.\n", + "#xmax[4] = 0." + ] + }, + { + "cell_type": "markdown", + "id": "f1bd17b9-c02d-4280-8715-c46efe6c03f4", + "metadata": {}, + "source": [ + "Check cost function on `xmin` and `xmax`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6d5ef67e-5d40-4bb0-a13d-65dc3d57bceb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((0.10149685980963595, 0.9998470957371233), (1.2747629440813006, 8.262599201630023e-6))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "obj_multi(xmin), obj_multi(xmax)" + ] + }, + { + "cell_type": "markdown", + "id": "3daceed1-2315-4eca-b7bd-b3a1e31cff09", + "metadata": { + "tags": [] + }, + "source": [ + "### Wrapper of the optimization process\n", + "\n", + "This is an optional step, but recommended to explore easily the impact of the many parameters taken by optimization algorithms.\n", + "\n", + "See [optimization termination conditions](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#termination-conditions) in NLopt documention for the meaning of `xtol_rel`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "08491363-e1ce-47d3-a425-4bc83888be12", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "optim_mg" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", + "\n", + "Parameters:\n", + "- `x0`: initial sizing (for the algorithms which need them)\n", + "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", + "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", + "- `maxeval`: maximum allowed number of calls to the objective function,\n", + " that is to the microgrid simulation\n", + "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", + "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", + "\n", + "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", + "but could be added to the parameters as well.\n", + "\"\"\"\n", + "function optim_mg(x0, shed_max, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-4, srand=1)\n", + " nx = length(x0) # number of optim variables\n", + " opt = Opt(algo, nx)\n", + " NLopt.srand(srand)\n", + " \n", + " opt.lower_bounds = xmin\n", + " opt.upper_bounds = xmax\n", + " opt.min_objective = (x, grad) -> obj(x, grad, shed_max)\n", + " opt.xtol_rel = xtol_rel\n", + " opt.maxeval = maxeval\n", + " \n", + " (fopt, xopt, ret) = optimize(opt, x0)\n", + " return xopt, ret, opt.numevals\n", + "end" + ] + }, + { + "cell_type": "markdown", + "id": "11264431-19b1-4349-bc90-19d88bba1b87", + "metadata": {}, + "source": [ + "### Run optimization & analyze results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: MAXEVAL_REACHED after 1000 iterations. \n", + "x*=[0.0, 14421.3, 20578.0, 3478.3, 100.0, 1520.5, 9680.3]\n", + "LCOE*: 0.6085859028176098\n", + "shed*: 7.567619784151898e-8\n" + ] + } + ], + "source": [ + "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "shed_max = 0.0 # in [0,1]\n", + "maxeval=1000\n", + "xopt, ret, numevals = optim_mg(x0, shed_max, algo, maxeval)\n", + "\n", + "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt*1000; digits=1)) # kW\n", + "lcoe_opt, shed_rate_opt = obj_multi(xopt)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, + { + "cell_type": "markdown", + "id": "58772966-74b1-431c-9f84-e8efe7352130", + "metadata": {}, + "source": [ + "optional local \"polishing\":" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "78b3e3f8-0de8-47f3-a8a4-40358feef172", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LN_SBPLX polish: XTOL_REACHED after 643 iterations. \n", + "x*=[0.0, 2350.4, 2176.4, 2133.3, 1212.5, 843.2, 16738.2]\n", + "LCOE*: 0.19632167925055366\n", + "shed*: 0.009999999270919192\n" + ] + } + ], + "source": [ + "algo_polish = :LN_SBPLX\n", + "xopt_polish, ret, numevals = optim_mg(xopt, shed_max, algo_polish)\n", + "\n", + "@printf(\"%s polish: %s after %d iterations. \\nx*=\", algo_polish, ret, numevals)\n", + "println(round.(xopt_polish*1000; digits=1)) # kW\n", + "lcoe_opt, shed_rate_opt = obj_multi(xopt_polish)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, + { + "cell_type": "markdown", + "id": "f543939c-c26d-40fb-aefa-129dff57ae1d", + "metadata": {}, + "source": [ + "Retrieve all performance statistics of the optimized sizing" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1e046d9e-21ec-4834-9bf3-5bc1c9be1f0a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "type OperationStats has no field npc", + "output_type": "error", + "traceback": [ + "type OperationStats has no field npc", + "", + "Stacktrace:", + " [1] getproperty(x::OperationStats, f::Symbol)", + " @ Base .\\Base.jl:37", + " [2] top-level scope", + " @ In[15]:2" + ] + } + ], + "source": [ + "traj_opt, stats_opt, costs_opt = simulate_microgrid(xopt_polish)\n", + "@printf(\"NPC*: %.2f M\\$ (compared to %.2f M\\$ in baseline)\\n\", costs_opt.npc/1e6, costs.npc/1e6)\n", + "@printf(\"rate of renewables: %.1f %%\\n\", stats_opt.renew_rate*100)\n", + "# Display all operation statistics\n", + "stats_opt" + ] + }, + { + "cell_type": "markdown", + "id": "464f74e6-2939-4bed-ac6f-87ec8ef99660", + "metadata": {}, + "source": [ + "#### Summary of some results for 1% max shedding rate\n", + "\n", + "Global search algorithms:\n", + "- **CRS2**: 0.190 720 \\$/kWh (with shedding ~ threshold).\n", + " x* = [889.0, 2013.0, 1570.9, 1354.0] → *best global algorithm choice so far, although effect of seed not studied*\n", + "- **DIRECT**: 0.191 297 \\$/kWh (with shedding < threshold) in 1000 iter.\n", + " x* = [910.4, 1837.8, 1543.3, 1375.7] \n", + " - \"polishing\" with SBPLX: 0.190 650 \\$/kWh (with shedding ~ threshold) in 207 extra iter.\n", + " x* = [887.1, 1992.4, 1543.3, 1375.6]\n", + " - increasing DIRECT maxeval to 10k doesn't bring much improvement (0.191 258 \\$/kWh)\n", + " compared to SBPLX polishing\n", + "- **ESCH**: 0.194 884 \\$/kWh with shedding OK (with shedding < threshold) in 1000 iter.\n", + " x* = [1028.1, 1769.6, 1420.5, 1365.1] → *much slower to converge than DIRECT or CRS*\n", + "\n", + "Local search algorithms:\n", + "- **SUBPLX**: 0.190 630 \\$/kWh with (with shedding ~ threshold) **in 479 iter** ( 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n", + "\n" + ], + "text/markdown": [ + "```\n", + "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", + "```\n", + "\n", + "Aggregate operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object).\n", + "\n", + "Discontinuous statistics can optionally be relaxed (smoothed) using the relaxation parameter `ε`:\n", + "\n", + " * 0.0 means no relaxation (default value)\n", + " * 1.0 yields the strongest relaxation\n", + "\n", + "Using relaxation (`ε` > 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n" + ], + "text/plain": [ + "\u001b[36m aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\u001b[39m\n", + "\n", + " Aggregate operation time series \u001b[36moper_traj\u001b[39m into yearly statistics for the the\n", + " microgrid \u001b[36mmg\u001b[39m (returned as an \u001b[36mOperationStats\u001b[39m object).\n", + "\n", + " Discontinuous statistics can optionally be relaxed (smoothed) using the\n", + " relaxation parameter \u001b[36mε\u001b[39m:\n", + "\n", + " • 0.0 means no relaxation (default value)\n", + "\n", + " • 1.0 yields the strongest relaxation\n", + "\n", + " Using relaxation (\u001b[36mε\u001b[39m > 0) is recommended when using gradient-based\n", + " optimization and then a “small enough” value between 0.05 and 0.30 is\n", + " suggested." + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "?aggregation" + ] + }, + { + "cell_type": "markdown", + "id": "37b788d2-a5c7-4444-9f45-fefa8929d6fe", + "metadata": {}, + "source": [ + "---\n", + "## *How we imagine the optimization interface in a future version...*\n", + "\n", + "Desirable feature: *dynamically select* which variables are optimized (e.g. using `getproperty` to dynamically access structure fields...)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2a51ac3b-4493-472b-8b0f-ac924edf5de7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-element Vector{Tuple{Vector{Symbol}, Float64, Float64, Float64}}:\n", + " ([:generator, :power_rated], 1000.0, 0.0, 2000.0)\n", + " ([:battery, :energy_max], 3000.0, 0.0, 6000.0)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#mg0 = baseline microgrid project, with given price and time series data\n", + "opti_params = [ # list of :component/variable name, initial, min, max values\n", + " ([:generator,:power_rated], 1000.0, 0., 2000.), \n", + " ([:battery,:energy_max], 3000.0, 0., 6000.)\n", + "]\n", + "#optim_mg(mg0, opti_params)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Microgrid_sizing_optimization.ipynb b/examples/Microgrid_sizing_optimization.ipynb index e34b47b..1e7912f 100644 --- a/examples/Microgrid_sizing_optimization.ipynb +++ b/examples/Microgrid_sizing_optimization.ipynb @@ -46,7 +46,9 @@ "cell_type": "code", "execution_count": 1, "id": "c2f04250-290c-4c38-93e6-074702ec0700", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "using Microgrids\n", @@ -70,7 +72,9 @@ "cell_type": "code", "execution_count": 2, "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -117,7 +121,9 @@ "cell_type": "code", "execution_count": 3, "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -183,7 +189,9 @@ "cell_type": "code", "execution_count": 4, "id": "d765dc6a-eeda-4727-b259-479176670dc1", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -220,7 +228,9 @@ "cell_type": "code", "execution_count": 5, "id": "470f5630-322c-497f-bac9-beb87f014f71", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -248,7 +258,9 @@ "cell_type": "code", "execution_count": 6, "id": "6b3eb2d2-1513-424b-baa0-a49ca5247bb4", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -297,7 +309,9 @@ "cell_type": "code", "execution_count": 7, "id": "8de2153d-559f-4a60-880e-e8ea5fa3b810", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -352,7 +366,9 @@ "cell_type": "code", "execution_count": 8, "id": "5ac92d7e-5de1-4ddf-a050-6897dbc8af2d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -389,7 +405,9 @@ "cell_type": "code", "execution_count": 9, "id": "9f65e094-4c58-4e9b-b4ee-b626f6504f6c", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Solar power forbidden (optional)\n", @@ -412,7 +430,9 @@ "cell_type": "code", "execution_count": 10, "id": "6d5ef67e-5d40-4bb0-a13d-65dc3d57bceb", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -432,7 +452,9 @@ { "cell_type": "markdown", "id": "3daceed1-2315-4eca-b7bd-b3a1e31cff09", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### Wrapper of the optimization process\n", "\n", @@ -445,7 +467,9 @@ "cell_type": "code", "execution_count": 11, "id": "08491363-e1ce-47d3-a425-4bc83888be12", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -501,7 +525,9 @@ "cell_type": "code", "execution_count": 12, "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -539,7 +565,9 @@ "cell_type": "code", "execution_count": 13, "id": "78b3e3f8-0de8-47f3-a8a4-40358feef172", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -575,7 +603,9 @@ "cell_type": "code", "execution_count": 14, "id": "1e046d9e-21ec-4834-9bf3-5bc1c9be1f0a", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -662,7 +692,9 @@ "cell_type": "code", "execution_count": 15, "id": "8f4a08f2-96d5-47f1-a9fb-41a419051be8", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -702,13 +734,15 @@ "cell_type": "code", "execution_count": 16, "id": "9dc290cb-ac52-44db-8ee3-c54dce0fe4be", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 1.967928 seconds (1.46 M allocations: 7.884 GiB, 6.18% gc time)\n" + " 5.030847 seconds (1.46 M allocations: 7.884 GiB, 10.79% gc time)\n" ] }, { @@ -740,13 +774,15 @@ "cell_type": "code", "execution_count": 17, "id": "16330858-ca64-45e6-b641-d197924e401d", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 162.559 μs (143 allocations: 826.58 KiB)\n" + " 203.800 μs (143 allocations: 826.58 KiB)\n" ] }, { @@ -803,7 +839,7 @@ "\\begin{verbatim}\n", "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", "\\end{verbatim}\n", - "Aggregates operation time series \\texttt{oper\\_traj} into yearly statistics for the the microgrid \\texttt{mg} (returned as an \\texttt{OperationStats} object).\n", + "Aggregate operation time series \\texttt{oper\\_traj} into yearly statistics for the the microgrid \\texttt{mg} (returned as an \\texttt{OperationStats} object).\n", "\n", "Discontinuous statistics can optionally be relaxed (smoothed) using the relaxation parameter \\texttt{ε}:\n", "\n", @@ -814,7 +850,7 @@ "\\item 1.0 yields the strongest relaxation\n", "\n", "\\end{itemize}\n", - "when using relaxation, a value between 0.05 and 0.30 is suggested\n", + "Using relaxation (\\texttt{ε} > 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n", "\n" ], "text/markdown": [ @@ -822,20 +858,20 @@ "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", "```\n", "\n", - "Aggregates operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object).\n", + "Aggregate operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object).\n", "\n", "Discontinuous statistics can optionally be relaxed (smoothed) using the relaxation parameter `ε`:\n", "\n", " * 0.0 means no relaxation (default value)\n", " * 1.0 yields the strongest relaxation\n", "\n", - "when using relaxation, a value between 0.05 and 0.30 is suggested\n" + "Using relaxation (`ε` > 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n" ], "text/plain": [ "\u001b[36m aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\u001b[39m\n", "\n", - " Aggregates operation time series \u001b[36moper_traj\u001b[39m into yearly statistics for the\n", - " the microgrid \u001b[36mmg\u001b[39m (returned as an \u001b[36mOperationStats\u001b[39m object).\n", + " Aggregate operation time series \u001b[36moper_traj\u001b[39m into yearly statistics for the the\n", + " microgrid \u001b[36mmg\u001b[39m (returned as an \u001b[36mOperationStats\u001b[39m object).\n", "\n", " Discontinuous statistics can optionally be relaxed (smoothed) using the\n", " relaxation parameter \u001b[36mε\u001b[39m:\n", @@ -844,7 +880,9 @@ "\n", " • 1.0 yields the strongest relaxation\n", "\n", - " when using relaxation, a value between 0.05 and 0.30 is suggested" + " Using relaxation (\u001b[36mε\u001b[39m > 0) is recommended when using gradient-based\n", + " optimization and then a “small enough” value between 0.05 and 0.30 is\n", + " suggested." ] }, "execution_count": 18, @@ -871,7 +909,9 @@ "cell_type": "code", "execution_count": 19, "id": "2a51ac3b-4493-472b-8b0f-ac924edf5de7", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -898,7 +938,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.2", + "display_name": "Julia 1.9.3", "language": "julia", 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z>s|%}(aZLn0KY=8gKmB&dzmA}dfCtQRJ>vzM(U>kO+)}LGj%4p^dWR%(2XdP>VqeS$Pe@8YZK)bLByQVsOyoj-CbSqm&Uux zy;g>;fHoR%ZD-Cu{16Za?7#}29-sTShSKa1&~_NkuD-reKx3R$8q78)3ES)Lu>h4e z+SftA>lcy!eb_BMQ`0oxU|=?GJ?7>PsW(Mz{*vjOY4G7@q69S3PZ=tr%9?xgKUIH# ydx2#Tu3Sg_{p)|9)K&g!wg3Guz@NIWE~pA5uEq^VMxGEq2Ft0wDt-Cp)Bgf;mS@KR literal 0 HcmV?d00001 diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 9e1c751..a0c9a7a 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -3,11 +3,11 @@ module Microgrids import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions export simulate, - NonDispatchableSource, - Project, DispatchableGenerator, Battery, Photovoltaic, PVInverter, WindPower, Microgrid, + NonDispatchableSource, ProductionUnit, Tank, TankCompound, + Project, DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, capacity_from_wind, - OperationTraj, OperationStats, - operation, aggregation, dispatch, production, + OperationTraj, OperationStats, increment, + operation, aggregation, dispatch, dispatch2, production, CostFactors, MicrogridCosts, component_costs, economics include("components.jl") diff --git a/src/components.jl b/src/components.jl index 7917af5..e52916a 100644 --- a/src/components.jl +++ b/src/components.jl @@ -22,27 +22,27 @@ end """ Dispatchable power source -(e.g. Diesel generator, Gas turbine, Fuel cell) - -# About the types of the fields +(e.g. Diesel generator, Gas turbine, Fuel cell with its associated tank) All component parameters should be `Float64` except for the *sizing parameter(s)* (here `power_rated`) which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type). """ -struct DispatchableGenerator{Topt<:Real} + + +struct ProductionUnit{Topt<:Real} # Main technical parameters "rated power (kW)" power_rated::Topt - "fuel consumption curve intercept (L/h/kW_max)" - fuel_intercept::Float64 - "fuel consumption curve slope (L/h/kW)" - fuel_slope::Float64 + "combustible consumption curve intercept (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used are linear " + consumption_intercept::Float64 + "input consumption curve slope (L/h/kW or Kg/h/kW or KW/Kg/h )" + consumption_slope::Float64 # Main economics parameters "fuel price (\$/L)" - fuel_price::Float64 + combustible_price::Float64 "initial investiment price (\$/kW)" investment_price::Float64 "operation & maintenance price (\$/kW/h of operation)" @@ -52,7 +52,7 @@ struct DispatchableGenerator{Topt<:Real} # Secondary technical parameters (which should have a default value) "minimum load ratio ∈ [0,1]" - load_ratio_min::Float64 + minimum_load_ratio::Float64 # Secondary economics parameters (which should have a default value) "replacement price, relative to initial investment" @@ -60,9 +60,60 @@ struct DispatchableGenerator{Topt<:Real} "salvage price, relative to initial investment" salvage_price_ratio::Float64 "fuel quantity unit (used in fuel price and consumption curve parameters)" - fuel_unit::String + input_unit::String + output_unit::String + +end +""" +Description to be added + +""" + +struct Tank{Topt<:Real} + # Main technical parameters + "rated capacity (kg or L)" + capacity::Topt + + # Main economics parameters + "initial investment price (\$/kg or \$/L)" + investment_price::Float64 + "operation and maintenance price (\$/kg/y or \$/L)/y" + om_price::Float64 + "calendar lifetime (y)" + lifetime::Float64 + + # Secondary technical parameters (which should have a default value) + "linear loss factor α (efficiency is about 1 − α) ∈ [0,1]" + loss_factor::Float64 # (auto - discharge rate ) + "initial level of tank ∈ [0,1]" + ini_filling_ratio ::Float64 + "minimum level of tank ∈ [0,1]" + min_filling_ratio::Float64 + "maximum level of tank ∈ [0,1]" + max_filling_ratio::Float64 + + # Secondary economics parameters (which should have a default value) + "replacement price, relative to initial investment" + replacement_price_ratio::Float64 + "salvage price, relative to initial investment" + salvage_price_ratio::Float64 +end + + + +struct DispatchableCompound{Topt<:Real} + generator + fuel_cell + end +struct TankCompound{Topt<:Real} + fuelTank + h2Tank + +end + + """ Battery energy storage (including AC/DC converter) @@ -78,7 +129,8 @@ All component parameters should be `Float64` except for the which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type). """ -struct Battery{Topt<:Real} + +struct Battery{Topt<:Real} # Main technical parameters "rated energy capacity (kWh)" energy_rated::Topt @@ -102,6 +154,8 @@ struct Battery{Topt<:Real} loss_factor::Float64 "minimum State of Charge ∈ [0,1]" SoC_min::Float64 + "maximum State of Charge ∈ [0,1]" + SoC_max::Float64 "initial State of Charge ∈ [0,1]" SoC_ini::Float64 @@ -112,6 +166,7 @@ struct Battery{Topt<:Real} salvage_price_ratio::Float64 end + "Base type for non-dispatchable sources (e.g. renewables like wind and solar)" abstract type NonDispatchableSource end @@ -233,10 +288,18 @@ struct Microgrid{Topt<:Real} project::Project "desired load time series (kW)" load::Vector{Float64} - "dispatchable generator" - generator::DispatchableGenerator{Topt} + " Dispatchable Compound" + dispatchables:: DispatchableCompound{Topt} + "electrolyzer" + electrolyzer + "Tanks compound" + tanks :: TankCompound{Topt} "energy storage (e.g. battery)" storage::Battery{Topt} "non-dispatchable sources (e.g. renewables like wind and solar)" - nondispatchables #::Vector{NonDispatchableSource} + nondispatchables end + + + + diff --git a/src/dispatch.jl b/src/dispatch.jl index f8a7836..772c227 100644 --- a/src/dispatch.jl +++ b/src/dispatch.jl @@ -48,3 +48,91 @@ function dispatch(Pnl_req, return Pnl, Pgen, Pbatt, Pcurt, Pshed end +function dispatch(Pnl_req, + Pbatt_cmax, Pbatt_dmax, + Pgen_min , Pgen_max, + Pelyz_min, Pelyz_max, + Pfc_min, Pfc_max) + Pgen = 0.0 + Pcurt = 0.0 + Pshed = 0.0 + Pelyz = 0.0 + Pfc = 0.0 + # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) + if Pnl_req >= 0.0 + Pbatt=min(Pbatt_dmax,Pnl_req) + + if Pnl_req - Pbatt > Pfc_min + Pfc=min(Pfc_max,Pnl_req-Pbatt) + end + + if Pnl_req - Pbatt - Pfc > Pgen_min + Pgen=min(Pgen_max,Pnl_req-Pbatt-Pfc) + end + + Pshed = Pnl_req - Pbatt - Pfc - Pgen + + # Pnl_req < 0 - VRE excess + elseif Pnl_req < 0.0 + Pbatt = max(Pbatt_cmax,Pnl_req) + + if Pbatt - Pnl_req > Pelyz_min + Pelyz = min(Pelyz_max,Pbatt-Pnl_req) + end + + Pcurt = Pbatt - Pelyz - Pnl_req + end + + Pnl = Pnl_req - Pshed + Pcurt + + return Pnl, Pgen , Pbatt, Pcurt, Pshed, Pelyz, Pfc +end + + +function dispatch2(Pnl_req, + Pbatt_cmax, Pbatt_dmax, + Pgen_min , Pgen_max, + Pelyz_min, Pelyz_max, + Pfc_min, Pfc_max) + Pgen = 0.0 + Pcurt = 0.0 + Pshed = 0.0 + Pelyz = 0.0 + Pfc = 0.0 + # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) + if Pnl_req >= 0.0 + + if Pnl_req > Pfc_min + + Pfc = min(Pfc_max, Pnl_req) + + end + + Pbatt = min(Pbatt_dmax, Pnl_req - Pfc) + + if Pnl_req - Pbatt - Pfc > Pgen_min + + Pgen = min(Pgen_max, Pnl_req - Pbatt - Pfc) + + end + + Pshed = Pnl_req - Pbatt - Pfc - Pgen + + # Pnl_req < 0 - VRE excess + elseif Pnl_req < 0.0 + + if -1 * Pnl_req > Pelyz_min + + Pelyz=min(Pelyz_max, - Pnl_req) + + end + + Pbatt=max(Pbatt_cmax,Pnl_req - Pelyz) + + Pcurt = Pbatt - Pelyz - Pnl_req + end + + Pnl = Pnl_req - Pshed + Pcurt + + return Pnl, Pgen , Pbatt, Pcurt, Pshed, Pelyz, Pfc +end \ No newline at end of file diff --git a/src/economics.jl b/src/economics.jl index 89954ba..e822add 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -120,10 +120,15 @@ also includes `system` cost (all components) and two key economic data: system:: CostFactors "costs of generator" generator:: CostFactors + "costs of energy storage" storage:: CostFactors "costs of each non-dispatchable source" nondispatchables:: Vector{CostFactors} + electrolyzer:: CostFactors + fuel_cell:: CostFactors + fuel_tank:: CostFactors + h2_tank:: CostFactors end ### component_costs methods @@ -197,20 +202,20 @@ function component_costs(mg_project::Project, lifetime::Real, end """ - component_costs(gen::DispatchableGenerator, mg_project::Project, oper_stats::OperationStats) + component_costs(gen::ProductionUnit, mg_project::Project, oper_stats::OperationStats) -Compute net present cost factors for a `DispatchableGenerator`. +Compute net present cost factors for a `ProductionUnit`. """ -function component_costs(gen::DispatchableGenerator, mg_project::Project, oper_stats::OperationStats) - rating = gen.power_rated - investment = gen.investment_price * rating - replacement = investment * gen.replacement_price_ratio - salvage = investment * gen.salvage_price_ratio - om_annual = gen.om_price_hours * oper_stats.gen_hours * rating - fuel_annual = gen.fuel_price * oper_stats.gen_fuel +function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_unit_hours, prod_unit_cons) + rating = prod_unit.power_rated + investment = prod_unit.investment_price * rating + replacement = investment * prod_unit.replacement_price_ratio + salvage = investment * prod_unit.salvage_price_ratio + om_annual = prod_unit.om_price_hours * rating + fuel_annual = prod_unit.combustible_price * prod_unit_cons # effective generator lifetime (in years) - lifetime = gen.lifetime_hours / oper_stats.gen_hours + lifetime = prod_unit.lifetime_hours / prod_unit_hours c = component_costs( mg_project, lifetime, @@ -220,6 +225,22 @@ function component_costs(gen::DispatchableGenerator, mg_project::Project, oper_s return c end +function component_costs(tank::Tank, mg_project::Project) + rating = tank.capacity + investment = tank.investment_price * rating + replacement = investment * tank.replacement_price_ratio + salvage = investment * tank.salvage_price_ratio + om_annual = tank.om_price * rating + fuel_annual = 0.0 + + c = component_costs( + mg_project, tank.lifetime, + investment, replacement, salvage, + om_annual, fuel_annual + ) + return c +end + """ component_costs(bt::Battery, mg_project::Project, oper_stats::OperationStats) @@ -324,8 +345,11 @@ See also: [`aggregation`](@ref) """ function economics(mg::Microgrid, oper_stats::OperationStats) # Dispatchable generator - gen_costs = component_costs(mg.generator, mg.project, oper_stats) - + gen_costs = component_costs(mg.dispatchables.generator[1], mg.project, oper_stats.gen_hours,oper_stats.gen_fuel) + elyz_costs = component_costs(mg.electrolyzer[1], mg.project, oper_stats.elyz_hours,oper_stats.elyz_consumed_energy) + fc_costs = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.h2_consumed) + fuel_tantk_cost = component_costs(mg.tanks.fuelTank, mg.project) + h2_tantk_cost = component_costs(mg.tanks.h2Tank, mg.project) # Energy storage sto_costs = component_costs(mg.storage, mg.project, oper_stats) @@ -342,14 +366,14 @@ function economics(mg::Microgrid, oper_stats::OperationStats) for i = 1:mg.project.lifetime] crf = 1/sum(discount_factors) # Cost of all components and NPC of the project - system_costs = gen_costs + sto_costs + sum(nd_costs) + system_costs = gen_costs + sto_costs + sum(nd_costs) + elyz_costs + fc_costs + fuel_tantk_cost + h2_tantk_cost npc = system_costs.total # levelized cost of energy annualized_cost = npc*crf # $/y lcoe = annualized_cost / oper_stats.served_energy # ($/y) / (kWh/y) → $/kWh costs = MicrogridCosts(lcoe, npc, - system_costs, gen_costs, sto_costs, nd_costs + system_costs, gen_costs, sto_costs, nd_costs, elyz_costs,fc_costs,fuel_tantk_cost,h2_tantk_cost ) return costs diff --git a/src/operation.jl b/src/operation.jl index acf1bb7..ca98abf 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -16,15 +16,15 @@ struct OperationTraj{T<:Real} # diesel generator "Diesel generator power at each time instant (kW)" Pgen::Vector{T} + Pelyz:: Vector{T} + Pfc :: Vector{T} # battery "Battery energy at each time instant (kWh)" Ebatt::Vector{T} "Battery power at each time instant (kW)" Pbatt::Vector{T} - "Maximum battery discharge power at time t (kW)" - Pbatt_dmax::Vector{T} - "Maximum battery charge power at time t (kW)" - Pbatt_cmax::Vector{T} + LoH :: Vector{T} + LoF :: Vector{T} # renewables sources "Renewables curtailment power at each time instant (kW)" power_curtailment::Vector{T} @@ -57,6 +57,17 @@ struct OperationStats "fuel consumption (L/y)" gen_fuel + fc_energy + + fc_hours + + h2_consumed + h2_produced + elyz_hours + elyz_consumed_energy + h2_chain_loss + + # Energy storage (e.g. battery) statistics "cycling of the energy storage (cycles/y)" storage_cycles @@ -91,7 +102,14 @@ const oper_stats_units = Dict( :shed_rate => "in [0,1]", :gen_energy => "kWh", :gen_hours => "h", - :gen_fuel => "L", # TODO: use instead the generator's fuel unit string + :gen_fuel => "L",# TODO: use instead the generator's fuel unit string + :fc_energy => "kWh", + :fc_hours => "h", + :h2_consumed => "kg", + :h2_produced => "kg", + :elyz_hours => "h", + :elyz_consumed_energy => "kWh", + :h2_chain_loss => "kWh", :storage_cycles => "", :storage_char_energy => "kWh", :storage_dis_energy => "kWh", @@ -121,7 +139,33 @@ function Base.show(io::IO, ::MIME"text/plain", stats::OperationStats) println(io, "- ", name, ": ", format_stat(stats, name; sigdigits=5), " ", unit) end end +""" +increment the running time, and fuel consumption of a productionUnit + +""" +function increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Real=0.0) + Pprod_unit_max = prod_unit.power_rated + Pprod_unit_eps = Pprod_unit_max*1e-6 + time_inc = 0 + cons_rate = 0 + if prod_unit_power > Pprod_unit_eps # prod_unit ON + Pprod_unit_norm = prod_unit_power / (ε * Pprod_unit_max) # can be Inf e.g. for ε=0.0 + + if Pprod_unit_norm <= 1.0 + time_inc = Pprod_unit_norm # relaxation of discontinuous generator statistics for small Pgen + cons_rate = Pprod_unit_norm * prod_unit.consumption_intercept * Pprod_unit_max + + prod_unit.consumption_slope * prod_unit_power # (L/h) + else + time_inc=1 + cons_rate = prod_unit.consumption_intercept * Pprod_unit_max + + prod_unit.consumption_slope * prod_unit_power # (L/h) + end + + end + return time_inc , cons_rate + +end """ operation(mg::Microgrid) @@ -144,42 +188,104 @@ function operation(mg::Microgrid) # Fixed parameters and short aliases K = length(mg.load) dt = mg.project.timestep - Pgen_max = mg.generator.power_rated + + Pgen_max = collect(gen.power_rated for gen in mg.dispatchables.generator) + Pgen_min = collect(mg.dispatchables.generator[i].minimum_load_ratio * Pgen_max[i] for i in eachindex(Pgen_max) ) + fuel_slope = collect(gen.consumption_slope for gen in mg.dispatchables.generator) + fuel_intercept = collect(gen.consumption_intercept for gen in mg.dispatchables.generator) + + LoF_max = mg.tanks.fuelTank.max_filling_ratio * mg.tanks.fuelTank.capacity + LoF_min = mg.tanks.fuelTank.min_filling_ratio * mg.tanks.fuelTank.capacity + Esto_max = mg.storage.energy_rated Esto_min = mg.storage.SoC_min * Esto_max Psto_pmax = mg.storage.discharge_rate * Esto_max Psto_pmin = -mg.storage.charge_rate * Esto_max # <0 in line with the generator convention for Psto sto_loss = mg.storage.loss_factor + Pelyz_max = collect(el.power_rated for el in mg.electrolyzer) + Pelyz_min = collect(mg.electrolyzer[i].minimum_load_ratio * Pelyz_max[i] for i in eachindex(Pelyz_max) ) + prod_rate = collect(el.consumption_slope for el in mg.electrolyzer) + + Pfc_max = collect(fc.power_rated for fc in mg.dispatchables.fuel_cell) + Pfc_min = collect(mg.dispatchables.fuel_cell[i].minimum_load_ratio * Pfc_max[i] for i in eachindex(Pfc_max) ) + cons_rate = collect(fc.consumption_slope for fc in mg.dispatchables.fuel_cell) + + LoH_max = mg.tanks.h2Tank.max_filling_ratio * mg.tanks.h2Tank.capacity + LoH_min = mg.tanks.h2Tank.min_filling_ratio * mg.tanks.h2Tank.capacity + + + # Initialization of loop variables Pnl = zeros(Topt,K) - Pgen = zeros(Topt,K) + Pgen = zeros(Topt,K,length(mg.dispatchables.generator)) + Pgen_dmax = zeros(Topt,K,length(mg.dispatchables.generator)) + Esto = zeros(Topt,K+1) Psto = zeros(Topt,K) + Psto_dmax = zeros(Topt,K) - Psto_cmax = zeros(Topt,K) + Psto_cmax = zeros(Topt,K) + Pspill = zeros(Topt,K) Pshed = zeros(Topt,K) - # Initial storage state + Pelyz = zeros(Topt,K,length(mg.electrolyzer)) + Pelyz_cmax = zeros(Topt,K,length(mg.electrolyzer)) + + Pfc = zeros(Topt,K,length(mg.dispatchables.fuel_cell)) + Pfc_pmax = zeros(Topt,K,length(mg.dispatchables.fuel_cell)) + + LoH = zeros(Topt,K+1) + LoF = zeros(Topt,K+1) + + # Initial battery storage state Esto_ini = mg.storage.SoC_ini * mg.storage.energy_rated Esto[1] = Esto_ini + # Initial h2 storage state + LoH_ini = mg.tanks.h2Tank.ini_filling_ratio * mg.tanks.h2Tank.capacity + LoH[1] = LoH_ini + + # Initial fuel tank state + + LoF_ini = mg.tanks.fuelTank.ini_filling_ratio * mg.tanks.fuelTank.capacity + LoF[1] = LoF_ini + for k=1:K - # Storage energy and power limits + # Storage energy and power limits Psto_emin = - (Esto_max - Esto[k]) / ((1 - sto_loss) * dt) Psto_emax = (Esto[k] - Esto_min) / ((1 + sto_loss) * dt) - Psto_dmax[k] = min(Psto_emax, Psto_pmax) - Psto_cmax[k] = max(Psto_emin, Psto_pmin) + Psto_dmax = min(Psto_emax, Psto_pmax) + Psto_cmax = max(Psto_emin, Psto_pmin) + + + Pelyz_emax = collect( (LoH_max - LoH[k] ) * prod_rate[i] * dt for i in eachindex(prod_rate)) + Pelyz_cmax = collect( min(Pelyz_emax[i],Pelyz_max[i]) for i in eachindex(Pelyz_emax)) + + + Pfc_emax = collect( (LoH[k] - LoH_min) / cons_rate[i] * dt for i in eachindex(cons_rate)) + Pfc_pmax = collect( min(Pfc_emax[i],Pfc_max[i]) for i in eachindex(Pfc_emax)) + + Pgen_emax = collect( (((LoF[K] - LoF_min) - fuel_intercept[i] * Pgen_max[i]* dt )/ fuel_slope[i] * dt ) for i in eachindex(cons_rate)) + Pgen_dmax = collect( min(Pgen_emax[i],Pgen_max[i]) for i in eachindex(Pgen_emax)) + # dispatch - Pnl[k], Pgen[k], Psto[k], Pspill[k], Pshed[k] = dispatch(Pnl_request[k], Psto_cmax[k], Psto_dmax[k], Pgen_max) + Pnl[k], Pgen[k,1], Psto[k], Pspill[k], Pshed[k], Pelyz[k,1], Pfc[k,1] = dispatch(Pnl_request[k], Psto_cmax, Psto_dmax, Pgen_min[1], Pgen_dmax[1], Pelyz_min[1],Pelyz_cmax[1], Pfc_min[1],Pfc_pmax[1]) - # Storage dynamics + # Battery storage dynamics Esto[k+1] = Esto[k] - (Psto[k] + sto_loss * abs(Psto[k])) * dt + + # Hydrogen storage dynamics + LoH[k+1] = LoH[k] + (sum(collect((Pelyz[k,i]/prod_rate[i] for i in eachindex(prod_rate)))) - sum(collect(Pfc[k,i]*cons_rate[i] for i in eachindex(cons_rate)))) * dt + + # fuel storage dynamics + LoF[k+1] = LoF[k] - sum(collect(((Pgen[k,i] / fuel_slope[i] ) + fuel_intercept[i]* Pgen_max[i]) for i in eachindex(fuel_slope))) * dt + end - oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen, Esto, Psto, Psto_dmax, Psto_cmax, Pspill) + oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pelyz[:,1], Pfc[:,1], Esto, Psto, LoH, LoF, Pspill) return oper_traj end @@ -225,6 +331,16 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) storage_loss_energy = storage_char_energy - storage_dis_energy - (Efin - Eini) # kWh/y + #Power-H2-Power statistics + elyz_consumed_energy =sum(oper_traj.Pelyz) * dt + elyz_produced_h2 = elyz_consumed_energy / mg.electrolyzer[1].consumption_slope + fc_produced_energy = sum(oper_traj.Pfc) * dt + fc_consumed_h2 = fc_produced_energy * mg.dispatchables.fuel_cell[1].consumption_slope + + LoHfin = oper_traj.LoH[end] + LoHini = oper_traj.LoH[1] + chain_loss = fc_produced_energy - elyz_consumed_energy -(LoHfin-LoHini)*mg.dispatchables.fuel_cell[1].consumption_slope + # Non-dispatchable (typ. renewables) sources statistics spilled_energy = sum(oper_traj.power_curtailment) * dt # kWh/y spilled_max = maximum(oper_traj.power_curtailment) # kW @@ -240,33 +356,33 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) shed_duration_max = 0.0 # h gen_hours = 0.0 # h/y gen_fuel = 0.0 # L/y + elyz_hours = 0.0 + fc_hours = 0.0 + fc_cons=0.0 # auxilliary counter: shed_duration = 0.0; # duration of current load shedding event (h) Pload_avg = load_energy/(K*dt) # kW - for k = 1:K + for K = 1:K # Dispatchable generator : operating hours and fuel consumption - Pgen = oper_traj.Pgen[k] - Pgen_max = mg.generator.power_rated - Pgen_eps = Pgen_max*1e-6 - if Pgen > Pgen_eps # generator ON - Pgen_norm = Pgen / (ε * Pgen_max) # can be Inf e.g. for ε=0.0 - if Pgen_norm <= 1.0 - # relaxation of discontinuous generator statistics for small Pgen - gen_hours += Pgen_norm * dt - fuel_rate = Pgen_norm * mg.generator.fuel_intercept * Pgen_max + - mg.generator.fuel_slope * Pgen # (L/h) - else - gen_hours += dt - fuel_rate = mg.generator.fuel_intercept * Pgen_max + - mg.generator.fuel_slope * Pgen # (L/h) - end - gen_fuel += fuel_rate * dt - end + time_inc, cons_rate = increment(oper_traj.Pgen[K],mg.dispatchables.generator[1],ε) + gen_hours += time_inc * dt + gen_fuel += cons_rate * dt + + + time_inc, cons_rate = increment(oper_traj.Pelyz[K],mg.electrolyzer[1],ε) + elyz_hours += time_inc * dt + + + time_inc, cons_rate = increment(oper_traj.Pfc[K],mg.dispatchables.fuel_cell[1],ε) + fc_hours += time_inc * dt + # fc_cons += cons_rate * dt + + # Load shedding: shedding duration and maximum shedding duration - Pshed = oper_traj.power_shedding[k] + Pshed = oper_traj.power_shedding[K] if Pshed > 0.0 Pshed_norm = Pshed / (ε * Pload_avg) # can be Inf e.g. for ε=0.0 if Pshed_norm <= 1.0 @@ -290,7 +406,10 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) # Load statistics served_energy, shed_energy, shed_max, shed_hours, shed_duration_max, shed_rate, # Dispatchable generator statistics - gen_energy, gen_hours, gen_fuel, + gen_energy, gen_hours, gen_fuel, fc_produced_energy, fc_hours, fc_consumed_h2, + # + elyz_produced_h2, elyz_hours, elyz_consumed_energy, chain_loss, + # Energy storage (e.g. battery) statistics storage_cycles, storage_char_energy, storage_dis_energy, storage_loss_energy, # Non-dispatchable (typ. renewables) sources statistics From 091a78a6daad9e0d9a6dce6a5e8b3c02e0ebd18a Mon Sep 17 00:00:00 2001 From: NIKIEMA Jean Date: Thu, 14 Dec 2023 14:20:17 +0100 Subject: [PATCH 02/19] work in progress on dispatch with H2 --- examples/Microgrid_Wind-Solar-H2.ipynb | 848 +++++++++++++++++- examples/Microgrid_Wind-Solar.ipynb | 22 + examples/Microgrid_Wind-Solar_data.jl | 2 +- .../Microgrid_sizingH2_optimization1.ipynb | 144 +-- examples/Microgrid_sizing_optimization.ipynb | 64 +- src/components.jl | 56 +- src/operation.jl | 80 +- 7 files changed, 1039 insertions(+), 177 deletions(-) diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index dc48e23..60dd73c 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -29,7 +29,15 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" + ] + } + ], "source": [ "using Microgrids\n", "using Printf, CSV, DataFrames, Statistics" @@ -497,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "id": "fad9eb7c-789d-49ab-aa2b-e309df77b3e5", "metadata": { "tags": [] @@ -509,7 +517,7 @@ "Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)" ] }, - "execution_count": 24, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -571,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "id": "6b00b378-1ee2-4682-8b45-f7e0fc1a012a", "metadata": { "tags": [] @@ -583,7 +591,7 @@ "TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0))" ] }, - "execution_count": 25, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -870,7 +878,31 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, + "id": "a812d24d-819c-40d2-993b-a78ebc342125", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1-element Vector{ProductionUnit{Float64}}:\n", + " ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[elyz,]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "id": "13483089-225f-4fe4-98c8-656a675ba84d", "metadata": { "tags": [] @@ -882,7 +914,7 @@ "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -910,7 +942,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "9038c442-f3bc-4fe7-8e3f-83e77a42d2e7", "metadata": { "tags": [] @@ -946,6 +978,196 @@ "(you can use [Benchmarktools](https://juliaci.github.io/BenchmarkTools.jl/)'s `@btime` for more accurate timing results)" ] }, + { + "cell_type": "code", + "execution_count": 26, + "id": "8ce71bfd-a4ad-4e41-984e-8a78ce890699", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "using BenchmarkTools" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "c3b07f26-c09c-4abe-bffa-1af9bd836736", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5.005707762557078" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "43850 /8760" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "045309d6-743b-41c9-8cec-78d73b0f9fc8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "search: \u001b[0m\u001b[1mP\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mu\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mU\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mt\u001b[22m \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mu\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "Dispatchable power source (e.g. Diesel generator, Gas turbine, Fuel cell or )\n", + "\n", + "All component parameters should be `Float64` except for the *sizing parameter(s)* (here `power_rated`) which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type).\n", + "\n", + "```julia\n", + "struct ProductionUnit{Topt<:Real}\n", + "```\n", + "\n", + "**parameters** : \n", + "\n", + " * `power_rated`: rated power (kW)\n", + " * `consumption_intercept`: combustible consumption curve intercept (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used are linear\n", + " * `consumption_slope`: input consumption curve slope (L/h/kW or Kg/h/kW or KW/Kg/h )\n", + " * `combustible_price`: fuel price (:/L)\n", + " * `investment_price`: initial investiment price (:/kW)\n", + " * `om_price_hours`: operation & maintenance price (:/kW/h of operation)\n", + " * `lifetime_hours`: generator lifetime (h of operation)\n", + " * `minimum_load_ratio`: minimum load ratio ∈ [0,1]\n", + " * `replacement_price_ratio`: replacement price, relative to initial investment\n", + " * `salvage_price_ratio`: salvage price, relative to initial investment\n", + " * `input_unit`: fuel quantity unit (used in fuel price and consumption curve parameters)\n", + " * `output_unit`\n" + ], + "text/plain": [ + " Dispatchable power source (e.g. Diesel generator, Gas turbine, Fuel cell or\n", + " )\n", + "\n", + " All component parameters should be \u001b[36mFloat64\u001b[39m except for the \u001b[4msizing\n", + " parameter(s)\u001b[24m (here \u001b[36mpower_rated\u001b[39m) which type is parametrized as \u001b[36mTopt\u001b[39m and may\n", + " be also \u001b[36mFloat64\u001b[39m or or any another \u001b[36mReal\u001b[39m type (e.g. ForwardDiff's dual number\n", + " type).\n", + "\n", + "\u001b[36m struct ProductionUnit{Topt<:Real}\u001b[39m\n", + "\n", + " \u001b[1mparameters\u001b[22m :\n", + "\n", + " • \u001b[36mpower_rated\u001b[39m: rated power (kW)\n", + "\n", + " • \u001b[36mconsumption_intercept\u001b[39m: combustible consumption curve intercept\n", + " (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used\n", + " are linear\n", + "\n", + " • \u001b[36mconsumption_slope\u001b[39m: input consumption curve slope (L/h/kW or\n", + " Kg/h/kW or KW/Kg/h )\n", + "\n", + " • \u001b[36mcombustible_price\u001b[39m: fuel price (:/L)\n", + "\n", + " • \u001b[36minvestment_price\u001b[39m: initial investiment price (:/kW)\n", + "\n", + " • \u001b[36mom_price_hours\u001b[39m: operation & maintenance price (:/kW/h of\n", + " operation)\n", + "\n", + " • \u001b[36mlifetime_hours\u001b[39m: generator lifetime (h of operation)\n", + "\n", + " • \u001b[36mminimum_load_ratio\u001b[39m: minimum load ratio ∈ [0,1]\n", + "\n", + " • \u001b[36mreplacement_price_ratio\u001b[39m: replacement price, relative to initial\n", + " investment\n", + "\n", + " • \u001b[36msalvage_price_ratio\u001b[39m: salvage price, relative to initial investment\n", + "\n", + " • \u001b[36minput_unit\u001b[39m: fuel quantity unit (used in fuel price and consumption\n", + " curve parameters)\n", + "\n", + " • \u001b[36moutput_unit\u001b[39m" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "?ProductionUnit" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5a0a208f-b289-4b91-bcdc-e3d141e8cfee", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1.920 ms (43850 allocations: 3.81 MiB)\n" + ] + } + ], + "source": [ + "@btime oper_traj = operation(microgrid);" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b3e05487-f5b5-4075-9102-3c8bbe7c4191", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1.918 ms (43850 allocations: 3.81 MiB)\n" + ] + } + ], + "source": [ + "@btime oper_traj = operation(microgrid);" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5e8d4714-4eed-4267-a7ff-7e030a830291", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 2.133 ms (52609 allocations: 4.35 MiB)\n" + ] + } + ], + "source": [ + "@btime oper_traj = operation(microgrid);" + ] + }, { "cell_type": "code", "execution_count": 28, @@ -958,12 +1180,556 @@ "name": "stdout", "output_type": "stream", "text": [ - " 0.073339 seconds (912.97 k allocations: 26.776 MiB)\n" + " 3.186 ms (79057 allocations: 5.96 MiB)\n" + ] + } + ], + "source": [ + "@btime oper_traj, oper_stats, mg_costs = simulate(microgrid);" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e399ac23-c55f-42d7-a10e-6b5cf42cb96e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MethodInstance for Microgrids.operation(::Microgrid{Float64})\n", + " from operation(\u001b[90mmg\u001b[39m::\u001b[1mMicrogrid\u001b[22m)\u001b[90m @\u001b[39m \u001b[90mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", + "Arguments\n", + " #self#\u001b[36m::Core.Const(Microgrids.operation)\u001b[39m\n", + " mg\u001b[36m::Microgrid{Float64}\u001b[39m\n", + "Locals\n", + " @_3\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", + " #15\u001b[36m::Microgrids.var\"#15#35\"\u001b[39m\n", + " #14\u001b[36m::Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + " #13\u001b[36m::Microgrids.var\"#13#33\"\u001b[39m\n", + " #12\u001b[36m::Microgrids.var\"#12#32\"\u001b[39m\n", + " #11\u001b[36m::Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + " #10\u001b[36m::Microgrids.var\"#10#30\"\u001b[39m\n", + " #9\u001b[36m::Microgrids.var\"#9#29\"\u001b[39m\n", + " #8\u001b[36m::Microgrids.var\"#8#28\"\u001b[39m\n", + " #7\u001b[36m::Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + " #6\u001b[36m::Microgrids.var\"#6#26\"\u001b[39m\n", + " #5\u001b[36m::Microgrids.var\"#5#25\"\u001b[39m\n", + " oper_traj\u001b[36m::OperationTraj{Float64}\u001b[39m\n", + " LoF_ini\u001b[36m::Float64\u001b[39m\n", + " LoH_ini\u001b[36m::Float64\u001b[39m\n", + " Esto_ini\u001b[36m::Float64\u001b[39m\n", + " LoF\u001b[36m::Vector{Float64}\u001b[39m\n", + " LoH\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pfc\u001b[36m::Matrix{Float64}\u001b[39m\n", + " Pelyz\u001b[36m::Matrix{Float64}\u001b[39m\n", + " Pshed\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pspill\u001b[36m::Vector{Float64}\u001b[39m\n", + " Psto\u001b[36m::Vector{Float64}\u001b[39m\n", + " Esto\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pgen\u001b[36m::Matrix{Float64}\u001b[39m\n", + " Pnl\u001b[36m::Vector{Float64}\u001b[39m\n", + " LoH_min\u001b[36m::Float64\u001b[39m\n", + " LoH_max\u001b[36m::Float64\u001b[39m\n", + " cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pfc_min\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pfc_max\u001b[36m::Vector{Float64}\u001b[39m\n", + " prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pelyz_min\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pelyz_max\u001b[36m::Vector{Float64}\u001b[39m\n", + " sto_loss\u001b[36m::Float64\u001b[39m\n", + " Psto_pmin\u001b[36m::Float64\u001b[39m\n", + " Psto_pmax\u001b[36m::Float64\u001b[39m\n", + " Esto_min\u001b[36m::Float64\u001b[39m\n", + " Esto_max\u001b[36m::Float64\u001b[39m\n", + " LoF_min\u001b[36m::Float64\u001b[39m\n", + " LoF_max\u001b[36m::Float64\u001b[39m\n", + " fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", + " fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pgen_min\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", + " dt\u001b[36m::Float64\u001b[39m\n", + " K\u001b[36m::Int64\u001b[39m\n", + " Pnl_request\u001b[36m::Vector{Float64}\u001b[39m\n", + " renew_potential\u001b[36m::Vector{Float64}\u001b[39m\n", + " renew_productions\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", + " Topt\u001b[36m::Type{Float64}\u001b[39m\n", + " #24\u001b[36m::Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + " #23\u001b[36m::Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + " #22\u001b[36m::Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + " @_57\u001b[36m::Int64\u001b[39m\n", + " #21\u001b[36m::Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + " #20\u001b[36m::Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}\u001b[39m\n", + " #19\u001b[36m::Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + " #18\u001b[36m::Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", + " #17\u001b[36m::Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + " #16\u001b[36m::Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", + " k\u001b[36m::Int64\u001b[39m\n", + " Pgen_dmax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pgen_emax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pfc_pmax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pfc_emax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pelyz_cmax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Pelyz_emax\u001b[36m::Vector{Float64}\u001b[39m\n", + " Psto_cmax\u001b[36m::Float64\u001b[39m\n", + " Psto_dmax\u001b[36m::Float64\u001b[39m\n", + " Psto_emax\u001b[36m::Float64\u001b[39m\n", + " Psto_emin\u001b[36m::Float64\u001b[39m\n", + "Body\u001b[36m::OperationTraj{Float64}\u001b[39m\n", + "\u001b[90m1 ─\u001b[39m Core.NewvarNode(:(oper_traj))\n", + "\u001b[90m│ \u001b[39m %2 = Microgrids.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %3 = Base.getproperty(%2, :parameters)\u001b[36m::Core.Const(svec(Float64))\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Topt = Base.getindex(%3, 1))\n", + "\u001b[90m│ \u001b[39m (#5 = %new(Microgrids.:(var\"#5#25\")))\n", + "\u001b[90m│ \u001b[39m %6 = #5\u001b[36m::Core.Const(Microgrids.var\"#5#25\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %7 = Base.getproperty(mg, :nondispatchables)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", + "\u001b[90m│ \u001b[39m %8 = Base.Generator(%6, %7)\u001b[91m\u001b[1m::Base.Generator{_A, Microgrids.var\"#5#25\"} where _A\u001b[22m\u001b[39m\n", + "\u001b[90m│ \u001b[39m (renew_productions = Microgrids.collect(%8))\n", + "\u001b[90m│ \u001b[39m %10 = Microgrids.sum(renew_productions)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", + "\u001b[90m│ \u001b[39m %11 = Core.apply_type(Microgrids.Vector, Topt::Core.Const(Float64))\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m (renew_potential = Core.typeassert(%10, %11))\n", + "\u001b[90m│ \u001b[39m %13 = Base.getproperty(mg, :load)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pnl_request = %13 - renew_potential)\n", + "\u001b[90m│ \u001b[39m %15 = Base.getproperty(mg, :load)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (K = Microgrids.length(%15))\n", + "\u001b[90m│ \u001b[39m %17 = Base.getproperty(mg, :project)\u001b[36m::Project\u001b[39m\n", + "\u001b[90m│ \u001b[39m (dt = Base.getproperty(%17, :timestep))\n", + "\u001b[90m│ \u001b[39m (#6 = %new(Microgrids.:(var\"#6#26\")))\n", + "\u001b[90m│ \u001b[39m %20 = #6\u001b[36m::Core.Const(Microgrids.var\"#6#26\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %21 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %22 = Base.getproperty(%21, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %23 = Base.Generator(%20, %22)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#6#26\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pgen_max = Microgrids.collect(%23))\n", + "\u001b[90m│ \u001b[39m %25 = Microgrids.:(var\"#7#27\")\u001b[36m::Core.Const(Microgrids.var\"#7#27\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %26 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %27 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %28 = Core.apply_type(%25, %26, %27)\u001b[36m::Core.Const(Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#7 = %new(%28, mg, Pgen_max))\n", + "\u001b[90m│ \u001b[39m %30 = #7\u001b[36m::Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %31 = Microgrids.eachindex(Pgen_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %32 = Base.Generator(%30, %31)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pgen_min = Microgrids.collect(%32))\n", + "\u001b[90m│ \u001b[39m (#8 = %new(Microgrids.:(var\"#8#28\")))\n", + "\u001b[90m│ \u001b[39m %35 = #8\u001b[36m::Core.Const(Microgrids.var\"#8#28\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %36 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %37 = Base.getproperty(%36, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %38 = Base.Generator(%35, %37)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#8#28\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (fuel_slope = Microgrids.collect(%38))\n", + "\u001b[90m│ \u001b[39m (#9 = %new(Microgrids.:(var\"#9#29\")))\n", + "\u001b[90m│ \u001b[39m %41 = #9\u001b[36m::Core.Const(Microgrids.var\"#9#29\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %42 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %43 = Base.getproperty(%42, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %44 = Base.Generator(%41, %43)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#9#29\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (fuel_intercept = Microgrids.collect(%44))\n", + "\u001b[90m│ \u001b[39m %46 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %47 = Base.getproperty(%46, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %48 = Base.getproperty(%47, :max_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %49 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %50 = Base.getproperty(%49, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %51 = Base.getproperty(%50, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoF_max = %48 * %51)\n", + "\u001b[90m│ \u001b[39m %53 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %54 = Base.getproperty(%53, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %55 = Base.getproperty(%54, :min_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %56 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %57 = Base.getproperty(%56, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %58 = Base.getproperty(%57, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoF_min = %55 * %58)\n", + "\u001b[90m│ \u001b[39m %60 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Esto_max = Base.getproperty(%60, :energy_rated))\n", + "\u001b[90m│ \u001b[39m %62 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %63 = Base.getproperty(%62, :SoC_min)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Esto_min = %63 * Esto_max)\n", + "\u001b[90m│ \u001b[39m %65 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %66 = Base.getproperty(%65, :discharge_rate)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Psto_pmax = %66 * Esto_max)\n", + "\u001b[90m│ \u001b[39m %68 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %69 = Base.getproperty(%68, :charge_rate)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %70 = -%69\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Psto_pmin = %70 * Esto_max)\n", + "\u001b[90m│ \u001b[39m %72 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (sto_loss = Base.getproperty(%72, :loss_factor))\n", + "\u001b[90m│ \u001b[39m (#10 = %new(Microgrids.:(var\"#10#30\")))\n", + "\u001b[90m│ \u001b[39m %75 = #10\u001b[36m::Core.Const(Microgrids.var\"#10#30\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %76 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %77 = Base.Generator(%75, %76)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#10#30\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pelyz_max = Microgrids.collect(%77))\n", + "\u001b[90m│ \u001b[39m %79 = Microgrids.:(var\"#11#31\")\u001b[36m::Core.Const(Microgrids.var\"#11#31\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %80 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %81 = Core.typeof(Pelyz_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %82 = Core.apply_type(%79, %80, %81)\u001b[36m::Core.Const(Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#11 = %new(%82, mg, Pelyz_max))\n", + "\u001b[90m│ \u001b[39m %84 = #11\u001b[36m::Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %85 = Microgrids.eachindex(Pelyz_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %86 = Base.Generator(%84, %85)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pelyz_min = Microgrids.collect(%86))\n", + "\u001b[90m│ \u001b[39m (#12 = %new(Microgrids.:(var\"#12#32\")))\n", + "\u001b[90m│ \u001b[39m %89 = #12\u001b[36m::Core.Const(Microgrids.var\"#12#32\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %90 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %91 = Base.Generator(%89, %90)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#12#32\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (prod_rate = Microgrids.collect(%91))\n", + "\u001b[90m│ \u001b[39m (#13 = %new(Microgrids.:(var\"#13#33\")))\n", + "\u001b[90m│ \u001b[39m %94 = #13\u001b[36m::Core.Const(Microgrids.var\"#13#33\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %95 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %96 = Base.getproperty(%95, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %97 = Base.Generator(%94, %96)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#13#33\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pfc_max = Microgrids.collect(%97))\n", + "\u001b[90m│ \u001b[39m %99 = Microgrids.:(var\"#14#34\")\u001b[36m::Core.Const(Microgrids.var\"#14#34\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %100 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %101 = Core.typeof(Pfc_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %102 = Core.apply_type(%99, %100, %101)\u001b[36m::Core.Const(Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#14 = %new(%102, mg, Pfc_max))\n", + "\u001b[90m│ \u001b[39m %104 = #14\u001b[36m::Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %105 = Microgrids.eachindex(Pfc_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %106 = Base.Generator(%104, %105)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pfc_min = Microgrids.collect(%106))\n", + "\u001b[90m│ \u001b[39m (#15 = %new(Microgrids.:(var\"#15#35\")))\n", + "\u001b[90m│ \u001b[39m %109 = #15\u001b[36m::Core.Const(Microgrids.var\"#15#35\"())\u001b[39m\n", + "\u001b[90m│ \u001b[39m %110 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %111 = Base.getproperty(%110, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %112 = Base.Generator(%109, %111)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#15#35\"}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (cons_rate = Microgrids.collect(%112))\n", + "\u001b[90m│ \u001b[39m %114 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %115 = Base.getproperty(%114, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %116 = Base.getproperty(%115, :max_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %117 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %118 = Base.getproperty(%117, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %119 = Base.getproperty(%118, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoH_max = %116 * %119)\n", + "\u001b[90m│ \u001b[39m %121 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %122 = Base.getproperty(%121, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %123 = Base.getproperty(%122, :min_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %124 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %125 = Base.getproperty(%124, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %126 = Base.getproperty(%125, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoH_min = %123 * %126)\n", + "\u001b[90m│ \u001b[39m (Pnl = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", + "\u001b[90m│ \u001b[39m %129 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %130 = K\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %131 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %132 = Base.getproperty(%131, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %133 = Microgrids.length(%132)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pgen = Microgrids.zeros(%129, %130, %133))\n", + "\u001b[90m│ \u001b[39m %135 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %136 = (K + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Esto = Microgrids.zeros(%135, %136))\n", + "\u001b[90m│ \u001b[39m (Psto = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", + "\u001b[90m│ \u001b[39m (Pspill = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", + "\u001b[90m│ \u001b[39m (Pshed = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", + "\u001b[90m│ \u001b[39m %141 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %142 = K\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %143 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %144 = Microgrids.length(%143)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pelyz = Microgrids.zeros(%141, %142, %144))\n", + "\u001b[90m│ \u001b[39m %146 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %147 = K\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %148 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %149 = Base.getproperty(%148, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %150 = Microgrids.length(%149)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pfc = Microgrids.zeros(%146, %147, %150))\n", + "\u001b[90m│ \u001b[39m %152 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %153 = (K + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoH = Microgrids.zeros(%152, %153))\n", + "\u001b[90m│ \u001b[39m %155 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %156 = (K + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoF = Microgrids.zeros(%155, %156))\n", + "\u001b[90m│ \u001b[39m %158 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %159 = Base.getproperty(%158, :SoC_ini)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %160 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %161 = Base.getproperty(%160, :energy_rated)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Esto_ini = %159 * %161)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Esto, Esto_ini, 1)\n", + "\u001b[90m│ \u001b[39m %164 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %165 = Base.getproperty(%164, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %166 = Base.getproperty(%165, :ini_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %167 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %168 = Base.getproperty(%167, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %169 = Base.getproperty(%168, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoH_ini = %166 * %169)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(LoH, LoH_ini, 1)\n", + "\u001b[90m│ \u001b[39m %172 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %173 = Base.getproperty(%172, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %174 = Base.getproperty(%173, :ini_filling_ratio)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %175 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %176 = Base.getproperty(%175, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %177 = Base.getproperty(%176, :capacity)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (LoF_ini = %174 * %177)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(LoF, LoF_ini, 1)\n", + "\u001b[90m│ \u001b[39m %180 = (1:K)\u001b[36m::Core.PartialStruct(UnitRange{Int64}, Any[Core.Const(1), Int64])\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_3 = Base.iterate(%180))\n", + "\u001b[90m│ \u001b[39m %182 = (@_3 === nothing)\u001b[36m::Bool\u001b[39m\n", + "\u001b[90m│ \u001b[39m %183 = Base.not_int(%182)\u001b[36m::Bool\u001b[39m\n", + "\u001b[90m└──\u001b[39m goto #4 if not %183\n", + "\u001b[90m2 ┄\u001b[39m %185 = @_3\u001b[36m::Tuple{Int64, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (k = Core.getfield(%185, 1))\n", + "\u001b[90m│ \u001b[39m %187 = Core.getfield(%185, 2)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %188 = Esto_max\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %189 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %190 = (%188 - %189)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %191 = -%190\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %192 = (1 - sto_loss)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %193 = (%192 * dt)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Psto_emin = %191 / %193)\n", + "\u001b[90m│ \u001b[39m %195 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %196 = (%195 - Esto_min)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %197 = (1 + sto_loss)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %198 = (%197 * dt)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Psto_emax = %196 / %198)\n", + "\u001b[90m│ \u001b[39m (Psto_dmax = Microgrids.min(Psto_emax, Psto_pmax))\n", + "\u001b[90m│ \u001b[39m (Psto_cmax = Microgrids.max(Psto_emin, Psto_pmin))\n", + "\u001b[90m│ \u001b[39m %202 = Microgrids.:(var\"#16#36\")\u001b[36m::Core.Const(Microgrids.var\"#16#36\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %203 = Core.typeof(LoH)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %204 = Core.typeof(LoH_max)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %205 = Core.typeof(prod_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %206 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %207 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %208 = Core.apply_type(%202, %203, %204, %205, %206, %207)\u001b[36m::Core.Const(Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %209 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %210 = LoH_max\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %211 = prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %212 = dt\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#16 = %new(%208, %209, %210, %211, %212, k))\n", + "\u001b[90m│ \u001b[39m %214 = #16\u001b[36m::Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %215 = Microgrids.eachindex(prod_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %216 = Base.Generator(%214, %215)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pelyz_emax = Microgrids.collect(%216))\n", + "\u001b[90m│ \u001b[39m %218 = Microgrids.:(var\"#17#37\")\u001b[36m::Core.Const(Microgrids.var\"#17#37\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %219 = Core.typeof(Pelyz_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %220 = Core.typeof(Pelyz_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %221 = Core.apply_type(%218, %219, %220)\u001b[36m::Core.Const(Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %222 = Pelyz_max\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#17 = %new(%221, %222, Pelyz_emax))\n", + "\u001b[90m│ \u001b[39m %224 = #17\u001b[36m::Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %225 = Microgrids.eachindex(Pelyz_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %226 = Base.Generator(%224, %225)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pelyz_cmax = Microgrids.collect(%226))\n", + "\u001b[90m│ \u001b[39m %228 = Microgrids.:(var\"#18#38\")\u001b[36m::Core.Const(Microgrids.var\"#18#38\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %229 = Core.typeof(LoH)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %230 = Core.typeof(LoH_min)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %231 = Core.typeof(cons_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %232 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %233 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %234 = Core.apply_type(%228, %229, %230, %231, %232, %233)\u001b[36m::Core.Const(Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %235 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %236 = LoH_min\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %237 = cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %238 = dt\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#18 = %new(%234, %235, %236, %237, %238, k))\n", + "\u001b[90m│ \u001b[39m %240 = #18\u001b[36m::Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %241 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %242 = Base.Generator(%240, %241)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pfc_emax = Microgrids.collect(%242))\n", + "\u001b[90m│ \u001b[39m %244 = Microgrids.:(var\"#19#39\")\u001b[36m::Core.Const(Microgrids.var\"#19#39\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %245 = Core.typeof(Pfc_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %246 = Core.typeof(Pfc_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %247 = Core.apply_type(%244, %245, %246)\u001b[36m::Core.Const(Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %248 = Pfc_max\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#19 = %new(%247, %248, Pfc_emax))\n", + "\u001b[90m│ \u001b[39m %250 = #19\u001b[36m::Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %251 = Microgrids.eachindex(Pfc_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %252 = Base.Generator(%250, %251)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pfc_pmax = Microgrids.collect(%252))\n", + "\u001b[90m│ \u001b[39m %254 = Microgrids.:(var\"#20#40\")\u001b[36m::Core.Const(Microgrids.var\"#20#40\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %255 = Core.typeof(LoF)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %256 = Core.typeof(LoF_min)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %257 = Core.typeof(fuel_intercept)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %258 = Core.typeof(fuel_slope)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %259 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %260 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %261 = Core.typeof(K)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %262 = Core.apply_type(%254, %255, %256, %257, %258, %259, %260, %261)\u001b[36m::Core.Const(Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %263 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %264 = LoF_min\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %265 = fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %266 = fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %267 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %268 = dt\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#20 = %new(%262, %263, %264, %265, %266, %267, %268, K))\n", + "\u001b[90m│ \u001b[39m %270 = #20\u001b[36m::Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %271 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %272 = Base.Generator(%270, %271)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pgen_emax = Microgrids.collect(%272))\n", + "\u001b[90m│ \u001b[39m %274 = Microgrids.:(var\"#21#41\")\u001b[36m::Core.Const(Microgrids.var\"#21#41\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %275 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %276 = Core.typeof(Pgen_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %277 = Core.apply_type(%274, %275, %276)\u001b[36m::Core.Const(Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %278 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#21 = %new(%277, %278, Pgen_emax))\n", + "\u001b[90m│ \u001b[39m %280 = #21\u001b[36m::Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %281 = Microgrids.eachindex(Pgen_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %282 = Base.Generator(%280, %281)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (Pgen_dmax = Microgrids.collect(%282))\n", + "\u001b[90m│ \u001b[39m %284 = Base.getindex(Pnl_request, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %285 = Psto_cmax\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %286 = Psto_dmax\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %287 = Base.getindex(Pgen_min, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %288 = Base.getindex(Pgen_dmax, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %289 = Base.getindex(Pelyz_min, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %290 = Base.getindex(Pelyz_cmax, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %291 = Base.getindex(Pfc_min, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %292 = Base.getindex(Pfc_pmax, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %293 = Microgrids.dispatch(%284, %285, %286, %287, %288, %289, %290, %291, %292)\u001b[36m::NTuple{7, Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %294 = Base.indexed_iterate(%293, 1)\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(2)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %295 = Core.getfield(%294, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%294, 2))\n", + "\u001b[90m│ \u001b[39m %297 = Base.indexed_iterate(%293, 2, @_57::Core.Const(2))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(3)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %298 = Core.getfield(%297, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%297, 2))\n", + "\u001b[90m│ \u001b[39m %300 = Base.indexed_iterate(%293, 3, @_57::Core.Const(3))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(4)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %301 = Core.getfield(%300, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%300, 2))\n", + "\u001b[90m│ \u001b[39m %303 = Base.indexed_iterate(%293, 4, @_57::Core.Const(4))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(5)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %304 = Core.getfield(%303, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%303, 2))\n", + "\u001b[90m│ \u001b[39m %306 = Base.indexed_iterate(%293, 5, @_57::Core.Const(5))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(6)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %307 = Core.getfield(%306, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%306, 2))\n", + "\u001b[90m│ \u001b[39m %309 = Base.indexed_iterate(%293, 6, @_57::Core.Const(6))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(7)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %310 = Core.getfield(%309, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%309, 2))\n", + "\u001b[90m│ \u001b[39m %312 = Base.indexed_iterate(%293, 7, @_57::Core.Const(7))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(8)])\u001b[39m\n", + "\u001b[90m│ \u001b[39m %313 = Core.getfield(%312, 1)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pnl, %295, k)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pgen, %298, k, 1)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Psto, %301, k)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pspill, %304, k)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pshed, %307, k)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pelyz, %310, k, 1)\n", + "\u001b[90m│ \u001b[39m Base.setindex!(Pfc, %313, k, 1)\n", + "\u001b[90m│ \u001b[39m %321 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %322 = Base.getindex(Psto, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %323 = sto_loss\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %324 = Base.getindex(Psto, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %325 = Microgrids.abs(%324)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %326 = (%323 * %325)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %327 = (%322 + %326)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %328 = (%327 * dt)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %329 = (%321 - %328)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %330 = Esto\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %331 = (k + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m Base.setindex!(%330, %329, %331)\n", + "\u001b[90m│ \u001b[39m %333 = Base.getindex(LoH, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %334 = Microgrids.:(var\"#22#42\")\u001b[36m::Core.Const(Microgrids.var\"#22#42\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %335 = Core.typeof(Pelyz)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %336 = Core.typeof(prod_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %337 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %338 = Core.apply_type(%334, %335, %336, %337)\u001b[36m::Core.Const(Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %339 = Pelyz\u001b[36m::Matrix{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %340 = prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#22 = %new(%338, %339, %340, k))\n", + "\u001b[90m│ \u001b[39m %342 = #22\u001b[36m::Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %343 = Microgrids.eachindex(prod_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %344 = Base.Generator(%342, %343)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %345 = Microgrids.collect(%344)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %346 = Microgrids.sum(%345)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %347 = Microgrids.:(var\"#23#43\")\u001b[36m::Core.Const(Microgrids.var\"#23#43\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %348 = Core.typeof(Pfc)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %349 = Core.typeof(cons_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %350 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %351 = Core.apply_type(%347, %348, %349, %350)\u001b[36m::Core.Const(Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %352 = Pfc\u001b[36m::Matrix{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %353 = cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#23 = %new(%351, %352, %353, k))\n", + "\u001b[90m│ \u001b[39m %355 = #23\u001b[36m::Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %356 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %357 = Base.Generator(%355, %356)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %358 = Microgrids.collect(%357)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %359 = Microgrids.sum(%358)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %360 = (%346 - %359)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %361 = (%360 * dt)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %362 = (%333 + %361)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %363 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %364 = (k + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m Base.setindex!(%363, %362, %364)\n", + "\u001b[90m│ \u001b[39m %366 = Base.getindex(LoF, k)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %367 = Microgrids.:(var\"#24#44\")\u001b[36m::Core.Const(Microgrids.var\"#24#44\")\u001b[39m\n", + "\u001b[90m│ \u001b[39m %368 = Core.typeof(Pgen)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %369 = Core.typeof(fuel_intercept)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %370 = Core.typeof(fuel_slope)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %371 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %372 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", + "\u001b[90m│ \u001b[39m %373 = Core.apply_type(%367, %368, %369, %370, %371, %372)\u001b[36m::Core.Const(Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64})\u001b[39m\n", + "\u001b[90m│ \u001b[39m %374 = Pgen\u001b[36m::Matrix{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %375 = fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %376 = fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %377 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (#24 = %new(%373, %374, %375, %376, %377, k))\n", + "\u001b[90m│ \u001b[39m %379 = #24\u001b[36m::Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %380 = Microgrids.eachindex(fuel_slope)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %381 = Base.Generator(%379, %380)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %382 = Microgrids.collect(%381)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %383 = Microgrids.sum(%382)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %384 = (%383 * dt)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %385 = (%366 - %384)\u001b[36m::Float64\u001b[39m\n", + "\u001b[90m│ \u001b[39m %386 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %387 = (k + 1)\u001b[36m::Int64\u001b[39m\n", + "\u001b[90m│ \u001b[39m Base.setindex!(%386, %385, %387)\n", + "\u001b[90m│ \u001b[39m (@_3 = Base.iterate(%180, %187))\n", + "\u001b[90m│ \u001b[39m %390 = (@_3 === nothing)\u001b[36m::Bool\u001b[39m\n", + "\u001b[90m│ \u001b[39m %391 = Base.not_int(%390)\u001b[36m::Bool\u001b[39m\n", + "\u001b[90m└──\u001b[39m goto #4 if not %391\n", + "\u001b[90m3 ─\u001b[39m goto #2\n", + "\u001b[90m4 ┄\u001b[39m %394 = Pnl\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %395 = Pshed\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %396 = renew_potential\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %397 = Base.getindex(Pgen, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %398 = Base.getindex(Pfc, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %399 = Base.getindex(Pelyz, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %400 = Esto\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %401 = Psto\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %402 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m %403 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", + "\u001b[90m│ \u001b[39m (oper_traj = Microgrids.OperationTraj(%394, %395, %396, %397, %398, %399, %400, %401, %402, %403, Pspill))\n", + "\u001b[90m└──\u001b[39m return oper_traj\n", + "\n" ] } ], "source": [ - "@time oper_traj, oper_stats, mg_costs = simulate(microgrid);" + "@code_warntype operation(microgrid);" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "05f198e7-eef7-47b9-ba7f-5170a52e461f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{verbatim}\n", + "@code_warntype\n", + "\\end{verbatim}\n", + "Evaluates the arguments to the function or macro call, determines their types, and calls \\href{@ref}{\\texttt{code\\_warntype}} on the resulting expression.\n", + "\n" + ], + "text/markdown": [ + "```\n", + "@code_warntype\n", + "```\n", + "\n", + "Evaluates the arguments to the function or macro call, determines their types, and calls [`code_warntype`](@ref) on the resulting expression.\n" + ], + "text/plain": [ + "\u001b[36m @code_warntype\u001b[39m\n", + "\n", + " Evaluates the arguments to the function or macro call, determines their\n", + " types, and calls \u001b[36mcode_warntype\u001b[39m on the resulting expression." + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "?@code_warntype" ] }, { @@ -986,7 +1752,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "id": "3695a442-4e04-4436-9374-70c85036beda", "metadata": { "tags": [] @@ -996,7 +1762,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Load shedding rate: 4.8%\n", + "Load shedding rate: 3.1%\n", "Renewable rate: 100.0%\n" ] } @@ -1016,7 +1782,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "id": "dcdb5710-b0c0-4fb7-b92f-71265b6f2489", "metadata": { "tags": [] @@ -1025,10 +1791,10 @@ { "data": { "text/plain": [ - "1133.9075685798962" + "1041.8586431923732" ] }, - "execution_count": 30, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1039,7 +1805,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "id": "0090bf4d-f684-492b-9583-bd56854329b0", "metadata": { "tags": [] @@ -1049,35 +1815,35 @@ "data": { "text/plain": [ "OperationStats with fields:\n", - "- served_energy: 6.4502e6 kWh\n", - "- shed_energy: 324750.0 kWh\n", + "- served_energy: 6.5642e6 kWh\n", + "- shed_energy: 210820.0 kWh\n", "- shed_max: 33396.0 kW\n", - "- shed_hours: 443.0 h\n", + "- shed_hours: 306.0 h\n", "- shed_duration_max: 56.0 h\n", - "- shed_rate: 0.047934 in [0,1]\n", + "- shed_rate: 0.031117 in [0,1]\n", "- gen_energy: 0.0 kWh\n", "- gen_hours: 0.0 h\n", "- gen_fuel: 0.0 L\n", - "- fc_energy: 1.0862e6 kWh\n", - "- fc_hours: 2535.0 h\n", - "- h2_consumed: 67889.0 kg\n", - "- h2_produced: 69889.0 kg\n", - "- elyz_hours: 4694.0 h\n", - "- elyz_consumed_energy: 3.9138e6 kWh\n", - "- h2_chain_loss: -2.8277e6 kWh\n", - "- storage_cycles: 37.192 \n", - "- storage_char_energy: 195260.0 kWh\n", - "- storage_dis_energy: 176660.0 kWh\n", - "- storage_loss_energy: 18596.0 kWh\n", - "- spilled_energy: 71228.0 kWh\n", - "- spilled_max: 3271.4 kW\n", - "- spilled_rate: 0.0076037 in [0,1]\n", + "- fc_energy: 626880.0 kWh\n", + "- fc_hours: 1215.0 h\n", + "- h2_consumed: 39180.0 kg\n", + "- h2_produced: 41180.0 kg\n", + "- elyz_hours: 2667.0 h\n", + "- elyz_consumed_energy: 2.3061e6 kWh\n", + "- h2_chain_loss: 1.6791e6 kWh\n", + "- storage_cycles: 157.88 \n", + "- storage_char_energy: 828880.0 kWh\n", + "- storage_dis_energy: 749940.0 kWh\n", + "- storage_loss_energy: 78941.0 kWh\n", + "- spilled_energy: 1.0453e6 kWh\n", + "- spilled_max: 3413.9 kW\n", + "- spilled_rate: 0.11159 in [0,1]\n", "- renew_potential_energy: 9.3676e6 kWh\n", - "- renew_energy: 9.2964e6 kWh\n", + "- renew_energy: 8.3223e6 kWh\n", "- renew_rate: 1.0 in [0,1]\n" ] }, - "execution_count": 31, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1088,7 +1854,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 34, "id": "59e59883-d545-4de7-9584-ac06b5110005", "metadata": { "tags": [] @@ -1097,16 +1863,16 @@ { "data": { "text/plain": [ - "10000.0" + "3148.701693426455" ] }, - "execution_count": 54, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "oper_traj.LoH[7000]" + "oper_traj.LoH[7680]" ] }, { @@ -1347,7 +2113,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", "metadata": { "tags": [] diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb index 3bf4149..1ae6eeb 100644 --- a/examples/Microgrid_Wind-Solar.ipynb +++ b/examples/Microgrid_Wind-Solar.ipynb @@ -769,6 +769,28 @@ "oper_traj, oper_stats, mg_costs = simulate(microgrid);" ] }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c127ba4e-0a97-4ca0-a450-11083e0bc81a", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", + "output_type": "error", + "traceback": [ + "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", + "" + ] + } + ], + "source": [ + "@btime oper_traj, oper_stats = operation(microgrid)" + ] + }, { "cell_type": "markdown", "id": "8ab2808c-d25a-4bac-858b-abd436664730", diff --git a/examples/Microgrid_Wind-Solar_data.jl b/examples/Microgrid_Wind-Solar_data.jl index 46ae89f..63b4285 100644 --- a/examples/Microgrid_Wind-Solar_data.jl +++ b/examples/Microgrid_Wind-Solar_data.jl @@ -75,7 +75,7 @@ investment_price_hytank = 0.8 # initial investment price ($/kg) om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y) lifetime_hytank = 25. # calendar lifetime (y) loss_factor_hytank = 0. # hydrogen used on site -LoH_ini_ratio = 0.2 # Initial load ratio ∈ [0,1] +LoH_ini_ratio = 0. # Initial load ratio ∈ [0,1] LoH_min_ratio = 0.1 # minimum load ratio ∈ [0,1] LoH_max_ratio = 1. # maximum load ratio ∈ [0,1] diff --git a/examples/Microgrid_sizingH2_optimization1.ipynb b/examples/Microgrid_sizingH2_optimization1.ipynb index 11cf921..493fe2f 100644 --- a/examples/Microgrid_sizingH2_optimization1.ipynb +++ b/examples/Microgrid_sizingH2_optimization1.ipynb @@ -49,15 +49,7 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" - ] - } - ], + "outputs": [], "source": [ "using Microgrids\n", "using NLopt # optimization solvers\n", @@ -373,8 +365,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Base. multi: (0.21461387127508236, 0.20080291410297912) mono: 19080.506024169186\n", - "Shed. multi: (0.20721897266661307, 0.20080291410297912) mono: 19080.49862927058\n", + "Base. multi: (0.21571461172077722, 0.20552617597582035) mono: 19552.833312193754\n", + "Shed. multi: (0.20827574936977203, 0.20552617597582035) mono: 19552.825873331403\n", "PV. multi: (0.10149685980966677, 0.923547868561659) mono: 91354.8883530257\n", "Wind. multi: (0.10040264224635914, 0.74395737102815) mono: 73395.83750545724\n" ] @@ -496,7 +488,7 @@ { "data": { "text/plain": [ - "((0.10149685980963595, 0.9998470957371233), (1.2747629440813006, 8.262599201630023e-6))" + "((0.10149685980963595, 0.9998470957371233), (1.2761120605599305, 8.262599201630023e-6))" ] }, "execution_count": 11, @@ -582,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", "metadata": { "tags": [] @@ -592,17 +584,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "GN_CRS2_LM algo: MAXEVAL_REACHED after 1000 iterations. \n", - "x*=[0.0, 14421.3, 20578.0, 3478.3, 100.0, 1520.5, 9680.3]\n", - "LCOE*: 0.6085859028176098\n", - "shed*: 7.567619784151898e-8\n" + "GN_CRS2_LM algo: MAXEVAL_REACHED after 10000 iterations. \n", + "x*=[0.0, 9764.2, 20727.4, 3356.4, 380.0, 1541.9, 16800.5]\n", + "LCOE*: 0.5786213467955624\n", + "shed*: 0.0\n" ] } ], "source": [ "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0.0 # in [0,1]\n", - "maxeval=1000\n", + "shed_max = 0. # in [0,1]\n", + "maxeval=10000\n", "xopt, ret, numevals = optim_mg(x0, shed_max, algo, maxeval)\n", "\n", "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", @@ -612,6 +604,38 @@ "println(\"shed*: \", shed_rate_opt)" ] }, + { + "cell_type": "code", + "execution_count": 24, + "id": "6a763252-1a24-4d35-a8e8-b45d9da51341", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: MAXEVAL_REACHED after 10000 iterations. \n", + "x*=[0.0, 3402.7, 2074.2, 2217.4, 1196.7, 1301.9, 16800.9]\n", + "LCOE*: 0.20798206778104475\n", + "shed*: 0.0009998209597289068\n" + ] + } + ], + "source": [ + "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "shed_max = 0.001 # in [0,1]\n", + "maxeval=10000\n", + "xopt, ret, numevals = optim_mg(x0, shed_max, algo, maxeval,1e-10,5)\n", + "\n", + "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt*1000; digits=1)) # kW\n", + "lcoe_opt, shed_rate_opt = obj_multi(xopt)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, { "cell_type": "markdown", "id": "58772966-74b1-431c-9f84-e8efe7352130", @@ -622,7 +646,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 25, "id": "78b3e3f8-0de8-47f3-a8a4-40358feef172", "metadata": { "tags": [] @@ -632,10 +656,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "LN_SBPLX polish: XTOL_REACHED after 643 iterations. \n", - "x*=[0.0, 2350.4, 2176.4, 2133.3, 1212.5, 843.2, 16738.2]\n", - "LCOE*: 0.19632167925055366\n", - "shed*: 0.009999999270919192\n" + "LN_SBPLX polish: XTOL_REACHED after 239 iterations. \n", + "x*=[0.0, 3402.5, 2074.2, 2217.4, 1196.6, 1301.8, 16801.0]\n", + "LCOE*: 0.20798046348779503\n", + "shed*: 0.000999888188853352\n" ] } ], @@ -800,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "id": "d3f47b0a-7b0d-4556-892f-4a123244d1e0", "metadata": { "tags": [] @@ -810,35 +834,35 @@ "data": { "text/plain": [ "OperationStats with fields:\n", - "- served_energy: 6.7119e6 kWh\n", - "- shed_energy: 63086.0 kWh\n", - "- shed_max: 1510.2 kW\n", - "- shed_hours: 248.0 h\n", - "- shed_duration_max: 38.0 h\n", - "- shed_rate: 0.0093116 in [0,1]\n", + "- served_energy: 6.775e6 kWh\n", + "- shed_energy: 0.0 kWh\n", + "- shed_max: 0.0 kW\n", + "- shed_hours: 0.0 h\n", + "- shed_duration_max: 0.0 h\n", + "- shed_rate: 0.0 in [0,1]\n", "- gen_energy: 0.0 kWh\n", "- gen_hours: 0.0 h\n", "- gen_fuel: 0.0 L\n", - "- fc_energy: 1.0979e6 kWh\n", - "- fc_hours: 2027.0 h\n", - "- h2_consumed: 68618.0 kg\n", - "- h2_produced: 66929.0 kg\n", - "- elyz_hours: 4285.0 h\n", - "- elyz_consumed_energy: 3.748e6 kWh\n", - "- h2_chain_loss: -2.65e6 kWh\n", - "- storage_cycles: 185.21 \n", - "- storage_char_energy: 385080.0 kWh\n", - "- storage_dis_energy: 350300.0 kWh\n", - "- storage_loss_energy: 36769.0 kWh\n", - "- spilled_energy: 192580.0 kWh\n", - "- spilled_max: 1672.7 kW\n", - "- spilled_rate: 0.020082 in [0,1]\n", - "- renew_potential_energy: 9.5894e6 kWh\n", - "- renew_energy: 9.3968e6 kWh\n", + "- fc_energy: 103580.0 kWh\n", + "- fc_hours: 143.0 h\n", + "- h2_consumed: 6473.8 kg\n", + "- h2_produced: 6954.6 kg\n", + "- elyz_hours: 3920.0 h\n", + "- elyz_consumed_energy: 389460.0 kWh\n", + "- h2_chain_loss: 285850.0 kWh\n", + "- storage_cycles: 41.344 \n", + "- storage_char_energy: 726430.0 kWh\n", + "- storage_dis_energy: 673370.0 kWh\n", + "- storage_loss_energy: 69990.0 kWh\n", + "- spilled_energy: 1.7264e7 kWh\n", + "- spilled_max: 8847.8 kW\n", + "- spilled_rate: 0.70818 in [0,1]\n", + "- renew_potential_energy: 2.4378e7 kWh\n", + "- renew_energy: 7.1139e6 kWh\n", "- renew_rate: 1.0 in [0,1]\n" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -873,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "b01d2fab-2c1c-40fb-9903-0e602224e9e2", "metadata": { "tags": [] @@ -958,12 +982,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "16330858-ca64-45e6-b641-d197924e401d", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 50.694 ms (899212 allocations: 26.56 MiB)\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.32398700745291287, 0.0002557645932088728)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "@btime obj_multi(x0)" ] diff --git a/examples/Microgrid_sizing_optimization.ipynb b/examples/Microgrid_sizing_optimization.ipynb index 1e7912f..8789bdb 100644 --- a/examples/Microgrid_sizing_optimization.ipynb +++ b/examples/Microgrid_sizing_optimization.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "c2f04250-290c-4c38-93e6-074702ec0700", "metadata": { "tags": [] @@ -70,12 +70,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", "metadata": { "tags": [] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b]\n", + "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling DataFrames [a93c6f00-e57d-5684-b7b6-d8193f3e46c0]\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -119,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", "metadata": { "tags": [] @@ -131,7 +139,7 @@ "simulate_microgrid" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -187,21 +195,25 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "d765dc6a-eeda-4727-b259-479176670dc1", "metadata": { "tags": [] }, "outputs": [ { - "data": { - "text/plain": [ - "([1.8, 5.0, 3.0, 0.9], 0.22924812869928668, 21.89002772908652)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `DispatchableGenerator` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `DispatchableGenerator` not defined", + "", + "Stacktrace:", + " [1] simulate_microgrid(x::Vector{Float64})", + " @ Main .\\In[4]:15", + " [2] top-level scope", + " @ In[5]:4" + ] } ], "source": [ @@ -226,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "470f5630-322c-497f-bac9-beb87f014f71", "metadata": { "tags": [] @@ -238,7 +250,7 @@ "obj_multi" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -772,28 +784,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "id": "16330858-ca64-45e6-b641-d197924e401d", "metadata": { "tags": [] }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " 203.800 μs (143 allocations: 826.58 KiB)\n" + "ename": "LoadError", + "evalue": "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", + "output_type": "error", + "traceback": [ + "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", + "" ] - }, - { - "data": { - "text/plain": [ - "(0.31411996962098027, 0.0)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ diff --git a/src/components.jl b/src/components.jl index e52916a..fcc4c6c 100644 --- a/src/components.jl +++ b/src/components.jl @@ -2,12 +2,6 @@ """ Microgrid project information - -Parameters: -- lifetime (y) -- discount rate ∈ [0,1] -- time step (h) -- currency: "\$", "€"... """ struct Project "project lifetime (y)" @@ -21,27 +15,26 @@ struct Project end """ -Dispatchable power source -(e.g. Diesel generator, Gas turbine, Fuel cell with its associated tank) +ProductionUnit +Unit used to product gaz or energy +(e.g. Diesel generator, Gas turbine, Fuel cell or Electrolyzer ) All component parameters should be `Float64` except for the *sizing parameter(s)* (here `power_rated`) which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type). """ - - struct ProductionUnit{Topt<:Real} # Main technical parameters "rated power (kW)" power_rated::Topt - "combustible consumption curve intercept (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used are linear " + "combustible consumption curve intercept (L/h/kW_max or kg/h/kW_max or kW_max/h/kg ); 0 for fuel cells and electrolyzer as the models used are linear " consumption_intercept::Float64 "input consumption curve slope (L/h/kW or Kg/h/kW or KW/Kg/h )" consumption_slope::Float64 # Main economics parameters - "fuel price (\$/L)" + "fuel price (\$/L, \$/Kg, \$/kW)" combustible_price::Float64 "initial investiment price (\$/kW)" investment_price::Float64 @@ -64,11 +57,16 @@ struct ProductionUnit{Topt<:Real} output_unit::String end -""" -Description to be added """ +Tank( Can be a fuel or a H2 tank) +# About the types of the fields +All component parameters should be `Float64` except for the +*sizing parameter(s)* (here `capacity`) +which type is parametrized as `Topt` and may be also `Float64` or +or any another `Real` type (e.g. ForwardDiff's dual number type). +""" struct Tank{Topt<:Real} # Main technical parameters "rated capacity (kg or L)" @@ -84,7 +82,7 @@ struct Tank{Topt<:Real} # Secondary technical parameters (which should have a default value) "linear loss factor α (efficiency is about 1 − α) ∈ [0,1]" - loss_factor::Float64 # (auto - discharge rate ) + loss_factor::Float64 # "initial level of tank ∈ [0,1]" ini_filling_ratio ::Float64 "minimum level of tank ∈ [0,1]" @@ -99,22 +97,31 @@ struct Tank{Topt<:Real} salvage_price_ratio::Float64 end - - +""" +Dispatchable compound +Dispatchable compound brings together the generators and fuel_cells that a `Microgrid` may contain. +""" struct DispatchableCompound{Topt<:Real} - generator - fuel_cell + "array of generators" + generator :: Vector{ProductionUnit{Topt}} + "array of fuel_cells" + fuel_cell :: Vector{ProductionUnit{Topt}} end +""" +Tank compound +Tank compound brings together the both tanks that a `Microgrid` may contain. +We assumed that a `Microgrid` can only have one `Tank` of each type. +""" struct TankCompound{Topt<:Real} - fuelTank - h2Tank + "fuel tank, can be used by all diesel generators of the `Microgrid`" + fuelTank :: Tank{Topt} + "hydrogen tank, can be used by all diesel generators of the `Microgrid`" + h2Tank :: Tank{Topt} end - - """ Battery energy storage (including AC/DC converter) @@ -129,7 +136,6 @@ All component parameters should be `Float64` except for the which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type). """ - struct Battery{Topt<:Real} # Main technical parameters "rated energy capacity (kWh)" @@ -291,7 +297,7 @@ struct Microgrid{Topt<:Real} " Dispatchable Compound" dispatchables:: DispatchableCompound{Topt} "electrolyzer" - electrolyzer + electrolyzer :: Vector{ProductionUnit{Topt}} "Tanks compound" tanks :: TankCompound{Topt} "energy storage (e.g. battery)" diff --git a/src/operation.jl b/src/operation.jl index ca98abf..1a26152 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -1,6 +1,7 @@ """Operation variables (time series) from a simulated Microgrid operation (simulation duration is assumed to be 1 year) +Works only for a `Microgrid` with 1 electrolyzer, one fuel_cell, and 1 one diesel generator . """ struct OperationTraj{T<:Real} # load @@ -13,17 +14,23 @@ struct OperationTraj{T<:Real} # non dispatchable (renewables) "renewable power potential (before spillage) (kW)" Prenew_pot::Vector{T} - # diesel generator + # dispatchables "Diesel generator power at each time instant (kW)" Pgen::Vector{T} - Pelyz:: Vector{T} + "fuel cell power at each time instant (kW)" Pfc :: Vector{T} + # electrolyzer + "Electrolyzer power at each time instant (kW)" + Pelyz:: Vector{T} # battery "Battery energy at each time instant (kWh)" Ebatt::Vector{T} "Battery power at each time instant (kW)" Pbatt::Vector{T} + # tanks + "Level of hydrogen at each time instant (kg)" LoH :: Vector{T} + "Level of fuel at each time instant (L)" LoF :: Vector{T} # renewables sources "Renewables curtailment power at each time instant (kW)" @@ -33,6 +40,7 @@ end """Aggregated statistics over the simulated Microgrid operation (simulation duration is assumed to be 1 year) +Works only for a `Microgrid` with 1 electrolyzer, one fuel_cell, and 1 one diesel generator . """ struct OperationStats # Load statistics @@ -49,7 +57,7 @@ struct OperationStats "ratio of shed energy to the desired load (∈ [0,1])" shed_rate - # Dispatchable generator statistics + # Dispatchable diesel generator statistics "energy supplied by the dispatchable generator (kWh/y)" gen_energy "cumulated operating hours of the dispatchable generator (h/y)" @@ -57,17 +65,22 @@ struct OperationStats "fuel consumption (L/y)" gen_fuel + # Hydrogen chain statistics + "energy supplied by the fuel cell (kWh/y)" fc_energy - + "cumulated operating hours of the fuel_cell (h/y)" fc_hours - + "total amount of consumed hydrogen" h2_consumed + "total amount of produced hydrogen" h2_produced + "cumulated operating hours of the electrolyzer (h/y)" elyz_hours + "energy consumed by the the electrolyzer to produce hydrogen (kWh/y)" elyz_consumed_energy + "energy lossed in the Power-Gaz-Power chain (kWh/y)" h2_chain_loss - # Energy storage (e.g. battery) statistics "cycling of the energy storage (cycles/y)" storage_cycles @@ -139,13 +152,17 @@ function Base.show(io::IO, ::MIME"text/plain", stats::OperationStats) println(io, "- ", name, ": ", format_stat(stats, name; sigdigits=5), " ", unit) end end + """ -increment the running time, and fuel consumption of a productionUnit + increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Real=0.0) +Allow to compute operating hours and fuel consumption of 'ProductionUnit'. """ function increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Real=0.0) Pprod_unit_max = prod_unit.power_rated Pprod_unit_eps = Pprod_unit_max*1e-6 + prod_unit_intercept = prod_unit.consumption_intercept + prod_unit_slope = prod_unit.consumption_slope time_inc = 0 cons_rate = 0 if prod_unit_power > Pprod_unit_eps # prod_unit ON @@ -153,12 +170,12 @@ function increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Rea if Pprod_unit_norm <= 1.0 time_inc = Pprod_unit_norm # relaxation of discontinuous generator statistics for small Pgen - cons_rate = Pprod_unit_norm * prod_unit.consumption_intercept * Pprod_unit_max + - prod_unit.consumption_slope * prod_unit_power # (L/h) + cons_rate = Pprod_unit_norm * prod_unit_intercept * Pprod_unit_max + + prod_unit_slope * prod_unit_power # (L/h) else time_inc=1 - cons_rate = prod_unit.consumption_intercept * Pprod_unit_max + - prod_unit.consumption_slope * prod_unit_power # (L/h) + cons_rate = prod_unit_intercept * Pprod_unit_max + + prod_unit_slope * prod_unit_power # (L/h) end end @@ -203,6 +220,7 @@ function operation(mg::Microgrid) Psto_pmin = -mg.storage.charge_rate * Esto_max # <0 in line with the generator convention for Psto sto_loss = mg.storage.loss_factor + n_elyz = length(mg.electrolyzer) Pelyz_max = collect(el.power_rated for el in mg.electrolyzer) Pelyz_min = collect(mg.electrolyzer[i].minimum_load_ratio * Pelyz_max[i] for i in eachindex(Pelyz_max) ) prod_rate = collect(el.consumption_slope for el in mg.electrolyzer) @@ -219,22 +237,19 @@ function operation(mg::Microgrid) # Initialization of loop variables Pnl = zeros(Topt,K) Pgen = zeros(Topt,K,length(mg.dispatchables.generator)) - Pgen_dmax = zeros(Topt,K,length(mg.dispatchables.generator)) + Esto = zeros(Topt,K+1) Psto = zeros(Topt,K) - Psto_dmax = zeros(Topt,K) - Psto_cmax = zeros(Topt,K) + Pspill = zeros(Topt,K) Pshed = zeros(Topt,K) Pelyz = zeros(Topt,K,length(mg.electrolyzer)) - Pelyz_cmax = zeros(Topt,K,length(mg.electrolyzer)) - Pfc = zeros(Topt,K,length(mg.dispatchables.fuel_cell)) - Pfc_pmax = zeros(Topt,K,length(mg.dispatchables.fuel_cell)) + LoH = zeros(Topt,K+1) LoF = zeros(Topt,K+1) @@ -251,6 +266,8 @@ function operation(mg::Microgrid) LoF_ini = mg.tanks.fuelTank.ini_filling_ratio * mg.tanks.fuelTank.capacity LoF[1] = LoF_ini + + Pelyz_emax = zeros(Topt, n_elyz) for k=1:K # Storage energy and power limits @@ -260,7 +277,8 @@ function operation(mg::Microgrid) Psto_cmax = max(Psto_emin, Psto_pmin) - Pelyz_emax = collect( (LoH_max - LoH[k] ) * prod_rate[i] * dt for i in eachindex(prod_rate)) + #Pelyz_emax = collect( (LoH_max - LoH[k] ) * prod_rate[i] * dt for i in eachindex(prod_rate)) + Pelyz_emax .= prod_rate .* ((LoH_max - LoH[k]) * dt) Pelyz_cmax = collect( min(Pelyz_emax[i],Pelyz_max[i]) for i in eachindex(Pelyz_emax)) @@ -278,19 +296,26 @@ function operation(mg::Microgrid) Esto[k+1] = Esto[k] - (Psto[k] + sto_loss * abs(Psto[k])) * dt # Hydrogen storage dynamics - LoH[k+1] = LoH[k] + (sum(collect((Pelyz[k,i]/prod_rate[i] for i in eachindex(prod_rate)))) - sum(collect(Pfc[k,i]*cons_rate[i] for i in eachindex(cons_rate)))) * dt + LoH[k+1] = LoH[k] + ( + sum(Pelyz[k,i]/prod_rate[i] for i in eachindex(prod_rate)) - + sum(Pfc[k,i]*cons_rate[i] for i in eachindex(cons_rate)) + ) * dt # fuel storage dynamics - LoF[k+1] = LoF[k] - sum(collect(((Pgen[k,i] / fuel_slope[i] ) + fuel_intercept[i]* Pgen_max[i]) for i in eachindex(fuel_slope))) * dt + LoF[k+1] = LoF[k] - sum( + Pgen[k,i] / fuel_slope[i] + fuel_intercept[i]* Pgen_max[i] + for i in eachindex(fuel_slope) + ) * dt end - oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pelyz[:,1], Pfc[:,1], Esto, Psto, LoH, LoF, Pspill) + oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1], Esto, Psto, LoH, LoF, Pspill) + return oper_traj end """ - aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0) +aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0) Aggregate operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object). @@ -332,14 +357,14 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) (Efin - Eini) # kWh/y #Power-H2-Power statistics - elyz_consumed_energy =sum(oper_traj.Pelyz) * dt + elyz_consumed_energy = sum(oper_traj.Pelyz) * dt elyz_produced_h2 = elyz_consumed_energy / mg.electrolyzer[1].consumption_slope fc_produced_energy = sum(oper_traj.Pfc) * dt fc_consumed_h2 = fc_produced_energy * mg.dispatchables.fuel_cell[1].consumption_slope LoHfin = oper_traj.LoH[end] LoHini = oper_traj.LoH[1] - chain_loss = fc_produced_energy - elyz_consumed_energy -(LoHfin-LoHini)*mg.dispatchables.fuel_cell[1].consumption_slope + chain_loss = elyz_consumed_energy - fc_produced_energy -(LoHfin-LoHini)*mg.dispatchables.fuel_cell[1].consumption_slope # Non-dispatchable (typ. renewables) sources statistics spilled_energy = sum(oper_traj.power_curtailment) * dt # kWh/y @@ -358,7 +383,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) gen_fuel = 0.0 # L/y elyz_hours = 0.0 fc_hours = 0.0 - fc_cons=0.0 + # auxilliary counter: shed_duration = 0.0; # duration of current load shedding event (h) @@ -377,10 +402,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) time_inc, cons_rate = increment(oper_traj.Pfc[K],mg.dispatchables.fuel_cell[1],ε) fc_hours += time_inc * dt - # fc_cons += cons_rate * dt - - - + # Load shedding: shedding duration and maximum shedding duration Pshed = oper_traj.power_shedding[K] if Pshed > 0.0 From 6d47dd512ea0a3a68ed7bfc45b35705a5a9d459d Mon Sep 17 00:00:00 2001 From: NIKIEMA Jean Date: Thu, 14 Dec 2023 14:23:36 +0100 Subject: [PATCH 03/19] First doc with Documenter + DocStringExtensions --- Project.toml | 6 +- docs/.gitignore | 2 + docs/Project.toml | 2 + docs/make.jl | 18 ++ docs/src/assets/logo.svg | 394 +++++++++++++++++++++++++++++++++++++++ docs/src/index.md | 16 ++ src/Microgrids.jl | 13 ++ 7 files changed, 449 insertions(+), 2 deletions(-) create mode 100644 docs/.gitignore create mode 100644 docs/Project.toml create mode 100644 docs/make.jl create mode 100644 docs/src/assets/logo.svg create mode 100644 docs/src/index.md diff --git a/Project.toml b/Project.toml index 362984f..00201e2 100644 --- a/Project.toml +++ b/Project.toml @@ -1,8 +1,10 @@ name = "Microgrids" uuid = "bd581358-d3fa-499e-a26e-e70307242c03" -authors = ["Pierre Haessig ", - "evelisea <59697806+evelisea@users.noreply.github.com>"] +authors = ["Pierre Haessig ", "evelisea <59697806+evelisea@users.noreply.github.com>"] version = "0.10.2" +[deps] +DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" + [compat] julia = "1.6" diff --git a/docs/.gitignore b/docs/.gitignore new file mode 100644 index 0000000..a303fff --- /dev/null +++ b/docs/.gitignore @@ -0,0 +1,2 @@ +build/ +site/ diff --git a/docs/Project.toml b/docs/Project.toml new file mode 100644 index 0000000..dfa65cd --- /dev/null +++ b/docs/Project.toml @@ -0,0 +1,2 @@ +[deps] +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" diff --git a/docs/make.jl b/docs/make.jl new file mode 100644 index 0000000..1a1795d --- /dev/null +++ b/docs/make.jl @@ -0,0 +1,18 @@ +using Documenter +using Microgrids +using DocStringExtensions + +makedocs( + sitename = "Microgrids", + format = Documenter.HTML(sidebar_sitename=false), + modules = [Microgrids], + pages = ["Microgrids" => "index.md"], + +) + +# Documenter can also automatically deploy documentation to gh-pages. +# See "Hosting Documentation" and deploydocs() in the Documenter manual +# for more information. +#=deploydocs( + repo = "" +)=# diff --git a/docs/src/assets/logo.svg b/docs/src/assets/logo.svg new file mode 100644 index 0000000..49a90f1 --- /dev/null +++ b/docs/src/assets/logo.svg @@ -0,0 +1,394 @@ + + + + + Logo for Microgrids.jl package + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Logo for Microgrids.jl package + + + Pierre Haessig + + + 2022-08 + + + CC-BY + + + + + + diff --git a/docs/src/index.md b/docs/src/index.md new file mode 100644 index 0000000..2552a67 --- /dev/null +++ b/docs/src/index.md @@ -0,0 +1,16 @@ +# Microgrids.jl + +Documentation for Microgrids.jl + + +## functions +```@autodocs +Modules = [Microgrids] +Order = [:function] +``` + +## Type +```@autodocs +Modules = [Microgrids] +Order = [ :type] +``` \ No newline at end of file diff --git a/src/Microgrids.jl b/src/Microgrids.jl index a0c9a7a..4b3620c 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -1,4 +1,17 @@ module Microgrids +using Documenter +using DocumenterTools +using DocStringExtensions + + +@template (TYPES) = +""" +$(TYPEDEF) +$(DOCSTRING) + +### Parameters: +$(FIELDS) +""" import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions From 3fb64cf29ce65a8aca2b2d3cb5a56ad65b3ef94b Mon Sep 17 00:00:00 2001 From: NIKIEMA Jean Date: Tue, 30 Jan 2024 14:51:11 +0100 Subject: [PATCH 04/19] campagne de tests 2 --- .vscode/settings.json | 2 + Project.toml | 4 + docs/src/intro.md | 9 + examples/Microgrid_Wind-Solar-H2.ipynb | 1494 +-- examples/Microgrid_Wind-Solar.ipynb | 24 +- examples/Microgrid_Wind-Solar_data.jl | 14 +- .../Microgrid_sizingH2_optimization1.ipynb | 194 +- examples/new_examples/Untitled.ipynb | 80 + .../data/Ouessant_2016_monthly_stats.png | Bin 0 -> 126789 bytes .../new_examples/data/Ouessant_data_2016.csv | 8761 +++++++++++++++++ examples/new_examples/data/SOURCES.md | 26 + examples/new_examples/df2_dataset.csv | 351 + examples/new_examples/df2_dataset2.csv | 351 + examples/new_examples/df2_dataset2.xlsx | Bin 0 -> 151903 bytes examples/new_examples/df3_dataset - Copie.csv | 601 ++ examples/new_examples/df3_dataset.csv | 601 ++ examples/new_examples/df4_dataset.csv | 601 ++ .../new_examples/images/MG_optim_diagram.png | Bin 0 -> 136890 bytes examples/new_examples/images/microgrid.png | Bin 0 -> 130445 bytes .../images/microgrid_sizing - h2.png | Bin 0 -> 177607 bytes .../new_examples/images/microgrid_sizing.png | Bin 0 -> 114539 bytes examples/new_examples/microgrid_def.ipynb | 661 ++ .../prices_scenario_comparison.ipynb | 2139 ++++ src/Microgrid_Wind-Solar_data.jl | 141 + src/Microgrids.jl | 73 +- src/components.jl | 4 +- src/dispatch.jl | 108 +- src/economics.jl | 7 +- src/operation.jl | 64 +- test2/Typetest.jl | 0 30 files changed, 15408 insertions(+), 902 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 docs/src/intro.md create mode 100644 examples/new_examples/Untitled.ipynb create mode 100644 examples/new_examples/data/Ouessant_2016_monthly_stats.png create mode 100644 examples/new_examples/data/Ouessant_data_2016.csv create mode 100644 examples/new_examples/data/SOURCES.md create mode 100644 examples/new_examples/df2_dataset.csv create mode 100644 examples/new_examples/df2_dataset2.csv create mode 100644 examples/new_examples/df2_dataset2.xlsx create mode 100644 examples/new_examples/df3_dataset - Copie.csv create mode 100644 examples/new_examples/df3_dataset.csv create mode 100644 examples/new_examples/df4_dataset.csv create mode 100644 examples/new_examples/images/MG_optim_diagram.png create mode 100644 examples/new_examples/images/microgrid.png create mode 100644 examples/new_examples/images/microgrid_sizing - h2.png create mode 100644 examples/new_examples/images/microgrid_sizing.png create mode 100644 examples/new_examples/microgrid_def.ipynb create mode 100644 examples/new_examples/prices_scenario_comparison.ipynb create mode 100644 src/Microgrid_Wind-Solar_data.jl create mode 100644 test2/Typetest.jl diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7a73a41 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,2 @@ +{ +} \ No newline at end of file diff --git a/Project.toml b/Project.toml index 00201e2..dc49e40 100644 --- a/Project.toml +++ b/Project.toml @@ -4,7 +4,11 @@ authors = ["Pierre Haessig ", "evelisea <5969 version = "0.10.2" [deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" +PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" [compat] julia = "1.6" diff --git a/docs/src/intro.md b/docs/src/intro.md new file mode 100644 index 0000000..100bd1c --- /dev/null +++ b/docs/src/intro.md @@ -0,0 +1,9 @@ +# Microgrids.jl + ### Tests the objective functions + +Sizing being tested: +- baseline sizing from the simulation notebook: perfect quality of service (QoS) with zero load shedding +- baseline modified with a halved generator sizing: very good QoS with a bit of load shedding → not penalized +- small PV and small wind generators only: low LCOE (i.e. the production-only LCOE of these sources) but but extremely bad QoS → huge penalty + +Documentation for Microgrids.jl diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index 60dd73c..0bb1396 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -50,9 +50,22 @@ "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "using PyPlot # For plotting only. Can be skipped or commented out" + "using PyPlot # For plotting only. Can be skipped or commented out\n", + "using PyCall\n", + "np=pyimport(\"numpy\")" ] }, { @@ -186,7 +199,7 @@ ], "source": [ "td = collect((0:nsteps-1)/24) # time in days\n", - "\n", + "#tm = collect((0:nsteps-1)/730)\n", "\"\"\"helper: plot time series with a temporal zoom\"\"\"\n", "function plot_ts_zoom(t, ts; zoom, color::String, title::String, xlabel::String, ylabel::String)\n", " fig, (ax1, ax2) = plt.subplots(1,2, figsize=(7.5,2.7), sharey=true,\n", @@ -949,7 +962,7 @@ }, "outputs": [], "source": [ - "oper_traj, oper_stats, mg_costs = simulate(microgrid);" + "oper_traj, oper_stats, mg_costs = simulate(microgrid,dispatch_2);" ] }, { @@ -1001,7 +1014,7 @@ { "data": { "text/plain": [ - "5.005707762557078" + "2.9680365296803655" ] }, "execution_count": 27, @@ -1010,13 +1023,13 @@ } ], "source": [ - "43850 /8760" + "26000 /8760" ] }, { "cell_type": "code", - "execution_count": 31, - "id": "045309d6-743b-41c9-8cec-78d73b0f9fc8", + "execution_count": 29, + "id": "748d17cd-cfae-44d9-ac10-856d095aa96c", "metadata": { "tags": [] }, @@ -1025,102 +1038,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "search: \u001b[0m\u001b[1mP\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mu\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mU\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mt\u001b[22m \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1md\u001b[22m\u001b[0m\u001b[1mu\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mt\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mn\u001b[22m\n", - "\n" + " 518.500 μs (55 allocations: 1.14 MiB)\n" ] - }, - { - "data": { - "text/markdown": [ - "Dispatchable power source (e.g. Diesel generator, Gas turbine, Fuel cell or )\n", - "\n", - "All component parameters should be `Float64` except for the *sizing parameter(s)* (here `power_rated`) which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type).\n", - "\n", - "```julia\n", - "struct ProductionUnit{Topt<:Real}\n", - "```\n", - "\n", - "**parameters** : \n", - "\n", - " * `power_rated`: rated power (kW)\n", - " * `consumption_intercept`: combustible consumption curve intercept (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used are linear\n", - " * `consumption_slope`: input consumption curve slope (L/h/kW or Kg/h/kW or KW/Kg/h )\n", - " * `combustible_price`: fuel price (:/L)\n", - " * `investment_price`: initial investiment price (:/kW)\n", - " * `om_price_hours`: operation & maintenance price (:/kW/h of operation)\n", - " * `lifetime_hours`: generator lifetime (h of operation)\n", - " * `minimum_load_ratio`: minimum load ratio ∈ [0,1]\n", - " * `replacement_price_ratio`: replacement price, relative to initial investment\n", - " * `salvage_price_ratio`: salvage price, relative to initial investment\n", - " * `input_unit`: fuel quantity unit (used in fuel price and consumption curve parameters)\n", - " * `output_unit`\n" - ], - "text/plain": [ - " Dispatchable power source (e.g. Diesel generator, Gas turbine, Fuel cell or\n", - " )\n", - "\n", - " All component parameters should be \u001b[36mFloat64\u001b[39m except for the \u001b[4msizing\n", - " parameter(s)\u001b[24m (here \u001b[36mpower_rated\u001b[39m) which type is parametrized as \u001b[36mTopt\u001b[39m and may\n", - " be also \u001b[36mFloat64\u001b[39m or or any another \u001b[36mReal\u001b[39m type (e.g. ForwardDiff's dual number\n", - " type).\n", - "\n", - "\u001b[36m struct ProductionUnit{Topt<:Real}\u001b[39m\n", - "\n", - " \u001b[1mparameters\u001b[22m :\n", - "\n", - " • \u001b[36mpower_rated\u001b[39m: rated power (kW)\n", - "\n", - " • \u001b[36mconsumption_intercept\u001b[39m: combustible consumption curve intercept\n", - " (L/h/kW_max ) 0for fuel cells and electrolyzer as the models used\n", - " are linear\n", - "\n", - " • \u001b[36mconsumption_slope\u001b[39m: input consumption curve slope (L/h/kW or\n", - " Kg/h/kW or KW/Kg/h )\n", - "\n", - " • \u001b[36mcombustible_price\u001b[39m: fuel price (:/L)\n", - "\n", - " • \u001b[36minvestment_price\u001b[39m: initial investiment price (:/kW)\n", - "\n", - " • \u001b[36mom_price_hours\u001b[39m: operation & maintenance price (:/kW/h of\n", - " operation)\n", - "\n", - " • \u001b[36mlifetime_hours\u001b[39m: generator lifetime (h of operation)\n", - "\n", - " • \u001b[36mminimum_load_ratio\u001b[39m: minimum load ratio ∈ [0,1]\n", - "\n", - " • \u001b[36mreplacement_price_ratio\u001b[39m: replacement price, relative to initial\n", - " investment\n", - "\n", - " • \u001b[36msalvage_price_ratio\u001b[39m: salvage price, relative to initial investment\n", - "\n", - " • \u001b[36minput_unit\u001b[39m: fuel quantity unit (used in fuel price and consumption\n", - " curve parameters)\n", - "\n", - " • \u001b[36moutput_unit\u001b[39m" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "?ProductionUnit" + "@btime oper_traj = operation(microgrid,dispatch_2);" ] }, { "cell_type": "code", - "execution_count": 28, - "id": "5a0a208f-b289-4b91-bcdc-e3d141e8cfee", + "execution_count": 29, + "id": "fcbd0801-7d24-400b-b49e-8513fc7d4eac", "metadata": { "tags": [] }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1.920 ms (43850 allocations: 3.81 MiB)\n" + "ename": "LoadError", + "evalue": "MethodError: no method matching operation(::Microgrid{Float64})\n\n\u001b[0mClosest candidates are:\n\u001b[0m operation(::Microgrid, \u001b[91m::Function\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[35mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", + "output_type": "error", + "traceback": [ + "MethodError: no method matching operation(::Microgrid{Float64})\n\n\u001b[0mClosest candidates are:\n\u001b[0m operation(::Microgrid, \u001b[91m::Function\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[35mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", + "", + "Stacktrace:", + " [1] var\"##core#304\"()", + " @ Main C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:489", + " [2] var\"##sample#305\"(::Tuple{}, __params::BenchmarkTools.Parameters)", + " @ Main C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:495", + " [3] _run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; verbose::Bool, pad::String, kwargs::Base.Pairs{Symbol, Integer, NTuple{4, Symbol}, NamedTuple{(:samples, :evals, :gctrial, :gcsample), Tuple{Int64, Int64, Bool, Bool}}})", + " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:99", + " [4] #invokelatest#2", + " @ .\\essentials.jl:821 [inlined]", + " [5] invokelatest", + " @ .\\essentials.jl:816 [inlined]", + " [6] #run_result#45", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:34 [inlined]", + " [7] run_result", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:34 [inlined]", + " [8] run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; progressid::Nothing, nleaves::Float64, ndone::Float64, kwargs::Base.Pairs{Symbol, Integer, NTuple{5, Symbol}, NamedTuple{(:verbose, :samples, :evals, :gctrial, :gcsample), Tuple{Bool, Int64, Int64, Bool, Bool}}})", + " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:117", + " [9] run (repeats 2 times)", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:117 [inlined]", + " [10] #warmup#54", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:169 [inlined]", + " [11] warmup(item::BenchmarkTools.Benchmark)", + " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:168", + " [12] top-level scope", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:575" ] } ], @@ -1130,47 +1095,7 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "b3e05487-f5b5-4075-9102-3c8bbe7c4191", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1.918 ms (43850 allocations: 3.81 MiB)\n" - ] - } - ], - "source": [ - "@btime oper_traj = operation(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "5e8d4714-4eed-4267-a7ff-7e030a830291", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2.133 ms (52609 allocations: 4.35 MiB)\n" - ] - } - ], - "source": [ - "@btime oper_traj = operation(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "id": "c9bf9e90-a9dc-43ca-95f8-4a3d76eb0cf1", "metadata": { "tags": [] @@ -1180,556 +1105,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " 3.186 ms (79057 allocations: 5.96 MiB)\n" + " 2.955 ms (223 allocations: 1.15 MiB)\n" ] } ], "source": [ - "@btime oper_traj, oper_stats, mg_costs = simulate(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "e399ac23-c55f-42d7-a10e-6b5cf42cb96e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MethodInstance for Microgrids.operation(::Microgrid{Float64})\n", - " from operation(\u001b[90mmg\u001b[39m::\u001b[1mMicrogrid\u001b[22m)\u001b[90m @\u001b[39m \u001b[90mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", - "Arguments\n", - " #self#\u001b[36m::Core.Const(Microgrids.operation)\u001b[39m\n", - " mg\u001b[36m::Microgrid{Float64}\u001b[39m\n", - "Locals\n", - " @_3\u001b[33m\u001b[1m::Union{Nothing, Tuple{Int64, Int64}}\u001b[22m\u001b[39m\n", - " #15\u001b[36m::Microgrids.var\"#15#35\"\u001b[39m\n", - " #14\u001b[36m::Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - " #13\u001b[36m::Microgrids.var\"#13#33\"\u001b[39m\n", - " #12\u001b[36m::Microgrids.var\"#12#32\"\u001b[39m\n", - " #11\u001b[36m::Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - " #10\u001b[36m::Microgrids.var\"#10#30\"\u001b[39m\n", - " #9\u001b[36m::Microgrids.var\"#9#29\"\u001b[39m\n", - " #8\u001b[36m::Microgrids.var\"#8#28\"\u001b[39m\n", - " #7\u001b[36m::Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - " #6\u001b[36m::Microgrids.var\"#6#26\"\u001b[39m\n", - " #5\u001b[36m::Microgrids.var\"#5#25\"\u001b[39m\n", - " oper_traj\u001b[36m::OperationTraj{Float64}\u001b[39m\n", - " LoF_ini\u001b[36m::Float64\u001b[39m\n", - " LoH_ini\u001b[36m::Float64\u001b[39m\n", - " Esto_ini\u001b[36m::Float64\u001b[39m\n", - " LoF\u001b[36m::Vector{Float64}\u001b[39m\n", - " LoH\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pfc\u001b[36m::Matrix{Float64}\u001b[39m\n", - " Pelyz\u001b[36m::Matrix{Float64}\u001b[39m\n", - " Pshed\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pspill\u001b[36m::Vector{Float64}\u001b[39m\n", - " Psto\u001b[36m::Vector{Float64}\u001b[39m\n", - " Esto\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pgen\u001b[36m::Matrix{Float64}\u001b[39m\n", - " Pnl\u001b[36m::Vector{Float64}\u001b[39m\n", - " LoH_min\u001b[36m::Float64\u001b[39m\n", - " LoH_max\u001b[36m::Float64\u001b[39m\n", - " cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pfc_min\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pfc_max\u001b[36m::Vector{Float64}\u001b[39m\n", - " prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pelyz_min\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pelyz_max\u001b[36m::Vector{Float64}\u001b[39m\n", - " sto_loss\u001b[36m::Float64\u001b[39m\n", - " Psto_pmin\u001b[36m::Float64\u001b[39m\n", - " Psto_pmax\u001b[36m::Float64\u001b[39m\n", - " Esto_min\u001b[36m::Float64\u001b[39m\n", - " Esto_max\u001b[36m::Float64\u001b[39m\n", - " LoF_min\u001b[36m::Float64\u001b[39m\n", - " LoF_max\u001b[36m::Float64\u001b[39m\n", - " fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", - " fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pgen_min\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", - " dt\u001b[36m::Float64\u001b[39m\n", - " K\u001b[36m::Int64\u001b[39m\n", - " Pnl_request\u001b[36m::Vector{Float64}\u001b[39m\n", - " renew_potential\u001b[36m::Vector{Float64}\u001b[39m\n", - " renew_productions\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", - " Topt\u001b[36m::Type{Float64}\u001b[39m\n", - " #24\u001b[36m::Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - " #23\u001b[36m::Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - " #22\u001b[36m::Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - " @_57\u001b[36m::Int64\u001b[39m\n", - " #21\u001b[36m::Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - " #20\u001b[36m::Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}\u001b[39m\n", - " #19\u001b[36m::Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - " #18\u001b[36m::Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", - " #17\u001b[36m::Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - " #16\u001b[36m::Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", - " k\u001b[36m::Int64\u001b[39m\n", - " Pgen_dmax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pgen_emax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pfc_pmax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pfc_emax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pelyz_cmax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Pelyz_emax\u001b[36m::Vector{Float64}\u001b[39m\n", - " Psto_cmax\u001b[36m::Float64\u001b[39m\n", - " Psto_dmax\u001b[36m::Float64\u001b[39m\n", - " Psto_emax\u001b[36m::Float64\u001b[39m\n", - " Psto_emin\u001b[36m::Float64\u001b[39m\n", - "Body\u001b[36m::OperationTraj{Float64}\u001b[39m\n", - "\u001b[90m1 ─\u001b[39m Core.NewvarNode(:(oper_traj))\n", - "\u001b[90m│ \u001b[39m %2 = Microgrids.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %3 = Base.getproperty(%2, :parameters)\u001b[36m::Core.Const(svec(Float64))\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Topt = Base.getindex(%3, 1))\n", - "\u001b[90m│ \u001b[39m (#5 = %new(Microgrids.:(var\"#5#25\")))\n", - "\u001b[90m│ \u001b[39m %6 = #5\u001b[36m::Core.Const(Microgrids.var\"#5#25\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %7 = Base.getproperty(mg, :nondispatchables)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", - "\u001b[90m│ \u001b[39m %8 = Base.Generator(%6, %7)\u001b[91m\u001b[1m::Base.Generator{_A, Microgrids.var\"#5#25\"} where _A\u001b[22m\u001b[39m\n", - "\u001b[90m│ \u001b[39m (renew_productions = Microgrids.collect(%8))\n", - "\u001b[90m│ \u001b[39m %10 = Microgrids.sum(renew_productions)\u001b[91m\u001b[1m::Any\u001b[22m\u001b[39m\n", - "\u001b[90m│ \u001b[39m %11 = Core.apply_type(Microgrids.Vector, Topt::Core.Const(Float64))\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m (renew_potential = Core.typeassert(%10, %11))\n", - "\u001b[90m│ \u001b[39m %13 = Base.getproperty(mg, :load)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pnl_request = %13 - renew_potential)\n", - "\u001b[90m│ \u001b[39m %15 = Base.getproperty(mg, :load)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (K = Microgrids.length(%15))\n", - "\u001b[90m│ \u001b[39m %17 = Base.getproperty(mg, :project)\u001b[36m::Project\u001b[39m\n", - "\u001b[90m│ \u001b[39m (dt = Base.getproperty(%17, :timestep))\n", - "\u001b[90m│ \u001b[39m (#6 = %new(Microgrids.:(var\"#6#26\")))\n", - "\u001b[90m│ \u001b[39m %20 = #6\u001b[36m::Core.Const(Microgrids.var\"#6#26\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %21 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %22 = Base.getproperty(%21, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %23 = Base.Generator(%20, %22)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#6#26\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pgen_max = Microgrids.collect(%23))\n", - "\u001b[90m│ \u001b[39m %25 = Microgrids.:(var\"#7#27\")\u001b[36m::Core.Const(Microgrids.var\"#7#27\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %26 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %27 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %28 = Core.apply_type(%25, %26, %27)\u001b[36m::Core.Const(Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#7 = %new(%28, mg, Pgen_max))\n", - "\u001b[90m│ \u001b[39m %30 = #7\u001b[36m::Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %31 = Microgrids.eachindex(Pgen_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %32 = Base.Generator(%30, %31)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#7#27\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pgen_min = Microgrids.collect(%32))\n", - "\u001b[90m│ \u001b[39m (#8 = %new(Microgrids.:(var\"#8#28\")))\n", - "\u001b[90m│ \u001b[39m %35 = #8\u001b[36m::Core.Const(Microgrids.var\"#8#28\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %36 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %37 = Base.getproperty(%36, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %38 = Base.Generator(%35, %37)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#8#28\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (fuel_slope = Microgrids.collect(%38))\n", - "\u001b[90m│ \u001b[39m (#9 = %new(Microgrids.:(var\"#9#29\")))\n", - "\u001b[90m│ \u001b[39m %41 = #9\u001b[36m::Core.Const(Microgrids.var\"#9#29\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %42 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %43 = Base.getproperty(%42, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %44 = Base.Generator(%41, %43)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#9#29\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (fuel_intercept = Microgrids.collect(%44))\n", - "\u001b[90m│ \u001b[39m %46 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %47 = Base.getproperty(%46, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %48 = Base.getproperty(%47, :max_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %49 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %50 = Base.getproperty(%49, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %51 = Base.getproperty(%50, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoF_max = %48 * %51)\n", - "\u001b[90m│ \u001b[39m %53 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %54 = Base.getproperty(%53, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %55 = Base.getproperty(%54, :min_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %56 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %57 = Base.getproperty(%56, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %58 = Base.getproperty(%57, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoF_min = %55 * %58)\n", - "\u001b[90m│ \u001b[39m %60 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Esto_max = Base.getproperty(%60, :energy_rated))\n", - "\u001b[90m│ \u001b[39m %62 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %63 = Base.getproperty(%62, :SoC_min)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Esto_min = %63 * Esto_max)\n", - "\u001b[90m│ \u001b[39m %65 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %66 = Base.getproperty(%65, :discharge_rate)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Psto_pmax = %66 * Esto_max)\n", - "\u001b[90m│ \u001b[39m %68 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %69 = Base.getproperty(%68, :charge_rate)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %70 = -%69\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Psto_pmin = %70 * Esto_max)\n", - "\u001b[90m│ \u001b[39m %72 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (sto_loss = Base.getproperty(%72, :loss_factor))\n", - "\u001b[90m│ \u001b[39m (#10 = %new(Microgrids.:(var\"#10#30\")))\n", - "\u001b[90m│ \u001b[39m %75 = #10\u001b[36m::Core.Const(Microgrids.var\"#10#30\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %76 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %77 = Base.Generator(%75, %76)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#10#30\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pelyz_max = Microgrids.collect(%77))\n", - "\u001b[90m│ \u001b[39m %79 = Microgrids.:(var\"#11#31\")\u001b[36m::Core.Const(Microgrids.var\"#11#31\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %80 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %81 = Core.typeof(Pelyz_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %82 = Core.apply_type(%79, %80, %81)\u001b[36m::Core.Const(Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#11 = %new(%82, mg, Pelyz_max))\n", - "\u001b[90m│ \u001b[39m %84 = #11\u001b[36m::Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %85 = Microgrids.eachindex(Pelyz_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %86 = Base.Generator(%84, %85)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#11#31\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pelyz_min = Microgrids.collect(%86))\n", - "\u001b[90m│ \u001b[39m (#12 = %new(Microgrids.:(var\"#12#32\")))\n", - "\u001b[90m│ \u001b[39m %89 = #12\u001b[36m::Core.Const(Microgrids.var\"#12#32\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %90 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %91 = Base.Generator(%89, %90)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#12#32\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (prod_rate = Microgrids.collect(%91))\n", - "\u001b[90m│ \u001b[39m (#13 = %new(Microgrids.:(var\"#13#33\")))\n", - "\u001b[90m│ \u001b[39m %94 = #13\u001b[36m::Core.Const(Microgrids.var\"#13#33\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %95 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %96 = Base.getproperty(%95, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %97 = Base.Generator(%94, %96)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#13#33\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pfc_max = Microgrids.collect(%97))\n", - "\u001b[90m│ \u001b[39m %99 = Microgrids.:(var\"#14#34\")\u001b[36m::Core.Const(Microgrids.var\"#14#34\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %100 = Core.typeof(mg)\u001b[36m::Core.Const(Microgrid{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %101 = Core.typeof(Pfc_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %102 = Core.apply_type(%99, %100, %101)\u001b[36m::Core.Const(Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#14 = %new(%102, mg, Pfc_max))\n", - "\u001b[90m│ \u001b[39m %104 = #14\u001b[36m::Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %105 = Microgrids.eachindex(Pfc_max)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %106 = Base.Generator(%104, %105)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#14#34\"{Microgrid{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pfc_min = Microgrids.collect(%106))\n", - "\u001b[90m│ \u001b[39m (#15 = %new(Microgrids.:(var\"#15#35\")))\n", - "\u001b[90m│ \u001b[39m %109 = #15\u001b[36m::Core.Const(Microgrids.var\"#15#35\"())\u001b[39m\n", - "\u001b[90m│ \u001b[39m %110 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %111 = Base.getproperty(%110, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %112 = Base.Generator(%109, %111)\u001b[36m::Base.Generator{Vector{ProductionUnit{Float64}}, Microgrids.var\"#15#35\"}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (cons_rate = Microgrids.collect(%112))\n", - "\u001b[90m│ \u001b[39m %114 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %115 = Base.getproperty(%114, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %116 = Base.getproperty(%115, :max_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %117 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %118 = Base.getproperty(%117, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %119 = Base.getproperty(%118, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoH_max = %116 * %119)\n", - "\u001b[90m│ \u001b[39m %121 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %122 = Base.getproperty(%121, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %123 = Base.getproperty(%122, :min_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %124 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %125 = Base.getproperty(%124, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %126 = Base.getproperty(%125, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoH_min = %123 * %126)\n", - "\u001b[90m│ \u001b[39m (Pnl = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", - "\u001b[90m│ \u001b[39m %129 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %130 = K\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %131 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %132 = Base.getproperty(%131, :generator)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %133 = Microgrids.length(%132)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pgen = Microgrids.zeros(%129, %130, %133))\n", - "\u001b[90m│ \u001b[39m %135 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %136 = (K + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Esto = Microgrids.zeros(%135, %136))\n", - "\u001b[90m│ \u001b[39m (Psto = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", - "\u001b[90m│ \u001b[39m (Pspill = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", - "\u001b[90m│ \u001b[39m (Pshed = Microgrids.zeros(Topt::Core.Const(Float64), K))\n", - "\u001b[90m│ \u001b[39m %141 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %142 = K\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %143 = Base.getproperty(mg, :electrolyzer)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %144 = Microgrids.length(%143)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pelyz = Microgrids.zeros(%141, %142, %144))\n", - "\u001b[90m│ \u001b[39m %146 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %147 = K\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %148 = Base.getproperty(mg, :dispatchables)\u001b[36m::DispatchableCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %149 = Base.getproperty(%148, :fuel_cell)\u001b[36m::Vector{ProductionUnit{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %150 = Microgrids.length(%149)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pfc = Microgrids.zeros(%146, %147, %150))\n", - "\u001b[90m│ \u001b[39m %152 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %153 = (K + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoH = Microgrids.zeros(%152, %153))\n", - "\u001b[90m│ \u001b[39m %155 = Topt\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %156 = (K + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoF = Microgrids.zeros(%155, %156))\n", - "\u001b[90m│ \u001b[39m %158 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %159 = Base.getproperty(%158, :SoC_ini)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %160 = Base.getproperty(mg, :storage)\u001b[36m::Battery{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %161 = Base.getproperty(%160, :energy_rated)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Esto_ini = %159 * %161)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Esto, Esto_ini, 1)\n", - "\u001b[90m│ \u001b[39m %164 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %165 = Base.getproperty(%164, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %166 = Base.getproperty(%165, :ini_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %167 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %168 = Base.getproperty(%167, :h2Tank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %169 = Base.getproperty(%168, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoH_ini = %166 * %169)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(LoH, LoH_ini, 1)\n", - "\u001b[90m│ \u001b[39m %172 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %173 = Base.getproperty(%172, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %174 = Base.getproperty(%173, :ini_filling_ratio)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %175 = Base.getproperty(mg, :tanks)\u001b[36m::TankCompound{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %176 = Base.getproperty(%175, :fuelTank)\u001b[36m::Tank{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %177 = Base.getproperty(%176, :capacity)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (LoF_ini = %174 * %177)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(LoF, LoF_ini, 1)\n", - "\u001b[90m│ \u001b[39m %180 = (1:K)\u001b[36m::Core.PartialStruct(UnitRange{Int64}, Any[Core.Const(1), Int64])\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_3 = Base.iterate(%180))\n", - "\u001b[90m│ \u001b[39m %182 = (@_3 === nothing)\u001b[36m::Bool\u001b[39m\n", - "\u001b[90m│ \u001b[39m %183 = Base.not_int(%182)\u001b[36m::Bool\u001b[39m\n", - "\u001b[90m└──\u001b[39m goto #4 if not %183\n", - "\u001b[90m2 ┄\u001b[39m %185 = @_3\u001b[36m::Tuple{Int64, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (k = Core.getfield(%185, 1))\n", - "\u001b[90m│ \u001b[39m %187 = Core.getfield(%185, 2)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %188 = Esto_max\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %189 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %190 = (%188 - %189)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %191 = -%190\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %192 = (1 - sto_loss)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %193 = (%192 * dt)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Psto_emin = %191 / %193)\n", - "\u001b[90m│ \u001b[39m %195 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %196 = (%195 - Esto_min)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %197 = (1 + sto_loss)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %198 = (%197 * dt)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Psto_emax = %196 / %198)\n", - "\u001b[90m│ \u001b[39m (Psto_dmax = Microgrids.min(Psto_emax, Psto_pmax))\n", - "\u001b[90m│ \u001b[39m (Psto_cmax = Microgrids.max(Psto_emin, Psto_pmin))\n", - "\u001b[90m│ \u001b[39m %202 = Microgrids.:(var\"#16#36\")\u001b[36m::Core.Const(Microgrids.var\"#16#36\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %203 = Core.typeof(LoH)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %204 = Core.typeof(LoH_max)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %205 = Core.typeof(prod_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %206 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %207 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %208 = Core.apply_type(%202, %203, %204, %205, %206, %207)\u001b[36m::Core.Const(Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %209 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %210 = LoH_max\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %211 = prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %212 = dt\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#16 = %new(%208, %209, %210, %211, %212, k))\n", - "\u001b[90m│ \u001b[39m %214 = #16\u001b[36m::Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %215 = Microgrids.eachindex(prod_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %216 = Base.Generator(%214, %215)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#16#36\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pelyz_emax = Microgrids.collect(%216))\n", - "\u001b[90m│ \u001b[39m %218 = Microgrids.:(var\"#17#37\")\u001b[36m::Core.Const(Microgrids.var\"#17#37\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %219 = Core.typeof(Pelyz_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %220 = Core.typeof(Pelyz_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %221 = Core.apply_type(%218, %219, %220)\u001b[36m::Core.Const(Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %222 = Pelyz_max\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#17 = %new(%221, %222, Pelyz_emax))\n", - "\u001b[90m│ \u001b[39m %224 = #17\u001b[36m::Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %225 = Microgrids.eachindex(Pelyz_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %226 = Base.Generator(%224, %225)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#17#37\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pelyz_cmax = Microgrids.collect(%226))\n", - "\u001b[90m│ \u001b[39m %228 = Microgrids.:(var\"#18#38\")\u001b[36m::Core.Const(Microgrids.var\"#18#38\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %229 = Core.typeof(LoH)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %230 = Core.typeof(LoH_min)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %231 = Core.typeof(cons_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %232 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %233 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %234 = Core.apply_type(%228, %229, %230, %231, %232, %233)\u001b[36m::Core.Const(Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %235 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %236 = LoH_min\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %237 = cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %238 = dt\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#18 = %new(%234, %235, %236, %237, %238, k))\n", - "\u001b[90m│ \u001b[39m %240 = #18\u001b[36m::Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %241 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %242 = Base.Generator(%240, %241)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#18#38\"{Vector{Float64}, Float64, Vector{Float64}, Float64, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pfc_emax = Microgrids.collect(%242))\n", - "\u001b[90m│ \u001b[39m %244 = Microgrids.:(var\"#19#39\")\u001b[36m::Core.Const(Microgrids.var\"#19#39\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %245 = Core.typeof(Pfc_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %246 = Core.typeof(Pfc_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %247 = Core.apply_type(%244, %245, %246)\u001b[36m::Core.Const(Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %248 = Pfc_max\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#19 = %new(%247, %248, Pfc_emax))\n", - "\u001b[90m│ \u001b[39m %250 = #19\u001b[36m::Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %251 = Microgrids.eachindex(Pfc_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %252 = Base.Generator(%250, %251)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#19#39\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pfc_pmax = Microgrids.collect(%252))\n", - "\u001b[90m│ \u001b[39m %254 = Microgrids.:(var\"#20#40\")\u001b[36m::Core.Const(Microgrids.var\"#20#40\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %255 = Core.typeof(LoF)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %256 = Core.typeof(LoF_min)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %257 = Core.typeof(fuel_intercept)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %258 = Core.typeof(fuel_slope)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %259 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %260 = Core.typeof(dt)\u001b[36m::Core.Const(Float64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %261 = Core.typeof(K)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %262 = Core.apply_type(%254, %255, %256, %257, %258, %259, %260, %261)\u001b[36m::Core.Const(Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %263 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %264 = LoF_min\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %265 = fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %266 = fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %267 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %268 = dt\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#20 = %new(%262, %263, %264, %265, %266, %267, %268, K))\n", - "\u001b[90m│ \u001b[39m %270 = #20\u001b[36m::Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %271 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %272 = Base.Generator(%270, %271)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#20#40\"{Vector{Float64}, Float64, Vector{Float64}, Vector{Float64}, Vector{Float64}, Float64, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pgen_emax = Microgrids.collect(%272))\n", - "\u001b[90m│ \u001b[39m %274 = Microgrids.:(var\"#21#41\")\u001b[36m::Core.Const(Microgrids.var\"#21#41\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %275 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %276 = Core.typeof(Pgen_emax)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %277 = Core.apply_type(%274, %275, %276)\u001b[36m::Core.Const(Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %278 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#21 = %new(%277, %278, Pgen_emax))\n", - "\u001b[90m│ \u001b[39m %280 = #21\u001b[36m::Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %281 = Microgrids.eachindex(Pgen_emax)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %282 = Base.Generator(%280, %281)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#21#41\"{Vector{Float64}, Vector{Float64}}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (Pgen_dmax = Microgrids.collect(%282))\n", - "\u001b[90m│ \u001b[39m %284 = Base.getindex(Pnl_request, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %285 = Psto_cmax\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %286 = Psto_dmax\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %287 = Base.getindex(Pgen_min, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %288 = Base.getindex(Pgen_dmax, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %289 = Base.getindex(Pelyz_min, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %290 = Base.getindex(Pelyz_cmax, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %291 = Base.getindex(Pfc_min, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %292 = Base.getindex(Pfc_pmax, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %293 = Microgrids.dispatch(%284, %285, %286, %287, %288, %289, %290, %291, %292)\u001b[36m::NTuple{7, Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %294 = Base.indexed_iterate(%293, 1)\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(2)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %295 = Core.getfield(%294, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%294, 2))\n", - "\u001b[90m│ \u001b[39m %297 = Base.indexed_iterate(%293, 2, @_57::Core.Const(2))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(3)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %298 = Core.getfield(%297, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%297, 2))\n", - "\u001b[90m│ \u001b[39m %300 = Base.indexed_iterate(%293, 3, @_57::Core.Const(3))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(4)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %301 = Core.getfield(%300, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%300, 2))\n", - "\u001b[90m│ \u001b[39m %303 = Base.indexed_iterate(%293, 4, @_57::Core.Const(4))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(5)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %304 = Core.getfield(%303, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%303, 2))\n", - "\u001b[90m│ \u001b[39m %306 = Base.indexed_iterate(%293, 5, @_57::Core.Const(5))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(6)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %307 = Core.getfield(%306, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%306, 2))\n", - "\u001b[90m│ \u001b[39m %309 = Base.indexed_iterate(%293, 6, @_57::Core.Const(6))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(7)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %310 = Core.getfield(%309, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m (@_57 = Core.getfield(%309, 2))\n", - "\u001b[90m│ \u001b[39m %312 = Base.indexed_iterate(%293, 7, @_57::Core.Const(7))\u001b[36m::Core.PartialStruct(Tuple{Float64, Int64}, Any[Float64, Core.Const(8)])\u001b[39m\n", - "\u001b[90m│ \u001b[39m %313 = Core.getfield(%312, 1)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pnl, %295, k)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pgen, %298, k, 1)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Psto, %301, k)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pspill, %304, k)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pshed, %307, k)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pelyz, %310, k, 1)\n", - "\u001b[90m│ \u001b[39m Base.setindex!(Pfc, %313, k, 1)\n", - "\u001b[90m│ \u001b[39m %321 = Base.getindex(Esto, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %322 = Base.getindex(Psto, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %323 = sto_loss\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %324 = Base.getindex(Psto, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %325 = Microgrids.abs(%324)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %326 = (%323 * %325)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %327 = (%322 + %326)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %328 = (%327 * dt)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %329 = (%321 - %328)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %330 = Esto\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %331 = (k + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m Base.setindex!(%330, %329, %331)\n", - "\u001b[90m│ \u001b[39m %333 = Base.getindex(LoH, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %334 = Microgrids.:(var\"#22#42\")\u001b[36m::Core.Const(Microgrids.var\"#22#42\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %335 = Core.typeof(Pelyz)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %336 = Core.typeof(prod_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %337 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %338 = Core.apply_type(%334, %335, %336, %337)\u001b[36m::Core.Const(Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %339 = Pelyz\u001b[36m::Matrix{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %340 = prod_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#22 = %new(%338, %339, %340, k))\n", - "\u001b[90m│ \u001b[39m %342 = #22\u001b[36m::Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %343 = Microgrids.eachindex(prod_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %344 = Base.Generator(%342, %343)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#22#42\"{Matrix{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %345 = Microgrids.collect(%344)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %346 = Microgrids.sum(%345)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %347 = Microgrids.:(var\"#23#43\")\u001b[36m::Core.Const(Microgrids.var\"#23#43\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %348 = Core.typeof(Pfc)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %349 = Core.typeof(cons_rate)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %350 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %351 = Core.apply_type(%347, %348, %349, %350)\u001b[36m::Core.Const(Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %352 = Pfc\u001b[36m::Matrix{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %353 = cons_rate\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#23 = %new(%351, %352, %353, k))\n", - "\u001b[90m│ \u001b[39m %355 = #23\u001b[36m::Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %356 = Microgrids.eachindex(cons_rate)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %357 = Base.Generator(%355, %356)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#23#43\"{Matrix{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %358 = Microgrids.collect(%357)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %359 = Microgrids.sum(%358)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %360 = (%346 - %359)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %361 = (%360 * dt)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %362 = (%333 + %361)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %363 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %364 = (k + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m Base.setindex!(%363, %362, %364)\n", - "\u001b[90m│ \u001b[39m %366 = Base.getindex(LoF, k)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %367 = Microgrids.:(var\"#24#44\")\u001b[36m::Core.Const(Microgrids.var\"#24#44\")\u001b[39m\n", - "\u001b[90m│ \u001b[39m %368 = Core.typeof(Pgen)\u001b[36m::Core.Const(Matrix{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %369 = Core.typeof(fuel_intercept)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %370 = Core.typeof(fuel_slope)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %371 = Core.typeof(Pgen_max)\u001b[36m::Core.Const(Vector{Float64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %372 = Core.typeof(k)\u001b[36m::Core.Const(Int64)\u001b[39m\n", - "\u001b[90m│ \u001b[39m %373 = Core.apply_type(%367, %368, %369, %370, %371, %372)\u001b[36m::Core.Const(Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64})\u001b[39m\n", - "\u001b[90m│ \u001b[39m %374 = Pgen\u001b[36m::Matrix{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %375 = fuel_intercept\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %376 = fuel_slope\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %377 = Pgen_max\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (#24 = %new(%373, %374, %375, %376, %377, k))\n", - "\u001b[90m│ \u001b[39m %379 = #24\u001b[36m::Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %380 = Microgrids.eachindex(fuel_slope)\u001b[36m::Base.OneTo{Int64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %381 = Base.Generator(%379, %380)\u001b[36m::Base.Generator{Base.OneTo{Int64}, Microgrids.var\"#24#44\"{Matrix{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %382 = Microgrids.collect(%381)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %383 = Microgrids.sum(%382)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %384 = (%383 * dt)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %385 = (%366 - %384)\u001b[36m::Float64\u001b[39m\n", - "\u001b[90m│ \u001b[39m %386 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %387 = (k + 1)\u001b[36m::Int64\u001b[39m\n", - "\u001b[90m│ \u001b[39m Base.setindex!(%386, %385, %387)\n", - "\u001b[90m│ \u001b[39m (@_3 = Base.iterate(%180, %187))\n", - "\u001b[90m│ \u001b[39m %390 = (@_3 === nothing)\u001b[36m::Bool\u001b[39m\n", - "\u001b[90m│ \u001b[39m %391 = Base.not_int(%390)\u001b[36m::Bool\u001b[39m\n", - "\u001b[90m└──\u001b[39m goto #4 if not %391\n", - "\u001b[90m3 ─\u001b[39m goto #2\n", - "\u001b[90m4 ┄\u001b[39m %394 = Pnl\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %395 = Pshed\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %396 = renew_potential\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %397 = Base.getindex(Pgen, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %398 = Base.getindex(Pfc, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %399 = Base.getindex(Pelyz, Microgrids.:(:), 1)\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %400 = Esto\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %401 = Psto\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %402 = LoH\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m %403 = LoF\u001b[36m::Vector{Float64}\u001b[39m\n", - "\u001b[90m│ \u001b[39m (oper_traj = Microgrids.OperationTraj(%394, %395, %396, %397, %398, %399, %400, %401, %402, %403, Pspill))\n", - "\u001b[90m└──\u001b[39m return oper_traj\n", - "\n" - ] - } - ], - "source": [ - "@code_warntype operation(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "05f198e7-eef7-47b9-ba7f-5170a52e461f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{verbatim}\n", - "@code_warntype\n", - "\\end{verbatim}\n", - "Evaluates the arguments to the function or macro call, determines their types, and calls \\href{@ref}{\\texttt{code\\_warntype}} on the resulting expression.\n", - "\n" - ], - "text/markdown": [ - "```\n", - "@code_warntype\n", - "```\n", - "\n", - "Evaluates the arguments to the function or macro call, determines their types, and calls [`code_warntype`](@ref) on the resulting expression.\n" - ], - "text/plain": [ - "\u001b[36m @code_warntype\u001b[39m\n", - "\n", - " Evaluates the arguments to the function or macro call, determines their\n", - " types, and calls \u001b[36mcode_warntype\u001b[39m on the resulting expression." - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "?@code_warntype" + "@btime oper_traj, oper_stats, mg_costs = simulate(microgrid,dispatch_2);" ] }, { @@ -1805,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 33, "id": "0090bf4d-f684-492b-9583-bd56854329b0", "metadata": { "tags": [] @@ -1817,15 +1198,15 @@ "OperationStats with fields:\n", "- served_energy: 6.5642e6 kWh\n", "- shed_energy: 210820.0 kWh\n", - "- shed_max: 33396.0 kW\n", - "- shed_hours: 306.0 h\n", + "- shed_max: 1520.2 kW\n", + "- shed_hours: 370.0 h\n", "- shed_duration_max: 56.0 h\n", "- shed_rate: 0.031117 in [0,1]\n", "- gen_energy: 0.0 kWh\n", "- gen_hours: 0.0 h\n", "- gen_fuel: 0.0 L\n", "- fc_energy: 626880.0 kWh\n", - "- fc_hours: 1215.0 h\n", + "- fc_hours: 1152.0 h\n", "- h2_consumed: 39180.0 kg\n", "- h2_produced: 41180.0 kg\n", "- elyz_hours: 2667.0 h\n", @@ -1843,7 +1224,7 @@ "- renew_rate: 1.0 in [0,1]\n" ] }, - "execution_count": 26, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1890,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "id": "8b904163-9ef8-4b87-a11c-d06ed53af5ac", "metadata": { "tags": [] @@ -1900,8 +1281,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Levelized Cost of Electricity: 0.253 $/kWh\n", - "Net Present Cost: 23.00 M$ (over 25 years at 5% discount rate)\n" + "Levelized Cost of Electricity: 0.228 $/kWh\n", + "Net Present Cost: 21.13 M$ (over 25 years at 5% discount rate)\n" ] } ], @@ -1921,7 +1302,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "id": "7e5fde52-1cd8-43a1-9f9b-c04d659d4f1a", "metadata": { "tags": [] @@ -1930,10 +1311,10 @@ { "data": { "text/plain": [ - "CostFactors(2.2999237665849324e7, 1.5458e7, 3.636477520274991e6, 4.623166166276827e6, 0.0, -718406.0207024994)" + "CostFactors(2.113122113200041e7, 1.5458e7, 1.719801651934103e6, 4.623166166276827e6, 0.0, -669746.6862105239)" ] }, - "execution_count": 33, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1952,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 37, "id": "82c503dc-02a8-4fcf-86c0-b0e3bc3784dd", "metadata": { "tags": [] @@ -1966,7 +1347,7 @@ " CostFactors(8.836910021888055e6, 6.3e6, 0.0, 2.5369100218880544e6, 0.0, -0.0)" ] }, - "execution_count": 57, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1985,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 38, "id": "82273c01-fa92-414a-af84-46bb793c7988", "metadata": { "tags": [] @@ -1997,7 +1378,7 @@ "(:total, :investment, :replacement, :om, :fuel, :salvage)" ] }, - "execution_count": 58, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2018,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 39, "id": "d0dc9505-34d3-4657-a40b-2d5aa684a19b", "metadata": { "tags": [] @@ -2027,10 +1408,10 @@ { "data": { "text/plain": [ - "CostFactors(23.0, 15.46, 3.64, 4.62, 0.0, -0.72)" + "CostFactors(21.13, 15.46, 1.72, 4.62, 0.0, -0.67)" ] }, - "execution_count": 59, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2049,7 +1430,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 40, "id": "5eb2896b-4e36-4eb8-adcc-fb8cbd67809d", "metadata": { "tags": [] @@ -2058,10 +1439,10 @@ { "data": { "text/plain": [ - "CostFactors(2.5, 1.8, 0.76, 0.25, 0.0, -0.31)" + "CostFactors(1.86, 1.8, 0.0, 0.25, 0.0, -0.19)" ] }, - "execution_count": 60, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2072,7 +1453,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 41, "id": "106877f9-9008-45de-8f68-05a1cb4bb6fa", "metadata": { "tags": [] @@ -2081,10 +1462,10 @@ { "data": { "text/plain": [ - "CostFactors(4.09, 2.0, 2.04, 0.28, 0.0, -0.23)" + "CostFactors(2.85, 2.0, 0.88, 0.28, 0.0, -0.31)" ] }, - "execution_count": 61, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2113,7 +1494,487 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 42, + "id": "4ddf950f-5aa6-48e8-ae8c-cf516bd07db9", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "search: \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m3D \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22mfile \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_date \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_trisurf \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_surface \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_ts_zoom\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "Plot y versus x as lines and/or markers.\n", + "\n", + "Call signatures::\n", + "\n", + " plot([x], y, [fmt], *, data=None, **kwargs)\n", + " plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n", + "\n", + "The coordinates of the points or line nodes are given by *x*, *y*.\n", + "\n", + "The optional parameter *fmt* is a convenient way for defining basic\n", + "formatting like color, marker and linestyle. It's a shortcut string\n", + "notation described in the *Notes* section below.\n", + "\n", + ">>> plot(x, y) # plot x and y using default line style and color\n", + ">>> plot(x, y, 'bo') # plot x and y using blue circle markers\n", + ">>> plot(y) # plot y using x as index array 0..N-1\n", + ">>> plot(y, 'r+') # ditto, but with red plusses\n", + "\n", + "You can use `.Line2D` properties as keyword arguments for more\n", + "control on the appearance. Line properties and *fmt* can be mixed.\n", + "The following two calls yield identical results:\n", + "\n", + ">>> plot(x, y, 'go--', linewidth=2, markersize=12)\n", + ">>> plot(x, y, color='green', marker='o', linestyle='dashed',\n", + "... linewidth=2, markersize=12)\n", + "\n", + "When conflicting with *fmt*, keyword arguments take precedence.\n", + "\n", + "\n", + "**Plotting labelled data**\n", + "\n", + "There's a convenient way for plotting objects with labelled data (i.e.\n", + "data that can be accessed by index ``obj['y']``). Instead of giving\n", + "the data in *x* and *y*, you can provide the object in the *data*\n", + "parameter and just give the labels for *x* and *y*::\n", + "\n", + ">>> plot('xlabel', 'ylabel', data=obj)\n", + "\n", + "All indexable objects are supported. This could e.g. be a `dict`, a\n", + "`pandas.DataFrame` or a structured numpy array.\n", + "\n", + "\n", + "**Plotting multiple sets of data**\n", + "\n", + "There are various ways to plot multiple sets of data.\n", + "\n", + "- The most straight forward way is just to call `plot` multiple times.\n", + " Example:\n", + "\n", + " >>> plot(x1, y1, 'bo')\n", + " >>> plot(x2, y2, 'go')\n", + "\n", + "- If *x* and/or *y* are 2D arrays a separate data set will be drawn\n", + " for every column. If both *x* and *y* are 2D, they must have the\n", + " same shape. If only one of them is 2D with shape (N, m) the other\n", + " must have length N and will be used for every data set m.\n", + "\n", + " Example:\n", + "\n", + " >>> x = [1, 2, 3]\n", + " >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n", + " >>> plot(x, y)\n", + "\n", + " is equivalent to:\n", + "\n", + " >>> for col in range(y.shape[1]):\n", + " ... plot(x, y[:, col])\n", + "\n", + "- The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n", + " groups::\n", + "\n", + " >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n", + "\n", + " In this case, any additional keyword argument applies to all\n", + " datasets. Also, this syntax cannot be combined with the *data*\n", + " parameter.\n", + "\n", + "By default, each line is assigned a different style specified by a\n", + "'style cycle'. The *fmt* and line property parameters are only\n", + "necessary if you want explicit deviations from these defaults.\n", + "Alternatively, you can also change the style cycle using\n", + ":rc:`axes.prop_cycle`.\n", + "\n", + "\n", + "Parameters\n", + "----------\n", + "x, y : array-like or scalar\n", + " The horizontal / vertical coordinates of the data points.\n", + " *x* values are optional and default to ``range(len(y))``.\n", + "\n", + " Commonly, these parameters are 1D arrays.\n", + "\n", + " They can also be scalars, or two-dimensional (in that case, the\n", + " columns represent separate data sets).\n", + "\n", + " These arguments cannot be passed as keywords.\n", + "\n", + "fmt : str, optional\n", + " A format string, e.g. 'ro' for red circles. See the *Notes*\n", + " section for a full description of the format strings.\n", + "\n", + " Format strings are just an abbreviation for quickly setting\n", + " basic line properties. All of these and more can also be\n", + " controlled by keyword arguments.\n", + "\n", + " This argument cannot be passed as keyword.\n", + "\n", + "data : indexable object, optional\n", + " An object with labelled data. If given, provide the label names to\n", + " plot in *x* and *y*.\n", + "\n", + " .. note::\n", + " Technically there's a slight ambiguity in calls where the\n", + " second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n", + " could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n", + " the former interpretation is chosen, but a warning is issued.\n", + " You may suppress the warning by adding an empty format string\n", + " ``plot('n', 'o', '', data=obj)``.\n", + "\n", + "Returns\n", + "-------\n", + "list of `.Line2D`\n", + " A list of lines representing the plotted data.\n", + "\n", + "Other Parameters\n", + "----------------\n", + "scalex, scaley : bool, default: True\n", + " These parameters determine if the view limits are adapted to the\n", + " data limits. The values are passed on to\n", + " `~.axes.Axes.autoscale_view`.\n", + "\n", + "**kwargs : `~matplotlib.lines.Line2D` properties, optional\n", + " *kwargs* are used to specify properties like a line label (for\n", + " auto legends), linewidth, antialiasing, marker face color.\n", + " Example::\n", + "\n", + " >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n", + " >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n", + "\n", + " If you specify multiple lines with one plot call, the kwargs apply\n", + " to all those lines. In case the label object is iterable, each\n", + " element is used as labels for each set of data.\n", + "\n", + " Here is a list of available `.Line2D` properties:\n", + "\n", + " Properties:\n", + " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n", + " alpha: scalar or None\n", + " animated: bool\n", + " antialiased or aa: bool\n", + " clip_box: `~matplotlib.transforms.BboxBase` or None\n", + " clip_on: bool\n", + " clip_path: Patch or (Path, Transform) or None\n", + " color or c: color\n", + " dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n", + " dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n", + " dashes: sequence of floats (on/off ink in points) or (None, None)\n", + " data: (2, N) array or two 1D arrays\n", + " drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n", + " figure: `~matplotlib.figure.Figure`\n", + " fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n", + " gapcolor: color or None\n", + " gid: str\n", + " in_layout: bool\n", + " label: object\n", + " linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n", + " linewidth or lw: float\n", + " marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n", + " markeredgecolor or mec: color\n", + " markeredgewidth or mew: float\n", + " markerfacecolor or mfc: color\n", + " markerfacecoloralt or mfcalt: color\n", + " markersize or ms: float\n", + " markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n", + " mouseover: bool\n", + " path_effects: list of `.AbstractPathEffect`\n", + " picker: float or callable[[Artist, Event], tuple[bool, dict]]\n", + " pickradius: float\n", + " rasterized: bool\n", + " sketch_params: (scale: float, length: float, randomness: float)\n", + " snap: bool or None\n", + " solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n", + " solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n", + " transform: unknown\n", + " url: str\n", + " visible: bool\n", + " xdata: 1D array\n", + " ydata: 1D array\n", + " zorder: float\n", + "\n", + "See Also\n", + "--------\n", + "scatter : XY scatter plot with markers of varying size and/or color (\n", + " sometimes also called bubble chart).\n", + "\n", + "Notes\n", + "-----\n", + "**Format Strings**\n", + "\n", + "A format string consists of a part for color, marker and line::\n", + "\n", + " fmt = '[marker][line][color]'\n", + "\n", + "Each of them is optional. If not provided, the value from the style\n", + "cycle is used. Exception: If ``line`` is given, but no ``marker``,\n", + "the data will be a line without markers.\n", + "\n", + "Other combinations such as ``[color][marker][line]`` are also\n", + "supported, but note that their parsing may be ambiguous.\n", + "\n", + "**Markers**\n", + "\n", + "============= ===============================\n", + "character description\n", + "============= ===============================\n", + "``'.'`` point marker\n", + "``','`` pixel marker\n", + "``'o'`` circle marker\n", + "``'v'`` triangle_down marker\n", + "``'^'`` triangle_up marker\n", + "``'<'`` triangle_left marker\n", + "``'>'`` triangle_right marker\n", + "``'1'`` tri_down marker\n", + "``'2'`` tri_up marker\n", + "``'3'`` tri_left marker\n", + "``'4'`` tri_right marker\n", + "``'8'`` octagon marker\n", + "``'s'`` square marker\n", + "``'p'`` pentagon marker\n", + "``'P'`` plus (filled) marker\n", + "``'*'`` star marker\n", + "``'h'`` hexagon1 marker\n", + "``'H'`` hexagon2 marker\n", + "``'+'`` plus marker\n", + "``'x'`` x marker\n", + "``'X'`` x (filled) marker\n", + "``'D'`` diamond marker\n", + "``'d'`` thin_diamond marker\n", + "``'|'`` vline marker\n", + "``'_'`` hline marker\n", + "============= ===============================\n", + "\n", + "**Line Styles**\n", + "\n", + "============= ===============================\n", + "character description\n", + "============= ===============================\n", + "``'-'`` solid line style\n", + "``'--'`` dashed line style\n", + "``'-.'`` dash-dot line style\n", + "``':'`` dotted line style\n", + "============= ===============================\n", + "\n", + "Example format strings::\n", + "\n", + " 'b' # blue markers with default shape\n", + " 'or' # red circles\n", + " '-g' # green solid line\n", + " '--' # dashed line with default color\n", + " '^k:' # black triangle_up markers connected by a dotted line\n", + "\n", + "**Colors**\n", + "\n", + "The supported color abbreviations are the single letter codes\n", + "\n", + "============= ===============================\n", + "character color\n", + "============= ===============================\n", + "``'b'`` blue\n", + "``'g'`` green\n", + "``'r'`` red\n", + "``'c'`` cyan\n", + "``'m'`` magenta\n", + "``'y'`` yellow\n", + "``'k'`` black\n", + "``'w'`` white\n", + "============= ===============================\n", + "\n", + "and the ``'CN'`` colors that index into the default property cycle.\n", + "\n", + "If the color is the only part of the format string, you can\n", + "additionally use any `matplotlib.colors` spec, e.g. full names\n", + "(``'green'``) or hex strings (``'#008000'``)." + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "?plot\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "75f21136-a63d-443e-a37d-69cfd6067e24", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "401.0798711924217" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.power_curtailment[1646]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "87b9d1d0-61fc-4cd2-b9f4-69fe3a487c24", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3472.0798711924217" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " oper_traj.Prenew_pot[1646]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "b9933b12-d650-45f1-898e-59aa8457e443", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.Pbatt[1646]" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "729db6ba-0cdb-41d7-9a40-9134698c7547", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2000.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.Pelyz[1646]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "0858a5ab-e94b-4abd-a0fc-a0e6eec04472", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1071.0" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload[1646]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7f418cd0-33b5-4a75-b4f7-522511644382", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2903.8797262849666" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oper_traj.Pelyz[1645]+Pload[1645]+oper_traj.Pbatt[1645]+oper_traj.power_curtailment[1645]" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ce7204b5-e389-47d2-a00f-dc06bef14ff9", + "metadata": {}, + "outputs": [ + { + "ename": "LoadError", + "evalue": "syntax: incomplete: premature end of input", + "output_type": "error", + "traceback": [ + "syntax: incomplete: premature end of input", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[49]:1" + ] + } + ], + "source": [ + "tm= " + ] + }, + { + "cell_type": "code", + "execution_count": 50, "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", "metadata": { "tags": [] @@ -2122,14 +1983,48 @@ "source": [ "function plot_oper_traj(microgrid, oper_traj)\n", " fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", - " ax1.plot(td, Pload, label=\"load\")\n", - " ax1.plot(td, oper_traj.Pfc, \"tab:red\", label=\"Pfc\")\n", + " ax1.plot(td, Pload, label=\"load\")\n", + "\n", " ax1.plot(td, oper_traj.Prenew_pot, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, oper_traj.Pelyz, \"tab:gray\", label=\"Pelyz\")\n", - " ax1.plot(td, oper_traj.Pfc, \"tab:pink\", label=\"Pfc\")\n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim([0, 1])\n", - " ax3.plot(td, oper_traj.Ebatt[1:end-1]/5000, \"C2\")\n", + " #ax1.plot(td, oper_traj.Pelyz, \"tab:gray\", label=\"Pelyz\")\n", + " # ax1.plot(td, oper_traj.Pfc, \"tab:pink\", label=\"Pfc\")\n", + " # ax1.plot(td, oper_traj.power_shedding, \"k\", label=\"Pshed\")\n", + " ax1.fill_between(td, min.(oper_traj.Prenew_pot,Pload), 0,\n", + " interpolate=\"True\",color=\"orange\", alpha=0.5)\n", + " \"\"\" ax1.fill_between(td, Pload, 0,\n", + " where = Pload.>oper_traj.Prenew_pot ,interpolate=\"True\",color=\"orange\", alpha=0.5)\n", + "\"\"\"\n", + " ax1.fill_between(td, oper_traj.Pfc .+ oper_traj.Pbatt.+ oper_traj.Prenew_pot,\n", + " oper_traj.Prenew_pot .+ oper_traj.Pbatt, where =oper_traj.Pfc.>0.0 ,interpolate = \"true\",\n", + " label=\"Pfc\", color=\"pink\", alpha=0.5 )\n", + " \n", + " ax1.fill_between(td, oper_traj.Pelyz .+ Pload.-oper_traj.Pbatt,\n", + " Pload.-oper_traj.Pbatt, where =oper_traj.Pelyz.>0.0 ,interpolate = \"true\",\n", + " label=\"Pfc\", color=\"grey\", alpha=0.5 )\n", + " #ax3=ax1.twinx()\n", + " #ax3.set_ylim([0, 1])\n", + " #ax1.plot(td, min.( max.(oper_traj.Pbatt,0).+oper_traj.Prenew_pot,Pload), \"C2\")\n", + " \n", + " ax1.fill_between(td,Pload.-oper_traj.Pbatt ,Pload,\n", + " where= oper_traj.Pbatt.<0.0, interpolate = \"True\" ,color=\"cyan\", alpha=0.5 )\n", + " \n", + " y1=zeros(Float64,8760)\n", + " y2=zeros(Float64,8760)\n", + " y2.= Pload .- oper_traj.Pbatt .+ oper_traj.Pelyz \n", + " y1.= oper_traj.power_curtailment .+ y2\n", + " \n", + " \n", + " ax1.fill_between(td,y1, \n", + " y2, where= oper_traj.power_curtailment.>0 , interpolate = \"True\" ,color=\"red\", alpha=0.5,hatch=\"\\\\\" )\n", + "\n", + " \n", + " \n", + " ax1.fill_between(td,oper_traj.power_shedding .+ oper_traj.Pbatt.+oper_traj.Prenew_pot.+ oper_traj.Pfc, \n", + " oper_traj.Pbatt.+oper_traj.Prenew_pot.+ oper_traj.Pfc,\n", + " where= oper_traj.power_shedding.>0.0, interpolate = \"true\" ,color=\"black\", alpha=0.5,hatch=\"\\\\\" )\n", + " ax1.fill_between(td,oper_traj.Pbatt.+oper_traj.Prenew_pot,oper_traj.Prenew_pot,\n", + " where= oper_traj.Pbatt.>0.0, interpolate = \"true\" ,color=\"C2\", alpha=0.5 )\n", + " \n", " \n", " ax1.legend()\n", " ax1.grid(true)\n", @@ -2153,16 +2048,181 @@ "\n", "if with_plots\n", " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", - " ax1.set_ylim(0, 5000)\n", + " ax1.set_xlim(297.2, 297.6)\n", " pygui(true)\n", " plt.show()\n", " \n", "end;" ] }, + { + "cell_type": "code", + "execution_count": 51, + "id": "8813ada5-0c1a-49d2-869f-b8aee4e4028f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "13-element Vector{Float64}:\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z=zeros(Float64,12)\n", + "tm=zeros(Float64,13)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "6022b5d3-9189-4e3b-8c30-512cdcb59e27", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for i=1:12 \n", + " z[i]= oper_traj.LoH[i*730+1]\n", + " tm[i]=(i-1)*730/24 \n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "e86b8fdf-b44c-491a-a18b-9357c4d22d30", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "365.0" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tm[13]=8760/24" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "1ed8ac9d-9730-4223-a33a-dd90b13d260c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "plot_oper_traj (generic function with 1 method)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "function plot_oper_traj(microgrid, oper_traj)\n", + " \n", + " \n", + " fig,( ax1, ax2) = plt.subplots(2,1)\n", + " y1 = min.(oper_traj.Prenew_pot,Pload)\n", + " y2 = max.( oper_traj.Pbatt,0.0)\n", + " y3 = oper_traj.Pfc\n", + " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", + " y5 = oper_traj.Pelyz\n", + " y6 = oper_traj.power_shedding\n", + " y7 = oper_traj.power_curtailment\n", + " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", + " hatches=[\" \", \"-\", \" \", \"+\", \" \", \"\\\\\", \"\\\\\"]\n", + " \n", + " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", + " \n", + " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", + " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", + " ax1.plot(td, Pload,lw=2, label=\"load\")\n", + " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", + " ax2.plot(td, oper_traj.LoH[1:end-1]/10000, \"C2\")\n", + " stacks[2].set_hatch(\"-\")\n", + " stacks[4].set_hatch(\"+\")\n", + " stacks[6].set_hatch(\"\\\\\") \n", + " stacks[7].set_hatch(\"\\\\\") \n", + " \n", + " ax3=ax1.twinx()\n", + " ax3.set_ylim(0, 2)\n", + " ax3.plot(td, oper_traj.Ebatt[1:end-1]/5000, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", + " ax2.plot(td, oper_traj.LoH[1:end-1], \"C2\",label=\"hourly H2 level\")\n", + " \n", + " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", + " \n", + " \n", + " ax1.set_title(\"hourly power repartition\")\n", + " ax2.set_title(\"H2 level\")\n", + " ax1.legend()\n", + " ax1.grid(true)\n", + " ax1.set(ylabel=\"kW\")\n", + " ax2.legend()\n", + " ax3.legend()\n", + " ax2.grid(true)\n", + " ax2.set(ylabel=\"kg\")\n", + " \n", + " \n", + " return fig, ax1, ax2\n", + "\n", + "end\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 63, + "id": "a36e31f8-f379-451a-973f-5db836035738", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if with_plots\n", + " fig, ax1, ax2= plot_oper_traj(microgrid, oper_traj)\n", + " \n", + " pygui(true)\n", + " plt.show()\n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 110, "id": "3ac88fac-41bd-48c6-8e43-fe108b5a4109", "metadata": { "tags": [] @@ -2175,7 +2235,7 @@ " 1310.75 1202.75 1040.17 1018.17 … 1214.92 1354.92 1416.79 1409.08" ] }, - "execution_count": 63, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -2184,6 +2244,14 @@ "mean(reshape(Pload, 24, :); dims=1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "76bb909e-709b-45ec-9a8f-ff0ae6ae133a", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 64, @@ -2374,7 +2442,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 80, "id": "256a0b88-4972-46f3-a29e-fb7016aee700", "metadata": { "tags": [] diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb index 1ae6eeb..d7f4516 100644 --- a/examples/Microgrid_Wind-Solar.ipynb +++ b/examples/Microgrid_Wind-Solar.ipynb @@ -167,21 +167,23 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", "metadata": { "tags": [] }, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "Figure(PyObject

)" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "LoadError", + "evalue": "UndefVarError: `nsteps` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `nsteps` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[1]:1" + ] } ], "source": [ @@ -1200,7 +1202,9 @@ "cell_type": "code", "execution_count": 28, "id": "256a0b88-4972-46f3-a29e-fb7016aee700", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { diff --git a/examples/Microgrid_Wind-Solar_data.jl b/examples/Microgrid_Wind-Solar_data.jl index 63b4285..88e564c 100644 --- a/examples/Microgrid_Wind-Solar_data.jl +++ b/examples/Microgrid_Wind-Solar_data.jl @@ -13,13 +13,13 @@ println("Data definition for Microgrid with wind, solar, storage and generator.. data = DataFrame(CSV.File("$(@__DIR__)/data/Ouessant_data_2016.csv")) # Simulation steps -nsteps = length(data.Load) -@assert(nsteps == 8760) # 1 year at an hourly timestep +nsteps = 2*length(data.Load) +@assert(nsteps == 17520) # 1 year at an hourly timestep # Split load, solar and wind data: -Pload = data.Load # kW -Ppv1k = data.Ppv1k ./ 1000; # convert to kW/kWp -wind_speed = data.Wind; # m/s +Pload = [data.Load;data.Load] # kW +Ppv1k = [data.Ppv1k; data.Ppv1k]./ 1000; # convert to kW/kWp +wind_speed = [data.Wind;data.Wind] # m/s # Calibrate wind speed data against a mast measurement ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean @@ -104,7 +104,7 @@ charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1) discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1) loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1] SoC_min = 0. # minimum State of Charge ∈ [0,1] -SoC_ini = 0. # initial State of Charge ∈ [0,1] +SoC_ini = 1. # initial State of Charge ∈ [0,1] SoC_max = 1. # initial State of Charge ∈ [0,1] #electrolyzer @@ -130,7 +130,7 @@ lifetime_pv = 25. # lifetime (y) derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1] # Wind power generation -power_rated_wind = 900. # rated power (kW) +power_rated_wind = 1800. # rated power (kW) investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) lifetime_wind = 25. # lifetime (y) diff --git a/examples/Microgrid_sizingH2_optimization1.ipynb b/examples/Microgrid_sizingH2_optimization1.ipynb index 493fe2f..58550a2 100644 --- a/examples/Microgrid_sizingH2_optimization1.ipynb +++ b/examples/Microgrid_sizingH2_optimization1.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 38, "id": "c2f04250-290c-4c38-93e6-074702ec0700", "metadata": { "tags": [] @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 39, "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", "metadata": { "tags": [] @@ -119,42 +119,12 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "simulate_microgrid" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "e9485d6b-7bfa-4d3b-bc85-3d93d1eacefa", + "metadata": {}, + "outputs": [], "source": [ - "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", - "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", - "\n", - "Returns stats, costs\n", - "\"\"\"\n", - " function simulate_microgrid(x)\n", - " project = Project(lifetime, discount_rate, timestep, \"€\")\n", - " # Split decision variables (converted MW → kW):\n", - " power_rated_gen = x[1]*1000\n", - " energy_rated_sto = x[2]*1000\n", - " power_rated_pv = x[3]*1000\n", - " power_rated_wind = x[4]*1000\n", - " power_rated_elyz = x[5]*1000\n", - " power_rated_fc = x[6]*1000\n", - " capacity_rated_hytank = x[7]*1000\n", - " \n", - "\n", - " # Create components\n", + "function default_microgrid()\n", " gen = ProductionUnit(power_rated_gen,\n", " fuel_intercept, fuel_slope, fuel_price,\n", " investment_price_gen, om_price_gen, lifetime_gen,\n", @@ -189,8 +159,49 @@ " [elyz,],tanks,batt, [\n", " pv,\n", " windgen\n", - " ]\n", - " )\n", + " ])\n", + " end" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "simulate_microgrid" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", + "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", + "\n", + "Returns mg, traj, stats, costs\n", + "\"\"\"\n", + " function simulate_microgrid(microgrid)\n", + " project = Project(lifetime, discount_rate, timestep, \"€\")\n", + " # Split decision variables (converted MW → kW):\n", + " #power_rated_gen = x[1]*1000\n", + " energy_rated_sto = x[1]*1000\n", + " power_rated_pv = x[2]*1000\n", + " power_rated_wind = x[3]*1000\n", + " power_rated_elyz = x[4]*1000\n", + " power_rated_fc = x[5]*1000\n", + " capacity_rated_hytank = x[6]*1000\n", + " \n", + "\n", + " # Create components\n", + " \n", " # Launch simulation:\n", " traj, stats, costs = simulate(mg)\n", "\n", @@ -200,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "id": "df58e986-633b-4d9c-8d25-dd1d6e8a2e9f", "metadata": { "tags": [] @@ -212,7 +223,7 @@ "0.0" ] }, - "execution_count": 4, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -231,32 +242,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 37, "id": "d765dc6a-eeda-4727-b259-479176670dc1", "metadata": { "tags": [] }, "outputs": [ { - "ename": "LoadError", - "evalue": "type OperationStats has no field lcoe", - "output_type": "error", - "traceback": [ - "type OperationStats has no field lcoe", - "", - "Stacktrace:", - " [1] getproperty(x::OperationStats, f::Symbol)", - " @ Base .\\Base.jl:37", - " [2] top-level scope", - " @ In[5]:5" - ] + "data": { + "text/plain": [ + "([5.0, 3.0, 0.9, 2.0, 1.8, 10.0], 0.21554333430784944, 16.351390939286258)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# Baseline sizing: same as in Microgrid_Wind-Solar.ipynb notebook\n", - "x_base = [power_rated_gen, energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", + "x_base = [energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", "# run simulation:\n", - "traj, stats, costs = simulate_microgrid(x_base)\n", + "mg, traj, stats, costs = simulate_microgrid(x_base)\n", "x_base, costs.lcoe, costs.npc/1e6" ] }, @@ -274,8 +281,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "470f5630-322c-497f-bac9-beb87f014f71", + "execution_count": 8, + "id": "62df72e3-ad0d-4c83-bf8b-3a7398b0c7e2", "metadata": { "tags": [] }, @@ -286,7 +293,7 @@ "obj_multi" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -304,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "6b3eb2d2-1513-424b-baa0-a49ca5247bb4", "metadata": { "tags": [] @@ -316,7 +323,7 @@ "obj" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -355,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "8de2153d-559f-4a60-880e-e8ea5fa3b810", "metadata": { "tags": [] @@ -365,10 +372,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Base. multi: (0.21571461172077722, 0.20552617597582035) mono: 19552.833312193754\n", - "Shed. multi: (0.20827574936977203, 0.20552617597582035) mono: 19552.825873331403\n", - "PV. multi: (0.10149685980966677, 0.923547868561659) mono: 91354.8883530257\n", - "Wind. multi: (0.10040264224635914, 0.74395737102815) mono: 73395.83750545724\n" + "Base. multi: (0.21554333430784944, 0.20552617597582035) mono: 19552.83314091634\n", + "Shed. multi: (0.21554333430784944, 0.20552617597582035) mono: 19552.83314091634\n", + "PV. multi: (0.17879988991758503, 0.923547868561659) mono: 91354.96565605582\n", + "Wind. multi: (0.12348466355712437, 0.74395737102815) mono: 73395.86058747856\n" ] } ], @@ -376,9 +383,9 @@ "# Test:\n", "shed_max = 0.01 # in [0,1]\n", "\n", - "x_shed = [0, energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", - "x_pv = [0. 0. 500. 0. 0. 0. 0.]/1000.\n", - "x_wind = [0. 0. 0. 500. 0. 0. 0.]/1000.\n", + "x_shed = [energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", + "x_pv = [0. 500. 0. 0. 0. 0.]/1000.\n", + "x_wind = [0. 0. 500. 0. 0. 0.]/1000.\n", "\n", "println(\"Base. multi: \", obj_multi(x_base), \" mono: \", obj(x_base,[], shed_max))\n", "println(\"Shed. multi: \", obj_multi(x_shed), \" mono: \", obj(x_shed,[], shed_max))\n", @@ -412,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "5ac92d7e-5de1-4ddf-a050-6897dbc8af2d", "metadata": { "tags": [] @@ -421,8 +428,7 @@ { "data": { "text/plain": [ - "7-element Vector{Float64}:\n", - " 0.0\n", + "6-element Vector{Float64}:\n", " 17.07\n", " 25.605\n", " 8.535\n", @@ -431,7 +437,7 @@ " 17.07" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -439,9 +445,9 @@ "source": [ "Pload_max = maximum(Pload)\n", "\n", - "xmin = [0., 0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", - "x0 = [0., 3.0, 3.0, 2.0, 1.0, 1.0, 5.0] * (Pload_max/1000)\n", - "xmax = [0., 10.0, 15.0, 5.0, 20., 1.2 , 10. ] * (Pload_max/1000)" + "xmin = [0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", + "x0 = [3.0, 3.0, 2.0, 1.0, 1.0, 5.0] * (Pload_max/1000)\n", + "xmax = [10.0, 15.0, 5.0, 20., 1.2 , 10. ] * (Pload_max/1000)" ] }, { @@ -479,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "6d5ef67e-5d40-4bb0-a13d-65dc3d57bceb", "metadata": { "tags": [] @@ -488,10 +494,10 @@ { "data": { "text/plain": [ - "((0.10149685980963595, 0.9998470957371233), (1.2761120605599305, 8.262599201630023e-6))" + "((38.75301191375701, 0.9998470957371233), (1.2820220908105344, 8.262599201630023e-6))" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -516,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "08491363-e1ce-47d3-a425-4bc83888be12", "metadata": { "tags": [] @@ -528,7 +534,7 @@ "optim_mg" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +554,7 @@ "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", "but could be added to the parameters as well.\n", "\"\"\"\n", - "function optim_mg(x0, shed_max, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-4, srand=1)\n", + "function optim_mg(x0, shed_max, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-8, srand=1)\n", " nx = length(x0) # number of optim variables\n", " opt = Opt(algo, nx)\n", " NLopt.srand(srand)\n", @@ -574,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 25, "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", "metadata": { "tags": [] @@ -584,17 +590,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "GN_CRS2_LM algo: MAXEVAL_REACHED after 10000 iterations. \n", - "x*=[0.0, 9764.2, 20727.4, 3356.4, 380.0, 1541.9, 16800.5]\n", - "LCOE*: 0.5786213467955624\n", - "shed*: 0.0\n" + "GN_CRS2_LM algo: XTOL_REACHED after 32418 iterations. \n", + "x*=[6920.8, 4227.9, 2138.7, 640.6, 1496.1, 13247.1]\n", + "LCOE*: 0.2527665492626518\n", + "shed*: 9.999433562439814e-5\n" ] } ], "source": [ "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0. # in [0,1]\n", - "maxeval=10000\n", + "shed_max = 0.0001 # in [0,1]\n", + "maxeval=100000\n", "xopt, ret, numevals = optim_mg(x0, shed_max, algo, maxeval)\n", "\n", "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", @@ -771,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "id": "f2545f43-8dd2-4254-84a2-4f643f69a994", "metadata": { "tags": [] @@ -781,10 +787,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "LN_SBPLX polish: XTOL_REACHED after 477 iterations. \n", - "x*=[0.0, 16138.7, 9004.4, 6778.6, 507.3, 1398.0, 1994.7]\n", - "LCOE*: 0.6114079791076036\n", - "shed*: 0.0\n" + "LN_SBPLX polish: XTOL_REACHED after 313 iterations. \n", + "x*=[2341.2, 2254.0, 2165.9, 1208.5, 845.2, 16007.4]\n", + "LCOE*: 0.20516631644385727\n", + "shed*: 0.00999967976963665\n" ] } ], diff --git a/examples/new_examples/Untitled.ipynb b/examples/new_examples/Untitled.ipynb new file mode 100644 index 0000000..f296714 --- /dev/null +++ b/examples/new_examples/Untitled.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "daa2a7b8-b634-4d53-8440-518b5d4aa50e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using CSV, DataFrames\n", + "using Microgrids\n", + "using NLopt # optimization solvers\n", + "using Printf # pretty print results\n", + "using PyPlot\n", + "using PyCall\n", + "using BenchmarkTools # for timing performance, optional\n", + "np=pyimport(\"numpy\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6daf95d7-90af-4daf-b642-643a803b9422", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg=default_microgrid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe39f510-2cb2-4ef7-ace1-f33b89389873", + 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-0,0 +1,26 @@ +# Sources for Ouessant load, PV, wind, temperature data + +Ouessant data 2016 (trimmed at 365 days), by Pierre Haessig, April 2021 + +## Load data + +Load data from EDF https://opendata-iles-ponant.edf.fr/, +collected and cleaned by Benjamin Bonvalet & Mohamed Chtiba, +in 2nd year Supélec student project 2018-2019. + +Unit: kW + +File `Ouessant_load_2016_filled_365j.csv` created by P. Haessig for HOMER ETS demo, 2019 +in `~/Travail/46 Séminaires/Efficient Tools Seminar/ETS HOMER/Conso Ouessant` + +## PV, wind, temperature data + +PV (W/kWp, 40° slope, 0° azimuth), Wind speed (m/s at 10 m) +and temperature (°C at 2 m) from PVGIS (c) European Communities, 2001-2021. + +File PVGIS_Ouessant_40deg_2013_2016.csv.xz created by P. Haessig, 2021, +in `~/Travail/11 Solar data/PVGIS/Ouessant hourly` + +## Merge + +Done by Python script `merge_PVGIS_load_data.py`. diff --git a/examples/new_examples/df2_dataset.csv b/examples/new_examples/df2_dataset.csv new file mode 100644 index 0000000..73655b7 --- /dev/null +++ b/examples/new_examples/df2_dataset.csv @@ -0,0 +1,351 @@ +fc_elyz_capex,shed_max,dispatch,algo,srand,ret,lcoe_opt,shed_rate_opt,capex,opex,xopt_sto,xopt_pv,xopt_wind,xopt_elyz,xopt_fc,xopt_hyTank +1000.0,0.0,1,GN_DIRECT,1,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 +1000.0,0.0,1,GN_DIRECT,2,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 +1000.0,0.0,1,GN_DIRECT,3,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 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z_|TL@NRJa3hNW}(5%q*eXsizV_qe7B!^G}MjDZ!5>3t;`_Z!4WCygn;88h^OpkLok z!*)(3F^-b6uf~P0YErS}6HvkcA4{nqpf`(rK6xFrG*K37HaoIbFaMRnz2Ke z4Nz1VXV9dWObY*g*s2~>CuB_E=sLIDfrU8tnlQ_P=iB$6kH+34wHzGq%-Se|vIu7t z3Zk8*gG_vb3d7CLUM@jY=4yFYSd!3%kl}f;iURBrvR;yKYa}$FjGRid#dlCF29U7jq1p z3+Ib3gv{wlaXkISY^W)bO&(Pv-OYwyt+0<&kL4X" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "using CSV, DataFrames\n", + "using Microgrids\n", + "using NLopt # optimization solvers\n", + "using Printf # pretty print results\n", + "using PyPlot\n", + "using PyCall\n", + "using BenchmarkTools # for timing performance, optional\n", + "np=pyimport(\"numpy\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "04b025c7-5c46-4efe-8497-83dc2637438e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_plots = isdefined(Main, :PyPlot)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5f54d21-8371-49d8-a81a-f170548a9bdf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a5a038f2-99e7-4727-8e3e-193569b45618", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "UndefVarError: `nsteps` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `nsteps` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[3]:1" + ] + } + ], + "source": [ + "td = collect((0:nsteps-1)/24) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "17f47887-a56f-4cac-940e-fb4ed0f2b7eb", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg= default_microgrid()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "824c7932-f17b-40e4-8ea6-f220ec80263c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "simulate_microgrid" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", + "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", + "\n", + "Returns mg, traj, stats, costs\n", + "\"\"\"\n", + " function simulate_microgrid(x,dispatch)\n", + " # Split decision variables (converted MW → kW):\n", + " \n", + " mg = new_microgrid(x,dispatch)\n", + " # Launch simulation:\n", + " traj, stats, costs = simulate(mg,dispatch)\n", + "\n", + " return traj ,stats, costs\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a34cb909-6b9b-47c6-aaea-a45c6d0ebff1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([5.0, 3.0, 1.8, 2.0, 1.8, 10.0], 0.26345419104330475, 23.59720165769015)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " # Baseline sizing: same as in Microgrid_Wind-Solar.ipynb notebook\n", + "x_base = [energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", + "# run simulation:\n", + "traj, stats, costs = simulate_microgrid(x_base,dispatch_2)\n", + "x_base, costs.lcoe, costs.npc/1e6" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f8c047df-66cc-47a2-9468-7c9082b82c1f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "plot_oper_traj (generic function with 1 method)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "function plot_oper_traj(mg, oper_traj)\n", + " \n", + " z=zeros(Float64,12)\n", + "tm=zeros(Float64,13)\n", + "tm[13]=8760/24\n", + " for i=1:12 \n", + " z[i]= oper_traj.LoH[i*730+1]\n", + " tm[i]=(i-1)*730/24 \n", + "end\n", + " fig,( ax1, ax2) = plt.subplots(2,1)\n", + " y1 = min.(oper_traj.Prenew_pot,mg.load)\n", + " y2 = max.( oper_traj.Pbatt,0.0)\n", + " y3 = oper_traj.Pfc\n", + " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", + " y5 = oper_traj.Pelyz\n", + " y6 = oper_traj.power_shedding\n", + " y7 = oper_traj.power_curtailment\n", + " \n", + " \n", + " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", + " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", + " \n", + " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", + " \n", + " \n", + " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", + " ax1.plot(td, mg.load,lw=2, label=\"load\")\n", + " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", + " ax2.plot(td, oper_traj.LoH[1:end-1]/mg.tanks.h2Tank.capacity, \"C2\")\n", + " stacks[2].set_hatch(\"-\")\n", + " stacks[4].set_hatch(\"+\")\n", + " stacks[6].set_hatch(\"\\\\\") \n", + " stacks[7].set_hatch(\"\\\\\") \n", + " \n", + " ax3=ax1.twinx()\n", + " ax3.set_ylim(0, 2)\n", + " ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", + " ax2.plot(td, oper_traj.LoH[1:end-1], \"C2\",label=\"hourly H2 level\")\n", + " \n", + " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", + " \n", + " \n", + " ax1.set_title(\"hourly power repartition\")\n", + " ax2.set_title(\"H2 level\")\n", + " ax1.legend()\n", + " ax1.grid(true)\n", + " ax1.set(ylabel=\"kW\")\n", + " ax2.legend()\n", + " ax3.legend()\n", + " ax2.grid(true)\n", + " ax2.set(ylabel=\"kg\")\n", + " \n", + " fig.tight_layout()\n", + "\n", + "\n", + " \n", + " return fig, ax1, ax2\n", + " \n", + "end\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4d8e8035-6db7-49e1-87ff-1d5884aa2789", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6-element Vector{Float64}:\n", + " 17.07\n", + " 25.605\n", + " 8.535\n", + " 34.14\n", + " 2.0484\n", + " 17.07" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload_max = maximum(Pload)\n", + "\n", + "xmin = [0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", + "x0 = [3.0, 3.0, 2.0, 1.0, 1.0, 5.0] * (Pload_max/1000)\n", + "xmax = [10.0, 15.0, 5.0, 20., 1.2 , 20. ] * (Pload_max/1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dd051772-f6d5-416a-a858-e3cac878b834", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "optim_mg" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", + "\n", + "Parameters:\n", + "- `x0`: initial sizing (for the algorithms which need them)\n", + "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", + "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", + "- `maxeval`: maximum allowed number of calls to the objective function,\n", + " that is to the microgrid simulation\n", + "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", + "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", + "\n", + "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", + "but could be added to the parameters as well.\n", + "\"\"\"\n", + "function optim_mg(x0, shed_max,dispatch, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-8, srand=1)\n", + " nx = length(x0) # number of optim variables\n", + " opt = Opt(algo, nx)\n", + " NLopt.srand(srand)\n", + " \n", + " opt.lower_bounds = xmin\n", + " opt.upper_bounds = xmax\n", + " opt.min_objective = (x, grad) -> obj(x, grad,dispatch, shed_max)\n", + " opt.xtol_rel = xtol_rel\n", + " opt.maxeval = maxeval\n", + " \n", + " (fopt, xopt, ret) = optimize(opt, x0)\n", + " return xopt, ret, opt.numevals\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "221d69a8-6262-4e95-9a3a-940a254914ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "obj_multi" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", + "function obj_multi(x,dispatch)\n", + " traj,stats, costs = simulate_microgrid(x,dispatch)\n", + " # Extract KPIs of interest\n", + " lcoe = costs.lcoe # $/kWh\n", + " shed_rate = stats.shed_rate; # in [0,1]\n", + " return lcoe, shed_rate\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0482bd16-e7b2-40b2-82ac-e4cc1505e7b4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "obj (generic function with 2 methods)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function obj(x, grad,dispatch, shed_max, w_shed_max=1e5)\n", + " lcoe, shed_rate = obj_multi(x,dispatch)\n", + " over_shed = shed_rate - shed_max\n", + " if over_shed > 0.0\n", + " penalty = w_shed_max*over_shed\n", + " else\n", + " penalty = 0.0\n", + " end\n", + " J = lcoe + penalty\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee419820-5562-49ae-9625-8e2384703a57", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97220b80-1eb3-431e-826c-12fcf5f04c7f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79006d3e-ab65-497d-89bd-a0c62fbfc2b9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1561109-812c-43c0-96be-773a032d4952", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "6305c0ad-6e41-49e0-9b3a-bc59009581ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: XTOL_REACHED after 69177 iterations. \n", + "x*=[6750.3, 4362.3, 2143.2, 887.7, 1495.3, 12606.8]\n", + "LCOE*: 0.25620326210747557\n", + "shed*: 9.999998227674223e-5\n" + ] + } + ], + "source": [ + "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "shed_max = 0.0001 # in [0,1]\n", + "maxeval=100000\n", + "xopt, ret, numevals = optim_mg(x0, shed_max,dispatch_1, algo, maxeval)\n", + "\n", + "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt*1000; digits=1)) # kW\n", + "lcoe_opt, shed_rate_opt = obj_multi(xopt,dispatch_1)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "1959b112-8609-49d0-a0e3-721b1dd5d7d1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OperationStats with fields:\n", + "- served_energy: 6.7682e6 kWh\n", + "- shed_energy: 6775.0 kWh\n", + "- shed_max: 256.54 kW\n", + "- shed_hours: 141.0 h\n", + "- shed_duration_max: 8.0 h\n", + "- shed_rate: 0.001 in [0,1]\n", + "- gen_energy: 0.0 kWh\n", + "- gen_hours: 0.0 h\n", + "- gen_fuel: 0.0 L\n", + "- fc_energy: 983780.0 kWh\n", + "- fc_hours: 1829.0 h\n", + "- h2_consumed: 61486.0 kg\n", + "- h2_produced: 64898.0 kg\n", + "- elyz_hours: 4051.0 h\n", + "- elyz_consumed_energy: 3.6343e6 kWh\n", + "- h2_chain_loss: 2.6503e6 kWh\n", + "- storage_cycles: 154.64 \n", + "- storage_char_energy: 483310.0 kWh\n", + "- storage_dis_energy: 440120.0 kWh\n", + "- storage_loss_energy: 46172.0 kWh\n", + "- spilled_energy: 785770.0 kWh\n", + "- spilled_max: 3136.2 kW\n", + "- spilled_rate: 0.076678 in [0,1]\n", + "- renew_potential_energy: 1.0248e7 kWh\n", + "- renew_energy: 9.4619e6 kWh\n", + "- renew_rate: 1.0 in [0,1]\n" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg, traj, stats, costs = simulate_microgrid(xopt,dispatch_1)\n", + "stats" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "08163c89-f8e5-48ff-b29c-942421942fb9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(Figure(PyObject

), PyObject , PyObject )" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot_oper_traj(mg,traj)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4da1dd01-e71f-49c9-96ea-09a8ad0e5726", + "metadata": {}, + "outputs": [ + { + "ename": "LoadError", + "evalue": "UndefVarError: `optim_mg` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `optim_mg` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[6]:4" + ] + } + ], + "source": [ + "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "shed_max = 0.001 # in [0,1]\n", + "maxeval=100000\n", + "xopt2, ret, numevals = optim_mg(x0, shed_max,dispatch_2, algo, maxeval)\n", + "\n", + "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt2*1000; digits=1)) # kW\n", + "lcoe_opt, shed_rate_opt = obj_multi(xopt2,dispatch_2)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "46dbc313-3f17-47f1-8589-dddbb5d76f79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OperationStats with fields:\n", + "- served_energy: 6.7682e6 kWh\n", + "- shed_energy: 6775.0 kWh\n", + "- shed_max: 1203.8 kW\n", + "- shed_hours: 65.0 h\n", + "- shed_duration_max: 11.0 h\n", + "- shed_rate: 0.001 in [0,1]\n", + "- gen_energy: 0.0 kWh\n", + "- gen_hours: 0.0 h\n", + "- gen_fuel: 0.0 L\n", + "- fc_energy: 1.3541e6 kWh\n", + "- fc_hours: 2843.0 h\n", + "- h2_consumed: 84630.0 kg\n", + "- h2_produced: 87968.0 kg\n", + "- elyz_hours: 5421.0 h\n", + "- elyz_consumed_energy: 4.9262e6 kWh\n", + "- h2_chain_loss: 3.5719e6 kWh\n", + "- storage_cycles: 46.307 \n", + "- storage_char_energy: 8484.3 kWh\n", + "- storage_dis_energy: 7844.2 kWh\n", + "- storage_loss_energy: 816.42 kWh\n", + "- spilled_energy: 534940.0 kWh\n", + "- spilled_max: 1974.7 kW\n", + "- spilled_rate: 0.049186 in [0,1]\n", + "- renew_potential_energy: 1.0876e7 kWh\n", + "- renew_energy: 1.0341e7 kWh\n", + "- renew_rate: 1.0 in [0,1]\n" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg2, traj2, stats2, costs2 = simulate_microgrid(xopt2,dispatch_2)\n", + "stats2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "88c56bff-3c06-4532-b16f-40f58a0433c8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "UndefVarError: `plot_oper_traj` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `plot_oper_traj` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[5]:1" + ] + } + ], + "source": [ + "plot_oper_traj(mg2,traj2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49cb7f56-213d-4e3a-8c53-80a355889344", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/new_examples/prices_scenario_comparison.ipynb b/examples/new_examples/prices_scenario_comparison.ipynb new file mode 100644 index 0000000..eabafc9 --- /dev/null +++ b/examples/new_examples/prices_scenario_comparison.ipynb @@ -0,0 +1,2139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a057597f-0993-4915-813c-915eff752105", + "metadata": {}, + "outputs": [], + "source": [ + "using CSV, DataFrames\n", + "using Microgrids\n", + "using NLopt # optimization solvers\n", + "using Printf # pretty print results\n", + "using PyPlot\n", + "using PyCall\n", + "using BenchmarkTools # for timing performance, optional" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e483c6f8-940c-4072-ac8a-4c86bd17fb82", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "using Printf # pretty print results\n", + "using Random, Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a87a676-9923-4d66-bac1-576ad1720dd8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "np=pyimport(\"numpy\");\n", + "td = collect((0:nsteps-1)/24) ;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7a3a52ff-9714-48c7-aad4-908c5f746401", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "true" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_plots = isdefined(Main, :PyPlot)" + ] + }, + { + "cell_type": "markdown", + "id": "11e43d38-ea1c-4256-9546-dba78952c001", + "metadata": {}, + "source": [ + "#### default Hydrogen prices scenario\n", + "#electrolyzer and fuel cell investment price : 1600\\$/kW" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "40e20332-bb1f-4ad4-a054-4de92fefe834", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1.8e6, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2.0e6, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(1.0e7, 500.0, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(5.0e6, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3.0e6, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1.8e6, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_base = new_microgrid()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a14a3ea4-a3c4-40d1-9f66-f092261b1985", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "500.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_base.tanks.h2Tank.investment_price" + ] + }, + { + "cell_type": "markdown", + "id": "8f7753e1-cfe3-4d1f-b4e6-1f8665b442e4", + "metadata": { + "tags": [] + }, + "source": [ + "#### Hydrogen High prices scenario\n", + "#electrolyzer and fuel cell investment price : 2200\\$/kW" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ef803eae-d36c-45e2-a124-4b3c236dbd10", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8-element Vector{Float64}:\n", + " 400.0\n", + " 0.0\n", + " 2200.0\n", + " 500.0\n", + " 2200.0\n", + " 350.0\n", + " 1200.0\n", + " 3500.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capex_hi= [400., 0.0, 2200., 500., 2200., 350., 1200., 3500.]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9c0f76fc-8d18-463b-bbc4-a27a0f881d16", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(2.2e6, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(500000.0, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(350000.0, 500.0, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(400000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(2.2e6, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_high= new_microgrid(capex_hi)" + ] + }, + { + "cell_type": "markdown", + "id": "819de33d-f525-4523-ab33-fedcde3675f3", + "metadata": {}, + "source": [ + "#### function to compare 2 operations trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c4b766e0-570d-4562-983b-7986cb97c364", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "comp_oper_traj (generic function with 1 method)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function comp_oper_traj(mg1,mg2, oper_traj1, oper_traj2, costs1,costs2)\n", + " \n", + " ns=Int64(nsteps/730)\n", + " z=zeros(Float64,ns)\n", + "tm=zeros(Float64,ns +1)\n", + " tm[end]=nsteps/24\n", + " for i=1:ns\n", + " z[i]= oper_traj1.LoH[i*730+1]\n", + " tm[i]=(i-1)*730/24 \n", + "end\n", + " fig,( ax1, ax2) = plt.subplots(2,1,sharex=true)\n", + " y1 = min.(oper_traj1.Prenew_pot,mg1.load)\n", + " y2 = max.( oper_traj1.Pbatt,0.0)\n", + " y3 = oper_traj1.Pfc\n", + " y4 = abs.(min.( oper_traj1.Pbatt,0.0))\n", + " y5 = oper_traj1.Pelyz\n", + " y6 = oper_traj1.power_shedding\n", + " y7 = oper_traj1.power_curtailment\n", + " \n", + " Y1=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", + " \n", + " \n", + " y1 = min.(oper_traj2.Prenew_pot,mg2.load)\n", + " y2 = max.( oper_traj2.Pbatt,0.0)\n", + " y3 = oper_traj2.Pfc\n", + " y4 = abs.(min.( oper_traj2.Pbatt,0.0))\n", + " y5 = oper_traj2.Pelyz\n", + " y6 = oper_traj2.power_shedding\n", + " y7 = oper_traj2.power_curtailment\n", + " \n", + " Y2=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", + " \n", + " \n", + " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", + " \n", + " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", + " \n", + " \n", + " ax1.plot(td, oper_traj1.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", + " ax1.plot(td, mg1.load,lw=2, label=\"load\")\n", + " stacks1 = ax1.stackplot(td,Y1,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", + " ax2.plot(td, oper_traj2.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", + " ax2.plot(td, mg2.load,lw=2, label=\"load\")\n", + " stacks2 = ax2.stackplot(td,Y2,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", + " \n", + " \n", + " stacks1[2].set_hatch(\"-\")\n", + " stacks1[4].set_hatch(\"+\")\n", + " stacks1[6].set_hatch(\"\\\\\") \n", + " stacks1[7].set_hatch(\"\\\\\") \n", + " \n", + " stacks2[2].set_hatch(\"-\")\n", + " stacks2[4].set_hatch(\"+\")\n", + " stacks2[6].set_hatch(\"\\\\\") \n", + " stacks2[7].set_hatch(\"\\\\\") \n", + " \n", + " ax3=ax1.twinx()\n", + " ax3.set_ylim(0, 2)\n", + " ax3.plot(td, oper_traj1.Ebatt[1:end-1]/mg1.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", + "\n", + " \n", + " \n", + " ax4=ax2.twinx()\n", + " ax4.set_ylim(0, 2)\n", + " ax4.plot(td, oper_traj2.Ebatt[1:end-1]/mg2.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", + "\n", + " \n", + " \n", + " \n", + " ax1.set_title(\"hourly power repartition\")\n", + " ax2.set_title(\"hourly power repartition\")\n", + " ax1.legend()\n", + " ax1.grid(true)\n", + " ax1.set(ylabel=\"kW\")\n", + " ax2.legend()\n", + " ax3.legend()\n", + " ax4.legend()\n", + " ax2.grid(true)\n", + " ax2.set(ylabel=\"kw\")\n", + " fig.tight_layout()\n", + " \n", + " return fig,(ax1, ax2,ax3,ax4)\n", + " \n", + "end\n" + ] + }, + { + "cell_type": "markdown", + "id": "d7b9e5a5-3de5-4371-afdb-7582fa493404", + "metadata": {}, + "source": [ + "#### optimization setup" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3a789b7d-eee8-4047-8bc2-34ad25ab09f0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "simulate_microgrid" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", + "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", + "\n", + "Returns mg, traj, stats, costs\n", + "\"\"\"\n", + " function simulate_microgrid(x,capex,dispatch)\n", + " mg=new_microgrid(x)\n", + " # Split decision variables (converted MW → kW):\n", + " oper_traj = operation(mg, dispatch)\n", + " ini=[0.0,oper_traj.LoH[end]/mg.tanks.h2Tank.capacity, oper_traj.Ebatt[end]/mg.storage.energy_rated]\n", + " mg=new_microgrid(x,capex,ini)\n", + " # Launch simulation:\n", + " traj, stats, costs = simulate(mg,dispatch)\n", + "\n", + " return traj ,stats, costs\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e695c1ab-4853-461a-8b3f-b6cd12dc3ecd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "obj_multi" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", + "function obj_multi(x,capex,dispatch)\n", + " traj,stats, costs = simulate_microgrid(x,capex,dispatch)\n", + " # Extract KPIs of interest\n", + " lcoe = costs.lcoe # $/kWh\n", + " shed_rate = stats.shed_rate; # in [0,1]\n", + " return lcoe, shed_rate, costs,traj\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "31a72ca9-cec2-4325-8758-9b2374847d0b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "obj (generic function with 2 methods)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function obj(x, grad,capex,dispatch, shed_max, w_shed_max=1e5)\n", + " lcoe, shed_rate,costs,traj = obj_multi(x,capex,dispatch)\n", + " over_shed = shed_rate - shed_max\n", + " if over_shed > 0.0\n", + " penalty = w_shed_max*over_shed\n", + " else\n", + " penalty = 0.0\n", + " end\n", + " J = lcoe + penalty\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "60c0f843-3bb6-4944-ab52-1a25ef54767d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6-element Vector{Float64}:\n", + " 34.14\n", + " 17.07\n", + " 1.8777000000000001\n", + " 8.535\n", + " 3.414\n", + " 25.605" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Pload_max = maximum(Pload)\n", + "\n", + "xmin = [0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", + "x0 = X * (1e-3)\n", + "xmax = [20.0, 10.0, 1.1, 5., 2. , 15. ] * (Pload_max/1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8c71df41-6029-4e2d-ada6-bec22e395d7f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6-element Vector{Float64}:\n", + " 5.0\n", + " 3.0\n", + " 1.8\n", + " 2.0\n", + " 1.8\n", + " 10.0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x0" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e6507580-4fd7-470f-a392-8477e9d598ef", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "optim_mg" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", + "\n", + "Parameters:\n", + "- `x0`: initial sizing (for the algorithms which need them)\n", + "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", + "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", + "- `maxeval`: maximum allowed number of calls to the objective function,\n", + " that is to the microgrid simulation\n", + "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", + "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", + "\n", + "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", + "but could be added to the parameters as well.\n", + "\"\"\"\n", + "function optim_mg(x0,capex, shed_max,dispatch, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-8, srand=1)\n", + " nx = length(x0) # number of optim variables\n", + " opt = Opt(algo, nx)\n", + " NLopt.srand(srand)\n", + " \n", + " opt.lower_bounds = xmin\n", + " opt.upper_bounds = xmax\n", + " opt.min_objective = (x, grad) -> obj(x, grad,capex,dispatch, shed_max)\n", + " opt.xtol_rel = xtol_rel\n", + " opt.maxeval = maxeval\n", + " \n", + " (fopt, xopt, ret) = optimize(opt, x0)\n", + " return xopt, ret, opt.numevals\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8344f0ba-9fdf-47bd-b84a-ef24bb942cf5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-element Vector{Function}:\n", + " dispatch_1 (generic function with 2 methods)\n", + " dispatch_2 (generic function with 1 method)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capexlist=[capex_def,capex_hi]\n", + "dispatchlist=[dispatch_1,dispatch_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "626688e2-98e9-438d-9494-d44aecc5621f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: XTOL_REACHED after 44902 iterations. \n", + "x*=[11380.11584, 16425.59372, 1499.74022, 354.72914, 1405.23487, 6377.35589]\n", + "LCOE*: 0.472324294850319\n", + "shed*: 0.0\n" + ] + } + ], + "source": [ + "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "shed_max = 0.00# in [0,1]\n", + "maxeval=100000\n", + "xtol_rel=1e-8\n", + "xopt_base, ret, numevals = optim_mg(x0,capex_def, shed_max,dispatch_1, algo, maxeval,xtol_rel,6)\n", + "\n", + "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt_base*1000; digits=5)) # kW\n", + "lcoe_opt, shed_rate_opt,costs,traj_opt = obj_multi(xopt_base,capex_def,dispatch_1)\n", + "println(\"LCOE*: \", lcoe_opt)\n", + "println(\"shed*: \", shed_rate_opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "b1afbd7f-1e6c-4743-ac94-05749b3a947e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13910787752736056" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traj_opt.LoH[1]/(xopt_base[end]*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0c5ad5a7-b088-46cc-981c-32a36013d445", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traj_opt.Ebatt[end]/(xopt_base[1]*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "22df5e8a-3293-48b6-8ed7-e00a0b22d866", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.13910787752736056" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traj_opt.LoH[end]/(xopt_base[end]*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "d4fbf0c6-8179-4027-ba00-8d171523ba0d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traj_opt.Ebatt[1]/(xopt_base[1]*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5bea2759-0a3f-42d4-a283-fe8da461c192", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3-element Vector{Float64}:\n", + " 0.0\n", + " 0.11191436669910618\n", + " 3.8487770259517386e-7" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ini_opt=[0.0,traj_opt.LoH[1]/(xopt_base[end]*1000),traj_opt.Ebatt[end]/(xopt_base[1]*1000)]" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "ef5f3774-8b30-4152-a449-1520c3d574b0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1405.2348706758517, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(354.7291392005546, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 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1.0)])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " mg=new_microgrid(xopt_base,capex_def,ini_opt)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "45dae8ba-c879-4fc8-8a0b-a0543efeba13", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(OperationTraj{Float64}([1215.7186774341255, 0.0, 0.0, 0.0, 0.0, -235.4169256696523, -404.6630978467483, -388.0389352980492, -333.3648992931039, -278.442595827634 … -354.7291392005545, -354.7291392005545, 1374.5398909533117, 1325.4614992280208, 1412.4708809444858, 1531.3027020820045, 1504.7141803771758, 1415.12269446997, 1476.7141803771758, 1427.8882665978665], [189.5160260512464, 1165.2888071173184, 845.1166282068943, 479.78521739130485, 95.05103381608512, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [47.76529651462817, 165.71119288268167, 368.8833717931057, 666.2147826086951, 982.9489661839149, 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182491.42007790215, 49995.32823853387, 0.0, -155033.69705166973), CostFactors(1.8739549897463678e6, 2.2483757930813627e6, 0.0, 198053.02369578512, 0.0, -572473.82703078), CostFactors(0.0, 0.0, 0.0, 0.0, 0.0, -0.0), CostFactors(3.198894698015956e6, 3.198669992765025e6, 0.0, 224.70525093164966, 0.0, -0.0)))" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " traj, stats, costs = simulate(mg,dispatch_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c6f0de38-f578-4ac7-8397-53c5ae2beb3e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if with_plots\n", + " fig1, ax1, ax2= plot_oper_traj(mg,traj)\n", + " \n", + " pygui(true)\n", + " plt.show()\n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "8e34ffff-e843-4d1d-9cd8-df925a9870e7", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PyObject " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loh_ini=zeros(Float64,100)\n", + "loh_end=zeros(Float64,100)\n", + "for i in 1:100\n", + " mg=new_microgrid(xopt_base,capex_def,[0.0,i/(100.0),0.0])\n", + " traj, stats, costs = simulate(mg,dispatch_2)\n", + " loh_ini[i]=traj.LoH[1]/(xopt_base[end]*1000)\n", + " loh_end[i]=traj.LoH[end]/(xopt_base[end]*1000)\n", + "end\n", + "fig,ax = plt.subplots()\n", + "ax.plot(loh_ini,loh_end)\n", + "ax.plot(loh_ini,loh_ini,label=\"x=x\")\n", + "ax.set(title=\"niveau d'hydrogène final en fonction du niveau initial\",xlabel=\"initial level\",ylabel=\"final level\")\n", + "ax.legend()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2f10eb38-b4d2-436f-904b-aa540d4f30c3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "100-element Vector{Float64}:\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " ⋮\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888\n", + " 0.11197005651847888" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loh_end" + ] + }, + { + "cell_type": "markdown", + "id": "16ee0cc1-edb4-4e6b-b77d-7e7e1b541ef6", + "metadata": {}, + "source": [ + "#### Pareto front of simple dispatch strategies" + ] + }, + { + "cell_type": "markdown", + "id": "6cddca75-5e07-4936-8050-5f6188f4ae56", + "metadata": {}, + "source": [ + "## Dispatch 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c70b32f-46ef-4761-9126-2b7b8def03ea", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "90cab27f-ebc4-4ef5-b0e9-91458d34eb6b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17-element Vector{Float64}:\n", + " 0.0\n", + " 2.5e-6\n", + " 5.0e-6\n", + " 7.5e-6\n", + " 1.0e-5\n", + " 1.25e-5\n", + " 1.5e-5\n", + " 1.75e-5\n", + " 2.0e-5\n", + " 2.25e-5\n", + " 2.5e-5\n", + " 2.75e-5\n", + " 3.0e-5\n", + " 3.25e-5\n", + " 3.5e-5\n", + " 3.75e-5\n", + " 4.0e-5" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_shed_1=collect(0.0:0.0000025:0.00004)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "47331a38-7b65-4925-bf07-62da78e201ef", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20-element Vector{Float64}:\n", + " 5.0e-5\n", + " 0.0001\n", + " 0.00015\n", + " 0.0002\n", + " 0.00025\n", + " 0.0003\n", + " 0.00035\n", + " 0.0004\n", + " 0.00045\n", + " 0.0005\n", + " 0.00055\n", + " 0.0006\n", + " 0.00065\n", + " 0.0007\n", + " 0.00075\n", + " 0.0008\n", + " 0.00085\n", + " 0.0009\n", + " 0.00095\n", + " 0.001" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_shed_2=collect(0.00005:0.00005:0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a783a8a1-44ad-4d35-aafa-fc423eadf04d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "37-element Vector{Float64}:\n", + " 0.0\n", + " 2.5e-6\n", + " 5.0e-6\n", + " 7.5e-6\n", + " 1.0e-5\n", + " 1.25e-5\n", + " 1.5e-5\n", + " 1.75e-5\n", + " 2.0e-5\n", + " 2.25e-5\n", + " 2.5e-5\n", + " 2.75e-5\n", + " 3.0e-5\n", + " ⋮\n", + " 0.00045\n", + " 0.0005\n", + " 0.00055\n", + " 0.0006\n", + " 0.00065\n", + " 0.0007\n", + " 0.00075\n", + " 0.0008\n", + " 0.00085\n", + " 0.0009\n", + " 0.00095\n", + " 0.001" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_shed_new=[pareto_shed_1;pareto_shed_2]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2815e28c-df86-4721-9cd3-57f1b397d2b9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: XTOL_REACHED after 27579 iterations. \n", + "x*=[23347.453054, 16995.434773, 1171.625098, 145.151887, 1069.897236, 3671.421094]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23474 iterations. \n", + "x*=[14479.12082, 12330.740779, 1454.01445, 357.983297, 1398.000414, 6604.40332]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 43408 iterations. \n", + "x*=[11292.04383, 12381.74684, 1513.152367, 403.535709, 1404.807522, 7483.875178]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 36424 iterations. \n", + "x*=[14368.285183, 12086.645801, 1466.594398, 377.82366, 1397.999828, 6644.018807]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 27129 iterations. \n", + "x*=[23288.523592, 13932.486344, 1503.644564, 153.792031, 1148.491, 3589.505247]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 35784 iterations. \n", + "x*=[9411.805542, 7854.990826, 1838.971553, 691.517119, 1467.810727, 8330.188014]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20457 iterations. \n", + "x*=[14087.670875, 11110.284058, 1784.509317, 329.459563, 1385.015408, 5878.375851]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 22029 iterations. \n", + "x*=[14405.253374, 11813.932567, 1496.122689, 383.340527, 1397.999977, 6584.038891]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 47439 iterations. \n", + "x*=[10611.466631, 11232.66635, 1512.622291, 564.376584, 1407.365635, 7994.852876]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 38529 iterations. \n", + "x*=[7131.232799, 10966.715354, 1812.920408, 638.289819, 1492.371105, 8242.184069]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 27793 iterations. \n", + "x*=[11264.102655, 6639.264557, 1877.7, 760.310972, 1487.403058, 8711.156151]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 53255 iterations. \n", + "x*=[8094.484481, 7860.159018, 1821.057204, 805.562698, 1464.654945, 8844.206887]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 59720 iterations. \n", + "x*=[9851.84816, 6717.642763, 1877.699976, 788.660908, 1469.781366, 8911.878499]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 28291 iterations. \n", + "x*=[9691.559945, 6717.642801, 1877.699995, 795.756824, 1476.270073, 8946.069339]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 34600 iterations. \n", + "x*=[10134.563139, 6639.847971, 1871.211462, 798.094289, 1475.43316, 8959.900061]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 45924 iterations. \n", + "x*=[9419.558271, 6405.206432, 1877.7, 868.966092, 1487.384934, 9289.30934]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 91208 iterations. \n", + "x*=[8600.945503, 6687.8133, 1877.425377, 873.069392, 1477.280538, 9208.912174]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 64488 iterations. \n", + "x*=[8044.561869, 6483.919668, 1877.699989, 961.552616, 1487.988272, 9527.776927]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 37047 iterations. \n", + "x*=[7756.01255, 5543.964486, 1877.7, 1022.518717, 1489.759176, 11280.19173]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20018 iterations. \n", + "x*=[7433.16685, 5424.047781, 1877.699992, 1024.595648, 1462.097288, 11686.878832]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23944 iterations. \n", + "x*=[6699.602337, 4410.279562, 1876.715621, 967.93892, 1454.402581, 14622.247988]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 29661 iterations. \n", + "x*=[6698.183894, 4419.573597, 1877.69999, 998.395432, 1425.187712, 14473.152826]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 34555 iterations. \n", + "x*=[5831.249527, 3962.219673, 1877.7, 939.264247, 1428.117955, 16339.083631]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 26570 iterations. \n", + "x*=[5832.553034, 3963.289726, 1877.699997, 937.80574, 1403.29558, 16318.477008]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 19486 iterations. \n", + "x*=[5440.601761, 3966.690102, 1877.7, 955.149671, 1398.0976, 16504.400391]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20077 iterations. \n", + "x*=[5425.779695, 3968.692118, 1877.7, 955.544439, 1384.652854, 16487.336099]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 21239 iterations. \n", + "x*=[5426.769876, 3971.500979, 1877.7, 953.302231, 1365.790773, 16463.057204]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16320 iterations. \n", + "x*=[5427.491355, 3995.177625, 1877.7, 944.750723, 1352.045948, 16413.865385]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 19664 iterations. \n", + "x*=[5435.27913, 3973.924764, 1877.7, 950.240831, 1341.432578, 16421.884026]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 14452 iterations. \n", + "x*=[5428.648025, 3974.997594, 1877.699994, 949.124293, 1330.01409, 16407.002081]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16699 iterations. \n", + "x*=[5429.31133, 3999.484389, 1877.7, 940.418351, 1317.381037, 16356.813923]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23355 iterations. \n", + "x*=[5213.88283, 3994.23801, 1877.7, 942.980111, 1311.276586, 16502.505703]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23601 iterations. \n", + "x*=[5430.217543, 3975.536951, 1877.7, 945.985404, 1300.122465, 16355.646134]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20378 iterations. \n", + "x*=[5182.995462, 4002.3016, 1877.699994, 937.683782, 1294.698428, 16485.381508]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16317 iterations. \n", + "x*=[5431.090714, 3975.836686, 1877.7, 943.946247, 1283.485224, 16323.922167]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23448 iterations. \n", + "x*=[5431.624254, 4004.956494, 1877.7, 934.622483, 1273.327917, 16267.159026]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 19040 iterations. \n", + "x*=[5432.052693, 4005.970452, 1877.7, 932.743124, 1265.16652, 16252.641495]\n" + ] + } + ], + "source": [ + " algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "pareto_lcoe1 = zeros(Float64,37)\n", + "maxeval=100000\n", + "for i in eachindex(pareto_shed_new)\n", + " xopt, ret, numevals = optim_mg(x0,capex_def, pareto_shed_new[i],dispatch_1, algo, maxeval)\n", + " lcoe_opt, shed_rate_opt = obj_multi(xopt,capex_def,dispatch_1)\n", + " mg = new_microgrid(xopt) ;\n", + " pareto_lcoe1[i]=lcoe_opt\n", + " @printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt*1000; digits=6)) # kW\n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c5028b91-d889-4aa9-a362-c73641e02914", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "37-element Vector{Float64}:\n", + " 0.5191261655500403\n", + " 0.4274416512728466\n", + " 0.4168988491304812\n", + " 0.42413328106116827\n", + " 0.4891206964493164\n", + " 0.36157491950261683\n", + " 0.4184650491601368\n", + " 0.42143634282172665\n", + " 0.39983759213324405\n", + " 0.3945343534500265\n", + " 0.359868392102726\n", + " 0.3575728953788394\n", + " 0.3535145026153155\n", + " ⋮\n", + " 0.33358420913900316\n", + " 0.33317240505760065\n", + " 0.33288403545080547\n", + " 0.33259178476622053\n", + " 0.3323038865016544\n", + " 0.3320324002658368\n", + " 0.33182090244724916\n", + " 0.3315309115556263\n", + " 0.3313519234645792\n", + " 0.3310820186183037\n", + " 0.33086752538870745\n", + " 0.3306481155866563" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_lcoe1" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "46f3150f-27fb-45dc-9489-353b1202b7c5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1-element Vector{PyObject}:\n", + " PyObject " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "plot(pareto_shed_new, pareto_lcoe1,\"or\")" + ] + }, + { + "cell_type": "markdown", + "id": "4eda341b-6228-4bff-8be9-943592999959", + "metadata": {}, + "source": [ + "#### Dispatch 2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4f921ae7-214f-42be-861b-24a0d7a81fca", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21-element Vector{Float64}:\n", + " 0.0\n", + " 5.0e-5\n", + " 0.0001\n", + " 0.00015\n", + " 0.0002\n", + " 0.00025\n", + " 0.0003\n", + " 0.00035\n", + " 0.0004\n", + " 0.00045\n", + " 0.0005\n", + " 0.00055\n", + " 0.0006\n", + " 0.00065\n", + " 0.0007\n", + " 0.00075\n", + " 0.0008\n", + " 0.00085\n", + " 0.0009\n", + " 0.00095\n", + " 0.001" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_shed=collect(0.0:0.00005:0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4b136793-6969-463b-94a9-5dc3706e82e8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: XTOL_REACHED after 15658 iterations. \n", + "x*=[385.10361, 5736.596414, 1877.7, 1824.243216, 1451.582529, 18784.003256]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16156 iterations. \n", + "x*=[283.262884, 5851.085497, 1877.7, 1787.004647, 1433.122784, 18681.967208]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 30896 iterations. \n", + "x*=[234.617202, 5718.847261, 1877.7, 1831.120711, 1418.332267, 18770.023747]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 19427 iterations. \n", + "x*=[213.034068, 5729.508642, 1877.7, 1827.352604, 1398.602756, 18744.084675]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 17800 iterations. \n", + "x*=[207.048184, 5733.970119, 1877.699999, 1825.041847, 1382.46422, 18725.700322]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 24532 iterations. \n", + "x*=[207.047483, 5743.432015, 1877.7, 1822.431568, 1365.557753, 18697.878631]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 19744 iterations. \n", + "x*=[207.047212, 5745.756866, 1877.7, 1820.428026, 1354.513028, 18677.917241]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 18188 iterations. \n", + "x*=[207.047855, 5755.624997, 1877.699999, 1816.554274, 1344.165622, 18651.691569]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20687 iterations. \n", + "x*=[207.048372, 5763.406932, 1877.7, 1813.356553, 1333.835591, 18628.641788]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16274 iterations. \n", + "x*=[207.069742, 5758.648372, 1877.69993, 1814.406736, 1323.943596, 18613.424205]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 21208 iterations. \n", + "x*=[204.332353, 5763.237138, 1877.7, 1812.45612, 1313.059678, 18593.573633]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 18598 iterations. \n", + "x*=[195.520723, 5766.096522, 1877.7, 1811.087593, 1306.283173, 18576.786285]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 23352 iterations. \n", + "x*=[201.723754, 5589.973737, 1877.699999, 1872.408294, 1298.259449, 18709.566988]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20915 iterations. \n", + "x*=[201.867367, 5587.171543, 1877.699997, 1872.408457, 1288.720283, 18699.002137]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 21158 iterations. \n", + "x*=[207.047022, 5576.870538, 1877.7, 1875.09313, 1280.250877, 18686.808198]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 20557 iterations. \n", + "x*=[207.04718, 5586.65935, 1877.7, 1870.391565, 1272.805158, 18663.79938]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 26457 iterations. \n", + "x*=[201.773157, 5589.011838, 1877.7, 1868.661675, 1265.513547, 18649.823463]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 16320 iterations. \n", + "x*=[201.078392, 5674.467114, 1877.699998, 1837.465756, 1258.128843, 18560.602928]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 24210 iterations. \n", + "x*=[181.771774, 5551.334946, 1877.7, 1881.373862, 1255.118735, 18658.113108]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 30038 iterations. \n", + "x*=[201.08262, 5558.494099, 1877.7, 1877.709364, 1245.537604, 18631.73682]\n", + "GN_CRS2_LM algo: XTOL_REACHED after 18864 iterations. \n", + "x*=[199.970551, 5623.957623, 1877.7, 1853.322059, 1238.732818, 18557.772978]\n" + ] + } + ], + "source": [ + " algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", + "pareto_lcoe2 = zeros(Float64,21)\n", + "maxeval=100000\n", + "xtol_rel=1e-8\n", + "srand=6\n", + "for i in eachindex(pareto_shed)\n", + " xopt, ret, numevals = optim_mg(x0,capex_def, pareto_shed[i],dispatch_2, algo, maxeval,xtol_rel,srand)\n", + " lcoe_opt, shed_rate_opt,costs = obj_multi(xopt,capex_def,dispatch_2)\n", + " pareto_lcoe2[i]=lcoe_opt\n", + " @printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", + "println(round.(xopt*1000; digits=6)) # kW\n", + " \n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "4c5597f2-67ab-4251-85b7-fc0f69a98051", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21-element Vector{Float64}:\n", + " 0.39078145064078484\n", + " 0.3897167595872884\n", + " 0.3889187421559924\n", + " 0.3882208773709961\n", + " 0.3877457126222478\n", + " 0.3872830140871662\n", + " 0.3869314617040828\n", + " 0.3866093997742454\n", + " 0.3862748492377523\n", + " 0.3859420172864854\n", + " 0.3855989821868398\n", + " 0.3853056534794736\n", + " 0.3850450309623384\n", + " 0.38475375716614985\n", + " 0.38445168894265797\n", + " 0.3841779167937007\n", + " 0.38391067889055736\n", + " 0.3836582968516015\n", + " 0.38340062329469277\n", + " 0.38318131724331905\n", + " 0.3829285732063151" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pareto_lcoe2" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "6b59a6da-3163-44c3-9d53-b658fbc27e53", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "1-element Vector{PyObject}:\n", + " PyObject " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plot(pareto_shed, pareto_lcoe2,\"ob\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "545e2ceb-d8c9-41e1-875f-ec52e88eb5e5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "ename": "LoadError", + "evalue": "UndefVarError: `mg_hi` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `mg_hi` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[24]:2" + ] + } + ], + "source": [ + "if with_plots\n", + " fig,(ax1,ax2,ax3,ax4) = comp_oper_traj(mg_base,mg_hi, traj1,traj2,costs1,costs2)\n", + " \n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "67910661-b3aa-49e0-83be-ff3fdf8fa7d6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
0×17 DataFrame
Rowfc_elyz_capexshed_maxdispatchalgosrandretnumevalslcoe_optshed_rate_optcapexopexxopt_stoxopt_pvxopt_windxopt_elyzxopt_fcxopt_hyTank
Float64Float64Int64StringInt64StringInt64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccccc}\n", + "\t& fc\\_elyz\\_capex & shed\\_max & dispatch & algo & srand & ret & numevals & lcoe\\_opt & shed\\_rate\\_opt & \\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Int64 & String & Int64 & String & Int64 & Float64 & Float64 & \\\\\n", + "\t\\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m0×17 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m fc_elyz_capex \u001b[0m\u001b[1m shed_max \u001b[0m\u001b[1m dispatch \u001b[0m\u001b[1m algo \u001b[0m\u001b[1m srand \u001b[0m\u001b[1m ret \u001b[0m\u001b[1m numevals \u001b[0m\u001b[1m lco\u001b[0m ⋯\n", + " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Flo\u001b[0m ⋯\n", + "─────┴──────────────────────────────────────────────────────────────────────────\n", + "\u001b[36m 10 columns omitted\u001b[0m" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = DataFrame( fc_elyz_capex=Float64[],shed_max=Float64[],dispatch=Int64[],algo=String[],\n", + " srand=Int[], ret=String[],numevals=Int64[],\n", + " lcoe_opt=Float64[], shed_rate_opt=Float64[], capex=Float64[],opex=Float64[],\n", + " xopt_sto=Float64[], xopt_pv=Float64[], xopt_wind=Float64[] ,xopt_elyz=Float64[], \n", + " xopt_fc=Float64[], xopt_hyTank=Float64[]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9f5dbe3-4795-4411-8ecc-06443e479ff9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "afb25a12-2269-4c22-9f7a-3ae5680bb956", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6×4 Matrix{Float64}:\n", + " 28.1193 28.1193 8.05818 12.5224\n", + " 15.5399 15.5399 5.9157 8.94308\n", + " 0.309005 0.309005 0.58717 0.394798\n", + " 1.5135 1.5135 0.0675057 6.99305\n", + " 0.952097 0.952097 1.66812 1.71168\n", + " 5.21002 5.21002 5.40184 14.3223" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"uniformly random vector between xmin and xmax, for a given PRNG seed\"\"\"\n", + "function x0_rng(seed, xmin, xmax)\n", + " n = length(xmin)\n", + " rng = MersenneTwister(seed)\n", + " xmin + rand(rng, n).*(xmax-xmin)\n", + "end\n", + "# Test with 3 different seeds:\n", + "[x0_rng(0, xmin, xmax) x0_rng(0, xmin, xmax) x0_rng(1, xmin, xmax) x0_rng(2, xmin, xmax)]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "55334e07-879f-4379-9867-eab2499d35b2", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5-element Vector{Float64}:\n", + " 0.0\n", + " 1.0e-5\n", + " 0.0001\n", + " 0.001\n", + " 0.01" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nrand = 10\n", + "srand_list = collect(1:nrand)\n", + "algo_list = [:GN_CRS2_LM, :GN_ISRES, :LN_SBPLX]\n", + "shed_max_list = [0.,0.00001,0.0001, 0.001, 0.01]" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b7019192-4f8c-453e-8773-84baeb0ef5ad", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "6-element Vector{Float64}:\n", + " 8.058178420902282\n", + " 5.915698915242573\n", + " 0.587169874444634\n", + " 0.06750573373843591\n", + " 1.668124201891417\n", + " 5.4018408162693134" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " x0 = x0_rng(1, xmin, xmax)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "5dc551bc-c188-46f3-9b17-999e1eefdba8", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2-element Vector{Vector{Float64}}:\n", + " [400.0, 0.0, 1600.0, 500.0, 1600.0, 350.0, 1200.0, 3500.0]\n", + " [400.0, 0.0, 2200.0, 500.0, 2200.0, 350.0, 1200.0, 3500.0]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "capexlist" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "3cfcf179-78f9-46be-8198-70b9b4200fba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1, GN_CRS2_LM algo: XTOL_REACHED after 21888 iterations. 2, GN_CRS2_LM algo: XTOL_REACHED after 23035 iterations. 3, GN_CRS2_LM algo: XTOL_REACHED after 20841 iterations. 4, GN_CRS2_LM algo: XTOL_REACHED after 33104 iterations. 5, GN_CRS2_LM algo: XTOL_REACHED after 19458 iterations. 6, GN_CRS2_LM algo: XTOL_REACHED after 34149 iterations. 7, GN_CRS2_LM algo: XTOL_REACHED after 13242 iterations. 8, GN_CRS2_LM algo: XTOL_REACHED after 13759 iterations. 9, GN_CRS2_LM algo: XTOL_REACHED after 15504 iterations. 10, GN_CRS2_LM algo: XTOL_REACHED after 17949 iterations. 11, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 12, GN_ISRES algo: XTOL_REACHED after 66964 iterations. 13, GN_ISRES algo: XTOL_REACHED after 72958 iterations. 14, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 15, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 16, GN_ISRES algo: XTOL_REACHED after 67785 iterations. 17, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 18, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 19, GN_ISRES algo: XTOL_REACHED after 68181 iterations. 20, GN_ISRES algo: XTOL_REACHED after 73784 iterations. 21, LN_SBPLX algo: XTOL_REACHED after 863 iterations. 22, LN_SBPLX algo: XTOL_REACHED after 1757 iterations. 23, LN_SBPLX algo: XTOL_REACHED after 1019 iterations. 24, LN_SBPLX algo: XTOL_REACHED after 1007 iterations. 25, LN_SBPLX algo: XTOL_REACHED after 1199 iterations. 26, LN_SBPLX algo: XTOL_REACHED after 1941 iterations. 27, LN_SBPLX algo: XTOL_REACHED after 1247 iterations. 28, LN_SBPLX algo: XTOL_REACHED after 1383 iterations. 29, LN_SBPLX algo: XTOL_REACHED after 1706 iterations. 30, LN_SBPLX algo: XTOL_REACHED after 1086 iterations. 31, GN_CRS2_LM algo: XTOL_REACHED after 15356 iterations. 32, GN_CRS2_LM algo: XTOL_REACHED after 16166 iterations. 33, GN_CRS2_LM algo: XTOL_REACHED after 15653 iterations. 34, GN_CRS2_LM algo: XTOL_REACHED after 14559 iterations. 35, GN_CRS2_LM algo: XTOL_REACHED after 15257 iterations. 36, GN_CRS2_LM algo: XTOL_REACHED after 18828 iterations. 37, GN_CRS2_LM algo: XTOL_REACHED after 22965 iterations. 38, GN_CRS2_LM algo: XTOL_REACHED after 14908 iterations. 39, GN_CRS2_LM algo: XTOL_REACHED after 16116 iterations. 40, GN_CRS2_LM algo: XTOL_REACHED after 14225 iterations. 41, GN_ISRES algo: XTOL_REACHED after 69022 iterations. 42, GN_ISRES algo: XTOL_REACHED after 69871 iterations. 43, GN_ISRES algo: XTOL_REACHED after 65848 iterations. 44, GN_ISRES algo: XTOL_REACHED after 65225 iterations. 45, GN_ISRES algo: XTOL_REACHED after 64431 iterations. 46, GN_ISRES algo: XTOL_REACHED after 68925 iterations. 47, GN_ISRES algo: XTOL_REACHED after 63912 iterations. 48, GN_ISRES algo: XTOL_REACHED after 62425 iterations. 49, GN_ISRES algo: XTOL_REACHED after 66683 iterations. 50, GN_ISRES algo: XTOL_REACHED after 67153 iterations. 51, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 52, LN_SBPLX algo: XTOL_REACHED after 2583 iterations. 53, LN_SBPLX algo: XTOL_REACHED after 790 iterations. 54, LN_SBPLX algo: XTOL_REACHED after 1682 iterations. 55, LN_SBPLX algo: XTOL_REACHED after 1009 iterations. 56, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 57, LN_SBPLX algo: XTOL_REACHED after 402 iterations. 58, LN_SBPLX algo: XTOL_REACHED after 2070 iterations. 59, LN_SBPLX algo: XTOL_REACHED after 1617 iterations. 60, LN_SBPLX algo: XTOL_REACHED after 694 iterations. 61, GN_CRS2_LM algo: XTOL_REACHED after 27534 iterations. 62, GN_CRS2_LM algo: XTOL_REACHED after 33577 iterations. 63, GN_CRS2_LM algo: XTOL_REACHED after 52262 iterations. 64, GN_CRS2_LM algo: XTOL_REACHED after 27103 iterations. 65, GN_CRS2_LM algo: XTOL_REACHED after 26649 iterations. 66, GN_CRS2_LM algo: XTOL_REACHED after 31949 iterations. 67, GN_CRS2_LM algo: XTOL_REACHED after 22780 iterations. 68, GN_CRS2_LM algo: XTOL_REACHED after 55790 iterations. 69, GN_CRS2_LM algo: XTOL_REACHED after 35955 iterations. 70, GN_CRS2_LM algo: XTOL_REACHED after 20555 iterations. 71, GN_ISRES algo: XTOL_REACHED after 73502 iterations. 72, GN_ISRES algo: XTOL_REACHED after 70926 iterations. 73, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 74, GN_ISRES algo: XTOL_REACHED after 69016 iterations. 75, GN_ISRES algo: XTOL_REACHED after 74690 iterations. 76, GN_ISRES algo: XTOL_REACHED after 71783 iterations. 77, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 78, GN_ISRES algo: XTOL_REACHED after 71600 iterations. 79, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 80, GN_ISRES algo: XTOL_REACHED after 70274 iterations. 81, LN_SBPLX algo: XTOL_REACHED after 705 iterations. 82, LN_SBPLX algo: XTOL_REACHED after 766 iterations. 83, LN_SBPLX algo: XTOL_REACHED after 859 iterations. 84, LN_SBPLX algo: XTOL_REACHED after 836 iterations. 85, LN_SBPLX algo: XTOL_REACHED after 898 iterations. 86, LN_SBPLX algo: XTOL_REACHED after 496 iterations. 87, LN_SBPLX algo: XTOL_REACHED after 1241 iterations. 88, LN_SBPLX algo: XTOL_REACHED after 1173 iterations. 89, LN_SBPLX algo: XTOL_REACHED after 2103 iterations. 90, LN_SBPLX algo: XTOL_REACHED after 1223 iterations. 91, GN_CRS2_LM algo: XTOL_REACHED after 16955 iterations. 92, GN_CRS2_LM algo: XTOL_REACHED after 17990 iterations. 93, GN_CRS2_LM algo: XTOL_REACHED after 22652 iterations. 94, GN_CRS2_LM algo: XTOL_REACHED after 16653 iterations. 95, GN_CRS2_LM algo: XTOL_REACHED after 18456 iterations. 96, GN_CRS2_LM algo: XTOL_REACHED after 17265 iterations. 97, GN_CRS2_LM algo: XTOL_REACHED after 15905 iterations. 98, GN_CRS2_LM algo: XTOL_REACHED after 16731 iterations. 99, GN_CRS2_LM algo: XTOL_REACHED after 19484 iterations. 100, GN_CRS2_LM algo: XTOL_REACHED after 14744 iterations. 101, GN_ISRES algo: XTOL_REACHED after 68648 iterations. 102, GN_ISRES algo: XTOL_REACHED after 65245 iterations. 103, GN_ISRES algo: XTOL_REACHED after 75041 iterations. 104, GN_ISRES algo: XTOL_REACHED after 69922 iterations. 105, GN_ISRES algo: XTOL_REACHED after 72519 iterations. 106, GN_ISRES algo: XTOL_REACHED after 88361 iterations. 107, GN_ISRES algo: XTOL_REACHED after 70582 iterations. 108, GN_ISRES algo: XTOL_REACHED after 70321 iterations. 109, GN_ISRES algo: XTOL_REACHED after 67986 iterations. 110, GN_ISRES algo: XTOL_REACHED after 72993 iterations. 111, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 112, LN_SBPLX algo: XTOL_REACHED after 1085 iterations. 113, LN_SBPLX algo: XTOL_REACHED after 791 iterations. 114, LN_SBPLX algo: XTOL_REACHED after 1449 iterations. 115, LN_SBPLX algo: XTOL_REACHED after 671 iterations. 116, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 117, LN_SBPLX algo: XTOL_REACHED after 1214 iterations. 118, LN_SBPLX algo: XTOL_REACHED after 1540 iterations. 119, LN_SBPLX algo: XTOL_REACHED after 2885 iterations. 120, LN_SBPLX algo: XTOL_REACHED after 1515 iterations. 121, GN_CRS2_LM algo: XTOL_REACHED after 27045 iterations. 122, GN_CRS2_LM algo: XTOL_REACHED after 18078 iterations. 123, GN_CRS2_LM algo: XTOL_REACHED after 53169 iterations. 124, GN_CRS2_LM algo: XTOL_REACHED after 17783 iterations. 125, GN_CRS2_LM algo: XTOL_REACHED after 25340 iterations. 126, GN_CRS2_LM algo: XTOL_REACHED after 50245 iterations. 127, GN_CRS2_LM algo: XTOL_REACHED after 24327 iterations. 128, GN_CRS2_LM algo: XTOL_REACHED after 40327 iterations. 129, GN_CRS2_LM algo: XTOL_REACHED after 25819 iterations. 130, GN_CRS2_LM algo: XTOL_REACHED after 22809 iterations. 131, GN_ISRES algo: XTOL_REACHED after 68260 iterations. 132, GN_ISRES algo: XTOL_REACHED after 70554 iterations. 133, GN_ISRES algo: XTOL_REACHED after 69795 iterations. 134, GN_ISRES algo: XTOL_REACHED after 78437 iterations. 135, GN_ISRES algo: XTOL_REACHED after 69303 iterations. 136, GN_ISRES algo: XTOL_REACHED after 70090 iterations. 137, GN_ISRES algo: XTOL_REACHED after 71151 iterations. 138, GN_ISRES algo: XTOL_REACHED after 73061 iterations. 139, GN_ISRES algo: XTOL_REACHED after 65846 iterations. 140, GN_ISRES algo: XTOL_REACHED after 69739 iterations. 141, LN_SBPLX algo: XTOL_REACHED after 1196 iterations. 142, LN_SBPLX algo: XTOL_REACHED after 1254 iterations. 143, LN_SBPLX algo: XTOL_REACHED after 981 iterations. 144, LN_SBPLX algo: XTOL_REACHED after 1088 iterations. 145, LN_SBPLX algo: XTOL_REACHED after 1541 iterations. 146, LN_SBPLX algo: XTOL_REACHED after 2116 iterations. 147, LN_SBPLX algo: XTOL_REACHED after 1274 iterations. 148, LN_SBPLX algo: XTOL_REACHED after 967 iterations. 149, LN_SBPLX algo: XTOL_REACHED after 1285 iterations. 150, LN_SBPLX algo: XTOL_REACHED after 721 iterations. 151, GN_CRS2_LM algo: XTOL_REACHED after 23526 iterations. 152, GN_CRS2_LM algo: XTOL_REACHED after 23743 iterations. 153, GN_CRS2_LM algo: XTOL_REACHED after 26097 iterations. 154, GN_CRS2_LM algo: XTOL_REACHED after 28478 iterations. 155, GN_CRS2_LM algo: XTOL_REACHED after 27480 iterations. 156, GN_CRS2_LM algo: XTOL_REACHED after 42015 iterations. 157, GN_CRS2_LM algo: XTOL_REACHED after 28229 iterations. 158, GN_CRS2_LM algo: XTOL_REACHED after 26720 iterations. 159, GN_CRS2_LM algo: XTOL_REACHED after 22279 iterations. 160, GN_CRS2_LM algo: XTOL_REACHED after 41152 iterations. 161, GN_ISRES algo: XTOL_REACHED after 75301 iterations. 162, GN_ISRES algo: XTOL_REACHED after 64747 iterations. 163, GN_ISRES algo: XTOL_REACHED after 65022 iterations. 164, GN_ISRES algo: XTOL_REACHED after 63800 iterations. 165, GN_ISRES algo: XTOL_REACHED after 71515 iterations. 166, GN_ISRES algo: XTOL_REACHED after 70722 iterations. 167, GN_ISRES algo: XTOL_REACHED after 67666 iterations. 168, GN_ISRES algo: XTOL_REACHED after 70520 iterations. 169, GN_ISRES algo: XTOL_REACHED after 74930 iterations. 170, GN_ISRES algo: XTOL_REACHED after 63631 iterations. 171, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 172, LN_SBPLX algo: XTOL_REACHED after 2043 iterations. 173, LN_SBPLX algo: XTOL_REACHED after 907 iterations. 174, LN_SBPLX algo: XTOL_REACHED after 1656 iterations. 175, LN_SBPLX algo: XTOL_REACHED after 1460 iterations. 176, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 177, LN_SBPLX algo: XTOL_REACHED after 408 iterations. 178, LN_SBPLX algo: XTOL_REACHED after 1114 iterations. 179, LN_SBPLX algo: XTOL_REACHED after 1941 iterations. 180, LN_SBPLX algo: XTOL_REACHED after 734 iterations. 181, GN_CRS2_LM algo: XTOL_REACHED after 16538 iterations. 182, GN_CRS2_LM algo: XTOL_REACHED after 23842 iterations. 183, GN_CRS2_LM algo: XTOL_REACHED after 14490 iterations. 184, GN_CRS2_LM algo: XTOL_REACHED after 20208 iterations. 185, GN_CRS2_LM algo: XTOL_REACHED after 28194 iterations. 186, GN_CRS2_LM algo: XTOL_REACHED after 22852 iterations. 187, GN_CRS2_LM algo: XTOL_REACHED after 19966 iterations. 188, GN_CRS2_LM algo: XTOL_REACHED after 20662 iterations. 189, GN_CRS2_LM algo: XTOL_REACHED after 20560 iterations. 190, GN_CRS2_LM algo: XTOL_REACHED after 19650 iterations. 191, GN_ISRES algo: XTOL_REACHED after 64851 iterations. 192, GN_ISRES algo: XTOL_REACHED after 66709 iterations. 193, GN_ISRES algo: XTOL_REACHED after 73943 iterations. 194, GN_ISRES algo: XTOL_REACHED after 68454 iterations. 195, GN_ISRES algo: XTOL_REACHED after 75000 iterations. 196, GN_ISRES algo: XTOL_REACHED after 60686 iterations. 197, GN_ISRES algo: XTOL_REACHED after 72941 iterations. 198, GN_ISRES algo: XTOL_REACHED after 75061 iterations. 199, GN_ISRES algo: XTOL_REACHED after 73249 iterations. 200, GN_ISRES algo: XTOL_REACHED after 70289 iterations. 201, LN_SBPLX algo: XTOL_REACHED after 1781 iterations. 202, LN_SBPLX algo: XTOL_REACHED after 2292 iterations. 203, LN_SBPLX algo: XTOL_REACHED after 921 iterations. 204, LN_SBPLX algo: XTOL_REACHED after 858 iterations. 205, LN_SBPLX algo: XTOL_REACHED after 1450 iterations. 206, LN_SBPLX algo: XTOL_REACHED after 837 iterations. 207, LN_SBPLX algo: XTOL_REACHED after 1961 iterations. 208, LN_SBPLX algo: XTOL_REACHED after 2656 iterations. 209, LN_SBPLX algo: XTOL_REACHED after 2038 iterations. 210, LN_SBPLX algo: XTOL_REACHED after 773 iterations. 211, GN_CRS2_LM algo: XTOL_REACHED after 23358 iterations. 212, GN_CRS2_LM algo: XTOL_REACHED after 20003 iterations. 213, GN_CRS2_LM algo: XTOL_REACHED after 18591 iterations. 214, GN_CRS2_LM algo: XTOL_REACHED after 24654 iterations. 215, GN_CRS2_LM algo: XTOL_REACHED after 21259 iterations. 216, GN_CRS2_LM algo: XTOL_REACHED after 24244 iterations. 217, GN_CRS2_LM algo: XTOL_REACHED after 19315 iterations. 218, GN_CRS2_LM algo: XTOL_REACHED after 24602 iterations. 219, GN_CRS2_LM algo: XTOL_REACHED after 19726 iterations. 220, GN_CRS2_LM algo: XTOL_REACHED after 22409 iterations. 221, GN_ISRES algo: XTOL_REACHED after 65438 iterations. 222, GN_ISRES algo: XTOL_REACHED after 69035 iterations. 223, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 224, GN_ISRES algo: XTOL_REACHED after 61265 iterations. 225, GN_ISRES algo: XTOL_REACHED after 64950 iterations. 226, GN_ISRES algo: XTOL_REACHED after 68113 iterations. 227, GN_ISRES algo: XTOL_REACHED after 70704 iterations. 228, GN_ISRES algo: XTOL_REACHED after 67190 iterations. 229, GN_ISRES algo: XTOL_REACHED after 61612 iterations. 230, GN_ISRES algo: XTOL_REACHED after 73379 iterations. 231, LN_SBPLX algo: XTOL_REACHED after 510 iterations. 232, LN_SBPLX algo: XTOL_REACHED after 1710 iterations. 233, LN_SBPLX algo: XTOL_REACHED after 727 iterations. 234, LN_SBPLX algo: XTOL_REACHED after 1886 iterations. 235, LN_SBPLX algo: XTOL_REACHED after 1323 iterations. 236, LN_SBPLX algo: XTOL_REACHED after 515 iterations. 237, LN_SBPLX algo: XTOL_REACHED after 6116 iterations. 238, LN_SBPLX algo: XTOL_REACHED after 1166 iterations. 239, LN_SBPLX algo: XTOL_REACHED after 1617 iterations. 240, LN_SBPLX algo: XTOL_REACHED after 607 iterations. 241, GN_CRS2_LM algo: XTOL_REACHED after 24710 iterations. 242, GN_CRS2_LM algo: XTOL_REACHED after 31916 iterations. 243, GN_CRS2_LM algo: XTOL_REACHED after 26202 iterations. 244, GN_CRS2_LM algo: XTOL_REACHED after 24525 iterations. 245, GN_CRS2_LM algo: XTOL_REACHED after 29903 iterations. 246, GN_CRS2_LM algo: XTOL_REACHED after 20729 iterations. 247, GN_CRS2_LM algo: XTOL_REACHED after 24564 iterations. 248, GN_CRS2_LM algo: XTOL_REACHED after 23860 iterations. 249, GN_CRS2_LM algo: XTOL_REACHED after 21809 iterations. 250, GN_CRS2_LM algo: XTOL_REACHED after 22572 iterations. 251, GN_ISRES algo: XTOL_REACHED after 96515 iterations. 252, GN_ISRES algo: XTOL_REACHED after 78342 iterations. 253, GN_ISRES algo: XTOL_REACHED after 80328 iterations. 254, GN_ISRES algo: XTOL_REACHED after 87769 iterations. 255, GN_ISRES algo: XTOL_REACHED after 79259 iterations. 256, GN_ISRES algo: XTOL_REACHED after 77745 iterations. 257, GN_ISRES algo: XTOL_REACHED after 80064 iterations. 258, GN_ISRES algo: XTOL_REACHED after 76440 iterations. 259, GN_ISRES algo: XTOL_REACHED after 81831 iterations. 260, GN_ISRES algo: XTOL_REACHED after 78199 iterations. 261, LN_SBPLX algo: XTOL_REACHED after 3840 iterations. 262, LN_SBPLX algo: XTOL_REACHED after 1602 iterations. 263, LN_SBPLX algo: XTOL_REACHED after 1093 iterations. 264, LN_SBPLX algo: XTOL_REACHED after 2075 iterations. 265, LN_SBPLX algo: XTOL_REACHED after 2850 iterations. 266, LN_SBPLX algo: XTOL_REACHED after 1153 iterations. 267, LN_SBPLX algo: XTOL_REACHED after 3279 iterations. 268, LN_SBPLX algo: XTOL_REACHED after 668 iterations. 269, LN_SBPLX algo: XTOL_REACHED after 4280 iterations. 270, LN_SBPLX algo: XTOL_REACHED after 2148 iterations. 271, GN_CRS2_LM algo: XTOL_REACHED after 40941 iterations. 272, GN_CRS2_LM algo: XTOL_REACHED after 24908 iterations. 273, GN_CRS2_LM algo: XTOL_REACHED after 26502 iterations. 274, GN_CRS2_LM algo: XTOL_REACHED after 31417 iterations. 275, GN_CRS2_LM algo: XTOL_REACHED after 23586 iterations. 276, GN_CRS2_LM algo: XTOL_REACHED after 24466 iterations. 277, GN_CRS2_LM algo: XTOL_REACHED after 21057 iterations. 278, GN_CRS2_LM algo: XTOL_REACHED after 33134 iterations. 279, GN_CRS2_LM algo: XTOL_REACHED after 23282 iterations. 280, GN_CRS2_LM algo: XTOL_REACHED after 21436 iterations. 281, GN_ISRES algo: XTOL_REACHED after 72081 iterations. 282, GN_ISRES algo: XTOL_REACHED after 68744 iterations. 283, GN_ISRES algo: XTOL_REACHED after 70963 iterations. 284, GN_ISRES algo: XTOL_REACHED after 72601 iterations. 285, GN_ISRES algo: XTOL_REACHED after 69032 iterations. 286, GN_ISRES algo: XTOL_REACHED after 84002 iterations. 287, GN_ISRES algo: XTOL_REACHED after 72183 iterations. 288, GN_ISRES algo: XTOL_REACHED after 66216 iterations. 289, GN_ISRES algo: XTOL_REACHED after 78353 iterations. 290, GN_ISRES algo: XTOL_REACHED after 70123 iterations. 291, LN_SBPLX algo: XTOL_REACHED after 1719 iterations. 292, LN_SBPLX algo: XTOL_REACHED after 1319 iterations. 293, LN_SBPLX algo: XTOL_REACHED after 1548 iterations. 294, LN_SBPLX algo: XTOL_REACHED after 1991 iterations. 295, LN_SBPLX algo: XTOL_REACHED after 1492 iterations. 296, LN_SBPLX algo: XTOL_REACHED after 1098 iterations. 297, LN_SBPLX algo: XTOL_REACHED after 2402 iterations. 298, LN_SBPLX algo: XTOL_REACHED after 949 iterations. 299, LN_SBPLX algo: XTOL_REACHED after 1252 iterations. 300, LN_SBPLX algo: XTOL_REACHED after 1400 iterations. 301, GN_CRS2_LM algo: XTOL_REACHED after 22606 iterations. 302, GN_CRS2_LM algo: XTOL_REACHED after 26310 iterations. 303, GN_CRS2_LM algo: XTOL_REACHED after 38425 iterations. 304, GN_CRS2_LM algo: XTOL_REACHED after 21306 iterations. 305, GN_CRS2_LM algo: XTOL_REACHED after 24840 iterations. 306, GN_CRS2_LM algo: XTOL_REACHED after 41424 iterations. 307, GN_CRS2_LM algo: XTOL_REACHED after 27892 iterations. 308, GN_CRS2_LM algo: XTOL_REACHED after 18117 iterations. 309, GN_CRS2_LM algo: XTOL_REACHED after 45052 iterations. 310, GN_CRS2_LM algo: XTOL_REACHED after 20077 iterations. 311, GN_ISRES algo: XTOL_REACHED after 74931 iterations. 312, GN_ISRES algo: XTOL_REACHED after 65689 iterations. 313, GN_ISRES algo: XTOL_REACHED after 74093 iterations. 314, GN_ISRES algo: XTOL_REACHED after 67892 iterations. 315, GN_ISRES algo: XTOL_REACHED after 71502 iterations. 316, GN_ISRES algo: XTOL_REACHED after 70363 iterations. 317, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 318, GN_ISRES algo: XTOL_REACHED after 68820 iterations. 319, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 320, GN_ISRES algo: XTOL_REACHED after 70031 iterations. 321, LN_SBPLX algo: XTOL_REACHED after 892 iterations. 322, LN_SBPLX algo: XTOL_REACHED after 1189 iterations. 323, LN_SBPLX algo: XTOL_REACHED after 928 iterations. 324, LN_SBPLX algo: XTOL_REACHED after 1113 iterations. 325, LN_SBPLX algo: XTOL_REACHED after 1157 iterations. 326, LN_SBPLX algo: XTOL_REACHED after 1591 iterations. 327, LN_SBPLX algo: XTOL_REACHED after 1005 iterations. 328, LN_SBPLX algo: XTOL_REACHED after 1885 iterations. 329, LN_SBPLX algo: XTOL_REACHED after 936 iterations. 330, LN_SBPLX algo: XTOL_REACHED after 1441 iterations. 331, GN_CRS2_LM algo: XTOL_REACHED after 14473 iterations. 332, GN_CRS2_LM algo: XTOL_REACHED after 16479 iterations. 333, GN_CRS2_LM algo: XTOL_REACHED after 14267 iterations. 334, GN_CRS2_LM algo: XTOL_REACHED after 14175 iterations. 335, GN_CRS2_LM algo: XTOL_REACHED after 13845 iterations. 336, GN_CRS2_LM algo: XTOL_REACHED after 12397 iterations. 337, GN_CRS2_LM algo: XTOL_REACHED after 15777 iterations. 338, GN_CRS2_LM algo: XTOL_REACHED after 13851 iterations. 339, GN_CRS2_LM algo: XTOL_REACHED after 14228 iterations. 340, GN_CRS2_LM algo: XTOL_REACHED after 14237 iterations. 341, GN_ISRES algo: XTOL_REACHED after 70658 iterations. 342, GN_ISRES algo: XTOL_REACHED after 74822 iterations. 343, GN_ISRES algo: XTOL_REACHED after 68917 iterations. 344, GN_ISRES algo: XTOL_REACHED after 71369 iterations. 345, GN_ISRES algo: XTOL_REACHED after 65860 iterations. 346, GN_ISRES algo: XTOL_REACHED after 74551 iterations. 347, GN_ISRES algo: XTOL_REACHED after 63153 iterations. 348, GN_ISRES algo: XTOL_REACHED after 67124 iterations. 349, GN_ISRES algo: XTOL_REACHED after 66322 iterations. 350, GN_ISRES algo: XTOL_REACHED after 73525 iterations. 351, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 352, LN_SBPLX algo: XTOL_REACHED after 2055 iterations. 353, LN_SBPLX algo: XTOL_REACHED after 1083 iterations. 354, LN_SBPLX algo: XTOL_REACHED after 2022 iterations. 355, LN_SBPLX algo: XTOL_REACHED after 896 iterations. 356, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 357, LN_SBPLX algo: XTOL_REACHED after 1404 iterations. 358, LN_SBPLX algo: XTOL_REACHED after 1088 iterations. 359, LN_SBPLX algo: XTOL_REACHED after 1592 iterations. 360, LN_SBPLX algo: XTOL_REACHED after 838 iterations. 361, GN_CRS2_LM algo: XTOL_REACHED after 19633 iterations. 362, GN_CRS2_LM algo: XTOL_REACHED after 27915 iterations. 363, GN_CRS2_LM algo: XTOL_REACHED after 21443 iterations. 364, GN_CRS2_LM algo: XTOL_REACHED after 31081 iterations. 365, GN_CRS2_LM algo: XTOL_REACHED after 34474 iterations. 366, GN_CRS2_LM algo: XTOL_REACHED after 20944 iterations. 367, GN_CRS2_LM algo: XTOL_REACHED after 68829 iterations. 368, GN_CRS2_LM algo: XTOL_REACHED after 35717 iterations. 369, GN_CRS2_LM algo: XTOL_REACHED after 41410 iterations. 370, GN_CRS2_LM algo: MAXEVAL_REACHED after 100000 iterations. 371, GN_ISRES algo: XTOL_REACHED after 77464 iterations. 372, GN_ISRES algo: XTOL_REACHED after 70600 iterations. 373, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 374, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 375, GN_ISRES algo: XTOL_REACHED after 74468 iterations. 376, GN_ISRES algo: XTOL_REACHED after 69560 iterations. 377, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 378, GN_ISRES algo: XTOL_REACHED after 66558 iterations. 379, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 380, GN_ISRES algo: XTOL_REACHED after 75767 iterations. 381, LN_SBPLX algo: XTOL_REACHED after 852 iterations. 382, LN_SBPLX algo: XTOL_REACHED after 1325 iterations. 383, LN_SBPLX algo: XTOL_REACHED after 953 iterations. 384, LN_SBPLX algo: XTOL_REACHED after 758 iterations. 385, LN_SBPLX algo: XTOL_REACHED after 1489 iterations. 386, LN_SBPLX algo: XTOL_REACHED after 688 iterations. 387, LN_SBPLX algo: XTOL_REACHED after 1359 iterations. 388, LN_SBPLX algo: XTOL_REACHED after 1288 iterations. 389, LN_SBPLX algo: XTOL_REACHED after 1745 iterations. 390, LN_SBPLX algo: XTOL_REACHED after 1908 iterations. 391, GN_CRS2_LM algo: XTOL_REACHED after 15341 iterations. 392, GN_CRS2_LM algo: XTOL_REACHED after 14264 iterations. 393, GN_CRS2_LM algo: XTOL_REACHED after 14601 iterations. 394, GN_CRS2_LM algo: XTOL_REACHED after 15980 iterations. 395, GN_CRS2_LM algo: XTOL_REACHED after 15569 iterations. 396, GN_CRS2_LM algo: XTOL_REACHED after 13733 iterations. 397, GN_CRS2_LM algo: XTOL_REACHED after 15537 iterations. 398, GN_CRS2_LM algo: XTOL_REACHED after 14136 iterations. 399, GN_CRS2_LM algo: XTOL_REACHED after 15655 iterations. 400, GN_CRS2_LM algo: XTOL_REACHED after 13799 iterations. 401, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 402, GN_ISRES algo: XTOL_REACHED after 66165 iterations. 403, GN_ISRES algo: XTOL_REACHED after 72708 iterations. 404, GN_ISRES algo: XTOL_REACHED after 68931 iterations. 405, GN_ISRES algo: XTOL_REACHED after 70705 iterations. 406, GN_ISRES algo: XTOL_REACHED after 70794 iterations. 407, GN_ISRES algo: XTOL_REACHED after 62430 iterations. 408, GN_ISRES algo: XTOL_REACHED after 68191 iterations. 409, GN_ISRES algo: XTOL_REACHED after 63479 iterations. 410, GN_ISRES algo: XTOL_REACHED after 77859 iterations. 411, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 412, LN_SBPLX algo: XTOL_REACHED after 1026 iterations. 413, LN_SBPLX algo: XTOL_REACHED after 850 iterations. 414, LN_SBPLX algo: XTOL_REACHED after 1349 iterations. 415, LN_SBPLX algo: XTOL_REACHED after 2010 iterations. 416, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 417, LN_SBPLX algo: XTOL_REACHED after 1754 iterations. 418, LN_SBPLX algo: XTOL_REACHED after 2081 iterations. 419, LN_SBPLX algo: XTOL_REACHED after 1309 iterations. 420, LN_SBPLX algo: XTOL_REACHED after 842 iterations. 421, GN_CRS2_LM algo: XTOL_REACHED after 46935 iterations. 422, GN_CRS2_LM algo: XTOL_REACHED after 28426 iterations. 423, GN_CRS2_LM algo: XTOL_REACHED after 40841 iterations. 424, GN_CRS2_LM algo: XTOL_REACHED after 29448 iterations. 425, GN_CRS2_LM algo: XTOL_REACHED after 27575 iterations. 426, GN_CRS2_LM algo: XTOL_REACHED after 25576 iterations. 427, GN_CRS2_LM algo: XTOL_REACHED after 25562 iterations. 428, GN_CRS2_LM algo: XTOL_REACHED after 40021 iterations. 429, GN_CRS2_LM algo: XTOL_REACHED after 31471 iterations. 430, GN_CRS2_LM algo: XTOL_REACHED after 56004 iterations. 431, GN_ISRES algo: XTOL_REACHED after 70770 iterations. 432, GN_ISRES algo: XTOL_REACHED after 70792 iterations. 433, GN_ISRES algo: XTOL_REACHED after 70044 iterations. 434, GN_ISRES algo: XTOL_REACHED after 72570 iterations. 435, GN_ISRES algo: XTOL_REACHED after 69314 iterations. 436, GN_ISRES algo: XTOL_REACHED after 65103 iterations. 437, GN_ISRES algo: XTOL_REACHED after 68601 iterations. 438, GN_ISRES algo: XTOL_REACHED after 67645 iterations. 439, GN_ISRES algo: XTOL_REACHED after 73987 iterations. 440, GN_ISRES algo: XTOL_REACHED after 75779 iterations. 441, LN_SBPLX algo: XTOL_REACHED after 1018 iterations. 442, LN_SBPLX algo: XTOL_REACHED after 1030 iterations. 443, LN_SBPLX algo: XTOL_REACHED after 856 iterations. 444, LN_SBPLX algo: XTOL_REACHED after 775 iterations. 445, LN_SBPLX algo: XTOL_REACHED after 1914 iterations. 446, LN_SBPLX algo: XTOL_REACHED after 1062 iterations. 447, LN_SBPLX algo: XTOL_REACHED after 1697 iterations. 448, LN_SBPLX algo: XTOL_REACHED after 1425 iterations. 449, LN_SBPLX algo: XTOL_REACHED after 1378 iterations. 450, LN_SBPLX algo: XTOL_REACHED after 1181 iterations. 451, GN_CRS2_LM algo: XTOL_REACHED after 19160 iterations. 452, GN_CRS2_LM algo: XTOL_REACHED after 22517 iterations. 453, GN_CRS2_LM algo: XTOL_REACHED after 17847 iterations. 454, GN_CRS2_LM algo: XTOL_REACHED after 21926 iterations. 455, GN_CRS2_LM algo: XTOL_REACHED after 18903 iterations. 456, GN_CRS2_LM algo: XTOL_REACHED after 17194 iterations. 457, GN_CRS2_LM algo: XTOL_REACHED after 15868 iterations. 458, GN_CRS2_LM algo: XTOL_REACHED after 19586 iterations. 459, GN_CRS2_LM algo: XTOL_REACHED after 23611 iterations. 460, GN_CRS2_LM algo: XTOL_REACHED after 17792 iterations. 461, GN_ISRES algo: XTOL_REACHED after 68614 iterations. 462, GN_ISRES algo: XTOL_REACHED after 67463 iterations. 463, GN_ISRES algo: XTOL_REACHED after 72160 iterations. 464, GN_ISRES algo: XTOL_REACHED after 73615 iterations. 465, GN_ISRES algo: XTOL_REACHED after 66520 iterations. 466, GN_ISRES algo: XTOL_REACHED after 69112 iterations. 467, GN_ISRES algo: XTOL_REACHED after 59609 iterations. 468, GN_ISRES algo: XTOL_REACHED after 64054 iterations. 469, GN_ISRES algo: XTOL_REACHED after 66795 iterations. 470, GN_ISRES algo: XTOL_REACHED after 70159 iterations. 471, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 472, LN_SBPLX algo: XTOL_REACHED after 1383 iterations. 473, LN_SBPLX algo: XTOL_REACHED after 874 iterations. 474, LN_SBPLX algo: XTOL_REACHED after 1701 iterations. 475, LN_SBPLX algo: XTOL_REACHED after 2095 iterations. 476, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 477, LN_SBPLX algo: XTOL_REACHED after 1648 iterations. 478, LN_SBPLX algo: XTOL_REACHED after 865 iterations. 479, LN_SBPLX algo: XTOL_REACHED after 1246 iterations. 480, LN_SBPLX algo: XTOL_REACHED after 810 iterations. 481, GN_CRS2_LM algo: XTOL_REACHED after 22240 iterations. 482, GN_CRS2_LM algo: XTOL_REACHED after 22494 iterations. 483, GN_CRS2_LM algo: XTOL_REACHED after 19766 iterations. 484, GN_CRS2_LM algo: XTOL_REACHED after 22813 iterations. 485, GN_CRS2_LM algo: XTOL_REACHED after 23353 iterations. 486, GN_CRS2_LM algo: XTOL_REACHED after 24616 iterations. 487, GN_CRS2_LM algo: XTOL_REACHED after 23935 iterations. 488, GN_CRS2_LM algo: XTOL_REACHED after 21075 iterations. 489, GN_CRS2_LM algo: XTOL_REACHED after 23847 iterations. 490, GN_CRS2_LM algo: XTOL_REACHED after 18241 iterations. 491, GN_ISRES algo: XTOL_REACHED after 68228 iterations. 492, GN_ISRES algo: XTOL_REACHED after 73350 iterations. 493, GN_ISRES algo: XTOL_REACHED after 82609 iterations. 494, GN_ISRES algo: XTOL_REACHED after 70788 iterations. 495, GN_ISRES algo: XTOL_REACHED after 71202 iterations. 496, GN_ISRES algo: XTOL_REACHED after 76069 iterations. 497, GN_ISRES algo: XTOL_REACHED after 75524 iterations. 498, GN_ISRES algo: XTOL_REACHED after 73479 iterations. 499, GN_ISRES algo: XTOL_REACHED after 69196 iterations. 500, GN_ISRES algo: XTOL_REACHED after 62904 iterations. 501, LN_SBPLX algo: XTOL_REACHED after 1034 iterations. 502, LN_SBPLX algo: XTOL_REACHED after 1075 iterations. 503, LN_SBPLX algo: XTOL_REACHED after 2276 iterations. 504, LN_SBPLX algo: XTOL_REACHED after 1477 iterations. 505, LN_SBPLX algo: XTOL_REACHED after 2399 iterations. 506, LN_SBPLX algo: XTOL_REACHED after 876 iterations. 507, LN_SBPLX algo: XTOL_REACHED after 3353 iterations. 508, LN_SBPLX algo: XTOL_REACHED after 1292 iterations. 509, LN_SBPLX algo: XTOL_REACHED after 1953 iterations. 510, LN_SBPLX algo: XTOL_REACHED after 955 iterations. 511, GN_CRS2_LM algo: XTOL_REACHED after 15408 iterations. 512, GN_CRS2_LM algo: XTOL_REACHED after 15288 iterations. 513, GN_CRS2_LM algo: XTOL_REACHED after 24561 iterations. 514, GN_CRS2_LM algo: XTOL_REACHED after 19171 iterations. 515, GN_CRS2_LM algo: XTOL_REACHED after 17644 iterations. 516, GN_CRS2_LM algo: XTOL_REACHED after 22012 iterations. 517, GN_CRS2_LM algo: XTOL_REACHED after 24766 iterations. 518, GN_CRS2_LM algo: XTOL_REACHED after 17903 iterations. 519, GN_CRS2_LM algo: XTOL_REACHED after 19550 iterations. 520, GN_CRS2_LM algo: XTOL_REACHED after 14556 iterations. 521, GN_ISRES algo: XTOL_REACHED after 70479 iterations. 522, GN_ISRES algo: XTOL_REACHED after 69016 iterations. 523, GN_ISRES algo: XTOL_REACHED after 66497 iterations. 524, GN_ISRES algo: XTOL_REACHED after 67782 iterations. 525, GN_ISRES algo: XTOL_REACHED after 64292 iterations. 526, GN_ISRES algo: XTOL_REACHED after 72103 iterations. 527, GN_ISRES algo: XTOL_REACHED after 61510 iterations. 528, GN_ISRES algo: XTOL_REACHED after 66862 iterations. 529, GN_ISRES algo: XTOL_REACHED after 75645 iterations. 530, GN_ISRES algo: XTOL_REACHED after 63460 iterations. 531, LN_SBPLX algo: XTOL_REACHED after 534 iterations. 532, LN_SBPLX algo: XTOL_REACHED after 1310 iterations. 533, LN_SBPLX algo: XTOL_REACHED after 768 iterations. 534, LN_SBPLX algo: XTOL_REACHED after 2290 iterations. 535, LN_SBPLX algo: XTOL_REACHED after 1159 iterations. 536, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 537, LN_SBPLX algo: XTOL_REACHED after 1399 iterations. 538, LN_SBPLX algo: XTOL_REACHED after 1398 iterations. 539, LN_SBPLX algo: XTOL_REACHED after 3156 iterations. 540, LN_SBPLX algo: XTOL_REACHED after 541 iterations. 541, GN_CRS2_LM algo: XTOL_REACHED after 27692 iterations. 542, GN_CRS2_LM algo: XTOL_REACHED after 61417 iterations. 543, GN_CRS2_LM algo: XTOL_REACHED after 24997 iterations. 544, GN_CRS2_LM algo: XTOL_REACHED after 32714 iterations. 545, GN_CRS2_LM algo: XTOL_REACHED after 27973 iterations. 546, GN_CRS2_LM algo: XTOL_REACHED after 24857 iterations. 547, GN_CRS2_LM algo: XTOL_REACHED after 25005 iterations. 548, GN_CRS2_LM algo: XTOL_REACHED after 46022 iterations. 549, GN_CRS2_LM algo: XTOL_REACHED after 21227 iterations. 550, GN_CRS2_LM algo: XTOL_REACHED after 28268 iterations. 551, GN_ISRES algo: XTOL_REACHED after 73545 iterations. 552, GN_ISRES algo: XTOL_REACHED after 74310 iterations. 553, GN_ISRES algo: XTOL_REACHED after 79551 iterations. 554, GN_ISRES algo: XTOL_REACHED after 94623 iterations. 555, GN_ISRES algo: XTOL_REACHED after 72923 iterations. 556, GN_ISRES algo: XTOL_REACHED after 86162 iterations. 557, GN_ISRES algo: XTOL_REACHED after 78704 iterations. 558, GN_ISRES algo: XTOL_REACHED after 88194 iterations. 559, GN_ISRES algo: XTOL_REACHED after 91274 iterations. 560, GN_ISRES algo: XTOL_REACHED after 74998 iterations. 561, LN_SBPLX algo: XTOL_REACHED after 1287 iterations. 562, LN_SBPLX algo: XTOL_REACHED after 1800 iterations. 563, LN_SBPLX algo: XTOL_REACHED after 1776 iterations. 564, LN_SBPLX algo: XTOL_REACHED after 1712 iterations. 565, LN_SBPLX algo: XTOL_REACHED after 2861 iterations. 566, LN_SBPLX algo: XTOL_REACHED after 1343 iterations. 567, LN_SBPLX algo: XTOL_REACHED after 3026 iterations. 568, LN_SBPLX algo: XTOL_REACHED after 616 iterations. 569, LN_SBPLX algo: XTOL_REACHED after 2644 iterations. 570, LN_SBPLX algo: XTOL_REACHED after 4020 iterations. 571, GN_CRS2_LM algo: XTOL_REACHED after 44998 iterations. 572, GN_CRS2_LM algo: XTOL_REACHED after 23092 iterations. 573, GN_CRS2_LM algo: XTOL_REACHED after 40531 iterations. 574, GN_CRS2_LM algo: XTOL_REACHED after 25857 iterations. 575, GN_CRS2_LM algo: XTOL_REACHED after 23137 iterations. 576, GN_CRS2_LM algo: XTOL_REACHED after 20374 iterations. 577, GN_CRS2_LM algo: XTOL_REACHED after 24680 iterations. 578, GN_CRS2_LM algo: XTOL_REACHED after 18143 iterations. 579, GN_CRS2_LM algo: XTOL_REACHED after 24696 iterations. 580, GN_CRS2_LM algo: XTOL_REACHED after 17921 iterations. 581, GN_ISRES algo: XTOL_REACHED after 69590 iterations. 582, GN_ISRES algo: XTOL_REACHED after 63389 iterations. 583, GN_ISRES algo: XTOL_REACHED after 65479 iterations. 584, GN_ISRES algo: XTOL_REACHED after 68875 iterations. 585, GN_ISRES algo: XTOL_REACHED after 77661 iterations. 586, GN_ISRES algo: XTOL_REACHED after 77780 iterations. 587, GN_ISRES algo: XTOL_REACHED after 72368 iterations. 588, GN_ISRES algo: XTOL_REACHED after 70138 iterations. 589, GN_ISRES algo: XTOL_REACHED after 68975 iterations. 590, GN_ISRES algo: XTOL_REACHED after 64701 iterations. 591, LN_SBPLX algo: XTOL_REACHED after 1365 iterations. 592, LN_SBPLX algo: XTOL_REACHED after 749 iterations. 593, LN_SBPLX algo: XTOL_REACHED after 819 iterations. 594, LN_SBPLX algo: XTOL_REACHED after 1666 iterations. 595, LN_SBPLX algo: XTOL_REACHED after 1693 iterations. 596, LN_SBPLX algo: XTOL_REACHED after 2365 iterations. 597, LN_SBPLX algo: XTOL_REACHED after 1816 iterations. 598, LN_SBPLX algo: XTOL_REACHED after 800 iterations. 599, LN_SBPLX algo: XTOL_REACHED after 1541 iterations. 600, LN_SBPLX algo: XTOL_REACHED after 1280 iterations." + ] + } + ], + "source": [ + "xtol_rel=1e-8\n", + "maxeval=100000\n", + "i=0\n", + "for capex in capexlist, shed in shed_max_list, dispatch in dispatchlist,algo in algo_list, srand in srand_list\n", + " x0 = x0_rng(srand, xmin, xmax)\n", + " xopt, ret, numevals = optim_mg(x0,capex, shed,dispatch, algo, maxeval,xtol_rel,srand)\n", + " lcoe_opt, shed_rate_opt,costs = obj_multi(xopt,capex,dispatch)\n", + " xopt = round.(xopt*1000; digits=2)\n", + " push!(df2,(capex[3],shed,findfirst(==(dispatch),dispatchlist),String(algo),srand,\n", + " String(ret),numevals,lcoe_opt,shed_rate_opt, costs.system.investment,costs.system.om, \n", + " xopt[1], xopt[2], xopt[3], xopt[4], xopt[5], xopt[6]\n", + " ))\n", + " i= i+1\n", + " @printf(\" %d, %s algo: %s after %d iterations.\",i, algo, ret, numevals)\n", + " \n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f315ecbf-790c-4a2c-a99e-ec3e0f85ca47", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "023269ee-048b-4d93-b02b-7950fba5148b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
600×17 DataFrame
575 rows omitted
Rowfc_elyz_capexshed_maxdispatchalgosrandretnumevalslcoe_optshed_rate_optcapexopexxopt_stoxopt_pvxopt_windxopt_elyzxopt_fcxopt_hyTank
Float64Float64Int64StringInt64StringInt64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
11600.00.01GN_CRS2_LM1XTOL_REACHED218880.5165050.03.54974e71.04221e730304.512227.81811.5976.75990.082732.21
21600.00.01GN_CRS2_LM2XTOL_REACHED230350.4946570.03.66674e79.1398e616979.516846.31251.93254.451398.05348.99
31600.00.01GN_CRS2_LM3XTOL_REACHED208410.5191260.03.72518e79.90446e623347.516995.41171.63145.151069.93671.42
41600.00.01GN_CRS2_LM4XTOL_REACHED331040.4287140.03.19499e77.85836e614406.912391.21482.9335.071398.06528.59
51600.00.01GN_CRS2_LM5XTOL_REACHED194580.5336250.03.77074e71.06257e724400.915913.21811.5961.05983.02513.25
61600.00.01GN_CRS2_LM6XTOL_REACHED341490.5191260.03.72522e79.90425e623347.516996.31171.27145.411069.873671.71
71600.00.01GN_CRS2_LM7XTOL_REACHED132420.5195430.03.72996e79.90014e623353.217070.01152.01157.621067.983687.44
81600.00.01GN_CRS2_LM8XTOL_REACHED137590.5195430.03.72996e79.90014e623353.217070.01152.01157.621067.983687.43
91600.00.01GN_CRS2_LM9XTOL_REACHED155040.5355480.03.78175e71.06989e724386.315853.31877.753.61983.02448.82
101600.00.01GN_CRS2_LM10XTOL_REACHED179490.5473050.03.83693e71.0981e726551.315782.51877.731.73983.02276.36
111600.00.01GN_ISRES1MAXEVAL_REACHED1000000.4878280.03.62623e79.20606e614760.116168.51652.6262.291428.74792.9
121600.00.01GN_ISRES2XTOL_REACHED669640.5212820.03.73204e71.00513e723316.316699.61338.22136.021077.493379.94
131600.00.01GN_ISRES3XTOL_REACHED729580.4857510.03.61593e79.18354e614328.616104.71688.17336.661398.04653.52
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
5892200.00.012GN_ISRES9XTOL_REACHED689750.3809590.012.74359e74.97881e62941.195880.711877.71173.11665.1715819.1
5902200.00.012GN_ISRES10XTOL_REACHED647010.3809470.012.74415e74.9743e62904.285882.441877.71174.09665.6615844.9
5912200.00.012LN_SBPLX1XTOL_REACHED13650.4855720.013.29395e79.69609e626156.316124.41038.70.00.00.0
5922200.00.012LN_SBPLX2XTOL_REACHED7490.493060.013.36263e79.78229e625669.217070.0959.450.00.00.0
5932200.00.012LN_SBPLX3XTOL_REACHED8190.6879230.015.17438e71.04343e719569.015522.91519.177744.03478.234102.88
5942200.00.012LN_SBPLX4XTOL_REACHED16660.3893010.012.8066e74.79278e6347.276181.091877.71608.57903.1215216.3
5952200.00.012LN_SBPLX5XTOL_REACHED16930.4651070.013.12498e79.68801e624387.312785.21877.70.00.00.0
5962200.00.012LN_SBPLX6XTOL_REACHED23650.4645340.013.10583e79.69861e625333.712349.71877.70.00.00.0
5972200.00.012LN_SBPLX7XTOL_REACHED18160.4746720.013.19262e79.61458e626636.814502.81257.160.00.00.0
5982200.00.012LN_SBPLX8XTOL_REACHED8000.4724080.013.44413e76.05422e6494.779221.421877.72337.442897.978601.11
5992200.00.012LN_SBPLX9XTOL_REACHED15410.4650050.013.10483e79.68347e625854.712404.71803.840.00.00.0
6002200.00.012LN_SBPLX10XTOL_REACHED12800.4858780.013.30676e79.69817e625400.116370.31066.620.00.00.0
" + ], + "text/latex": [ + "\\begin{tabular}{r|cccccccc}\n", + "\t& fc\\_elyz\\_capex & shed\\_max & dispatch & algo & srand & ret & numevals & \\\\\n", + "\t\\hline\n", + "\t& Float64 & Float64 & Int64 & String & Int64 & String & Int64 & \\\\\n", + "\t\\hline\n", + "\t1 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 1 & XTOL\\_REACHED & 21888 & $\\dots$ \\\\\n", + "\t2 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 2 & XTOL\\_REACHED & 23035 & $\\dots$ \\\\\n", + "\t3 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 3 & XTOL\\_REACHED & 20841 & $\\dots$ \\\\\n", + "\t4 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 4 & XTOL\\_REACHED & 33104 & $\\dots$ \\\\\n", + "\t5 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 5 & XTOL\\_REACHED & 19458 & $\\dots$ \\\\\n", + "\t6 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 6 & XTOL\\_REACHED & 34149 & $\\dots$ \\\\\n", + "\t7 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 7 & XTOL\\_REACHED & 13242 & $\\dots$ \\\\\n", + "\t8 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 8 & XTOL\\_REACHED & 13759 & $\\dots$ \\\\\n", + "\t9 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 9 & XTOL\\_REACHED & 15504 & $\\dots$ \\\\\n", + "\t10 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 10 & XTOL\\_REACHED & 17949 & $\\dots$ \\\\\n", + "\t11 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 1 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", + "\t12 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 2 & XTOL\\_REACHED & 66964 & $\\dots$ \\\\\n", + "\t13 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 3 & XTOL\\_REACHED & 72958 & $\\dots$ \\\\\n", + "\t14 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 4 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", + "\t15 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 5 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", + "\t16 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 6 & XTOL\\_REACHED & 67785 & $\\dots$ \\\\\n", + "\t17 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 7 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", + "\t18 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 8 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", + "\t19 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 9 & XTOL\\_REACHED & 68181 & $\\dots$ \\\\\n", + "\t20 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 10 & XTOL\\_REACHED & 73784 & $\\dots$ \\\\\n", + "\t21 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 1 & XTOL\\_REACHED & 863 & $\\dots$ \\\\\n", + "\t22 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 2 & XTOL\\_REACHED & 1757 & $\\dots$ \\\\\n", + "\t23 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 3 & XTOL\\_REACHED & 1019 & $\\dots$ \\\\\n", + "\t24 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 4 & XTOL\\_REACHED & 1007 & $\\dots$ \\\\\n", + "\t25 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 5 & XTOL\\_REACHED & 1199 & $\\dots$ \\\\\n", + "\t26 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 6 & XTOL\\_REACHED & 1941 & $\\dots$ \\\\\n", + "\t27 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 7 & XTOL\\_REACHED & 1247 & $\\dots$ \\\\\n", + "\t28 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 8 & XTOL\\_REACHED & 1383 & $\\dots$ \\\\\n", + "\t29 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 9 & XTOL\\_REACHED & 1706 & $\\dots$ \\\\\n", + "\t30 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 10 & XTOL\\_REACHED & 1086 & $\\dots$ \\\\\n", + "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\u001b[1m600×17 DataFrame\u001b[0m\n", + "\u001b[1m Row \u001b[0m│\u001b[1m fc_elyz_capex \u001b[0m\u001b[1m shed_max \u001b[0m\u001b[1m dispatch \u001b[0m\u001b[1m algo \u001b[0m\u001b[1m srand \u001b[0m\u001b[1m ret \u001b[0m\u001b[1m \u001b[0m ⋯\n", + " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m \u001b[0m ⋯\n", + "─────┼──────────────────────────────────────────────────────────────────────────\n", + " 1 │ 1600.0 0.0 1 GN_CRS2_LM 1 XTOL_REACHED ⋯\n", + " 2 │ 1600.0 0.0 1 GN_CRS2_LM 2 XTOL_REACHED\n", + " 3 │ 1600.0 0.0 1 GN_CRS2_LM 3 XTOL_REACHED\n", + " 4 │ 1600.0 0.0 1 GN_CRS2_LM 4 XTOL_REACHED\n", + " 5 │ 1600.0 0.0 1 GN_CRS2_LM 5 XTOL_REACHED ⋯\n", + " 6 │ 1600.0 0.0 1 GN_CRS2_LM 6 XTOL_REACHED\n", + " 7 │ 1600.0 0.0 1 GN_CRS2_LM 7 XTOL_REACHED\n", + " 8 │ 1600.0 0.0 1 GN_CRS2_LM 8 XTOL_REACHED\n", + " 9 │ 1600.0 0.0 1 GN_CRS2_LM 9 XTOL_REACHED ⋯\n", + " 10 │ 1600.0 0.0 1 GN_CRS2_LM 10 XTOL_REACHED\n", + " 11 │ 1600.0 0.0 1 GN_ISRES 1 MAXEVAL_REACHED\n", + " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", + " 591 │ 2200.0 0.01 2 LN_SBPLX 1 XTOL_REACHED\n", + " 592 │ 2200.0 0.01 2 LN_SBPLX 2 XTOL_REACHED ⋯\n", + " 593 │ 2200.0 0.01 2 LN_SBPLX 3 XTOL_REACHED\n", + " 594 │ 2200.0 0.01 2 LN_SBPLX 4 XTOL_REACHED\n", + " 595 │ 2200.0 0.01 2 LN_SBPLX 5 XTOL_REACHED\n", + " 596 │ 2200.0 0.01 2 LN_SBPLX 6 XTOL_REACHED ⋯\n", + " 597 │ 2200.0 0.01 2 LN_SBPLX 7 XTOL_REACHED\n", + " 598 │ 2200.0 0.01 2 LN_SBPLX 8 XTOL_REACHED\n", + " 599 │ 2200.0 0.01 2 LN_SBPLX 9 XTOL_REACHED\n", + " 600 │ 2200.0 0.01 2 LN_SBPLX 10 XTOL_REACHED ⋯\n", + "\u001b[36m 11 columns and 579 rows omitted\u001b[0m" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "9ea7de07-7fe7-45ff-acfa-681509ed243a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"df4_dataset.csv\"" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CSV.write(\"df4_dataset.csv\", df2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05b41e59-dd80-4d5b-b7b8-e3b00d570091", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df_gsrand = groupby(df2, [ :dispatch, :shed_max, :algo])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e0262e-a76e-47e0-99ad-50c5543876a2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "df_stats = combine(df_gsrand,\n", + " :lcoe_opt => minimum, :lcoe_opt => mean,:lcoe_opt=> maximum\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "59c0f2bc-269c-44c3-8bb9-7d5322fe0aac", + "metadata": { + "tags": [] + }, + 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}, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "function plot_oper_traj(mg, oper_traj)\n", + " ns=Int64(nsteps/730)\n", + " z=zeros(Float64,ns)\n", + "tm=zeros(Float64,ns +1)\n", + " tm[end]=nsteps/24\n", + " for i=1:ns\n", + " z[i]= oper_traj.LoH[i*730+1]\n", + " tm[i]=(i-1)*730/24 \n", + "end\n", + " fig,( ax1, ax2) = plt.subplots(2,1)\n", + " y1 = min.(oper_traj.Prenew_pot,mg.load)\n", + " y2 = max.( oper_traj.Pbatt,0.0)\n", + " y3 = oper_traj.Pfc\n", + " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", + " y5 = oper_traj.Pelyz\n", + " y6 = oper_traj.power_shedding\n", + " y7 = oper_traj.power_curtailment\n", + " \n", + " \n", + " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", + " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", + " \n", + " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", + " \n", + " \n", + " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", + " ax1.plot(td, mg.load,lw=2, label=\"load\")\n", + " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", + " \n", + " stacks[2].set_hatch(\"-\")\n", + " stacks[4].set_hatch(\"+\")\n", + " stacks[6].set_hatch(\"\\\\\") \n", + " stacks[7].set_hatch(\"\\\\\") \n", + " \n", + " ax3=ax1.twinx()\n", + " ax3.set_ylim(0, 2)\n", + " ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", + " \n", + " ax4=ax2.twinx()\n", + " \n", + " ax4.plot(td, oper_traj.LoH[1:end-1], \"C2\")\n", + " \n", + " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", + " \n", + " \n", + " ax1.set_title(\"hourly power repartition\")\n", + " ax2.set_title(\"H2 level\")\n", + " ax1.legend()\n", + " ax1.grid(true)\n", + " ax1.set(ylabel=\"kW\")\n", + " ax2.legend()\n", + " ax3.legend()\n", + " ax2.grid(true)\n", + " ax2.set(ylabel=\"kg\")\n", + " \n", + " fig.tight_layout()\n", + "\n", + "\n", + " \n", + " return fig, ax1, ax2\n", + " \n", + "end" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bea92522-3fb7-4444-9ce1-889d3d0a4a8b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if with_plots\n", + " fig1, ax1, ax2= plot_oper_traj(mg_best_1,traj_opt_1)\n", + " \n", + " pygui(true)\n", + " plt.show()\n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "93e473b9-e8a7-4575-a285-65b9b987ecef", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "if with_plots\n", + " fig2, ax1, ax2= plot_oper_traj(mg_best_2,traj_opt_2)\n", + " \n", + " pygui(true)\n", + " plt.show()\n", + " \n", + "end;" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "b1ecc08c-1241-4e48-84bb-a57b291b3475", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(Figure(PyObject
), (PyObject , PyObject , PyObject , PyObject ))" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "comp_oper_traj(mg_best_1,mg_best_2,traj_opt_1,traj_opt_2,costs_opt_1,costs_opt_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "0a6f9dbf-1a76-48d4-8060-5e0c11791493", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mg_best_1.tanks.h2Tank.investment_price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86f26f9c-d2e8-4d66-8873-22ebb765259f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Julia 1.9.3", + "language": "julia", + "name": "julia-1.9" + }, + "language_info": { + "file_extension": ".jl", + "mimetype": "application/julia", + "name": "julia", + "version": "1.9.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/Microgrid_Wind-Solar_data.jl b/src/Microgrid_Wind-Solar_data.jl new file mode 100644 index 0000000..f4d9611 --- /dev/null +++ b/src/Microgrid_Wind-Solar_data.jl @@ -0,0 +1,141 @@ +# Parameters and time series for a Microgrid project with *wind* and *solar* sources, +# plus a *battery* and a *dispatchable generator*. +# +# Values gathered from the Microgrid_Wind-Solar.ipynb notebook, +# to be used in the sizing optimization notebook. + +using Microgrids # for capacity_from_wind +using CSV, DataFrames + +println("Data definition for Microgrid with wind, solar, storage and generator...") + +### Input time series +data = DataFrame(CSV.File("C:/Users/nikiemaje/Documents/Macopie/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) + +# Simulation steps +nsteps = length(data.Load) +@assert(nsteps == 8760) # 1 year at an hourly timestep + +# Split load, solar and wind data: +Pload = data.Load# kW +Ppv1k = data.Ppv1k./ 1000; # convert to kW/kWp +wind_speed = data.Wind # m/s + +# Calibrate wind speed data against a mast measurement +ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean +wind_speed = ws_gain*wind_speed + +# Generate wind power capacity factor time series from wind speed +S_D52 = pi * (52/2)^2 # rotor swept area m² +TSP_D52 = 900e3/S_D52 # W/m² +v_out = 25.0 # m/s +Cp_D52, α_D52 = 0.521, 3.1; # fitted from actual power curve +cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52) + +### Microgrid parameters + +# Project parameters +lifetime = 25 # yr +discount_rate = 0.05 +timestep = 1. # h + +# Parameters chosen to be common to all Components (but they could differ) +replacement_price_ratio = 1.0 +salvage_price_ratio = 1.0 + +# Dispatchable generator (Diesel) +power_rated_gen = 2000. # rated power (kW) → /2 to see some load shedding +# fuel parameters: +fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max) +fuel_slope = 0.240 # fuel curve slope (L/h/kW) +# prices: +fuel_price = 1. # fuel price ($/L) +investment_price_gen = 400. # initial investiment price ($/kW) +om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation) +# other technical parameters +lifetime_gen = 15000. # generator lifetime (h) +load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1] +input_unit_gen = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) +output_unit_gen = "KWh" + +#fuel tank + +capacity_rated_ftank = 0. # rated power capacity (L) +investment_price_ftank = 0.0 # initial investment price ($/L) +om_price_ftank = 0.# operation and maintenance price ($/L/y) +lifetime_ftank = 25. # calendar lifetime (y) +loss_factor_ftank = 0. # hydrogen used on site +fuel_ini_ratio = 0. # Initial load ratio ∈ [0,1] +fuel_min_ratio = 0. # minimum load ratio ∈ [0,1] +fuel_max_ratio = 1. # maximum load ratio ∈ [0,1] + +#H2 Tank +capacity_rated_hytank = 10000. # rated power capacity (kg) +investment_price_hytank = 500. # initial investment price ($/kg) +hy_price = 14. # initial hydrogen price ($/kg) +om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y) +lifetime_hytank = 25. # calendar lifetime (y) +loss_factor_hytank = 0. # hydrogen used on site +LoH_ini_ratio = 0.1 # Initial load ratio ∈ [0,1] +LoH_min_ratio = 0.1 # minimum load ratio ∈ [0,1] +LoH_max_ratio = 1. # maximum load ratio ∈ [0,1] + + +#Fuel cell +power_rated_fc = 1800. # rated power capacity (KW) +cons_intercept_fc=0. # +cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé) +cons_price_fc = 0. # +investment_price_fc = 1000. # initial investment price ($/KW) +om_price_fc = 10. # operation and maintenance price ($/kW/y) +lifetime_fc = 45000. # Fuel Cell lifetime (h) +load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] +input_unit_fc= "Kg" +output_unit_fc="KWh" + + +# Battery energy storage +energy_rated_sto = 5000. # rated energy capacity (kWh) +investment_price_sto = 350. # initial investiment price ($/kWh) +om_price_sto = 10. # operation and maintenance price ($/kWh/y) +lifetime_sto = 15. # calendar lifetime (y) +lifetime_cycles = 3000. # maximum number of cycles over life (1) +# Parameters with default values +charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1) +discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1) +loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1] +SoC_min = 0. # minimum State of Charge ∈ [0,1] +SoC_ini = 0. # initial State of Charge ∈ [0,1] +SoC_max = 1. # initial State of Charge ∈ [0,1] + +#electrolyzer +power_rated_elyz = 2000. # rated power capacity (Kw) +cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2) +cons_slope_elyz = 56. # consumption rate (KWhé/KgH2) +cons_price_elyz = 0.# +investment_price_elyz = 1000. # initial investment price ($/kW) +om_price_elyz = 10. # operation and maintenance price ($/kW/y) +lifetime_elyz = 45000. #Electrolyzer lifetime (h) +load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1] + +input_unit_elyz= "KWh" +output_unit_elyz="Kg" + +# Photovoltaic (PV) generation +power_rated_pv = 3000. # rated power (kW) +irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²) +investment_price_pv = 1200. # initial investiment price ($/kW) +om_price_pv = 20.# operation and maintenance price ($/kW/y) +lifetime_pv = 25. # lifetime (y) +# Parameters with default values +derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1] + +# Wind power generation +power_rated_wind = 1800. # rated power (kW) +investment_price_wind = 3500. # initial investiment price ($/kW) +om_price_wind = 100.# operation and maintenance price ($/kW/y) +lifetime_wind = 25. # lifetime (y) + +X=[5000., 3000., 1800., 2000., 1800., 10000.] +capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] +ini_filling_state=[0.0,0.0,0.0] diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 4b3620c..4859d5a 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -1,9 +1,9 @@ module Microgrids -using Documenter -using DocumenterTools +using CSV, DataFrames using DocStringExtensions + @template (TYPES) = """ $(TYPEDEF) @@ -13,6 +13,13 @@ $(DOCSTRING) $(FIELDS) """ +include("components.jl") +include("dispatch.jl") +include("production.jl") +include("operation.jl") +include("economics.jl") +include("Microgrid_Wind-Solar_data.jl") + import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions export simulate, @@ -20,14 +27,11 @@ export simulate, Project, DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, capacity_from_wind, OperationTraj, OperationStats, increment, - operation, aggregation, dispatch, dispatch2, production, - CostFactors, MicrogridCosts, component_costs, economics + operation, aggregation, dispatch_1,dispatch_2, production, nsteps, + CostFactors, MicrogridCosts, component_costs, economics, new_microgrid, + energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank,Pload,X,capex_def + -include("components.jl") -include("dispatch.jl") -include("production.jl") -include("operation.jl") -include("economics.jl") """ simulate(mg::Microgrid, ε::Real=0.0) @@ -47,10 +51,9 @@ Returns: - Operational statistics from `sim_operation` - Microgrid project costs from `sim_economics` """ -function simulate(mg::Microgrid, ε::Real=0.0) +function simulate(mg::Microgrid, dispatch::Function,ε::Real=0.0) # Run the microgrid operation - oper_traj = operation(mg) - + oper_traj = operation(mg, dispatch) # Aggregate the operation variables oper_stats = aggregation(mg, oper_traj, ε) @@ -60,4 +63,50 @@ function simulate(mg::Microgrid, ε::Real=0.0) return oper_traj, oper_stats, mg_costs end + + + """ + new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}) + +TBW +""" +function new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state) + project = Project(lifetime, discount_rate, timestep, "€") + gen = ProductionUnit(power_rated_gen, + fuel_intercept, fuel_slope, fuel_price, + capex[1], om_price_gen, lifetime_gen, + load_ratio_min_gen, replacement_price_ratio, + salvage_price_ratio, input_unit_gen,output_unit_gen) + ftank = Tank(capacity_rated_ftank,capex[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,initial_fill_rate[1], + fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio) + fuel_cell = ProductionUnit(x[5]*1000,cons_intercept_fc, cons_rate_fc,cons_price_fc,capex[3], om_price_fc,lifetime_fc, + load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc) + hytank = Tank(x[6]*1000,capex[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,initial_fill_rate[2], + LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio) + dispatchables = DispatchableCompound{Float64}([gen], [fuel_cell]) + tanks = TankCompound{Float64}(ftank,hytank) + + elyz = ProductionUnit(x[4]*1000,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, capex[5], om_price_elyz, lifetime_elyz, + load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz) + + batt = Battery(x[1]*1000, + capex[6], om_price_sto, lifetime_sto, lifetime_cycles, + charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,initial_fill_rate[3], + replacement_price_ratio, salvage_price_ratio) + pv = Photovoltaic(x[2]*1000, irradiance, + capex[7], om_price_pv, + lifetime_pv, derating_factor_pv, + replacement_price_ratio, salvage_price_ratio) + windgen = WindPower(x[3]*1000, cf_wind, + capex[8], om_price_wind, + lifetime_wind, + replacement_price_ratio, salvage_price_ratio) + + mg = Microgrid(project, Pload,dispatchables, + [elyz,],tanks,batt, [ + pv, + windgen + ]) + return mg + end end \ No newline at end of file diff --git a/src/components.jl b/src/components.jl index fcc4c6c..9f1cefe 100644 --- a/src/components.jl +++ b/src/components.jl @@ -90,6 +90,9 @@ struct Tank{Topt<:Real} "maximum level of tank ∈ [0,1]" max_filling_ratio::Float64 + "fuel price (\$/L, \$/Kg, \$/kW)" + combustible_price::Float64 + # Secondary economics parameters (which should have a default value) "replacement price, relative to initial investment" replacement_price_ratio::Float64 @@ -140,7 +143,6 @@ struct Battery{Topt<:Real} # Main technical parameters "rated energy capacity (kWh)" energy_rated::Topt - # Main economics parameters "initial investment price (\$/kWh)" investment_price::Float64 diff --git a/src/dispatch.jl b/src/dispatch.jl index 772c227..34685e9 100644 --- a/src/dispatch.jl +++ b/src/dispatch.jl @@ -1,5 +1,8 @@ """ - dispatch(Pnl_req, Pbatt_cmax, Pbatt_dmax, Pgen_max) + dispatch_1(Pnl_req, + Pbatt_cmax, + Pbatt_dmax, + Pgen_max) Return the dispatch decision for a rule-based strategy. @@ -9,11 +12,11 @@ Return the dispatch decision for a rule-based strategy. - `Pbatt_dmax`: the maximum battery discharge power at time t. - `Pgen_max`: the diesel generator rated power. """ -function dispatch(Pnl_req, +function dispatch_1(Pnl_req, Pbatt_cmax, Pbatt_dmax, Pgen_max) - Pcurt = 0.0 + Pspill = 0.0 Pshed = 0.0 # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) if Pnl_req >= 0.0 @@ -41,79 +44,117 @@ function dispatch(Pnl_req, Pbatt = Pnl_req else Pbatt = Pbatt_cmax # min(battery) - Pcurt = Pbatt - Pnl_req + Pspill = Pbatt - Pnl_req end end - Pnl = Pnl_req - Pshed + Pcurt - return Pnl, Pgen, Pbatt, Pcurt, Pshed + Pnl = Pnl_req - Pshed + Pspill + return Pnl, Pgen, Pbatt, Pspill, Pshed end -function dispatch(Pnl_req, +""" + dispatch_1(Pnl_req, + Pbatt_cmax, Pbatt_dmax, + Pgen_min , Pgen_max, + Pelyz_min, Pelyz_max, + Pfc_min, Pfc_max) + +# Arguments +- `Pnl_req`: the requested load at time t. +- `Pbatt_cmax`: the maximum battery charge power at time t. +- `Pbatt_dmax`: the maximum battery discharge power at time t. +- `Pgen_max`: the diesel generator rated power. +- `Pelyz_min` : the minimum electrolyzer power at time t. +- `Pelyz_max` : the maximum electrolyzer power at time t. +- 'Pfc_min' : the minimum electrolyzer power at time t. +- 'Pfc_max' : the minimum electrolyzer power at time t. +""" +function dispatch_1(Pnl_req, Pbatt_cmax, Pbatt_dmax, Pgen_min , Pgen_max, Pelyz_min, Pelyz_max, Pfc_min, Pfc_max) Pgen = 0.0 - Pcurt = 0.0 + Pspill = 0.0 Pshed = 0.0 Pelyz = 0.0 Pfc = 0.0 - # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) + if Pnl_req >= 0.0 - Pbatt=min(Pbatt_dmax,Pnl_req) + ## Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) + # battery discharging --> Pbatt positive + Pbatt = min(Pbatt_dmax,Pnl_req) if Pnl_req - Pbatt > Pfc_min - Pfc=min(Pfc_max,Pnl_req-Pbatt) + # fuel_cell starts on if the battery can't supply the power net load request + Pfc = min(Pfc_max,Pnl_req-Pbatt) end - + if Pnl_req - Pbatt - Pfc > Pgen_min - Pgen=min(Pgen_max,Pnl_req-Pbatt-Pfc) + # Diesel generator starts on if the battery & the fuel_cell can't supply the power net load request + Pgen = min(Pgen_max,Pnl_req-Pbatt-Pfc) end Pshed = Pnl_req - Pbatt - Pfc - Pgen - # Pnl_req < 0 - VRE excess + ## Pnl_req < 0 - VRE excess elseif Pnl_req < 0.0 + # battery charging --> Pbatt negative Pbatt = max(Pbatt_cmax,Pnl_req) if Pbatt - Pnl_req > Pelyz_min + #Electrolyzer starts on if its remain enough power Pelyz = min(Pelyz_max,Pbatt-Pnl_req) end - Pcurt = Pbatt - Pelyz - Pnl_req + Pspill = Pbatt - Pelyz - Pnl_req end - Pnl = Pnl_req - Pshed + Pcurt + Pnl = Pnl_req - Pshed + Pspill - return Pnl, Pgen , Pbatt, Pcurt, Pshed, Pelyz, Pfc + return Pnl, Pgen , Pbatt, Pspill, Pshed, Pelyz, Pfc end +""" + dispatch_2(Pnl_req, + Pbatt_cmax, Pbatt_dmax, + Pgen_min , Pgen_max, + Pelyz_min, Pelyz_max, + Pfc_min, Pfc_max) +This dispatch strategy use H2 chain components (Fuel_cell and Electrolyzer) prior, and after, the battery. -function dispatch2(Pnl_req, +# Arguments +- `Pnl_req`: the requested load at time t. +- `Pbatt_cmax`: the maximum battery charge power at time t. +- `Pbatt_dmax`: the maximum battery discharge power at time t. +- `Pgen_max`: the diesel generator rated power. +- `Pelyz_min` : the minimum electrolyzer power at time t. +- `Pelyz_max` : the maximum electrolyzer power at time t. +- 'Pfc_min' : the minimum electrolyzer power at time t. +- 'Pfc_max' : the minimum electrolyzer power at time t. +""" +function dispatch_2(Pnl_req, Pbatt_cmax, Pbatt_dmax, Pgen_min , Pgen_max, Pelyz_min, Pelyz_max, Pfc_min, Pfc_max) Pgen = 0.0 - Pcurt = 0.0 + Pspill = 0.0 Pshed = 0.0 Pelyz = 0.0 Pfc = 0.0 # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) if Pnl_req >= 0.0 - + # fuel_cell starts on if possible . the battery can't supply the power net load request if Pnl_req > Pfc_min - Pfc = min(Pfc_max, Pnl_req) - end + # Battery starts on if the fuel cell can't supply the power net load request Pbatt = min(Pbatt_dmax, Pnl_req - Pfc) - - if Pnl_req - Pbatt - Pfc > Pgen_min - + + if Pnl_req - Pbatt - Pfc > Pgen_min + # Diesel generator starts on if the battery & the fuel_cell can't supply the power net load request Pgen = min(Pgen_max, Pnl_req - Pbatt - Pfc) - end Pshed = Pnl_req - Pbatt - Pfc - Pgen @@ -122,17 +163,18 @@ function dispatch2(Pnl_req, elseif Pnl_req < 0.0 if -1 * Pnl_req > Pelyz_min - + #Electrolyzer starts on Pelyz=min(Pelyz_max, - Pnl_req) - + end - - Pbatt=max(Pbatt_cmax,Pnl_req - Pelyz) - Pcurt = Pbatt - Pelyz - Pnl_req + # Battery starts on if its remain enough power + Pbatt=max(Pbatt_cmax,Pnl_req + Pelyz) + + Pspill = Pbatt - Pelyz - Pnl_req end - Pnl = Pnl_req - Pshed + Pcurt + Pnl = Pnl_req - Pshed + Pspill - return Pnl, Pgen , Pbatt, Pcurt, Pshed, Pelyz, Pfc + return Pnl, Pgen , Pbatt, Pspill, Pshed, Pelyz, Pfc end \ No newline at end of file diff --git a/src/economics.jl b/src/economics.jl index e822add..1c08e41 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -225,9 +225,14 @@ function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_un return c end +""" + component_costs(tank::Tank, mg_project::Project) + +Compute net present cost factors for a `Tank`. +""" function component_costs(tank::Tank, mg_project::Project) rating = tank.capacity - investment = tank.investment_price * rating + investment = tank.investment_price * rating + (tank.ini_filling_ratio * tank.combustible_price * rating ) replacement = investment * tank.replacement_price_ratio salvage = investment * tank.salvage_price_ratio om_annual = tank.om_price * rating diff --git a/src/operation.jl b/src/operation.jl index 1a26152..d044cc9 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -185,12 +185,12 @@ function increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Rea end """ - operation(mg::Microgrid) + operation(mg::Microgrid, dispatch::Function) Simulate the annual operation of the microgrid `mg` and return the hourly operation variables `OperationTraj`. """ -function operation(mg::Microgrid) +function operation(mg::Microgrid, dispatch::Function) # Type of all variables: Float64 or ForwardDiff.Dual{...} Topt = typeof(mg).parameters[1] @@ -206,6 +206,7 @@ function operation(mg::Microgrid) K = length(mg.load) dt = mg.project.timestep + n_gen = length(mg.dispatchables.generator) Pgen_max = collect(gen.power_rated for gen in mg.dispatchables.generator) Pgen_min = collect(mg.dispatchables.generator[i].minimum_load_ratio * Pgen_max[i] for i in eachindex(Pgen_max) ) fuel_slope = collect(gen.consumption_slope for gen in mg.dispatchables.generator) @@ -225,6 +226,7 @@ function operation(mg::Microgrid) Pelyz_min = collect(mg.electrolyzer[i].minimum_load_ratio * Pelyz_max[i] for i in eachindex(Pelyz_max) ) prod_rate = collect(el.consumption_slope for el in mg.electrolyzer) + n_fc = length(mg.dispatchables.fuel_cell) Pfc_max = collect(fc.power_rated for fc in mg.dispatchables.fuel_cell) Pfc_min = collect(mg.dispatchables.fuel_cell[i].minimum_load_ratio * Pfc_max[i] for i in eachindex(Pfc_max) ) cons_rate = collect(fc.consumption_slope for fc in mg.dispatchables.fuel_cell) @@ -232,24 +234,26 @@ function operation(mg::Microgrid) LoH_max = mg.tanks.h2Tank.max_filling_ratio * mg.tanks.h2Tank.capacity LoH_min = mg.tanks.h2Tank.min_filling_ratio * mg.tanks.h2Tank.capacity - - # Initialization of loop variables Pnl = zeros(Topt,K) - Pgen = zeros(Topt,K,length(mg.dispatchables.generator)) - + Pgen = zeros(Topt,K,n_elyz) + Pgen_dmax = zeros(Topt, n_gen) + Pgen_emax = zeros(Topt, n_gen) + Esto = zeros(Topt,K+1) Psto = zeros(Topt,K) - - Pspill = zeros(Topt,K) Pshed = zeros(Topt,K) - Pelyz = zeros(Topt,K,length(mg.electrolyzer)) - Pfc = zeros(Topt,K,length(mg.dispatchables.fuel_cell)) - + Pelyz = zeros(Topt,K, n_elyz) + Pelyz_emax = zeros(Topt, n_elyz) + Pelyz_cmax = zeros(Topt, n_elyz) + + Pfc = zeros(Topt,K, n_fc) + Pfc_emax = zeros(Topt, n_fc) + Pfc_pmax = zeros(Topt, n_fc) LoH = zeros(Topt,K+1) LoF = zeros(Topt,K+1) @@ -258,36 +262,34 @@ function operation(mg::Microgrid) Esto_ini = mg.storage.SoC_ini * mg.storage.energy_rated Esto[1] = Esto_ini - # Initial h2 storage state + # Initial H2 storage state LoH_ini = mg.tanks.h2Tank.ini_filling_ratio * mg.tanks.h2Tank.capacity LoH[1] = LoH_ini # Initial fuel tank state - LoF_ini = mg.tanks.fuelTank.ini_filling_ratio * mg.tanks.fuelTank.capacity LoF[1] = LoF_ini - - Pelyz_emax = zeros(Topt, n_elyz) + + for k=1:K # Storage energy and power limits Psto_emin = - (Esto_max - Esto[k]) / ((1 - sto_loss) * dt) Psto_emax = (Esto[k] - Esto_min) / ((1 + sto_loss) * dt) Psto_dmax = min(Psto_emax, Psto_pmax) Psto_cmax = max(Psto_emin, Psto_pmin) - - - #Pelyz_emax = collect( (LoH_max - LoH[k] ) * prod_rate[i] * dt for i in eachindex(prod_rate)) + + # Electrolyzers power limits Pelyz_emax .= prod_rate .* ((LoH_max - LoH[k]) * dt) - Pelyz_cmax = collect( min(Pelyz_emax[i],Pelyz_max[i]) for i in eachindex(Pelyz_emax)) - - - Pfc_emax = collect( (LoH[k] - LoH_min) / cons_rate[i] * dt for i in eachindex(cons_rate)) - Pfc_pmax = collect( min(Pfc_emax[i],Pfc_max[i]) for i in eachindex(Pfc_emax)) + Pelyz_cmax .= min.(Pelyz_emax,Pelyz_max) - Pgen_emax = collect( (((LoF[K] - LoF_min) - fuel_intercept[i] * Pgen_max[i]* dt )/ fuel_slope[i] * dt ) for i in eachindex(cons_rate)) - Pgen_dmax = collect( min(Pgen_emax[i],Pgen_max[i]) for i in eachindex(Pgen_emax)) + # fuel_cells power limits + Pfc_emax .= max.(dt * (LoH[k] - LoH_min) ./ cons_rate , 0.0 ) + Pfc_pmax .= min.(Pfc_emax,Pfc_max) + # Diesel generators power limits + Pgen_emax .= ((LoF[K] - LoF_min)/dt .- fuel_intercept.* Pgen_max )./ (fuel_slope) + Pgen_dmax .= min.(Pgen_emax,Pgen_max) # dispatch Pnl[k], Pgen[k,1], Psto[k], Pspill[k], Pshed[k], Pelyz[k,1], Pfc[k,1] = dispatch(Pnl_request[k], Psto_cmax, Psto_dmax, Pgen_min[1], Pgen_dmax[1], Pelyz_min[1],Pelyz_cmax[1], Pfc_min[1],Pfc_pmax[1]) @@ -356,7 +358,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) storage_loss_energy = storage_char_energy - storage_dis_energy - (Efin - Eini) # kWh/y - #Power-H2-Power statistics + # Power-H2-Power statistics elyz_consumed_energy = sum(oper_traj.Pelyz) * dt elyz_produced_h2 = elyz_consumed_energy / mg.electrolyzer[1].consumption_slope fc_produced_energy = sum(oper_traj.Pfc) * dt @@ -395,11 +397,11 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) gen_hours += time_inc * dt gen_fuel += cons_rate * dt - + # Electrolyzer : operating hours time_inc, cons_rate = increment(oper_traj.Pelyz[K],mg.electrolyzer[1],ε) elyz_hours += time_inc * dt - + # Fuel cell : operating hours time_inc, cons_rate = increment(oper_traj.Pfc[K],mg.dispatchables.fuel_cell[1],ε) fc_hours += time_inc * dt @@ -428,9 +430,9 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) # Load statistics served_energy, shed_energy, shed_max, shed_hours, shed_duration_max, shed_rate, # Dispatchable generator statistics - gen_energy, gen_hours, gen_fuel, fc_produced_energy, fc_hours, fc_consumed_h2, - # - elyz_produced_h2, elyz_hours, elyz_consumed_energy, chain_loss, + gen_energy, gen_hours, gen_fuel, + #H2 chain statistics + fc_produced_energy, fc_hours, fc_consumed_h2, elyz_produced_h2, elyz_hours, elyz_consumed_energy, chain_loss, # Energy storage (e.g. battery) statistics storage_cycles, storage_char_energy, storage_dis_energy, storage_loss_energy, diff --git a/test2/Typetest.jl b/test2/Typetest.jl new file mode 100644 index 0000000..e69de29 From 4023dc39b0e9a90a64eafbc05b81e7035bdbd7d5 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 2 Feb 2024 09:54:51 +0100 Subject: [PATCH 05/19] test 3 --- Project.toml | 1 + .../Microgrid_sizingH2_optimization1.ipynb | 8 +- .../prices_scenario_comparison.ipynb | 156 ++++++++++-------- src/Microgrid_Wind-Solar_data.jl | 2 +- 4 files changed, 93 insertions(+), 74 deletions(-) diff --git a/Project.toml b/Project.toml index dc49e40..7874bd7 100644 --- a/Project.toml +++ b/Project.toml @@ -7,6 +7,7 @@ version = "0.10.2" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd" PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" diff --git a/examples/Microgrid_sizingH2_optimization1.ipynb b/examples/Microgrid_sizingH2_optimization1.ipynb index 58550a2..cdfb0ed 100644 --- a/examples/Microgrid_sizingH2_optimization1.ipynb +++ b/examples/Microgrid_sizingH2_optimization1.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 1, "id": "c2f04250-290c-4c38-93e6-074702ec0700", "metadata": { "tags": [] @@ -1154,15 +1154,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/examples/new_examples/prices_scenario_comparison.ipynb b/examples/new_examples/prices_scenario_comparison.ipynb index eabafc9..ce26e5c 100644 --- a/examples/new_examples/prices_scenario_comparison.ipynb +++ b/examples/new_examples/prices_scenario_comparison.ipynb @@ -4,15 +4,17 @@ "cell_type": "code", "execution_count": 1, "id": "a057597f-0993-4915-813c-915eff752105", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "using CSV, DataFrames\n", "using Microgrids\n", "using NLopt # optimization solvers\n", "using Printf # pretty print results\n", - "using PyPlot\n", "using PyCall\n", + "using PyPlot\n", "using BenchmarkTools # for timing performance, optional" ] }, @@ -312,7 +314,9 @@ "cell_type": "code", "execution_count": 10, "id": "3a789b7d-eee8-4047-8bc2-34ad25ab09f0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -348,7 +352,9 @@ "cell_type": "code", "execution_count": 11, "id": "e695c1ab-4853-461a-8b3f-b6cd12dc3ecd", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -376,7 +382,9 @@ "cell_type": "code", "execution_count": 12, "id": "31a72ca9-cec2-4325-8758-9b2374847d0b", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { @@ -516,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "id": "8344f0ba-9fdf-47bd-b84a-ef24bb942cf5", "metadata": { "tags": [] @@ -530,7 +538,7 @@ " dispatch_2 (generic function with 1 method)" ] }, - "execution_count": 25, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -542,9 +550,11 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 18, "id": "626688e2-98e9-438d-9494-d44aecc5621f", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -573,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 19, "id": "b1afbd7f-1e6c-4743-ac94-05749b3a947e", "metadata": { "tags": [] @@ -585,7 +595,7 @@ "0.13910787752736056" ] }, - "execution_count": 41, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -596,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 20, "id": "0c5ad5a7-b088-46cc-981c-32a36013d445", "metadata": { "tags": [] @@ -608,7 +618,7 @@ "0.0" ] }, - "execution_count": 42, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -619,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 21, "id": "22df5e8a-3293-48b6-8ed7-e00a0b22d866", "metadata": { "tags": [] @@ -631,7 +641,7 @@ "0.13910787752736056" ] }, - "execution_count": 43, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -642,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 22, "id": "d4fbf0c6-8179-4027-ba00-8d171523ba0d", "metadata": { "tags": [] @@ -654,7 +664,7 @@ "0.0" ] }, - "execution_count": 44, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -665,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 23, "id": "5bea2759-0a3f-42d4-a283-fe8da461c192", "metadata": { "tags": [] @@ -676,11 +686,11 @@ "text/plain": [ "3-element Vector{Float64}:\n", " 0.0\n", - " 0.11191436669910618\n", - " 3.8487770259517386e-7" + " 0.13910787752736056\n", + " 0.0" ] }, - "execution_count": 31, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -691,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 24, "id": "ef5f3774-8b30-4152-a449-1520c3d574b0", "metadata": { "tags": [] @@ -700,10 +710,10 @@ { "data": { "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 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"execution_count": 47, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "ArgumentError: ref of NULL PyObject", + "output_type": "error", + "traceback": [ + "ArgumentError: ref of NULL PyObject", + "", + "Stacktrace:", + " [1] _getproperty(o::PyObject, s::Symbol)", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:299", + " [2] __getproperty", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:312 [inlined]", + " [3] getproperty(o::PyObject, s::Symbol)", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:318", + " [4] top-level scope", + " @ In[27]:9" + ] } ], "source": [ @@ -791,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 28, "id": "2f10eb38-b4d2-436f-904b-aa540d4f30c3", "metadata": { "tags": [] @@ -801,35 +819,35 @@ "data": { "text/plain": [ "100-element Vector{Float64}:\n", - " 0.11197005651847888\n", - " 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0.11197005651847888\n", - " 0.11197005651847888" + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002\n", + " 0.10000000000000002" ] }, - "execution_count": 39, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1950,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 17, "id": "fe7e451c-e293-4eb4-8674-58d988276adb", "metadata": { "tags": [] @@ -1962,7 +1980,7 @@ "plot_oper_traj (generic function with 1 method)" ] }, - "execution_count": 35, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2123,15 +2141,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/src/Microgrid_Wind-Solar_data.jl b/src/Microgrid_Wind-Solar_data.jl index f4d9611..fd20637 100644 --- a/src/Microgrid_Wind-Solar_data.jl +++ b/src/Microgrid_Wind-Solar_data.jl @@ -10,7 +10,7 @@ using CSV, DataFrames println("Data definition for Microgrid with wind, solar, storage and generator...") ### Input time series -data = DataFrame(CSV.File("C:/Users/nikiemaje/Documents/Macopie/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) +data = DataFrame(CSV.File("C:/Users/nikiemaje/Documents/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) # Simulation steps nsteps = length(data.Load) From 7529590d4ab11967155c5b8b11ac32b5608a7f80 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 9 Feb 2024 10:41:04 +0100 Subject: [PATCH 06/19] -uyjuy --- Project.toml | 1 + examples/Microgrid_Wind-Solar-H2.ipynb | 35 +++- examples/Microgrid_Wind-Solar.ipynb | 180 +++++++++++++++---- examples/Microgrid_sizing_optimization.ipynb | 6 +- src/Microgrid_Wind-Solar_data.jl | 10 +- 5 files changed, 181 insertions(+), 51 deletions(-) diff --git a/Project.toml b/Project.toml index 7874bd7..ce5ed0f 100644 --- a/Project.toml +++ b/Project.toml @@ -7,6 +7,7 @@ version = "0.10.2" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd" PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index 0bb1396..71cd88f 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -265,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", "metadata": { "tags": [] @@ -280,17 +280,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", "metadata": { "tags": [] }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wind power capacity factor: 39.70 %\n" + "ename": "LoadError", + "evalue": "UndefVarError: `capacity_from_wind` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `capacity_from_wind` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[4]:1" ] } ], @@ -299,6 +304,18 @@ "@printf(\"Wind power capacity factor: %.2f %%\\n\", mean(cf_wind)*100) # (pretty high in Ouessant)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b42c5d2-924b-40c1-9749-8d874de75566", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", + " ax1.plot(td, 0.5*Cp_D52*1.225/TSP_D52*(wind_speed^3), label=\"cf\")\n", + " ax1.plot(td, cf_wind, \"tab:red\", label=\"cf-corrected\")" + ] + }, { "cell_type": "markdown", "id": "d6734baf-c018-4130-9c6d-c5d37b237abb", @@ -2476,15 +2493,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb index d7f4516..6b28c73 100644 --- a/examples/Microgrid_Wind-Solar.ipynb +++ b/examples/Microgrid_Wind-Solar.ipynb @@ -24,20 +24,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "855af8dd-d21d-4587-84cb-ae2b932deeaa", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" - ] - } - ], + "outputs": [], "source": [ "using Microgrids\n", "using Printf, CSV, DataFrames, Statistics" @@ -45,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "f7cc6435-9bd2-487f-943f-225c452db904", "metadata": { "tags": [] @@ -57,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "c910f9e4-1c92-44ca-8ce3-8c520ec9d231", "metadata": { "tags": [] @@ -69,7 +61,7 @@ "true" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "27688e3f-f2d5-4ee1-b4f5-ed1d451c44d6", "metadata": { "tags": [] @@ -136,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "c24c3df6-4062-450d-ba2d-32d71af7a823", "metadata": { "tags": [] @@ -148,7 +140,7 @@ "1453.0" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -167,23 +159,21 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", "metadata": { "tags": [] }, "outputs": [ { - "ename": "LoadError", - "evalue": "UndefVarError: `nsteps` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `nsteps` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[1]:1" - ] + "data": { + "image/png": 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", + "text/plain": [ + "Figure(PyObject
)" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -254,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", "metadata": { "tags": [] @@ -263,13 +253,13 @@ "source": [ "S_D52 = pi * (52/2)^2 # rotor swept area m²\n", "TSP_D52 = 900e3/S_D52 # W/m²\n", - "v_out = 25.0 # m/s\n", + "v_out = 10.0 # m/s\n", "Cp_D52, α_D52 = 0.521, 3.1; # fitted from actual power curve" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", "metadata": { "tags": [] @@ -279,7 +269,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wind power capacity factor: 39.70 %\n" + "Wind power capacity factor: 15.21 %\n" ] } ], @@ -288,6 +278,128 @@ "@printf(\"Wind power capacity factor: %.2f %%\\n\", mean(cf_wind)*100) # (pretty high in Ouessant)" ] }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3ca02a25-7af8-4752-b2cf-22d2038aaae4", + "metadata": {}, + "outputs": [], + "source": [ + "ctest=0.5*Cp_D52*1.225/TSP_D52*(wind_speed.^3);" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3c65badd-b724-4726-a784-5b3f99cee532", + "metadata": {}, + "outputs": [ + { + "ename": "LoadError", + "evalue": "PyError ($(Expr(:escape, :(ccall(#= C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) \nValueError('x and y must have same first dimension, but have shapes (8760,) and (1,)')\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py\", line 1721, in plot\n lines = [*self._get_lines(self, *args, data=data, **kwargs)]\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 303, in __call__\n yield from self._plot_args(\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 499, in _plot_args\n raise ValueError(f\"x and y must have same first dimension, but \"\n", + "output_type": "error", + "traceback": [ + "PyError ($(Expr(:escape, :(ccall(#= C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) \nValueError('x and y must have same first dimension, but have shapes (8760,) and (1,)')\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py\", line 1721, in plot\n lines = [*self._get_lines(self, *args, data=data, **kwargs)]\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 303, in __call__\n yield from self._plot_args(\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 499, in _plot_args\n raise ValueError(f\"x and y must have same first dimension, but \"\n", + "", + "Stacktrace:", + " [1] pyerr_check", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:75 [inlined]", + " [2] pyerr_check", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:79 [inlined]", + " [3] _handle_error(msg::String)", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:96", + " [4] macro expansion", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:110 [inlined]", + " [5] #107", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 [inlined]", + " [6] disable_sigint", + " @ .\\c.jl:473 [inlined]", + " [7] __pycall!", + " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:42 [inlined]", + " [8] _pycall!(ret::PyCall.PyObject, o::PyCall.PyObject, args::Tuple{Vector{Float64}, Float64, String}, nargs::Int64, kw::PyCall.PyObject)", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:29", + " [9] _pycall!(ret::PyCall.PyObject, o::PyCall.PyObject, args::Tuple{Vector{Float64}, Float64, String}, kwargs::@Kwargs{label::String})", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:11", + " [10] (::PyCall.PyObject)(::Vector{Float64}, ::Vararg{Any}; kwargs::@Kwargs{label::String})", + " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:86", + " [11] top-level scope", + " @ In[37]:3" + ] + } + ], + "source": [ + "fig, (ax1, ax2,ax3) = plt.subplots(3,1, sharex=true)\n", + " ax1.plot(td, ctest, label=\"cf\")\n", + " ax2.plot(td, -log.(exp(-3.1) .+ exp.(-3.1*td)) / 3.1, \"tab:red\", label=\"cf-corrected\")\n", + " ax3.plot(td, cf_wind, \"tab:red\", label=\"cf-corrected\")\n", + " \n", + "pygui(true)\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "ed9b6a54-b62e-4eac-b128-ddc2ad0473f5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9999999444469194" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d= -log(exp(-3.1) + exp(-3.1*te)) / 3.1" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "2bac3dc2-13dc-4ffa-829f-ff3ba38181ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9886446785035883" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "te= 0.5*Cp_D52*(1.225/TSP_D52)* 10.95^3" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "98c25cd8-07ed-4865-8760-380262eb0fa2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1799.999900004455" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv=d*1800" + ] + }, { "cell_type": "markdown", "id": "d6734baf-c018-4130-9c6d-c5d37b237abb", @@ -1237,15 +1349,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/examples/Microgrid_sizing_optimization.ipynb b/examples/Microgrid_sizing_optimization.ipynb index 8789bdb..eb07759 100644 --- a/examples/Microgrid_sizing_optimization.ipynb +++ b/examples/Microgrid_sizing_optimization.ipynb @@ -942,15 +942,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.9.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/src/Microgrid_Wind-Solar_data.jl b/src/Microgrid_Wind-Solar_data.jl index fd20637..bd65cb4 100644 --- a/src/Microgrid_Wind-Solar_data.jl +++ b/src/Microgrid_Wind-Solar_data.jl @@ -10,7 +10,7 @@ using CSV, DataFrames println("Data definition for Microgrid with wind, solar, storage and generator...") ### Input time series -data = DataFrame(CSV.File("C:/Users/nikiemaje/Documents/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) +data = DataFrame(CSV.File("C:/Users/nikiemaje/OneDrive - CentraleSupelec/Documents/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) # Simulation steps nsteps = length(data.Load) @@ -86,8 +86,8 @@ power_rated_fc = 1800. # rated power capacity (KW) cons_intercept_fc=0. # cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé) cons_price_fc = 0. # -investment_price_fc = 1000. # initial investment price ($/KW) -om_price_fc = 10. # operation and maintenance price ($/kW/y) +investment_price_fc = 1600. # initial investment price ($/KW) +om_price_fc = 16. # operation and maintenance price ($/kW/y) lifetime_fc = 45000. # Fuel Cell lifetime (h) load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] input_unit_fc= "Kg" @@ -113,8 +113,8 @@ power_rated_elyz = 2000. # rated power capacity (Kw) cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2) cons_slope_elyz = 56. # consumption rate (KWhé/KgH2) cons_price_elyz = 0.# -investment_price_elyz = 1000. # initial investment price ($/kW) -om_price_elyz = 10. # operation and maintenance price ($/kW/y) +investment_price_elyz = 1600. # initial investment price ($/kW) +om_price_elyz = 16. # operation and maintenance price ($/kW/y) lifetime_elyz = 45000. #Electrolyzer lifetime (h) load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1] From 6ca87a352343224bc89a5067e00efbc013ae692b Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 14 Jun 2024 15:52:26 +0200 Subject: [PATCH 07/19] first viable version of the h2 branch --- Project.toml | 7 +- ...> Microgrid-H2_sizing_optimization1.ipynb} | 449 +- examples/Microgrid_Wind-Solar-H2.ipynb | 1524 +-- examples/Microgrid_Wind-Solar.ipynb | 2 +- examples/Microgrid_Wind-Solar_data.jl | 137 - examples/Microgrid_sizing_optimization.ipynb | 958 -- examples/Useful_functions.jl | 290 + .../data/Microgrid_Wind-Solar-H2_data.jl | 45 +- examples/images/Microgrid_h2.png | Bin 0 -> 124663 bytes examples/images/mg_sizing_h2.png | Bin 0 -> 164322 bytes examples/images/microgrid_sizing - h2.png | Bin 177607 -> 0 bytes examples/new_examples/Untitled.ipynb | 80 - .../data/Ouessant_2016_monthly_stats.png | Bin 126789 -> 0 bytes .../new_examples/data/Ouessant_data_2016.csv | 8761 ----------------- examples/new_examples/data/SOURCES.md | 26 - examples/new_examples/df2_dataset.csv | 351 - examples/new_examples/df2_dataset2.csv | 351 - examples/new_examples/df2_dataset2.xlsx | Bin 151903 -> 0 bytes examples/new_examples/df3_dataset - Copie.csv | 601 -- examples/new_examples/df3_dataset.csv | 601 -- examples/new_examples/df4_dataset.csv | 601 -- .../new_examples/images/MG_optim_diagram.png | Bin 136890 -> 0 bytes examples/new_examples/images/microgrid.png | Bin 130445 -> 0 bytes .../images/microgrid_sizing - h2.png | Bin 177607 -> 0 bytes .../new_examples/images/microgrid_sizing.png | Bin 114539 -> 0 bytes examples/new_examples/microgrid_def.ipynb | 661 -- .../prices_scenario_comparison.ipynb | 2157 ---- perf/Microgrids.jl_performance_analysis.ipynb | 20 +- src/Microgrids.jl | 54 +- src/components.jl | 11 +- src/dispatch.jl | 79 +- src/economics.jl | 110 +- src/operation.jl | 228 +- test2/Typetest.jl | 0 34 files changed, 1052 insertions(+), 17052 deletions(-) rename examples/{Microgrid_sizingH2_optimization1.ipynb => Microgrid-H2_sizing_optimization1.ipynb} (75%) delete mode 100644 examples/Microgrid_Wind-Solar_data.jl delete mode 100644 examples/Microgrid_sizing_optimization.ipynb create mode 100644 examples/Useful_functions.jl rename src/Microgrid_Wind-Solar_data.jl => examples/data/Microgrid_Wind-Solar-H2_data.jl (75%) create mode 100644 examples/images/Microgrid_h2.png create mode 100644 examples/images/mg_sizing_h2.png delete mode 100644 examples/images/microgrid_sizing - h2.png delete mode 100644 examples/new_examples/Untitled.ipynb delete mode 100644 examples/new_examples/data/Ouessant_2016_monthly_stats.png delete mode 100644 examples/new_examples/data/Ouessant_data_2016.csv delete mode 100644 examples/new_examples/data/SOURCES.md delete mode 100644 examples/new_examples/df2_dataset.csv delete mode 100644 examples/new_examples/df2_dataset2.csv delete mode 100644 examples/new_examples/df2_dataset2.xlsx delete mode 100644 examples/new_examples/df3_dataset - Copie.csv delete mode 100644 examples/new_examples/df3_dataset.csv delete mode 100644 examples/new_examples/df4_dataset.csv delete mode 100644 examples/new_examples/images/MG_optim_diagram.png delete mode 100644 examples/new_examples/images/microgrid.png delete mode 100644 examples/new_examples/images/microgrid_sizing - h2.png delete mode 100644 examples/new_examples/images/microgrid_sizing.png delete mode 100644 examples/new_examples/microgrid_def.ipynb delete mode 100644 examples/new_examples/prices_scenario_comparison.ipynb delete mode 100644 test2/Typetest.jl diff --git a/Project.toml b/Project.toml index ce5ed0f..f991e4a 100644 --- a/Project.toml +++ b/Project.toml @@ -1,16 +1,13 @@ name = "Microgrids" uuid = "bd581358-d3fa-499e-a26e-e70307242c03" -authors = ["Pierre Haessig ", "evelisea <59697806+evelisea@users.noreply.github.com>"] -version = "0.10.2" +authors = ["Pierre Haessig ", "evelisea <59697806+evelisea@users.noreply.github.com>","Jean NIKIEMA "] +version = "0.10.5" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" -NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd" -PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0" -PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee" [compat] julia = "1.6" diff --git a/examples/Microgrid_sizingH2_optimization1.ipynb b/examples/Microgrid-H2_sizing_optimization1.ipynb similarity index 75% rename from examples/Microgrid_sizingH2_optimization1.ipynb rename to examples/Microgrid-H2_sizing_optimization1.ipynb index cdfb0ed..a62c49a 100644 --- a/examples/Microgrid_sizingH2_optimization1.ipynb +++ b/examples/Microgrid-H2_sizing_optimization1.ipynb @@ -14,7 +14,7 @@ "\n", "Like in the previous example, the sizing is done for a microgrid project with *wind* and *solar* sources, plus a *battery* and a *dispatchable generator*.\n", "\n", - "schematic of a microgrid with a symbolic representation of the notion of sizing\n", + "schematic of a microgrid with a symbolic representation of the notion of sizing\n", "\n", "The main steps of the optimal sizing process are:\n", "1. set up the problem (in particular loading project data like prices and time series)\n", @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "c2f04250-290c-4c38-93e6-074702ec0700", "metadata": { "tags": [] @@ -57,6 +57,14 @@ "using BenchmarkTools # for timing performance, optional" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "93f6f85d-a51e-49bb-accd-1aed220e51b8", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "5e20bad8-81a3-4a6b-8c1e-6848a20c0d55", @@ -70,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", "metadata": { "tags": [] @@ -80,12 +88,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Data definition for Microgrid with wind, solar, storage and generator...\n" + "Data definition for Microgrid with wind, solar and Hybrid Storage System...\n" ] } ], "source": [ - "include(\"./Microgrid_Wind-Solar_data.jl\");" + "include(\"data/Microgrid_Wind-Solar-H2_data.jl\");" ] }, { @@ -119,99 +127,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e9485d6b-7bfa-4d3b-bc85-3d93d1eacefa", - "metadata": {}, - "outputs": [], - "source": [ - "function default_microgrid()\n", - " gen = ProductionUnit(power_rated_gen,\n", - " fuel_intercept, fuel_slope, fuel_price,\n", - " investment_price_gen, om_price_gen, lifetime_gen,\n", - " load_ratio_min_gen, replacement_price_ratio,\n", - " salvage_price_ratio, input_unit_gen,output_unit_gen)\n", - " ftank = Tank(capacity_rated_ftank,investment_price_ftank, om_price_ftank,lifetime_ftank,loss_factor_ftank,fuel_ini_ratio,\n", - " fuel_min_ratio, fuel_max_ratio,replacement_price_ratio, salvage_price_ratio)\n", - " fuel_cell = ProductionUnit(power_rated_fc,cons_intercept_fc, cons_rate_fc,cons_price_fc,investment_price_fc, om_price_fc,lifetime_fc,\n", - " load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc)\n", - " hytank = Tank(capacity_rated_hytank,investment_price_hytank, om_price_hytank,lifetime_hytank,loss_factor_hytank,LoH_ini_ratio,\n", - " LoH_min_ratio, LoH_max_ratio,replacement_price_ratio, salvage_price_ratio)\n", - " dispatchables = DispatchableCompound{Float64}([gen,], [fuel_cell])\n", - " tanks = TankCompound{Float64}(ftank,hytank)\n", - " \n", - " elyz = ProductionUnit(power_rated_elyz,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, investment_price_elyz, om_price_elyz, lifetime_elyz,\n", - " load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz)\n", - "\n", - " batt = Battery(energy_rated_sto,\n", - " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", - " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_ini,SoC_max,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " pv = Photovoltaic(power_rated_pv, irradiance,\n", - " investment_price_pv, om_price_pv,\n", - " lifetime_pv, derating_factor_pv,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " windgen = WindPower(power_rated_wind, cf_wind,\n", - " investment_price_wind, om_price_wind,\n", - " lifetime_wind,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " \n", - " mg = Microgrid(project, Pload,dispatchables,\n", - " [elyz,],tanks,batt, [\n", - " pv,\n", - " windgen\n", - " ])\n", - " end" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "simulate_microgrid" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", - "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", - "\n", - "Returns mg, traj, stats, costs\n", - "\"\"\"\n", - " function simulate_microgrid(microgrid)\n", - " project = Project(lifetime, discount_rate, timestep, \"€\")\n", - " # Split decision variables (converted MW → kW):\n", - " #power_rated_gen = x[1]*1000\n", - " energy_rated_sto = x[1]*1000\n", - " power_rated_pv = x[2]*1000\n", - " power_rated_wind = x[3]*1000\n", - " power_rated_elyz = x[4]*1000\n", - " power_rated_fc = x[5]*1000\n", - " capacity_rated_hytank = x[6]*1000\n", - " \n", - "\n", - " # Create components\n", - " \n", - " # Launch simulation:\n", - " traj, stats, costs = simulate(mg)\n", - "\n", - " return mg, traj ,stats, costs\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "id": "df58e986-633b-4d9c-8d25-dd1d6e8a2e9f", "metadata": { "tags": [] @@ -220,16 +136,16 @@ { "data": { "text/plain": [ - "0.0" + "plot_cashflow_comp (generic function with 1 method)" ] }, - "execution_count": 34, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "capacity_rated_ftank" + "include(\"Useful_functions.jl\")" ] }, { @@ -242,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "id": "d765dc6a-eeda-4727-b259-479176670dc1", "metadata": { "tags": [] @@ -251,10 +167,10 @@ { "data": { "text/plain": [ - "([5.0, 3.0, 0.9, 2.0, 1.8, 10.0], 0.21554333430784944, 16.351390939286258)" + "([5.0, 5.0, 1.8, 2.0, 1.8, 12.0], 0.22395801068026286, 0.036108065923919755)" ] }, - "execution_count": 37, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -281,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "62df72e3-ad0d-4c83-bf8b-3a7398b0c7e2", "metadata": { "tags": [] @@ -293,15 +209,15 @@ "obj_multi" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", - "function obj_multi(x)\n", - " mg, traj,stats, costs = simulate_microgrid(x)\n", + "function obj_multi(x= X,capex=capex_def,dispatch=dispatch_1)\n", + " mg,traj,stats, costs = simulate_microgrid(1000*x,capex,dispatch)\n", " # Extract KPIs of interest\n", " lcoe = costs.lcoe # $/kWh\n", " shed_rate = stats.shed_rate; # in [0,1]\n", @@ -311,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "id": "6b3eb2d2-1513-424b-baa0-a49ca5247bb4", "metadata": { "tags": [] @@ -323,7 +239,7 @@ "obj" ] }, - "execution_count": 9, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -335,8 +251,8 @@ "\n", "load shedding penalty threshold `shed_max` should be in [0,1[\n", "\"\"\"\n", - "function obj(x, grad, shed_max, w_shed_max=1e5)\n", - " lcoe, shed_rate = obj_multi(x)\n", + "function obj(x,grad, shed_max,capex=capex_def,dispatch=dispatch_1, w_shed_max=1e5)\n", + " lcoe, shed_rate = obj_multi(x,capex,dispatch)\n", " over_shed = shed_rate - shed_max\n", " if over_shed > 0.0\n", " penalty = w_shed_max*over_shed\n", @@ -362,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "id": "8de2153d-559f-4a60-880e-e8ea5fa3b810", "metadata": { "tags": [] @@ -372,10 +288,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Base. multi: (0.21554333430784944, 0.20552617597582035) mono: 19552.83314091634\n", - "Shed. multi: (0.21554333430784944, 0.20552617597582035) mono: 19552.83314091634\n", - "PV. multi: (0.17879988991758503, 0.923547868561659) mono: 91354.96565605582\n", - "Wind. multi: (0.12348466355712437, 0.74395737102815) mono: 73395.86058747856\n" + "Base. multi: (0.3803908621427198, 0.0024194966929672324) mono: 0.3803908621427198\n", + "Shed. multi: (0.3803908621427198, 0.0024194966929672324) mono: 0.3803908621427198\n", + "PV. multi: (0.10149685980966677, 0.923547868561659) mono: 91354.8883530257\n", + "Wind. multi: (0.10040264224635914, 0.74395737102815) mono: 73395.83750545724\n" ] } ], @@ -384,8 +300,8 @@ "shed_max = 0.01 # in [0,1]\n", "\n", "x_shed = [energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", - "x_pv = [0. 500. 0. 0. 0. 0.]/1000.\n", - "x_wind = [0. 0. 500. 0. 0. 0.]/1000.\n", + "x_pv = [0., 500. , 0., 0., 0. ,0.]/1000.\n", + "x_wind = [0. , 0. ,500., 0., 0., 0.]/1000.\n", "\n", "println(\"Base. multi: \", obj_multi(x_base), \" mono: \", obj(x_base,[], shed_max))\n", "println(\"Shed. multi: \", obj_multi(x_shed), \" mono: \", obj(x_shed,[], shed_max))\n", @@ -419,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 63, "id": "5ac92d7e-5de1-4ddf-a050-6897dbc8af2d", "metadata": { "tags": [] @@ -437,7 +353,7 @@ " 17.07" ] }, - "execution_count": 11, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 66, "id": "9f65e094-4c58-4e9b-b4ee-b626f6504f6c", "metadata": { "tags": [] @@ -485,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 69, "id": "6d5ef67e-5d40-4bb0-a13d-65dc3d57bceb", "metadata": { "tags": [] @@ -494,10 +410,10 @@ { "data": { "text/plain": [ - "((38.75301191375701, 0.9998470957371233), (1.2820220908105344, 8.262599201630023e-6))" + "((0.10149685980963595, 0.9998470957371233), (1.9936149574677808, 0.0))" ] }, - "execution_count": 12, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 72, "id": "08491363-e1ce-47d3-a425-4bc83888be12", "metadata": { "tags": [] @@ -534,7 +450,7 @@ "optim_mg" ] }, - "execution_count": 14, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -580,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 75, "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", "metadata": { "tags": [] @@ -590,10 +506,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "GN_CRS2_LM algo: XTOL_REACHED after 32418 iterations. \n", - "x*=[6920.8, 4227.9, 2138.7, 640.6, 1496.1, 13247.1]\n", - "LCOE*: 0.2527665492626518\n", - "shed*: 9.999433562439814e-5\n" + "GN_CRS2_LM algo: XTOL_REACHED after 28192 iterations. \n", + "x*=[1977.1, 1986.0, 2732.9, 1278.9, 1413.0, 10048.3]\n", + "LCOE*: 0.32716261972097593\n", + "shed*: 9.999999935696436e-5\n" ] } ], @@ -612,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 76, "id": "6a763252-1a24-4d35-a8e8-b45d9da51341", "metadata": { "tags": [] @@ -623,9 +539,9 @@ "output_type": "stream", "text": [ "GN_CRS2_LM algo: MAXEVAL_REACHED after 10000 iterations. \n", - "x*=[0.0, 3402.7, 2074.2, 2217.4, 1196.7, 1301.9, 16800.9]\n", - "LCOE*: 0.20798206778104475\n", - "shed*: 0.0009998209597289068\n" + "x*=[1968.0, 1986.4, 2732.0, 1281.0, 1244.1, 9756.6]\n", + "LCOE*: 0.32053999052563825\n", + "shed*: 0.0009996243532919401\n" ] } ], @@ -652,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 78, "id": "78b3e3f8-0de8-47f3-a8a4-40358feef172", "metadata": { "tags": [] @@ -662,10 +578,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "LN_SBPLX polish: XTOL_REACHED after 239 iterations. \n", - "x*=[0.0, 3402.5, 2074.2, 2217.4, 1196.6, 1301.8, 16801.0]\n", - "LCOE*: 0.20798046348779503\n", - "shed*: 0.000999888188853352\n" + "LN_SBPLX polish: XTOL_REACHED after 608 iterations. \n", + "x*=[1967.8, 1986.5, 2731.9, 1281.1, 1244.1, 9756.6]\n", + "LCOE*: 0.32053892743925294\n", + "shed*: 0.0009999999995675497\n" ] } ], @@ -690,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 80, "id": "1e046d9e-21ec-4834-9bf3-5bc1c9be1f0a", "metadata": { "tags": [] @@ -707,7 +623,7 @@ " [1] getproperty(x::OperationStats, f::Symbol)", " @ Base .\\Base.jl:37", " [2] top-level scope", - " @ In[15]:2" + " @ In[80]:2" ] } ], @@ -756,12 +672,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "id": "8f4a08f2-96d5-47f1-a9fb-41a419051be8", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GN_CRS2_LM algo: MAXEVAL_REACHED after 1000 iterations. \n", + "x*=[1460.5, 1552.4, 2840.9, 1265.4, 1544.6, 10768.3]\n", + "LCOE*: 0.33392376327303325\n", + "shed*: 0.0\n" + ] + } + ], "source": [ "algo = :GN_CRS2_LM # could be one of LN_SBPLX, GN_DIRECT, GN_CRS2_LM, GN_ESCH...\n", "shed_max = 0.00 # in [0,1]\n", @@ -777,7 +704,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 84, "id": "f2545f43-8dd2-4254-84a2-4f643f69a994", "metadata": { "tags": [] @@ -787,10 +714,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "LN_SBPLX polish: XTOL_REACHED after 313 iterations. \n", - "x*=[2341.2, 2254.0, 2165.9, 1208.5, 845.2, 16007.4]\n", - "LCOE*: 0.20516631644385727\n", - "shed*: 0.00999967976963665\n" + "LN_SBPLX polish: MAXEVAL_REACHED after 1000 iterations. \n", + "x*=[1314.8, 1560.7, 2841.5, 1340.1, 1484.6, 10279.0]\n", + "LCOE*: 0.33058074968228673\n", + "shed*: 0.0\n" ] } ], @@ -807,30 +734,17 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "ff55bbfb-dd3f-437c-b7b2-c9fa55dc9644", "metadata": { "tags": [] }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `costs_opt` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `costs_opt` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[28]:1" - ] - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 92, "id": "d3f47b0a-7b0d-4556-892f-4a123244d1e0", "metadata": { "tags": [] @@ -840,35 +754,64 @@ "data": { "text/plain": [ "OperationStats with fields:\n", - "- served_energy: 6.775e6 kWh\n", - "- shed_energy: 0.0 kWh\n", - "- shed_max: 0.0 kW\n", - "- shed_hours: 0.0 h\n", - "- shed_duration_max: 0.0 h\n", - "- shed_rate: 0.0 in [0,1]\n", + "- served_energy: 11488.0 kWh\n", + "- shed_energy: 6.7635e6 kWh\n", + "- shed_max: 1704.4 kW\n", + "- shed_hours: 8760.0 h\n", + "- shed_duration_max: 8760.0 h\n", + "- shed_rate: 0.9983 in [0,1]\n", "- gen_energy: 0.0 kWh\n", "- gen_hours: 0.0 h\n", + "- gen_starts: 0.0 \n", "- gen_fuel: 0.0 L\n", - "- fc_energy: 103580.0 kWh\n", - "- fc_hours: 143.0 h\n", - "- h2_consumed: 6473.8 kg\n", - "- h2_produced: 6954.6 kg\n", - "- elyz_hours: 3920.0 h\n", - "- elyz_consumed_energy: 389460.0 kWh\n", - "- h2_chain_loss: 285850.0 kWh\n", - "- storage_cycles: 41.344 \n", - "- storage_char_energy: 726430.0 kWh\n", - "- storage_dis_energy: 673370.0 kWh\n", - "- storage_loss_energy: 69990.0 kWh\n", - "- spilled_energy: 1.7264e7 kWh\n", - "- spilled_max: 8847.8 kW\n", - "- spilled_rate: 0.70818 in [0,1]\n", - "- renew_potential_energy: 2.4378e7 kWh\n", - "- renew_energy: 7.1139e6 kWh\n", + "- gen_rt_at_nom: 0.0 h\n", + "- gen_min_rp: 0.0 KW\n", + "- gen_avg_rp: NaN kW\n", + "- gen_avg_rt: NaN h\n", + "- gen_max_rt: 0.0 h\n", + "- gen_min_rt: 8760.0 h\n", + "- gen_ru2: 0.0 \n", + "- gen_ro2: 0.0 \n", + "- fc_energy: 0.0 kWh\n", + "- fc_hours: 0.0 h\n", + "- fc_starts: 0.0 \n", + "- h2_consumed: 0.0 kg\n", + "- fc_rt_at_nom: 0.0 h\n", + "- fc_min_rp: 1.5446 KW\n", + "- fc_avg_rp: NaN kW\n", + "- fc_avg_rt: NaN h\n", + "- fc_max_rt: 0.0 h\n", + "- fc_min_rt: 8760.0 h\n", + "- fc_ru2: 0.0 \n", + "- fc_ro2: 0.0 \n", + "- h2_produced: 0.0 kg\n", + "- elyz_hours: 0.0 h\n", + "- elyz_starts: 0.0 \n", + "- elyz_consumed_energy: 0.0 kWh\n", + "- elyz_rt_at_nom: 0.0 h\n", + "- elyz_min_rp: 1.2654 KW\n", + "- elyz_avg_rp: NaN kW\n", + "- elyz_avg_rt: NaN h\n", + "- elyz_max_rt: 0.0 h\n", + "- elyz_min_rt: 8760.0 h\n", + "- elyz_ru2: 0.0 \n", + "- elyz_ro2: 0.0 \n", + "- h2_chain_loss: 0.0 kWh\n", + "- storage_cycles: 0.0 \n", + "- storage_char_energy: -0.0 kWh\n", + "- storage_dis_energy: 0.0 kWh\n", + "- storage_loss_energy: -0.0 kWh\n", + "- spilled_energy: 0.0 kWh\n", + "- spilled_max: 0.0 kW\n", + "- spilled_rate: 0.0 in [0,1]\n", + "- dumped_energy: 0.0 \n", + "- dump_max: 0.0 kW\n", + "- renew_potential_energy: 11488.0 kWh\n", + "- renew_energy: 11488.0 kWh\n", "- renew_rate: 1.0 in [0,1]\n" ] }, - "execution_count": 19, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -880,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 94, "id": "f0843d0b-f0bf-4442-95bc-c5b9ae360720", "metadata": { "tags": [] @@ -889,10 +832,10 @@ { "data": { "text/plain": [ - "58.38101140623111" + "0.030672080135118743" ] }, - "execution_count": 29, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -901,59 +844,6 @@ "costs.npc/1e6" ] }, - { - "cell_type": "code", - "execution_count": 21, - "id": "b01d2fab-2c1c-40fb-9903-0e602224e9e2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using Printf, CSV, DataFrames, Statistics\n", - "using PyPlot\n", - "with_plots = isdefined(Main, :PyPlot)\n", - "td = collect((0:nsteps-1)/24) # time in days\n", - "\n", - "function plot_oper_traj(microgrid, oper_traj)\n", - " fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", - " ax1.plot(td, Pload, label=\"load\")\n", - " ax1.plot(td, oper_traj.Pfc, \"tab:red\", label=\"Pfc\")\n", - " ax1.plot(td, oper_traj.Prenew_pot, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, oper_traj.Pelyz, \"tab:gray\", label=\"Pelyz\")\n", - " ax1.plot(td, oper_traj.Pfc, \"tab:pink\", label=\"Pfc\")\n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim([0, 1.25])\n", - " ax3.plot(td, oper_traj.Ebatt[2:end]/(xopt[2]*1000), \"C2\")\n", - " \n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(\n", - " ylabel=\"kW\"\n", - " )\n", - " \n", - " ax2.grid(true)\n", - " ax2.set(\n", - " title=\"H2 Tank level\",\n", - " xlabel=\"time (d)\",\n", - " ylabel=\"kWh\"\n", - " \n", - " )\n", - " ax2.plot(td, oper_traj.LoH[1:end-1]/(xopt[7]*1000), \"C2\")\n", - " \n", - " fig.tight_layout()\n", - " return fig, (ax1, ax2)\n", - "end\n", - "\n", - "if with_plots\n", - " fig, (ax1, ax2) = plot_oper_traj(mg, traj)\n", - " ax1.set_ylim(0, 4250)\n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, { "cell_type": "markdown", "id": "6d4a8f44-eebd-49cd-bcf3-2e44fd3372c7", @@ -966,12 +856,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "id": "9dc290cb-ac52-44db-8ee3-c54dce0fe4be", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 12.204017 seconds (8.72 M allocations: 23.794 GiB, 6.44% gc time, 0.03% compilation time)\n" + ] + }, + { + "data": { + "text/plain": [ + "([1.1066217604588284, 1.2478340192043902, 2.8452114580922436, 1.3346913580246922, 1.5173352605463255, 11.380016060108272], :MAXEVAL_REACHED, 10000)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "@time xopt, ret, numevals = optim_mg(x0, shed_max, :GN_DIRECT, 10000)" ] @@ -988,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 99, "id": "16330858-ca64-45e6-b641-d197924e401d", "metadata": { "tags": [] @@ -998,16 +906,16 @@ "name": "stdout", "output_type": "stream", "text": [ - " 50.694 ms (899212 allocations: 26.56 MiB)\n" + " 619.500 μs (867 allocations: 2.44 MiB)\n" ] }, { "data": { "text/plain": [ - "(0.32398700745291287, 0.0002557645932088728)" + "(0.43369115477332165, 0.0)" ] }, - "execution_count": 15, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -1037,9 +945,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 101, "id": "5d974d16-f0fc-4f9a-a48c-1f7b2bc48786", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", @@ -1052,9 +962,8 @@ { "data": { "text/latex": [ - "\\begin{verbatim}\n", - "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", - "\\end{verbatim}\n", + "aggregation(mg::Microgrid, oper\\_traj::OperationTraj, ε::Real=1.0)\n", + "\n", "Aggregate operation time series \\texttt{oper\\_traj} into yearly statistics for the the microgrid \\texttt{mg} (returned as an \\texttt{OperationStats} object).\n", "\n", "Discontinuous statistics can optionally be relaxed (smoothed) using the relaxation parameter \\texttt{ε}:\n", @@ -1070,9 +979,7 @@ "\n" ], "text/markdown": [ - "```\n", "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", - "```\n", "\n", "Aggregate operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object).\n", "\n", @@ -1084,7 +991,7 @@ "Using relaxation (`ε` > 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n" ], "text/plain": [ - "\u001b[36m aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\u001b[39m\n", + " aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", "\n", " Aggregate operation time series \u001b[36moper_traj\u001b[39m into yearly statistics for the the\n", " microgrid \u001b[36mmg\u001b[39m (returned as an \u001b[36mOperationStats\u001b[39m object).\n", @@ -1101,7 +1008,7 @@ " suggested." ] }, - "execution_count": 18, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -1123,7 +1030,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 103, "id": "2a51ac3b-4493-472b-8b0f-ac924edf5de7", "metadata": { "tags": [] @@ -1137,7 +1044,7 @@ " ([:battery, :energy_max], 3000.0, 0.0, 6000.0)" ] }, - "execution_count": 19, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -1150,11 +1057,27 @@ "]\n", "#optim_mg(mg0, opti_params)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e11c50ab-99ee-415c-959c-ec0d89b52452", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4706c0fe-e5a1-4f3c-9bb2-2ba4785080a1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.0_threads", "language": "julia", "name": "julia-1.10" }, diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index 71cd88f..ee5107c 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -10,7 +10,7 @@ "Case of Microgrid project with *wind* and *solar* sources,\n", "plus a *battery* and a *dispatchable generator*:\n", "\n", - "schematic of a microgrid with wind and H2\n", + "schematic of a microgrid with wind and H2\n", "\n", "Demo of main data structures and functions of [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl).\n", "Main steps are:\n", @@ -24,20 +24,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "855af8dd-d21d-4587-84cb-ae2b932deeaa", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Microgrids [bd581358-d3fa-499e-a26e-e70307242c03]\n" - ] - } - ], + "outputs": [], "source": [ "using Microgrids\n", "using Printf, CSV, DataFrames, Statistics" @@ -45,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "f7cc6435-9bd2-487f-943f-225c452db904", "metadata": { "tags": [] @@ -57,7 +49,7 @@ "PyObject " ] }, - "execution_count": 2, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -70,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "c910f9e4-1c92-44ca-8ce3-8c520ec9d231", "metadata": { "tags": [] @@ -82,7 +74,7 @@ "true" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -107,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "27688e3f-f2d5-4ee1-b4f5-ed1d451c44d6", "metadata": { "tags": [] @@ -149,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "c24c3df6-4062-450d-ba2d-32d71af7a823", "metadata": { "tags": [] @@ -161,7 +153,7 @@ "1453.0" ] }, - "execution_count": 5, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -180,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", "metadata": { "tags": [] @@ -188,7 +180,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "Figure(PyObject
)" ] @@ -265,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", "metadata": { "tags": [] @@ -280,22 +272,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 20, "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", "metadata": { "tags": [] }, "outputs": [ { - "ename": "LoadError", - "evalue": "UndefVarError: `capacity_from_wind` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `capacity_from_wind` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[4]:1" + "name": "stdout", + "output_type": "stream", + "text": [ + "Wind power capacity factor: 39.70 %\n" ] } ], @@ -304,18 +291,6 @@ "@printf(\"Wind power capacity factor: %.2f %%\\n\", mean(cf_wind)*100) # (pretty high in Ouessant)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b42c5d2-924b-40c1-9749-8d874de75566", - "metadata": {}, - "outputs": [], - "source": [ - "fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", - " ax1.plot(td, 0.5*Cp_D52*1.225/TSP_D52*(wind_speed^3), label=\"cf\")\n", - " ax1.plot(td, cf_wind, \"tab:red\", label=\"cf-corrected\")" - ] - }, { "cell_type": "markdown", "id": "d6734baf-c018-4130-9c6d-c5d37b237abb", @@ -338,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "dfa81d70-79a5-4e45-88c4-aab803d5d708", "metadata": { "tags": [] @@ -350,7 +325,7 @@ "Project(25, 0.05, 1.0, \"€\")" ] }, - "execution_count": 9, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -376,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "id": "6a9a9819-2f3a-4305-9710-b62d6d21f8f6", "metadata": { "tags": [] @@ -387,6 +362,34 @@ "salvage_price_ratio = 1.0;" ] }, + { + "cell_type": "markdown", + "id": "337811d9-9d2b-46be-8c25-46c09d4d552c", + "metadata": {}, + "source": [ + "Load default parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f79e611e-7cc3-43b1-b801-b40cf784aa1d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data definition for Microgrid with wind, solar and Hybrid Storage System...\n" + ] + } + ], + "source": [ + "include(\"data/Microgrid_Wind-Solar-H2_data.jl\"); " + ] + }, { "cell_type": "markdown", "id": "08d88c9c-5eaf-4044-bced-3f914481b061", @@ -399,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "id": "324dd7e0-50e7-4cb6-9017-5b5f8e897773", "metadata": { "tags": [] @@ -408,34 +411,20 @@ { "data": { "text/plain": [ - "ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")" + "ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 0.0, 25.0, 15000.0, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "power_rated_gen = 0. # rated power (kW) → /2 to see some load shedding\n", - "# fuel parameters:\n", - "fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max)\n", - "fuel_slope = 0.240 # fuel curve slope (L/h/kW)\n", - "# prices:\n", - "fuel_price = 1. # fuel price ($/L)\n", - "investment_price_gen = 400. # initial investiment price ($/kW)\n", - "om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation)\n", - "# other technical parameters\n", - "lifetime_gen = 15000. # generator lifetime (h)\n", - "load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1]\n", - "input_unit_gen = \"L\" # fuel quantity unit (used in fuel price and consumption curve parameters)\n", - "output_unit_gen = \"KWh\"\n", - "\n", - "generator = ProductionUnit(power_rated_gen,\n", - " fuel_intercept, fuel_slope, fuel_price,\n", - " investment_price_gen, om_price_gen, lifetime_gen,\n", - " load_ratio_min_gen,\n", - " replacement_price_ratio, salvage_price_ratio, input_unit_gen,output_unit_gen)\n" + "gen = ProductionUnit(0.0,\n", + " fuel_intercept, fuel_slope, fuel_price,\n", + " capex_def[1], om_price_hour_gen, om_price_gen, lifetime_gen_y,lifetime_gen_h,lifetime_gen_starts,\n", + " load_ratio_min_gen, replacement_price_ratio,\n", + " salvage_price_ratio, input_unit_gen,output_unit_gen)\n" ] }, { @@ -448,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 32, "id": "9079e059-fd4a-4d5e-9358-29758fb06c86", "metadata": { "tags": [] @@ -457,26 +446,17 @@ { "data": { "text/plain": [ - "Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0)" + "Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0)" ] }, - "execution_count": 12, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "capacity_rated_ftank = 0. # rated power capacity (L)\n", - "investment_price_ftank = 0.0 # initial investment price ($/L)\n", - "om_price_ftank = 0.# operation and maintenance price ($/L/y)\n", - "lifetime_ftank = 25. # calendar lifetime (y)\n", - "loss_factor_ftank = 0. # hydrogen used on site \n", - "fuel_ini_ratio = 1. # Initial load ratio ∈ [0,1]\n", - "fuel_min_ratio = 0. # minimum load ratio ∈ [0,1]\n", - "fuel_max_ratio = 1. # maximum load ratio ∈ [0,1]\n", - "\n", - "ftank = Tank(capacity_rated_ftank,investment_price_ftank, om_price_ftank,lifetime_ftank,loss_factor_ftank,fuel_ini_ratio,\n", - " fuel_min_ratio, fuel_max_ratio,replacement_price_ratio, salvage_price_ratio)" + "ftank = Tank(capacity_rated_ftank,capex_def[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,0.0,\n", + " fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio)" ] }, { @@ -489,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "id": "2c1bd7f7-24a0-43f5-822b-d77927ab76c8", "metadata": { "tags": [] @@ -498,28 +478,17 @@ { "data": { "text/plain": [ - "ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")" + "ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1600.0, 0.0, 45.0, 15.0, 45000.0, 5000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")" ] }, - "execution_count": 13, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "power_rated_fc = 1800. # rated power capacity (KW)\n", - "cons_intercept_fc=0. #\n", - "cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé)\n", - "cons_price_fc = 0. #\n", - "investment_price_fc = 1000. # initial investment price ($/KW)\n", - "om_price_fc = 10. # operation and maintenance price ($/kW/y)\n", - "lifetime_fc = 45000. # Fuel Cell lifetime (h)\n", - "load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1]\n", - "input_unit_fc= \"Kg\"\n", - "output_unit_fc=\"KWh\"\n", - "\n", - "fuel_cell = ProductionUnit(power_rated_fc,cons_intercept_fc, cons_rate_fc,cons_price_fc,investment_price_fc, om_price_fc,lifetime_fc,\n", - " load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc)\n", + "fuel_cell = ProductionUnit(1800.,cons_intercept_fc, cons_rate_fc,cons_price_fc,capex_def[3], om_price_hour_fc, om_price_fc,lifetime_fc_y,lifetime_fc_h,lifetime_fc_starts,\n", + " load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc)\n", "\n", "\n", "\n" @@ -535,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "fad9eb7c-789d-49ab-aa2b-e309df77b3e5", "metadata": { "tags": [] @@ -544,26 +513,17 @@ { "data": { "text/plain": [ - "Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)" + "Tank{Float64}(12000.0, 500.0, 4.0, 25.0, 0.0, 0.5, 0.0, 1.0, 14.0, 1.0, 1.0)" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "capacity_rated_hytank = 10000. # rated power capacity (kg)\n", - "investment_price_hytank = 0.8 # initial investment price ($/kg)\n", - "om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y)\n", - "lifetime_hytank = 25. # calendar lifetime (y)\n", - "loss_factor_hytank = 0. # hydrogen used on site \n", - "LoH_ini_ratio = 0. # Initial load ratio ∈ [0,1]\n", - "LoH_min_ratio = 0.2 # minimum load ratio ∈ [0,1]\n", - "LoH_max_ratio = 1. # maximum load ratio ∈ [0,1]\n", - "\n", - "hytank = Tank(capacity_rated_hytank,investment_price_hytank, om_price_hytank,lifetime_hytank,loss_factor_hytank,LoH_ini_ratio,\n", - " LoH_min_ratio, LoH_max_ratio,replacement_price_ratio, salvage_price_ratio)\n" + "hytank = Tank(12000.,capex_def[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,0.5,\n", + " LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio)\n" ] }, { @@ -576,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "id": "1cab4a19-ae90-486d-b6c6-4863fda458f5", "metadata": { "tags": [] @@ -585,16 +545,16 @@ { "data": { "text/plain": [ - "DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")])" + "DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 0.0, 25.0, 15000.0, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1600.0, 0.0, 45.0, 15.0, 45000.0, 5000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")])" ] }, - "execution_count": 15, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dispatchables = DispatchableCompound{Float64}([generator,], [fuel_cell])" + "dispatchables = DispatchableCompound{Float64}([gen,], [fuel_cell])" ] }, { @@ -609,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 41, "id": "6b00b378-1ee2-4682-8b45-f7e0fc1a012a", "metadata": { "tags": [] @@ -618,10 +578,10 @@ { "data": { "text/plain": [ - "TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0))" + "TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(12000.0, 500.0, 4.0, 25.0, 0.0, 0.5, 0.0, 1.0, 14.0, 1.0, 1.0))" ] }, - "execution_count": 16, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -644,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 43, "id": "f744269a-b6bc-4fce-804a-66311843ed76", "metadata": { "tags": [] @@ -656,29 +616,16 @@ "Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0)" ] }, - "execution_count": 17, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "energy_rated_sto = 5000. # rated energy capacity (kWh)\n", - "investment_price_sto = 350. # initial investiment price ($/kWh)\n", - "om_price_sto = 10. # operation and maintenance price ($/kWh/y)\n", - "lifetime_sto = 15. # calendar lifetime (y)\n", - "lifetime_cycles = 3000. # maximum number of cycles over life (1)\n", - "# Parameters with default values\n", - "charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1)\n", - "discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1)\n", - "loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1]\n", - "SoC_min = 0. # minimum State of Charge ∈ [0,1]\n", - "SoC_max = 1. # minimum State of Charge ∈ [0,1]\n", - "SoC_ini = 0. # initial State of Charge ∈ [0,1]\n", - "\n", - "battery = Battery(energy_rated_sto,\n", - " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", - " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,SoC_ini,\n", - " replacement_price_ratio, salvage_price_ratio)" + "battery = Battery(5000.,\n", + " capex_def[6], om_price_sto, lifetime_sto, lifetime_cycles,\n", + " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,0.0,\n", + " replacement_price_ratio, salvage_price_ratio)" ] }, { @@ -694,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 45, "id": "059c09d0-4fb5-408a-975c-228c971c9b0f", "metadata": { "tags": [] @@ -703,29 +650,17 @@ { "data": { "text/plain": [ - "ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")" + "ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1600.0, 0.0, 44.0, 20.0, 45000.0, 5000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")" ] }, - "execution_count": 18, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "power_rated_elyz = 2000. # rated power capacity (Kw)\n", - "cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2)\n", - "cons_slope_elyz = 56. # consumption rate (KWhé/KgH2)\n", - "cons_price_elyz = 0.#\n", - "investment_price_elyz = 1000. # initial investment price ($/kW)\n", - "om_price_elyz = 10. # operation and maintenance price ($/kW/y)\n", - "lifetime_elyz = 45000. #Electrolyzer lifetime (h)\n", - "load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1]\n", - "\n", - "input_unit_elyz= \"KWh\"\n", - "output_unit_elyz=\"Kg\"\n", - "\n", - "elyz = ProductionUnit(power_rated_elyz,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, investment_price_elyz, om_price_elyz, lifetime_elyz,\n", - " load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz)\n" + "elyz = ProductionUnit(2000.,cons_intercept_elyz,cons_rate_elyz,cons_price_elyz, capex_def[5], om_price_hour_elyz,om_price_elyz, lifetime_elyz_y,lifetime_elyz_h,lifetime_elyz_starts,\n", + " load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz)\n" ] }, { @@ -740,7 +675,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 47, "id": "10ce4795-54f9-4e77-bf7f-886851756d84", "metadata": { "tags": [] @@ -749,23 +684,15 @@ { "data": { "text/plain": [ - "Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)" + "Photovoltaic{Float64}(5000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)" ] }, - "execution_count": 19, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "power_rated_pv = 3000. # rated power (kW)\n", - "irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²)\n", - "investment_price_pv = 1200. # initial investiment price ($/kW)\n", - "om_price_pv = 20.# operation and maintenance price ($/kW/y)\n", - "lifetime_pv = 25. # lifetime (y)\n", - "# Parameters with default values\n", - "derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1]\n", - "\n", "photovoltaic = Photovoltaic(power_rated_pv, irradiance,\n", " investment_price_pv, om_price_pv,\n", " lifetime_pv, derating_factor_pv,\n", @@ -783,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 49, "id": "1a047087-37e1-4f3f-a936-c562ebfb6f8d", "metadata": { "tags": [] @@ -791,7 +718,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "Figure(PyObject
)" ] @@ -825,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 51, "id": "fefdc955-8473-4f10-b19d-6674bedf773e", "metadata": { "tags": [] @@ -837,17 +764,12 @@ "WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" ] }, - "execution_count": 21, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "power_rated_wind = 1800. # rated power (kW)\n", - "investment_price_wind = 3500. # initial investiment price ($/kW)\n", - "om_price_wind = 100.# operation and maintenance price ($/kW/y)\n", - "lifetime_wind = 25. # lifetime (y)\n", - "\n", "windgen = WindPower(power_rated_wind, cf_wind,\n", " investment_price_wind, om_price_wind,\n", " lifetime_wind,\n", @@ -865,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 53, "id": "ea472eff-4cdd-42aa-8515-a1b72dc66c62", "metadata": { "tags": [] @@ -873,7 +795,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "Figure(PyObject
)" ] @@ -908,31 +830,7 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "a812d24d-819c-40d2-993b-a78ebc342125", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1-element Vector{ProductionUnit{Float64}}:\n", - " ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[elyz,]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, + "execution_count": 55, "id": "13483089-225f-4fe4-98c8-656a675ba84d", "metadata": { "tags": [] @@ -941,17 +839,15 @@ { "data": { "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" + "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(0.0, 0.0, 0.24, 1.0, 400.0, 0.02, 0.0, 25.0, 15000.0, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1600.0, 0.0, 45.0, 15.0, 45000.0, 5000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1600.0, 0.0, 44.0, 20.0, 45000.0, 5000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(12000.0, 500.0, 4.0, 25.0, 0.0, 0.5, 0.0, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(5000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" ] }, - "execution_count": 24, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", - "\n", "microgrid = Microgrid(project, Pload,dispatchables,\n", " [elyz,],tanks,battery, [\n", " photovoltaic,\n", @@ -960,6 +856,14 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8beb6d3-1f06-478e-962b-aa38af42e061", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "0e43de8d-82fe-4d33-9010-cbd6b620cca9", @@ -972,14 +876,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 58, "id": "9038c442-f3bc-4fe7-8e3f-83e77a42d2e7", "metadata": { "tags": [] }, "outputs": [], "source": [ - "oper_traj, oper_stats, mg_costs = simulate(microgrid,dispatch_2);" + "oper_traj, oper_stats, mg_costs = simulate(microgrid,dispatch_1);" ] }, { @@ -1010,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 63, "id": "8ce71bfd-a4ad-4e41-984e-8a78ce890699", "metadata": { "tags": [] @@ -1022,30 +926,7 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "c3b07f26-c09c-4abe-bffa-1af9bd836736", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2.9680365296803655" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "26000 /8760" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 64, "id": "748d17cd-cfae-44d9-ac10-856d095aa96c", "metadata": { "tags": [] @@ -1055,79 +936,12 @@ "name": "stdout", "output_type": "stream", "text": [ - " 518.500 μs (55 allocations: 1.14 MiB)\n" - ] - } - ], - "source": [ - "@btime oper_traj = operation(microgrid,dispatch_2);" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "fcbd0801-7d24-400b-b49e-8513fc7d4eac", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "MethodError: no method matching operation(::Microgrid{Float64})\n\n\u001b[0mClosest candidates are:\n\u001b[0m operation(::Microgrid, \u001b[91m::Function\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[35mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", - "output_type": "error", - "traceback": [ - "MethodError: no method matching operation(::Microgrid{Float64})\n\n\u001b[0mClosest candidates are:\n\u001b[0m operation(::Microgrid, \u001b[91m::Function\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[35mMicrogrids\u001b[39m \u001b[90mC:\\Users\\nikiemaje\\Documents\\Macopie\\Microgrids.jl\\src\\\u001b[39m\u001b[90m\u001b[4moperation.jl:193\u001b[24m\u001b[39m\n", - "", - "Stacktrace:", - " [1] var\"##core#304\"()", - " @ Main C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:489", - " [2] var\"##sample#305\"(::Tuple{}, __params::BenchmarkTools.Parameters)", - " @ Main C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:495", - " [3] _run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; verbose::Bool, pad::String, kwargs::Base.Pairs{Symbol, Integer, NTuple{4, Symbol}, NamedTuple{(:samples, :evals, :gctrial, :gcsample), Tuple{Int64, Int64, Bool, Bool}}})", - " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:99", - " [4] #invokelatest#2", - " @ .\\essentials.jl:821 [inlined]", - " [5] invokelatest", - " @ .\\essentials.jl:816 [inlined]", - " [6] #run_result#45", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:34 [inlined]", - " [7] run_result", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:34 [inlined]", - " [8] run(b::BenchmarkTools.Benchmark, p::BenchmarkTools.Parameters; progressid::Nothing, nleaves::Float64, ndone::Float64, kwargs::Base.Pairs{Symbol, Integer, NTuple{5, Symbol}, NamedTuple{(:verbose, :samples, :evals, :gctrial, :gcsample), Tuple{Bool, Int64, Int64, Bool, Bool}}})", - " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:117", - " [9] run (repeats 2 times)", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:117 [inlined]", - " [10] #warmup#54", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:169 [inlined]", - " [11] warmup(item::BenchmarkTools.Benchmark)", - " @ BenchmarkTools C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:168", - " [12] top-level scope", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\BenchmarkTools\\0owsb\\src\\execution.jl:575" - ] - } - ], - "source": [ - "@btime oper_traj = operation(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "c9bf9e90-a9dc-43ca-95f8-4a3d76eb0cf1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2.955 ms (223 allocations: 1.15 MiB)\n" + " 439.800 μs (57 allocations: 1.21 MiB)\n" ] } ], "source": [ - "@btime oper_traj, oper_stats, mg_costs = simulate(microgrid,dispatch_2);" + "@btime oper_traj = operation(microgrid,dispatch_1);" ] }, { @@ -1150,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 67, "id": "3695a442-4e04-4436-9374-70c85036beda", "metadata": { "tags": [] @@ -1160,7 +974,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Load shedding rate: 3.1%\n", + "Load shedding rate: 0.2%\n", "Renewable rate: 100.0%\n" ] } @@ -1180,30 +994,7 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "dcdb5710-b0c0-4fb7-b92f-71265b6f2489", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1041.8586431923732" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_traj.Pbatt[3000]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, + "execution_count": 70, "id": "0090bf4d-f684-492b-9583-bd56854329b0", "metadata": { "tags": [] @@ -1213,35 +1004,64 @@ "data": { "text/plain": [ "OperationStats with fields:\n", - "- served_energy: 6.5642e6 kWh\n", - "- shed_energy: 210820.0 kWh\n", + "- served_energy: 6.7622e6 kWh\n", + "- shed_energy: 12747.0 kWh\n", "- shed_max: 1520.2 kW\n", - "- shed_hours: 370.0 h\n", - "- shed_duration_max: 56.0 h\n", - "- shed_rate: 0.031117 in [0,1]\n", + "- shed_hours: 10.0 h\n", + "- shed_duration_max: 6.0 h\n", + "- shed_rate: 0.0018814 in [0,1]\n", "- gen_energy: 0.0 kWh\n", "- gen_hours: 0.0 h\n", + "- gen_starts: 0.0 \n", "- gen_fuel: 0.0 L\n", - "- fc_energy: 626880.0 kWh\n", - "- fc_hours: 1152.0 h\n", - "- h2_consumed: 39180.0 kg\n", - "- h2_produced: 41180.0 kg\n", - "- elyz_hours: 2667.0 h\n", - "- elyz_consumed_energy: 2.3061e6 kWh\n", - "- h2_chain_loss: 1.6791e6 kWh\n", - "- storage_cycles: 157.88 \n", - "- storage_char_energy: 828880.0 kWh\n", - "- storage_dis_energy: 749940.0 kWh\n", - "- storage_loss_energy: 78941.0 kWh\n", - "- spilled_energy: 1.0453e6 kWh\n", - "- spilled_max: 3413.9 kW\n", - "- spilled_rate: 0.11159 in [0,1]\n", - "- renew_potential_energy: 9.3676e6 kWh\n", - "- renew_energy: 8.3223e6 kWh\n", + "- gen_rt_at_nom: 0.0 h\n", + "- gen_min_rp: 0.0 KW\n", + "- gen_avg_rp: NaN kW\n", + "- gen_avg_rt: NaN h\n", + "- gen_max_rt: 0.0 h\n", + "- gen_min_rt: 8760.0 h\n", + "- gen_ru2: 0.0 \n", + "- gen_ro2: 0.0 \n", + "- fc_energy: 651690.0 kWh\n", + "- fc_hours: 1169.0 h\n", + "- fc_starts: 133.0 \n", + "- h2_consumed: 40730.0 kg\n", + "- fc_rt_at_nom: 0.0 h\n", + "- fc_min_rp: 90.0 KW\n", + "- fc_avg_rp: 557.47 kW\n", + "- fc_avg_rt: 8.7895 h\n", + "- fc_max_rt: 63.0 h\n", + "- fc_min_rt: 1.0 h\n", + "- fc_ru2: 6.0 \n", + "- fc_ro2: 127.0 \n", + "- h2_produced: 34730.0 kg\n", + "- elyz_hours: 2050.0 h\n", + "- elyz_starts: 161.0 \n", + "- elyz_consumed_energy: 1.9449e6 kWh\n", + "- elyz_rt_at_nom: 309.0 h\n", + "- elyz_min_rp: 100.45 KW\n", + "- elyz_avg_rp: 948.73 kW\n", + "- elyz_avg_rt: 12.733 h\n", + "- elyz_max_rt: 102.0 h\n", + "- elyz_min_rt: 1.0 h\n", + "- elyz_ru2: 15.0 \n", + "- elyz_ro2: 146.0 \n", + "- h2_chain_loss: 1.2936e6 kWh\n", + "- storage_cycles: 174.45 \n", + "- storage_char_energy: 915880.0 kWh\n", + "- storage_dis_energy: 828650.0 kWh\n", + "- storage_loss_energy: 87227.0 kWh\n", + "- spilled_energy: 3.2968e6 kWh\n", + "- spilled_max: 5044.6 kW\n", + "- spilled_rate: 0.28819 in [0,1]\n", + "- dumped_energy: 0.0 \n", + "- dump_max: 0.0 kW\n", + "- renew_potential_energy: 1.1439e7 kWh\n", + "- renew_energy: 8.1427e6 kWh\n", "- renew_rate: 1.0 in [0,1]\n" ] }, - "execution_count": 33, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1250,29 +1070,6 @@ "oper_stats" ] }, - { - "cell_type": "code", - "execution_count": 34, - "id": "59e59883-d545-4de7-9584-ac06b5110005", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3148.701693426455" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_traj.LoH[7680]" - ] - }, { "cell_type": "markdown", "id": "5120f110-3a6b-4073-8be7-d69804dfb1b3", @@ -1288,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 83, "id": "8b904163-9ef8-4b87-a11c-d06ed53af5ac", "metadata": { "tags": [] @@ -1298,8 +1095,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Levelized Cost of Electricity: 0.228 $/kWh\n", - "Net Present Cost: 21.13 M$ (over 25 years at 5% discount rate)\n" + "Levelized Cost of Electricity: 0.380 $/kWh\n", + "Net Present Cost: 36.19 M$ (over 25 years at 5% discount rate)\n" ] } ], @@ -1319,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 88, "id": "7e5fde52-1cd8-43a1-9f9b-c04d659d4f1a", "metadata": { "tags": [] @@ -1328,10 +1125,10 @@ { "data": { "text/plain": [ - "CostFactors(2.113122113200041e7, 1.5458e7, 1.719801651934103e6, 4.623166166276827e6, 0.0, -669746.6862105239)" + "CostFactors(3.619206592391976e7, 2.6214e7, 3.4331555093547925e6, 7.709387677626477e6, 0.0, -1.1644772630615076e6)" ] }, - "execution_count": 36, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -1350,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 92, "id": "82c503dc-02a8-4fcf-86c0-b0e3bc3784dd", "metadata": { "tags": [] @@ -1360,11 +1157,11 @@ "data": { "text/plain": [ "2-element Vector{CostFactors}:\n", - " CostFactors(4.445636673962684e6, 3.6e6, 0.0, 845636.6739626848, 0.0, -0.0)\n", + " CostFactors(7.409394456604475e6, 6.0e6, 0.0, 1.4093944566044747e6, 0.0, -0.0)\n", " CostFactors(8.836910021888055e6, 6.3e6, 0.0, 2.5369100218880544e6, 0.0, -0.0)" ] }, - "execution_count": 37, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -1383,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 98, "id": "82273c01-fa92-414a-af84-46bb793c7988", "metadata": { "tags": [] @@ -1395,7 +1192,7 @@ "(:total, :investment, :replacement, :om, :fuel, :salvage)" ] }, - "execution_count": 38, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -1416,7 +1213,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 100, "id": "d0dc9505-34d3-4657-a40b-2d5aa684a19b", "metadata": { "tags": [] @@ -1425,10 +1222,10 @@ { "data": { "text/plain": [ - "CostFactors(21.13, 15.46, 1.72, 4.62, 0.0, -0.67)" + "CostFactors(36.19, 26.21, 3.43, 7.71, 0.0, -1.16)" ] }, - "execution_count": 39, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -1447,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 102, "id": "5eb2896b-4e36-4eb8-adcc-fb8cbd67809d", "metadata": { "tags": [] @@ -1456,10 +1253,10 @@ { "data": { "text/plain": [ - "CostFactors(1.86, 1.8, 0.0, 0.25, 0.0, -0.19)" + "CostFactors(5.12, 2.88, 1.39, 1.14, 0.0, -0.28)" ] }, - "execution_count": 40, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } @@ -1470,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 103, "id": "106877f9-9008-45de-8f68-05a1cb4bb6fa", "metadata": { "tags": [] @@ -1479,10 +1276,10 @@ { "data": { "text/plain": [ - "CostFactors(2.85, 2.0, 0.88, 0.28, 0.0, -0.31)" + "CostFactors(4.94, 3.2, 1.21, 1.24, 0.0, -0.71)" ] }, - "execution_count": 41, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -1499,339 +1296,33 @@ "### Operation trajectories" ] }, - { - "cell_type": "markdown", - "id": "3f2b70c2-08ef-458e-9090-919ff4e3efa6", - "metadata": {}, - "source": [ - "`oper_traj` is of type `OperationTraj` with fields such as `Pgen` (generator power), `Pbatt` (battery power)...\n", - "\n", - "**Graph 1** Zoom to first week of January: high load, wind at maximum, few solar → battery often empty" - ] - }, { "cell_type": "code", - "execution_count": 42, - "id": "4ddf950f-5aa6-48e8-ae8c-cf516bd07db9", + "execution_count": 120, + "id": "5eb68af5-e92d-44be-886f-fe9b2a8e4dcc", "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, "tags": [] }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "search: \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m3D \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22mfile \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_date \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_trisurf \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_surface \u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1ml\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mt\u001b[22m_ts_zoom\n", - "\n" - ] - }, { "data": { "text/plain": [ - "Plot y versus x as lines and/or markers.\n", - "\n", - "Call signatures::\n", - "\n", - " plot([x], y, [fmt], *, data=None, **kwargs)\n", - " plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n", - "\n", - "The coordinates of the points or line nodes are given by *x*, *y*.\n", - "\n", - "The optional parameter *fmt* is a convenient way for defining basic\n", - "formatting like color, marker and linestyle. It's a shortcut string\n", - "notation described in the *Notes* section below.\n", - "\n", - ">>> plot(x, y) # plot x and y using default line style and color\n", - ">>> plot(x, y, 'bo') # plot x and y using blue circle markers\n", - ">>> plot(y) # plot y using x as index array 0..N-1\n", - ">>> plot(y, 'r+') # ditto, but with red plusses\n", - "\n", - "You can use `.Line2D` properties as keyword arguments for more\n", - "control on the appearance. Line properties and *fmt* can be mixed.\n", - "The following two calls yield identical results:\n", - "\n", - ">>> plot(x, y, 'go--', linewidth=2, markersize=12)\n", - ">>> plot(x, y, color='green', marker='o', linestyle='dashed',\n", - "... linewidth=2, markersize=12)\n", - "\n", - "When conflicting with *fmt*, keyword arguments take precedence.\n", - "\n", - "\n", - "**Plotting labelled data**\n", - "\n", - "There's a convenient way for plotting objects with labelled data (i.e.\n", - "data that can be accessed by index ``obj['y']``). Instead of giving\n", - "the data in *x* and *y*, you can provide the object in the *data*\n", - "parameter and just give the labels for *x* and *y*::\n", - "\n", - ">>> plot('xlabel', 'ylabel', data=obj)\n", - "\n", - "All indexable objects are supported. This could e.g. be a `dict`, a\n", - "`pandas.DataFrame` or a structured numpy array.\n", - "\n", - "\n", - "**Plotting multiple sets of data**\n", - "\n", - "There are various ways to plot multiple sets of data.\n", - "\n", - "- The most straight forward way is just to call `plot` multiple times.\n", - " Example:\n", - "\n", - " >>> plot(x1, y1, 'bo')\n", - " >>> plot(x2, y2, 'go')\n", - "\n", - "- If *x* and/or *y* are 2D arrays a separate data set will be drawn\n", - " for every column. If both *x* and *y* are 2D, they must have the\n", - " same shape. If only one of them is 2D with shape (N, m) the other\n", - " must have length N and will be used for every data set m.\n", - "\n", - " Example:\n", - "\n", - " >>> x = [1, 2, 3]\n", - " >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n", - " >>> plot(x, y)\n", - "\n", - " is equivalent to:\n", - "\n", - " >>> for col in range(y.shape[1]):\n", - " ... plot(x, y[:, col])\n", - "\n", - "- The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n", - " groups::\n", - "\n", - " >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n", - "\n", - " In this case, any additional keyword argument applies to all\n", - " datasets. Also, this syntax cannot be combined with the *data*\n", - " parameter.\n", - "\n", - "By default, each line is assigned a different style specified by a\n", - "'style cycle'. The *fmt* and line property parameters are only\n", - "necessary if you want explicit deviations from these defaults.\n", - "Alternatively, you can also change the style cycle using\n", - ":rc:`axes.prop_cycle`.\n", - "\n", - "\n", - "Parameters\n", - "----------\n", - "x, y : array-like or scalar\n", - " The horizontal / vertical coordinates of the data points.\n", - " *x* values are optional and default to ``range(len(y))``.\n", - "\n", - " Commonly, these parameters are 1D arrays.\n", - "\n", - " They can also be scalars, or two-dimensional (in that case, the\n", - " columns represent separate data sets).\n", - "\n", - " These arguments cannot be passed as keywords.\n", - "\n", - "fmt : str, optional\n", - " A format string, e.g. 'ro' for red circles. See the *Notes*\n", - " section for a full description of the format strings.\n", - "\n", - " Format strings are just an abbreviation for quickly setting\n", - " basic line properties. All of these and more can also be\n", - " controlled by keyword arguments.\n", - "\n", - " This argument cannot be passed as keyword.\n", - "\n", - "data : indexable object, optional\n", - " An object with labelled data. If given, provide the label names to\n", - " plot in *x* and *y*.\n", - "\n", - " .. note::\n", - " Technically there's a slight ambiguity in calls where the\n", - " second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n", - " could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n", - " the former interpretation is chosen, but a warning is issued.\n", - " You may suppress the warning by adding an empty format string\n", - " ``plot('n', 'o', '', data=obj)``.\n", - "\n", - "Returns\n", - "-------\n", - "list of `.Line2D`\n", - " A list of lines representing the plotted data.\n", - "\n", - "Other Parameters\n", - "----------------\n", - "scalex, scaley : bool, default: True\n", - " These parameters determine if the view limits are adapted to the\n", - " data limits. The values are passed on to\n", - " `~.axes.Axes.autoscale_view`.\n", - "\n", - "**kwargs : `~matplotlib.lines.Line2D` properties, optional\n", - " *kwargs* are used to specify properties like a line label (for\n", - " auto legends), linewidth, antialiasing, marker face color.\n", - " Example::\n", - "\n", - " >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n", - " >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n", - "\n", - " If you specify multiple lines with one plot call, the kwargs apply\n", - " to all those lines. In case the label object is iterable, each\n", - " element is used as labels for each set of data.\n", - "\n", - " Here is a list of available `.Line2D` properties:\n", - "\n", - " Properties:\n", - " agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n", - " alpha: scalar or None\n", - " animated: bool\n", - " antialiased or aa: bool\n", - " clip_box: `~matplotlib.transforms.BboxBase` or None\n", - " clip_on: bool\n", - " clip_path: Patch or (Path, Transform) or None\n", - " color or c: color\n", - " dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n", - " dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n", - " dashes: sequence of floats (on/off ink in points) or (None, None)\n", - " data: (2, N) array or two 1D arrays\n", - " drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n", - " figure: `~matplotlib.figure.Figure`\n", - " fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n", - " gapcolor: color or None\n", - " gid: str\n", - " in_layout: bool\n", - " label: object\n", - " linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n", - " linewidth or lw: float\n", - " marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n", - " markeredgecolor or mec: color\n", - " markeredgewidth or mew: float\n", - " markerfacecolor or mfc: color\n", - " markerfacecoloralt or mfcalt: color\n", - " markersize or ms: float\n", - " markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n", - " mouseover: bool\n", - " path_effects: list of `.AbstractPathEffect`\n", - " picker: float or callable[[Artist, Event], tuple[bool, dict]]\n", - " pickradius: float\n", - " rasterized: bool\n", - " sketch_params: (scale: float, length: float, randomness: float)\n", - " snap: bool or None\n", - " solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n", - " solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n", - " transform: unknown\n", - " url: str\n", - " visible: bool\n", - " xdata: 1D array\n", - " ydata: 1D array\n", - " zorder: float\n", - "\n", - "See Also\n", - "--------\n", - "scatter : XY scatter plot with markers of varying size and/or color (\n", - " sometimes also called bubble chart).\n", - "\n", - "Notes\n", - "-----\n", - "**Format Strings**\n", - "\n", - "A format string consists of a part for color, marker and line::\n", - "\n", - " fmt = '[marker][line][color]'\n", - "\n", - "Each of them is optional. If not provided, the value from the style\n", - "cycle is used. Exception: If ``line`` is given, but no ``marker``,\n", - "the data will be a line without markers.\n", - "\n", - "Other combinations such as ``[color][marker][line]`` are also\n", - "supported, but note that their parsing may be ambiguous.\n", - "\n", - "**Markers**\n", - "\n", - "============= ===============================\n", - "character description\n", - "============= ===============================\n", - "``'.'`` point marker\n", - "``','`` pixel marker\n", - "``'o'`` circle marker\n", - "``'v'`` triangle_down marker\n", - "``'^'`` triangle_up marker\n", - "``'<'`` triangle_left marker\n", - "``'>'`` triangle_right marker\n", - "``'1'`` tri_down marker\n", - "``'2'`` tri_up marker\n", - "``'3'`` tri_left marker\n", - "``'4'`` tri_right marker\n", - "``'8'`` octagon marker\n", - "``'s'`` square marker\n", - "``'p'`` pentagon marker\n", - "``'P'`` plus (filled) marker\n", - "``'*'`` star marker\n", - "``'h'`` hexagon1 marker\n", - "``'H'`` hexagon2 marker\n", - "``'+'`` plus marker\n", - "``'x'`` x marker\n", - "``'X'`` x (filled) marker\n", - "``'D'`` diamond marker\n", - "``'d'`` thin_diamond marker\n", - "``'|'`` vline marker\n", - "``'_'`` hline marker\n", - "============= ===============================\n", - "\n", - "**Line Styles**\n", - "\n", - "============= ===============================\n", - "character description\n", - "============= ===============================\n", - "``'-'`` solid line style\n", - "``'--'`` dashed line style\n", - "``'-.'`` dash-dot line style\n", - "``':'`` dotted line style\n", - "============= ===============================\n", - "\n", - "Example format strings::\n", - "\n", - " 'b' # blue markers with default shape\n", - " 'or' # red circles\n", - " '-g' # green solid line\n", - " '--' # dashed line with default color\n", - " '^k:' # black triangle_up markers connected by a dotted line\n", - "\n", - "**Colors**\n", - "\n", - "The supported color abbreviations are the single letter codes\n", - "\n", - "============= ===============================\n", - "character color\n", - "============= ===============================\n", - "``'b'`` blue\n", - "``'g'`` green\n", - "``'r'`` red\n", - "``'c'`` cyan\n", - "``'m'`` magenta\n", - "``'y'`` yellow\n", - "``'k'`` black\n", - "``'w'`` white\n", - "============= ===============================\n", - "\n", - "and the ``'CN'`` colors that index into the default property cycle.\n", - "\n", - "If the color is the only part of the format string, you can\n", - "additionally use any `matplotlib.colors` spec, e.g. full names\n", - "(``'green'``) or hex strings (``'#008000'``)." + "plot_cashflow_comp (generic function with 1 method)" ] }, - "execution_count": 42, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "?plot\n", - "\n", - "\n" + "include(\"Useful_functions.jl\")" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "75f21136-a63d-443e-a37d-69cfd6067e24", + "execution_count": 124, + "id": "b42cbddb-c2c2-4012-a9ac-1f28898e5ae8", "metadata": { "tags": [] }, @@ -1839,614 +1330,16 @@ { "data": { "text/plain": [ - "401.0798711924217" + "(Figure(PyObject
), PyObject , PyObject )" ] }, - "execution_count": 43, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "oper_traj.power_curtailment[1646]" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "87b9d1d0-61fc-4cd2-b9f4-69fe3a487c24", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3472.0798711924217" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " oper_traj.Prenew_pot[1646]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "b9933b12-d650-45f1-898e-59aa8457e443", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.0" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_traj.Pbatt[1646]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "729db6ba-0cdb-41d7-9a40-9134698c7547", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2000.0" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_traj.Pelyz[1646]" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "0858a5ab-e94b-4abd-a0fc-a0e6eec04472", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1071.0" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Pload[1646]" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7f418cd0-33b5-4a75-b4f7-522511644382", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2903.8797262849666" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_traj.Pelyz[1645]+Pload[1645]+oper_traj.Pbatt[1645]+oper_traj.power_curtailment[1645]" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ce7204b5-e389-47d2-a00f-dc06bef14ff9", - "metadata": {}, - "outputs": [ - { - "ename": "LoadError", - "evalue": "syntax: incomplete: premature end of input", - "output_type": "error", - "traceback": [ - "syntax: incomplete: premature end of input", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[49]:1" - ] - } - ], - "source": [ - "tm= " - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "function plot_oper_traj(microgrid, oper_traj)\n", - " fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", - " ax1.plot(td, Pload, label=\"load\")\n", - "\n", - " ax1.plot(td, oper_traj.Prenew_pot, \"tab:orange\", label=\"ren\")\n", - " #ax1.plot(td, oper_traj.Pelyz, \"tab:gray\", label=\"Pelyz\")\n", - " # ax1.plot(td, oper_traj.Pfc, \"tab:pink\", label=\"Pfc\")\n", - " # ax1.plot(td, oper_traj.power_shedding, \"k\", label=\"Pshed\")\n", - " ax1.fill_between(td, min.(oper_traj.Prenew_pot,Pload), 0,\n", - " interpolate=\"True\",color=\"orange\", alpha=0.5)\n", - " \"\"\" ax1.fill_between(td, Pload, 0,\n", - " where = Pload.>oper_traj.Prenew_pot ,interpolate=\"True\",color=\"orange\", alpha=0.5)\n", - "\"\"\"\n", - " ax1.fill_between(td, oper_traj.Pfc .+ oper_traj.Pbatt.+ oper_traj.Prenew_pot,\n", - " oper_traj.Prenew_pot .+ oper_traj.Pbatt, where =oper_traj.Pfc.>0.0 ,interpolate = \"true\",\n", - " label=\"Pfc\", color=\"pink\", alpha=0.5 )\n", - " \n", - " ax1.fill_between(td, oper_traj.Pelyz .+ Pload.-oper_traj.Pbatt,\n", - " Pload.-oper_traj.Pbatt, where =oper_traj.Pelyz.>0.0 ,interpolate = \"true\",\n", - " label=\"Pfc\", color=\"grey\", alpha=0.5 )\n", - " #ax3=ax1.twinx()\n", - " #ax3.set_ylim([0, 1])\n", - " #ax1.plot(td, min.( max.(oper_traj.Pbatt,0).+oper_traj.Prenew_pot,Pload), \"C2\")\n", - " \n", - " ax1.fill_between(td,Pload.-oper_traj.Pbatt ,Pload,\n", - " where= oper_traj.Pbatt.<0.0, interpolate = \"True\" ,color=\"cyan\", alpha=0.5 )\n", - " \n", - " y1=zeros(Float64,8760)\n", - " y2=zeros(Float64,8760)\n", - " y2.= Pload .- oper_traj.Pbatt .+ oper_traj.Pelyz \n", - " y1.= oper_traj.power_curtailment .+ y2\n", - " \n", - " \n", - " ax1.fill_between(td,y1, \n", - " y2, where= oper_traj.power_curtailment.>0 , interpolate = \"True\" ,color=\"red\", alpha=0.5,hatch=\"\\\\\" )\n", - "\n", - " \n", - " \n", - " ax1.fill_between(td,oper_traj.power_shedding .+ oper_traj.Pbatt.+oper_traj.Prenew_pot.+ oper_traj.Pfc, \n", - " oper_traj.Pbatt.+oper_traj.Prenew_pot.+ oper_traj.Pfc,\n", - " where= oper_traj.power_shedding.>0.0, interpolate = \"true\" ,color=\"black\", alpha=0.5,hatch=\"\\\\\" )\n", - " ax1.fill_between(td,oper_traj.Pbatt.+oper_traj.Prenew_pot,oper_traj.Prenew_pot,\n", - " where= oper_traj.Pbatt.>0.0, interpolate = \"true\" ,color=\"C2\", alpha=0.5 )\n", - " \n", - " \n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(\n", - " ylabel=\"kW\"\n", - " )\n", - " \n", - " ax2.grid(true)\n", - " ax2.set(\n", - " title=\"H2 Tank level\",\n", - " xlabel=\"time (d)\",\n", - " ylabel=\"kWh\"\n", - " \n", - " )\n", - " \n", - " ax2.plot(td, oper_traj.LoH[1:end-1]/10000, \"C2\")\n", - " \n", - " fig.tight_layout()\n", - " return fig, (ax1, ax2)\n", - "end\n", - "\n", - "if with_plots\n", - " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", - " ax1.set_xlim(297.2, 297.6)\n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "8813ada5-0c1a-49d2-869f-b8aee4e4028f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "13-element Vector{Float64}:\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z=zeros(Float64,12)\n", - "tm=zeros(Float64,13)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "6022b5d3-9189-4e3b-8c30-512cdcb59e27", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for i=1:12 \n", - " z[i]= oper_traj.LoH[i*730+1]\n", - " tm[i]=(i-1)*730/24 \n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "e86b8fdf-b44c-491a-a18b-9357c4d22d30", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "365.0" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tm[13]=8760/24" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "1ed8ac9d-9730-4223-a33a-dd90b13d260c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "plot_oper_traj (generic function with 1 method)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "function plot_oper_traj(microgrid, oper_traj)\n", - " \n", - " \n", - " fig,( ax1, ax2) = plt.subplots(2,1)\n", - " y1 = min.(oper_traj.Prenew_pot,Pload)\n", - " y2 = max.( oper_traj.Pbatt,0.0)\n", - " y3 = oper_traj.Pfc\n", - " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", - " y5 = oper_traj.Pelyz\n", - " y6 = oper_traj.power_shedding\n", - " y7 = oper_traj.power_curtailment\n", - " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", - " hatches=[\" \", \"-\", \" \", \"+\", \" \", \"\\\\\", \"\\\\\"]\n", - " \n", - " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", - " \n", - " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", - " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, Pload,lw=2, label=\"load\")\n", - " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", - " ax2.plot(td, oper_traj.LoH[1:end-1]/10000, \"C2\")\n", - " stacks[2].set_hatch(\"-\")\n", - " stacks[4].set_hatch(\"+\")\n", - " stacks[6].set_hatch(\"\\\\\") \n", - " stacks[7].set_hatch(\"\\\\\") \n", - " \n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim(0, 2)\n", - " ax3.plot(td, oper_traj.Ebatt[1:end-1]/5000, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", - " ax2.plot(td, oper_traj.LoH[1:end-1], \"C2\",label=\"hourly H2 level\")\n", - " \n", - " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", - " \n", - " \n", - " ax1.set_title(\"hourly power repartition\")\n", - " ax2.set_title(\"H2 level\")\n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(ylabel=\"kW\")\n", - " ax2.legend()\n", - " ax3.legend()\n", - " ax2.grid(true)\n", - " ax2.set(ylabel=\"kg\")\n", - " \n", - " \n", - " return fig, ax1, ax2\n", - "\n", - "end\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "a36e31f8-f379-451a-973f-5db836035738", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if with_plots\n", - " fig, ax1, ax2= plot_oper_traj(microgrid, oper_traj)\n", - " \n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "3ac88fac-41bd-48c6-8e43-fe108b5a4109", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1×365 Matrix{Float64}:\n", - " 1310.75 1202.75 1040.17 1018.17 … 1214.92 1354.92 1416.79 1409.08" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mean(reshape(Pload, 24, :); dims=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76bb909e-709b-45ec-9a8f-ff0ae6ae133a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "548cfc6b-3343-4468-85c6-22ab2699842b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "search: \u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1me\u001b[22m\u001b[0m\u001b[1ms\u001b[22m\u001b[0m\u001b[1mh\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1me\u001b[22m p\u001b[0m\u001b[1mr\u001b[22momot\u001b[0m\u001b[1me\u001b[22m_\u001b[0m\u001b[1ms\u001b[22m\u001b[0m\u001b[1mh\u001b[22m\u001b[0m\u001b[1ma\u001b[22m\u001b[0m\u001b[1mp\u001b[22m\u001b[0m\u001b[1me\u001b[22m\n", - "\n" - ] - }, - { - "data": { - "text/latex": [ - "\\begin{verbatim}\n", - "reshape(A, dims...) -> AbstractArray\n", - "reshape(A, dims) -> AbstractArray\n", - "\\end{verbatim}\n", - "Return an array with the same data as \\texttt{A}, but with different dimension sizes or number of dimensions. The two arrays share the same underlying data, so that the result is mutable if and only if \\texttt{A} is mutable, and setting elements of one alters the values of the other.\n", - "\n", - "The new dimensions may be specified either as a list of arguments or as a shape tuple. At most one dimension may be specified with a \\texttt{:}, in which case its length is computed such that its product with all the specified dimensions is equal to the length of the original array \\texttt{A}. The total number of elements must not change.\n", - "\n", - "\\section{Examples}\n", - "\\begin{verbatim}\n", - "julia> A = Vector(1:16)\n", - "16-element Vector{Int64}:\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " 9\n", - " 10\n", - " 11\n", - " 12\n", - " 13\n", - " 14\n", - " 15\n", - " 16\n", - "\n", - "julia> reshape(A, (4, 4))\n", - "4×4 Matrix{Int64}:\n", - " 1 5 9 13\n", - " 2 6 10 14\n", - " 3 7 11 15\n", - " 4 8 12 16\n", - "\n", - "julia> reshape(A, 2, :)\n", - "2×8 Matrix{Int64}:\n", - " 1 3 5 7 9 11 13 15\n", - " 2 4 6 8 10 12 14 16\n", - "\n", - "julia> reshape(1:6, 2, 3)\n", - "2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\n", - " 1 3 5\n", - " 2 4 6\n", - "\\end{verbatim}\n" - ], - "text/markdown": [ - "```\n", - "reshape(A, dims...) -> AbstractArray\n", - "reshape(A, dims) -> AbstractArray\n", - "```\n", - "\n", - "Return an array with the same data as `A`, but with different dimension sizes or number of dimensions. The two arrays share the same underlying data, so that the result is mutable if and only if `A` is mutable, and setting elements of one alters the values of the other.\n", - "\n", - "The new dimensions may be specified either as a list of arguments or as a shape tuple. At most one dimension may be specified with a `:`, in which case its length is computed such that its product with all the specified dimensions is equal to the length of the original array `A`. The total number of elements must not change.\n", - "\n", - "# Examples\n", - "\n", - "```jldoctest\n", - "julia> A = Vector(1:16)\n", - "16-element Vector{Int64}:\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " 9\n", - " 10\n", - " 11\n", - " 12\n", - " 13\n", - " 14\n", - " 15\n", - " 16\n", - "\n", - "julia> reshape(A, (4, 4))\n", - "4×4 Matrix{Int64}:\n", - " 1 5 9 13\n", - " 2 6 10 14\n", - " 3 7 11 15\n", - " 4 8 12 16\n", - "\n", - "julia> reshape(A, 2, :)\n", - "2×8 Matrix{Int64}:\n", - " 1 3 5 7 9 11 13 15\n", - " 2 4 6 8 10 12 14 16\n", - "\n", - "julia> reshape(1:6, 2, 3)\n", - "2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\n", - " 1 3 5\n", - " 2 4 6\n", - "```\n" - ], - "text/plain": [ - "\u001b[36m reshape(A, dims...) -> AbstractArray\u001b[39m\n", - "\u001b[36m reshape(A, dims) -> AbstractArray\u001b[39m\n", - "\n", - " Return an array with the same data as \u001b[36mA\u001b[39m, but with different dimension sizes\n", - " or number of dimensions. The two arrays share the same underlying data, so\n", - " that the result is mutable if and only if \u001b[36mA\u001b[39m is mutable, and setting elements\n", - " of one alters the values of the other.\n", - "\n", - " The new dimensions may be specified either as a list of arguments or as a\n", - " shape tuple. At most one dimension may be specified with a \u001b[36m:\u001b[39m, in which case\n", - " its length is computed such that its product with all the specified\n", - " dimensions is equal to the length of the original array \u001b[36mA\u001b[39m. The total number\n", - " of elements must not change.\n", - "\n", - "\u001b[1m Examples\u001b[22m\n", - "\u001b[1m ≡≡≡≡≡≡≡≡≡≡\u001b[22m\n", - "\n", - "\u001b[36m julia> A = Vector(1:16)\u001b[39m\n", - "\u001b[36m 16-element Vector{Int64}:\u001b[39m\n", - "\u001b[36m 1\u001b[39m\n", - "\u001b[36m 2\u001b[39m\n", - "\u001b[36m 3\u001b[39m\n", - "\u001b[36m 4\u001b[39m\n", - "\u001b[36m 5\u001b[39m\n", - "\u001b[36m 6\u001b[39m\n", - "\u001b[36m 7\u001b[39m\n", - "\u001b[36m 8\u001b[39m\n", - "\u001b[36m 9\u001b[39m\n", - "\u001b[36m 10\u001b[39m\n", - "\u001b[36m 11\u001b[39m\n", - "\u001b[36m 12\u001b[39m\n", - "\u001b[36m 13\u001b[39m\n", - "\u001b[36m 14\u001b[39m\n", - "\u001b[36m 15\u001b[39m\n", - "\u001b[36m 16\u001b[39m\n", - "\u001b[36m \u001b[39m\n", - "\u001b[36m julia> reshape(A, (4, 4))\u001b[39m\n", - "\u001b[36m 4×4 Matrix{Int64}:\u001b[39m\n", - "\u001b[36m 1 5 9 13\u001b[39m\n", - "\u001b[36m 2 6 10 14\u001b[39m\n", - "\u001b[36m 3 7 11 15\u001b[39m\n", - "\u001b[36m 4 8 12 16\u001b[39m\n", - "\u001b[36m \u001b[39m\n", - "\u001b[36m julia> reshape(A, 2, :)\u001b[39m\n", - "\u001b[36m 2×8 Matrix{Int64}:\u001b[39m\n", - "\u001b[36m 1 3 5 7 9 11 13 15\u001b[39m\n", - "\u001b[36m 2 4 6 8 10 12 14 16\u001b[39m\n", - "\u001b[36m \u001b[39m\n", - "\u001b[36m julia> reshape(1:6, 2, 3)\u001b[39m\n", - "\u001b[36m 2×3 reshape(::UnitRange{Int64}, 2, 3) with eltype Int64:\u001b[39m\n", - "\u001b[36m 1 3 5\u001b[39m\n", - "\u001b[36m 2 4 6\u001b[39m" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "?reshape" + " plot_oper_traj(microgrid, oper_traj)" ] }, { @@ -2457,21 +1350,6 @@ "**Graph 2** Zoom to one week in summer: much solar → battery often full → spillage" ] }, - { - "cell_type": "code", - "execution_count": 80, - "id": "256a0b88-4972-46f3-a29e-fb7016aee700", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if with_plots\n", - " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", - " ax1.set_xlim(150, 151)\n", - "end;" - ] - }, { "cell_type": "markdown", "id": "4570d462-880a-461f-b847-8c23cebe3a08", @@ -2493,7 +1371,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.0_threads", "language": "julia", "name": "julia-1.10" }, diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb index 6b28c73..a018b2b 100644 --- a/examples/Microgrid_Wind-Solar.ipynb +++ b/examples/Microgrid_Wind-Solar.ipynb @@ -1349,7 +1349,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.10.0", + "display_name": "Julia 1.10.0_threads", "language": "julia", "name": "julia-1.10" }, diff --git a/examples/Microgrid_Wind-Solar_data.jl b/examples/Microgrid_Wind-Solar_data.jl deleted file mode 100644 index 88e564c..0000000 --- a/examples/Microgrid_Wind-Solar_data.jl +++ /dev/null @@ -1,137 +0,0 @@ -# Parameters and time series for a Microgrid project with *wind* and *solar* sources, -# plus a *battery* and a *dispatchable generator*. -# -# Values gathered from the Microgrid_Wind-Solar.ipynb notebook, -# to be used in the sizing optimization notebook. - -using Microgrids # for capacity_from_wind -using CSV, DataFrames - -println("Data definition for Microgrid with wind, solar, storage and generator...") - -### Input time series -data = DataFrame(CSV.File("$(@__DIR__)/data/Ouessant_data_2016.csv")) - -# Simulation steps -nsteps = 2*length(data.Load) -@assert(nsteps == 17520) # 1 year at an hourly timestep - -# Split load, solar and wind data: -Pload = [data.Load;data.Load] # kW -Ppv1k = [data.Ppv1k; data.Ppv1k]./ 1000; # convert to kW/kWp -wind_speed = [data.Wind;data.Wind] # m/s - -# Calibrate wind speed data against a mast measurement -ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean -wind_speed = ws_gain*wind_speed - -# Generate wind power capacity factor time series from wind speed -S_D52 = pi * (52/2)^2 # rotor swept area m² -TSP_D52 = 900e3/S_D52 # W/m² -v_out = 25.0 # m/s -Cp_D52, α_D52 = 0.521, 3.1; # fitted from actual power curve -cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52) - -### Microgrid parameters - -# Project parameters -lifetime = 25 # yr -discount_rate = 0.05 -timestep = 1. # h - -# Parameters chosen to be common to all Components (but they could differ) -replacement_price_ratio = 1.0 -salvage_price_ratio = 1.0 - -# Dispatchable generator (Diesel) -power_rated_gen = 2000. # rated power (kW) → /2 to see some load shedding -# fuel parameters: -fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max) -fuel_slope = 0.240 # fuel curve slope (L/h/kW) -# prices: -fuel_price = 1. # fuel price ($/L) -investment_price_gen = 400. # initial investiment price ($/kW) -om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation) -# other technical parameters -lifetime_gen = 15000. # generator lifetime (h) -load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1] -input_unit_gen = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) -output_unit_gen = "KWh" - -#fuel tank - -capacity_rated_ftank = 0. # rated power capacity (L) -investment_price_ftank = 0.0 # initial investment price ($/L) -om_price_ftank = 0.# operation and maintenance price ($/L/y) -lifetime_ftank = 25. # calendar lifetime (y) -loss_factor_ftank = 0. # hydrogen used on site -fuel_ini_ratio = 0. # Initial load ratio ∈ [0,1] -fuel_min_ratio = 0. # minimum load ratio ∈ [0,1] -fuel_max_ratio = 1. # maximum load ratio ∈ [0,1] - -#H2 Tank -capacity_rated_hytank = 10000. # rated power capacity (kg) -investment_price_hytank = 0.8 # initial investment price ($/kg) -om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y) -lifetime_hytank = 25. # calendar lifetime (y) -loss_factor_hytank = 0. # hydrogen used on site -LoH_ini_ratio = 0. # Initial load ratio ∈ [0,1] -LoH_min_ratio = 0.1 # minimum load ratio ∈ [0,1] -LoH_max_ratio = 1. # maximum load ratio ∈ [0,1] - - -#Fuel cell -power_rated_fc = 1800. # rated power capacity (KW) -cons_intercept_fc=0. # -cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé) -cons_price_fc = 0. # -investment_price_fc = 1000. # initial investment price ($/KW) -om_price_fc = 10. # operation and maintenance price ($/kW/y) -lifetime_fc = 45000. # Fuel Cell lifetime (h) -load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] -input_unit_fc= "Kg" -output_unit_fc="KWh" - - -# Battery energy storage -energy_rated_sto = 5000. # rated energy capacity (kWh) -investment_price_sto = 350. # initial investiment price ($/kWh) -om_price_sto = 10. # operation and maintenance price ($/kWh/y) -lifetime_sto = 15. # calendar lifetime (y) -lifetime_cycles = 3000. # maximum number of cycles over life (1) -# Parameters with default values -charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1) -discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1) -loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1] -SoC_min = 0. # minimum State of Charge ∈ [0,1] -SoC_ini = 1. # initial State of Charge ∈ [0,1] -SoC_max = 1. # initial State of Charge ∈ [0,1] - -#electrolyzer -power_rated_elyz = 2000. # rated power capacity (Kw) -cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2) -cons_slope_elyz = 56. # consumption rate (KWhé/KgH2) -cons_price_elyz = 0.# -investment_price_elyz = 1000. # initial investment price ($/kW) -om_price_elyz = 10. # operation and maintenance price ($/kW/y) -lifetime_elyz = 45000. #Electrolyzer lifetime (h) -load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1] - -input_unit_elyz= "KWh" -output_unit_elyz="Kg" - -# Photovoltaic (PV) generation -power_rated_pv = 3000. # rated power (kW) -irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²) -investment_price_pv = 1200. # initial investiment price ($/kW) -om_price_pv = 20.# operation and maintenance price ($/kW/y) -lifetime_pv = 25. # lifetime (y) -# Parameters with default values -derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1] - -# Wind power generation -power_rated_wind = 1800. # rated power (kW) -investment_price_wind = 3500. # initial investiment price ($/kW) -om_price_wind = 100.# operation and maintenance price ($/kW/y) -lifetime_wind = 25. # lifetime (y) - diff --git a/examples/Microgrid_sizing_optimization.ipynb b/examples/Microgrid_sizing_optimization.ipynb deleted file mode 100644 index eb07759..0000000 --- a/examples/Microgrid_sizing_optimization.ipynb +++ /dev/null @@ -1,958 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b14f854f-51e9-4253-8946-d7ca39628ed0", - "metadata": {}, - "source": [ - "# Microgrid sizing optimization\n", - "\n", - "Following the first example about [Microgrid performance simulation](Microgrid_Wind-Solar.ipynb)\n", - "using [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl),\n", - "this notebook delves into **optimal microgrid sizing**.\n", - "This means finding the optimal size (rated power or energy) of each component by solving an optimization problem where the objective function is composed of Key Performance Indicators (KPIs) related to the economic and technical performance of the project.\n", - "\n", - "Like in the previous example, the sizing is done for a microgrid project with *wind* and *solar* sources, plus a *battery* and a *dispatchable generator*.\n", - "\n", - "schematic of a microgrid with a symbolic representation of the notion of sizing\n", - "\n", - "The main steps of the optimal sizing process are:\n", - "1. set up the problem (in particular loading project data like prices and time series)\n", - "2. run the optimization\n", - "3. analyze the results\n", - "\n", - "## About the optimization formulation and process\n", - "\n", - "### Optimization formulation\n", - "\n", - "In this illustrative sizing example, the optimization objective (criterion) is:\n", - "- minimize the Levelized Costs of Electricity `lcoe` (€/kWh)\n", - "- while keeping the load shedding rate below some threshold (ex.: shedding rate ≤ 1%)\n", - "\n", - "but other indicators could be used as well: rate of renewables above some threshold...\n", - "\n", - "The above problem is a constrained optimization problem. Since we'll use unconstrained optimization solvers, we will reformulate it as: objective = lcoe + penalty for excess of shedding.\n", - "\n", - "### Optimization process\n", - "\n", - "This is a “blackbox” type of optimization problem: the optimization algorithm calls iteratively the Microgrid simulator with sizings to be tested until convergence (or reaching the maximum allowed number of iterations):\n", - "\n", - "schematic of microgrid sizing optimization process: iterations call of the simulator by optimizer\n", - "\n", - "The code is meant for using the [NLopt](https://nlopt.readthedocs.io/en/latest/) library of optimization solvers (through its [NLopt.jl](https://github.com/JuliaOpt/NLopt.jl/) wrapper), but it could be adapted to other optimizers with minor changes (see remark below about the modularity of the code)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c2f04250-290c-4c38-93e6-074702ec0700", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using Microgrids\n", - "using NLopt # optimization solvers\n", - "using Printf # pretty print results\n", - "using BenchmarkTools # for timing performance, optional" - ] - }, - { - "cell_type": "markdown", - "id": "5e20bad8-81a3-4a6b-8c1e-6848a20c0d55", - "metadata": {}, - "source": [ - "## Load Microgrid project data\n", - "\n", - "Loading parameters and time series for a Microgrid project with *wind* and *solar* sources, plus a *battery* and a *dispatchable generator*. \n", - "Values gathered from the [Microgrid_Wind-Solar.ipynb]((Microgrid_Wind-Solar.ipynb)) notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a078bb9d-37a9-45f0-bb00-4404a44dcb30", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling CSV [336ed68f-0bac-5ca0-87d4-7b16caf5d00b]\n", - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling DataFrames [a93c6f00-e57d-5684-b7b6-d8193f3e46c0]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data definition for Microgrid with wind, solar, storage and generator...\n" - ] - } - ], - "source": [ - "include(\"./Microgrid_Wind-Solar_data.jl\");" - ] - }, - { - "cell_type": "markdown", - "id": "9431f37f-4a44-42f7-a017-4935125731f2", - "metadata": {}, - "source": [ - "## Setting up the cost function (criterion) to be optimized\n", - "\n", - "The key coding objective is to **encapsulate** the microgrid simulators (`simulate` function of `Microgrids.jl` package) into an objective function that can be called by the optimization algorithm, that is which respects its expected interface (here NLopt).\n", - "\n", - "To increase the modularity which facilites using optimization solvers others that NLopt's we implement the encapsulation by **3 nested functions**:\n", - "\n", - "1. Simulation of Microgrid project described by a sizing vector `x` (vector input) → returns all simulation statistics\n", - "2. Extract KPIs of interest to build a multi-objective criterion: here lcoe and shedding rate\n", - "3. Combine these KPIs as one mono-objective criterion: here LCOE + penalty if shedding rate > shed_max\n", - " - and match the interface expected by NLopt's optimizers\n", - "\n", - "but if one cares more about compactness, this could be assembled into one single function." - ] - }, - { - "cell_type": "markdown", - "id": "adb0b6bb-54f7-4849-b1b0-b236681be8d9", - "metadata": {}, - "source": [ - "### Wrapper of the Microgrid simulator\n", - "\n", - "accept a sizing vector `x`, then create all `Microgrids.jl` components and call `simulate`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b1b119b9-0e9c-447d-b708-ef64e11b0d0d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "simulate_microgrid" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", - "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", - "\n", - "Returns stats, costs\n", - "\"\"\"\n", - " function simulate_microgrid(x)\n", - " project = Project(lifetime, discount_rate, timestep, \"€\")\n", - " # Split decision variables (converted MW → kW):\n", - " power_rated_gen = x[1]*1000\n", - " energy_rated_sto = x[2]*1000\n", - " power_rated_pv = x[3]*1000\n", - " power_rated_wind = x[4]*1000\n", - "\n", - " # Create components\n", - " gen = DispatchableGenerator(power_rated_gen,\n", - " fuel_intercept, fuel_slope, fuel_price,\n", - " investment_price_gen, om_price_gen, lifetime_gen,\n", - " load_ratio_min,\n", - " replacement_price_ratio, salvage_price_ratio, fuel_unit)\n", - " batt = Battery(energy_rated_sto,\n", - " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", - " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_ini,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " pv = Photovoltaic(power_rated_pv, irradiance,\n", - " investment_price_pv, om_price_pv,\n", - " lifetime_pv, derating_factor_pv,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " windgen = WindPower(power_rated_wind, cf_wind,\n", - " investment_price_wind, om_price_wind,\n", - " lifetime_wind,\n", - " replacement_price_ratio, salvage_price_ratio)\n", - " mg = Microgrid(project, Pload, gen, batt, [pv, windgen])\n", - "\n", - " # Launch simulation:\n", - " traj, stats, costs = simulate(mg)\n", - "\n", - " return stats, costs\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "e5321132-a5bf-467b-839b-99e36a2e781c", - "metadata": {}, - "source": [ - "Test of the simulator wrapper (on a baseline sizing):" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d765dc6a-eeda-4727-b259-479176670dc1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `DispatchableGenerator` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `DispatchableGenerator` not defined", - "", - "Stacktrace:", - " [1] simulate_microgrid(x::Vector{Float64})", - " @ Main .\\In[4]:15", - " [2] top-level scope", - " @ In[5]:4" - ] - } - ], - "source": [ - "# Baseline sizing: same as in Microgrid_Wind-Solar.ipynb notebook\n", - "x_base = [power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]/1000.\n", - "# run simulation:\n", - "stats, costs = simulate_microgrid(x_base)\n", - "x_base, costs.lcoe, costs.npc/1e6" - ] - }, - { - "cell_type": "markdown", - "id": "6e51a19f-0f68-44e4-ae09-ad11806f5747", - "metadata": {}, - "source": [ - "### Build the objective functions (criteria)\n", - "\n", - "- first bi-objective function x ↦ (lcoe, shedding rate)(x)\n", - "- then wrapped into a mono objective x ↦ J(x) by using a penalty for the excess of shedding rate\n", - " - and match the interface expected by NLopt's optimizers" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "470f5630-322c-497f-bac9-beb87f014f71", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "obj_multi" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", - "function obj_multi(x)\n", - " stats, costs = simulate_microgrid(x)\n", - " # Extract KPIs of interest\n", - " lcoe = costs.lcoe # $/kWh\n", - " shed_rate = stats.shed_rate; # in [0,1]\n", - " return lcoe, shed_rate\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6b3eb2d2-1513-424b-baa0-a49ca5247bb4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "obj" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Mono-objective criterion: LCOE + penalty if shedding rate > `shed_max`\n", - "\n", - "with signature adapted to NLopt with `grad` as 2nd argument\n", - "\n", - "load shedding penalty threshold `shed_max` should be in [0,1[\n", - "\"\"\"\n", - "function obj(x, grad, shed_max, w_shed_max=1e5)\n", - " lcoe, shed_rate = obj_multi(x)\n", - " over_shed = shed_rate - shed_max\n", - " if over_shed > 0.0\n", - " penalty = w_shed_max*over_shed\n", - " else\n", - " penalty = 0.0\n", - " end\n", - " J = lcoe + penalty\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "c57f7af9-ea2f-40a8-b502-ba98b472410c", - "metadata": {}, - "source": [ - "### Tests the objective functions\n", - "\n", - "Sizing being tested:\n", - "- baseline sizing from the simulation notebook: perfect quality of service (QoS) with zero load shedding\n", - "- baseline modified with a halved generator sizing: very good QoS with a bit of load shedding → not penalized\n", - "- small PV and small wind generators only: low LCOE (i.e. the production-only LCOE of these sources) but but extremely bad QoS → huge penalty" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8de2153d-559f-4a60-880e-e8ea5fa3b810", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Base. multi: (0.22924812869928668, 0.0) mono: 0.22924812869928668\n", - "Shed. multi: (0.20671000160924613, 0.009602858175478355) mono: 0.20671000160924613\n", - "PV. multi: (0.10149685980966677, 0.923547868561659) mono: 91354.8883530257\n", - "Wind. multi: (0.10040264224635914, 0.74395737102815) mono: 73395.83750545724\n" - ] - } - ], - "source": [ - "# Test:\n", - "shed_max = 0.01 # in [0,1]\n", - "\n", - "x_shed = [power_rated_gen/2, energy_rated_sto, power_rated_pv, power_rated_wind]/1000.\n", - "x_pv = [0. 0. 500. 0.]/1000.\n", - "x_wind = [0. 0. 0. 500.]/1000.\n", - "\n", - "println(\"Base. multi: \", obj_multi(x_base), \" mono: \", obj(x_base,[], shed_max))\n", - "println(\"Shed. multi: \", obj_multi(x_shed), \" mono: \", obj(x_shed,[], shed_max))\n", - "println(\"PV. multi: \", obj_multi(x_pv), \" mono: \", obj(x_pv,[], shed_max))\n", - "println(\"Wind. multi: \", obj_multi(x_wind), \" mono: \", obj(x_wind,[], shed_max))" - ] - }, - { - "cell_type": "markdown", - "id": "dfbf1ccf-a7df-474a-991c-9437bd83d8ab", - "metadata": {}, - "source": [ - "## Optimization" - ] - }, - { - "cell_type": "markdown", - "id": "d15efd32-c2ab-4d9a-98cf-5918cdf0b9b1", - "metadata": {}, - "source": [ - "### Setting up the optimization problem" - ] - }, - { - "cell_type": "markdown", - "id": "87ccea54-0d83-4b01-8f7e-de37c8dfa0d9", - "metadata": {}, - "source": [ - "bounds of the design space and starting point: derived from maximal load power" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5ac92d7e-5de1-4ddf-a050-6897dbc8af2d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4-element Vector{Float64}:\n", - " 2.0484\n", - " 17.07\n", - " 17.07\n", - " 8.535" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Pload_max = maximum(Pload)\n", - "\n", - "xmin = [0., 0., 1e-3, 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", - "x0 = [1.0, 3.0, 3.0, 2.0] * (Pload_max/1000)\n", - "xmax = [1.2, 10.0, 10.0, 5.0] * (Pload_max/1000)" - ] - }, - { - "cell_type": "markdown", - "id": "6c2bc984-bae8-403f-8130-d50efc8f5717", - "metadata": {}, - "source": [ - "Optionally ban some choices:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9f65e094-4c58-4e9b-b4ee-b626f6504f6c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Solar power forbidden (optional)\n", - "#x0[3] = 1e-3\n", - "#xmax[3] = 1e-3\n", - "# Wind power forbidden (optional)\n", - "#x0[4] = 0.\n", - "#xmax[4] = 0." - ] - }, - { - "cell_type": "markdown", - "id": "f1bd17b9-c02d-4280-8715-c46efe6c03f4", - "metadata": {}, - "source": [ - "Check cost function on `xmin` and `xmax`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6d5ef67e-5d40-4bb0-a13d-65dc3d57bceb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((0.10149685980963595, 0.9998470957371233), (0.8229416738277807, 0.0))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "obj_multi(xmin), obj_multi(xmax)" - ] - }, - { - "cell_type": "markdown", - "id": "3daceed1-2315-4eca-b7bd-b3a1e31cff09", - "metadata": { - "tags": [] - }, - "source": [ - "### Wrapper of the optimization process\n", - "\n", - "This is an optional step, but recommended to explore easily the impact of the many parameters taken by optimization algorithms.\n", - "\n", - "See [optimization termination conditions](https://nlopt.readthedocs.io/en/latest/NLopt_Introduction/#termination-conditions) in NLopt documention for the meaning of `xtol_rel`" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "08491363-e1ce-47d3-a425-4bc83888be12", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "optim_mg" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", - "\n", - "Parameters:\n", - "- `x0`: initial sizing (for the algorithms which need them)\n", - "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", - "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", - "- `maxeval`: maximum allowed number of calls to the objective function,\n", - " that is to the microgrid simulation\n", - "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", - "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", - "\n", - "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", - "but could be added to the parameters as well.\n", - "\"\"\"\n", - "function optim_mg(x0, shed_max, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-4, srand=1)\n", - " nx = length(x0) # number of optim variables\n", - " opt = Opt(algo, nx)\n", - " NLopt.srand(srand)\n", - " \n", - " opt.lower_bounds = xmin\n", - " opt.upper_bounds = xmax\n", - " opt.min_objective = (x, grad) -> obj(x, grad, shed_max)\n", - " opt.xtol_rel = xtol_rel\n", - " opt.maxeval = maxeval\n", - " \n", - " (fopt, xopt, ret) = optimize(opt, x0)\n", - " return xopt, ret, opt.numevals\n", - "end" - ] - }, - { - "cell_type": "markdown", - "id": "11264431-19b1-4349-bc90-19d88bba1b87", - "metadata": {}, - "source": [ - "### Run optimization & analyze results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "d32fcbf4-42fc-42aa-a4ad-1b8ca24a984d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GN_CRS2_LM algo: MAXEVAL_REACHED after 1000 iterations. \n", - "x*=[889.0, 2013.0, 1570.9, 1354.0]\n", - "LCOE*: 0.19072074165935016\n", - "shed*: 0.009961277810994507\n" - ] - } - ], - "source": [ - "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0.01 # in [0,1]\n", - "maxeval=1000\n", - "xopt, ret, numevals = optim_mg(x0, shed_max, algo, maxeval)\n", - "\n", - "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt*1000; digits=1)) # kW\n", - "lcoe_opt, shed_rate_opt = obj_multi(xopt)\n", - "println(\"LCOE*: \", lcoe_opt)\n", - "println(\"shed*: \", shed_rate_opt)" - ] - }, - { - "cell_type": "markdown", - "id": "58772966-74b1-431c-9f84-e8efe7352130", - "metadata": {}, - "source": [ - "optional local \"polishing\":" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "78b3e3f8-0de8-47f3-a8a4-40358feef172", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "LN_SBPLX polish: XTOL_REACHED after 260 iterations. \n", - "x*=[888.5, 1881.3, 1570.9, 1354.0]\n", - "LCOE*: 0.1906609830904905\n", - "shed*: 0.009999998875359207\n" - ] - } - ], - "source": [ - "algo_polish = :LN_SBPLX\n", - "xopt_polish, ret, numevals = optim_mg(xopt, shed_max, algo_polish)\n", - "\n", - "@printf(\"%s polish: %s after %d iterations. \\nx*=\", algo_polish, ret, numevals)\n", - "println(round.(xopt_polish*1000; digits=1)) # kW\n", - "lcoe_opt, shed_rate_opt = obj_multi(xopt_polish)\n", - "println(\"LCOE*: \", lcoe_opt)\n", - "println(\"shed*: \", shed_rate_opt)" - ] - }, - { - "cell_type": "markdown", - "id": "f543939c-c26d-40fb-aefa-129dff57ae1d", - "metadata": {}, - "source": [ - "Retrieve all performance statistics of the optimized sizing" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1e046d9e-21ec-4834-9bf3-5bc1c9be1f0a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NPC*: 18.03 M$ (compared to 21.89 M$ in baseline)\n", - "rate of renewables: 75.3 %\n" - ] - }, - { - "data": { - "text/plain": [ - "OperationStats with fields:\n", - "- served_energy: 6.7075e6 kWh\n", - "- shed_energy: 67487.0 kWh\n", - "- shed_max: 680.56 kW\n", - "- shed_hours: 370.0 h\n", - "- shed_duration_max: 21.0 h\n", - "- shed_rate: 0.0099613 in [0,1]\n", - "- gen_energy: 1.6592e6 kWh\n", - "- gen_hours: 3462.0 h\n", - "- gen_fuel: 398200.0 L\n", - "- storage_cycles: 190.11 \n", - "- storage_char_energy: 401830.0 kWh\n", - "- storage_dis_energy: 363560.0 kWh\n", - "- storage_loss_energy: 38269.0 kWh\n", - "- spilled_energy: 1.2495e6 kWh\n", - "- spilled_max: 1941.1 kW\n", - "- spilled_rate: 0.1972 in [0,1]\n", - "- renew_potential_energy: 6.3361e6 kWh\n", - "- renew_energy: 5.0866e6 kWh\n", - "- renew_rate: 0.75264 in [0,1]\n" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "stats_opt, costs_opt = simulate_microgrid(xopt)\n", - "@printf(\"NPC*: %.2f M\\$ (compared to %.2f M\\$ in baseline)\\n\", costs_opt.npc/1e6, costs.npc/1e6)\n", - "@printf(\"rate of renewables: %.1f %%\\n\", stats_opt.renew_rate*100)\n", - "# Display all operation statistics\n", - "stats_opt" - ] - }, - { - "cell_type": "markdown", - "id": "464f74e6-2939-4bed-ac6f-87ec8ef99660", - "metadata": {}, - "source": [ - "#### Summary of some results for 1% max shedding rate\n", - "\n", - "Global search algorithms:\n", - "- **CRS2**: 0.190 720 \\$/kWh (with shedding ~ threshold).\n", - " x* = [889.0, 2013.0, 1570.9, 1354.0] → *best global algorithm choice so far, although effect of seed not studied*\n", - "- **DIRECT**: 0.191 297 \\$/kWh (with shedding < threshold) in 1000 iter.\n", - " x* = [910.4, 1837.8, 1543.3, 1375.7] \n", - " - \"polishing\" with SBPLX: 0.190 650 \\$/kWh (with shedding ~ threshold) in 207 extra iter.\n", - " x* = [887.1, 1992.4, 1543.3, 1375.6]\n", - " - increasing DIRECT maxeval to 10k doesn't bring much improvement (0.191 258 \\$/kWh)\n", - " compared to SBPLX polishing\n", - "- **ESCH**: 0.194 884 \\$/kWh with shedding OK (with shedding < threshold) in 1000 iter.\n", - " x* = [1028.1, 1769.6, 1420.5, 1365.1] → *much slower to converge than DIRECT or CRS*\n", - "\n", - "Local search algorithms:\n", - "- **SUBPLX**: 0.190 630 \\$/kWh with (with shedding ~ threshold) **in 479 iter** ( 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n", - "\n" - ], - "text/markdown": [ - "```\n", - "aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\n", - "```\n", - "\n", - "Aggregate operation time series `oper_traj` into yearly statistics for the the microgrid `mg` (returned as an `OperationStats` object).\n", - "\n", - "Discontinuous statistics can optionally be relaxed (smoothed) using the relaxation parameter `ε`:\n", - "\n", - " * 0.0 means no relaxation (default value)\n", - " * 1.0 yields the strongest relaxation\n", - "\n", - "Using relaxation (`ε` > 0) is recommended when using gradient-based optimization and then a “small enough” value between 0.05 and 0.30 is suggested.\n" - ], - "text/plain": [ - "\u001b[36m aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=1.0)\u001b[39m\n", - "\n", - " Aggregate operation time series \u001b[36moper_traj\u001b[39m into yearly statistics for the the\n", - " microgrid \u001b[36mmg\u001b[39m (returned as an \u001b[36mOperationStats\u001b[39m object).\n", - "\n", - " Discontinuous statistics can optionally be relaxed (smoothed) using the\n", - " relaxation parameter \u001b[36mε\u001b[39m:\n", - "\n", - " • 0.0 means no relaxation (default value)\n", - "\n", - " • 1.0 yields the strongest relaxation\n", - "\n", - " Using relaxation (\u001b[36mε\u001b[39m > 0) is recommended when using gradient-based\n", - " optimization and then a “small enough” value between 0.05 and 0.30 is\n", - " suggested." - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "?aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "37b788d2-a5c7-4444-9f45-fefa8929d6fe", - "metadata": {}, - "source": [ - "---\n", - "## *How we imagine the optimization interface in a future version...*\n", - "\n", - "Desirable feature: *dynamically select* which variables are optimized (e.g. using `getproperty` to dynamically access structure fields...)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2a51ac3b-4493-472b-8b0f-ac924edf5de7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Vector{Tuple{Vector{Symbol}, Float64, Float64, Float64}}:\n", - " ([:generator, :power_rated], 1000.0, 0.0, 2000.0)\n", - " ([:battery, :energy_max], 3000.0, 0.0, 6000.0)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#mg0 = baseline microgrid project, with given price and time series data\n", - "opti_params = [ # list of :component/variable name, initial, min, max values\n", - " ([:generator,:power_rated], 1000.0, 0., 2000.), \n", - " ([:battery,:energy_max], 3000.0, 0., 6000.)\n", - "]\n", - "#optim_mg(mg0, opti_params)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.10.0", - "language": "julia", - "name": "julia-1.10" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Useful_functions.jl b/examples/Useful_functions.jl new file mode 100644 index 0000000..7bd979c --- /dev/null +++ b/examples/Useful_functions.jl @@ -0,0 +1,290 @@ +td = collect((0:nsteps-1)/24); + +""" +This file must be included after the instructions: + - Using(Microgrids.jl) + - Include ("../examples/data/Microgrid_Wind-Solar-H2_data.jl") +""" + +""" + Create a Microgrid of size `x` + with x=[energy_rated_sto, power_rated_pv, power_rated_wind, power_rated_elyz, power_rated_fc, capacity_rated_hy_tank] + You can also specify capex prices and initial filling rate of either Hydrogen tank or batteries. + new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}) + +""" + + +function new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state) + project = Project(lifetime, discount_rate, timestep, "€") + gen = ProductionUnit(0.0, + fuel_intercept, fuel_slope, fuel_price, + capex[1], om_price_hour_gen, om_price_gen, lifetime_gen_y,lifetime_gen_h,lifetime_gen_starts, + load_ratio_min_gen, replacement_price_ratio, + salvage_price_ratio, input_unit_gen,output_unit_gen) + ftank = Tank(capacity_rated_ftank,capex[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,initial_fill_rate[1], + fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio) + fuel_cell = ProductionUnit(x[5],cons_intercept_fc, cons_rate_fc,cons_price_fc,capex[3], om_price_hour_fc, om_price_fc,lifetime_fc_y,lifetime_fc_h,lifetime_fc_starts, + load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc) + hytank = Tank(x[6],capex[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,initial_fill_rate[2], + LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio) + dispatchables = DispatchableCompound{Float64}([gen], [fuel_cell]) + tanks = TankCompound{Float64}(ftank,hytank) + + elyz = ProductionUnit(x[4],cons_intercept_elyz,cons_rate_elyz,cons_price_elyz, capex[5], om_price_hour_elyz,om_price_elyz, lifetime_elyz_y,lifetime_elyz_h,lifetime_elyz_starts, + load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz) + + batt = Battery(x[1], + capex[6], om_price_sto, lifetime_sto, lifetime_cycles, + charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,initial_fill_rate[3], + replacement_price_ratio, salvage_price_ratio) + pv = Photovoltaic(x[2], irradiance, + capex[7], om_price_pv, + lifetime_pv, derating_factor_pv, + replacement_price_ratio, salvage_price_ratio) + windgen = WindPower(x[3], cf_wind, + capex[8], om_price_wind, + lifetime_wind, + replacement_price_ratio, salvage_price_ratio) + + mg = Microgrid(project, Pload,dispatchables, + [elyz,],tanks,batt, [ + pv, + windgen + ]) + return mg + end + +""" +Simulate the performance of a Microgrid project +Returns mg, traj, stats, costs +""" + function simulate_microgrid(x=X,capex=capex_def,dispatch=dispatch_1) + mg=new_microgrid(x) + # Split decision variables (converted MW → kW): + oper_traj = operation(mg, dispatch) + if mg.tanks.h2Tank.capacity>0.0 + a = oper_traj.LoH[end]/mg.tanks.h2Tank.capacity + else + a=0.0 + end + + if mg.storage.energy_rated >0.0 + b = oper_traj.Ebatt[end]/mg.storage.energy_rated + else + b=0.0 + end + + ini=[0.0,a,b] + mg=new_microgrid(x,capex,ini) + # Launch simulation: + traj, stats, costs = simulate(mg,dispatch) + + return mg, traj ,stats, costs +end + +""" +Plot the instantaneous power sharing of a microgrid furing one year +plot_oper_traj(mg, oper_traj) +""" +function plot_oper_traj(mg, oper_traj) + ns=Int64(nsteps/730) + z=zeros(Float64,ns) +tm=zeros(Float64,ns +1) + tm[end]=nsteps/24 + for i=1:ns + z[i]= oper_traj.LoH[i*730+1] + tm[i]=(i-1)*730/24 + end + fig,( ax1, ax2) = plt.subplots(2,1) + y1 = min.(oper_traj.Prenew_pot,mg.load) + y2 = max.( oper_traj.Pbatt,0.0) + y3 = oper_traj.Pfc + y4 = abs.(min.( oper_traj.Pbatt,0.0)) + y5 = oper_traj.Pelyz + y6 = oper_traj.power_shedding + y7 = oper_traj.power_curtailment + + + y=np.vstack([y1,y2,y3,y4,y5,y6,y7]) + fieldNames =(["Pren used by the load","battery_discharge","fuel cell","battery_charge", "elyz","shedding","spillage"]) + + fieldColors = (["orange","green","pink","green","magenta","black","red"]) + + + ax1.plot(td, oper_traj.Prenew_pot,lw=2, "tab:orange", label="ren") + ax1.plot(td, mg.load,lw=2, label="load") + stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8) + + stacks[2].set_hatch("-") + stacks[4].set_hatch("+") + stacks[6].set_hatch("\\") + stacks[7].set_hatch("\\") + + ax3=ax1.twinx() + ax3.set_ylim(0, 2) + ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls="--","C2",label="hourly battery level") + + # ax4=ax2.twinx() + + ax2.plot(td, oper_traj.LoH[1:end-1], "C2",label="level of H2") + + # ax2.stairs(z,tm,fill="True",label="level of H2 at the end of each month") + + + ax1.set_title("hourly power repartition") + ax2.set_title("H2 level") + ax1.legend() + ax1.grid(true) + ax1.set(ylabel="kW") + ax2.legend() + ax3.legend() + ax2.grid(true) + ax2.set(ylabel="kg") + + fig.tight_layout() + pygui(true) + plt.show() + return fig, ax1, ax2 + +end + +function comp_oper_traj(mg1,mg2, oper_traj1, oper_traj2, costs1,costs2) + + ns=Int64(nsteps/730) + z=zeros(Float64,ns) + tm=zeros(Float64,ns +1) + tm[end]=nsteps/24 + for i=1:ns + z[i]= oper_traj1.LoH[i*730+1] + tm[i]=(i-1)*730/24 + end + fig,( ax1, ax2) = plt.subplots(2,1,sharex=true) + y1 = min.(oper_traj1.Prenew_pot,mg1.load) + y2 = max.( oper_traj1.Pbatt,0.0) + y3 = oper_traj1.Pfc + y4 = abs.(min.( oper_traj1.Pbatt,0.0)) + y5 = oper_traj1.Pelyz + y6 = oper_traj1.power_shedding + y7 = oper_traj1.power_curtailment + + Y1=np.vstack([y1,y2,y3,y4,y5,y6,y7]) + + + y1 = min.(oper_traj2.Prenew_pot,mg2.load) + y2 = max.( oper_traj2.Pbatt,0.0) + y3 = oper_traj2.Pfc + y4 = abs.(min.( oper_traj2.Pbatt,0.0)) + y5 = oper_traj2.Pelyz + y6 = oper_traj2.power_shedding + y7 = oper_traj2.power_curtailment + + Y2=np.vstack([y1,y2,y3,y4,y5,y6,y7]) + + + fieldNames =(["Pren used by the load","battery_discharge","fuel cell","battery_charge", "elyz","shedding","spillage"]) + + fieldColors = (["orange","green","pink","green","magenta","black","red"]) + + + ax1.plot(td, oper_traj1.Prenew_pot,lw=2, "tab:orange", label="ren") + ax1.plot(td, mg1.load,lw=2, label="load") + stacks1 = ax1.stackplot(td,Y1,labels=fieldNames, colors = fieldColors,alpha=0.8) + ax2.plot(td, oper_traj2.Prenew_pot,lw=2, "tab:orange", label="ren") + ax2.plot(td, mg2.load,lw=2, label="load") + stacks2 = ax2.stackplot(td,Y2,labels=fieldNames, colors = fieldColors,alpha=0.8) + + + stacks1[2].set_hatch("-") + stacks1[4].set_hatch("+") + stacks1[6].set_hatch("\\") + stacks1[7].set_hatch("\\") + + stacks2[2].set_hatch("-") + stacks2[4].set_hatch("+") + stacks2[6].set_hatch("\\") + stacks2[7].set_hatch("\\") + + ax3=ax1.twinx() + ax3.set_ylim(0, 2) + ax3.plot(td, oper_traj1.Ebatt[1:end-1]/mg1.storage.energy_rated, ls="--","C2",label="hourly battery level") + + + + ax4=ax2.twinx() + ax4.set_ylim(0, 2) + ax4.plot(td, oper_traj2.Ebatt[1:end-1]/mg2.storage.energy_rated, ls="--","C2",label="hourly battery level") + + + + + ax1.set_title("hourly power repartition") + ax2.set_title("hourly power repartition") + ax1.legend() + ax1.grid(true) + ax1.set(ylabel="kW") + ax2.legend() + ax3.legend() + ax4.legend() + ax2.grid(true) + ax2.set(ylabel="kw") + fig.tight_layout() + + return fig,(ax1, ax2,ax3,ax4) + + end + """ + plot_cashflow(cashflow) + +TBW +""" +function plot_cashflow(cashflow) + fig,ax=plt.subplots() + + years=collect(range(0,25,26)) + x=[cashflow.pv, cashflow.wind, cashflow.fc ,cashflow.elyz, cashflow.batt, cashflow.h2_tank] + names=["PV" "Wind" "fc" "elyz" "batt" "h2_tank"] + + + bottom = zeros(Float64,26) + + + for i=1:6 + p = ax.bar(years,x[i], 0.8, label=names[i], bottom=bottom,align="edge",capstyle="projecting") + bottom .+= x[i] + end + ax.set_title("cashflow diagrams") + ax.legend(loc="lower right") + ax.set_ylim(-3e7,2e7) + pygui(true) + plt.show() + return fig, ax + end + + function plot_cashflow_comp(cashflow_1,cashflow_2) + fig,ax=plt.subplots() + + years=collect(range(0,25,26)) + x1=[cashflow_1.wind, cashflow_1.pv, cashflow_1.fc ,cashflow_1.elyz, cashflow_1.batt, cashflow_1.h2_tank] + x2=[cashflow_2.wind, cashflow_2.pv, cashflow_2.fc ,cashflow_2.elyz, cashflow_2.batt, cashflow_2.h2_tank] + names=["Wind" "PV" "fc" "elyz" "batt" "h2_tank"] + + width = 0.4 # the width of the bars + color=["lavender","orange","darkviolet","lightcyan","darkgreen","cornflowerblue"] + bottom_1 = zeros(Float64,26) + bottom_2 = zeros(Float64,26) + + for i=1:6 + rects_1 = ax.bar(years,x1[i],width, label=names[i], bottom=bottom_1,color=color[i],align="edge",capstyle="projecting",ec="darkgreen") + rects_2 = ax.bar(years .+ width,x2[i],width, label=names[i], bottom=bottom_2,color=color[i],align="edge",capstyle="projecting",ec="darkred") + bottom_1 .+= x1[i] + bottom_2 .+= x2[i] + #ax.bar_label(rects, padding=3) + end + ax.set_title("cashflow diagrams") + ax.legend(loc="lower right") + pygui(true) + plt.show() + plt.savefig("cash_flow_comp.png") + return fig, ax + end \ No newline at end of file diff --git a/src/Microgrid_Wind-Solar_data.jl b/examples/data/Microgrid_Wind-Solar-H2_data.jl similarity index 75% rename from src/Microgrid_Wind-Solar_data.jl rename to examples/data/Microgrid_Wind-Solar-H2_data.jl index bd65cb4..6bff26a 100644 --- a/src/Microgrid_Wind-Solar_data.jl +++ b/examples/data/Microgrid_Wind-Solar-H2_data.jl @@ -7,10 +7,10 @@ using Microgrids # for capacity_from_wind using CSV, DataFrames -println("Data definition for Microgrid with wind, solar, storage and generator...") +println("Data definition for Microgrid with wind, solar and Hybrid Storage System...") ### Input time series -data = DataFrame(CSV.File("C:/Users/nikiemaje/OneDrive - CentraleSupelec/Documents/Microgrids.jl/examples/data/Ouessant_data_2016.csv")) +data = DataFrame(CSV.File("$(@__DIR__)/Ouessant_data_2016.csv")) # Simulation steps nsteps = length(data.Load) @@ -51,9 +51,12 @@ fuel_slope = 0.240 # fuel curve slope (L/h/kW) # prices: fuel_price = 1. # fuel price ($/L) investment_price_gen = 400. # initial investiment price ($/kW) -om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation) +om_price_hour_gen = 0.02 # operation & maintenance price ($/kW/h of operation) +om_price_gen = 0.0 # # other technical parameters -lifetime_gen = 15000. # generator lifetime (h) +lifetime_gen_y = 25. +lifetime_gen_h = 15000. # generator lifetime (h) +lifetime_gen_starts = 15000. # generator lmax starts on load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1] input_unit_gen = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) output_unit_gen = "KWh" @@ -70,14 +73,14 @@ fuel_min_ratio = 0. # minimum load ratio ∈ [0,1] fuel_max_ratio = 1. # maximum load ratio ∈ [0,1] #H2 Tank -capacity_rated_hytank = 10000. # rated power capacity (kg) -investment_price_hytank = 500. # initial investment price ($/kg) +capacity_rated_hytank = 12000. # rated power capacity (kg) +investment_price_hytank = 400. # initial investment price ($/kg) hy_price = 14. # initial hydrogen price ($/kg) -om_price_hytank = 0.0025 # operation and maintenance price ($/kg/y) +om_price_hytank = 4. # operation and maintenance price ($/kg/y) lifetime_hytank = 25. # calendar lifetime (y) loss_factor_hytank = 0. # hydrogen used on site -LoH_ini_ratio = 0.1 # Initial load ratio ∈ [0,1] -LoH_min_ratio = 0.1 # minimum load ratio ∈ [0,1] +LoH_ini_ratio = 0.0 # Initial load ratio ∈ [0,1] +LoH_min_ratio = 0.0 # minimum load ratio ∈ [0,1] LoH_max_ratio = 1. # maximum load ratio ∈ [0,1] @@ -87,8 +90,11 @@ cons_intercept_fc=0. # cons_rate_fc = 0.0625 # consumption rate (KgH2/KWhé) cons_price_fc = 0. # investment_price_fc = 1600. # initial investment price ($/KW) -om_price_fc = 16. # operation and maintenance price ($/kW/y) -lifetime_fc = 45000. # Fuel Cell lifetime (h) +om_price_fc = 45. # operation and maintenance price ($/kW/y) +om_price_hour_fc = 0. # operation and maintenance price ($/kW/h) +lifetime_fc_y = 15. +lifetime_fc_h = 45000. # Fuel Cell lifetime (h) +lifetime_fc_starts = 5000. # fuel cell maximum starts on load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] input_unit_fc= "Kg" output_unit_fc="KWh" @@ -109,20 +115,23 @@ SoC_ini = 0. # initial State of Charge ∈ [0,1] SoC_max = 1. # initial State of Charge ∈ [0,1] #electrolyzer -power_rated_elyz = 2000. # rated power capacity (Kw) +power_rated_elyz = 2000. # rated power capacity (kW) cons_intercept_elyz= 0. # consumption rate (KWhé/KgH2) -cons_slope_elyz = 56. # consumption rate (KWhé/KgH2) +cons_rate_elyz = 56. # consumption rate (KWhé/KgH2) cons_price_elyz = 0.# investment_price_elyz = 1600. # initial investment price ($/kW) -om_price_elyz = 16. # operation and maintenance price ($/kW/y) -lifetime_elyz = 45000. #Electrolyzer lifetime (h) +om_price_elyz = 44. # operation and maintenance price ($/kW/y) +om_price_hour_elyz = 0. # operation and maintenance price ($/kW/h) +lifetime_elyz_y = 20. +lifetime_elyz_h = 45000. #Electrolyzer lifetime (h) +lifetime_elyz_starts = 5000. #Electrolyzer maximum starts on load_min_ratio_elyz = 0.05 # minimum load ratio ∈ [0,1] input_unit_elyz= "KWh" output_unit_elyz="Kg" # Photovoltaic (PV) generation -power_rated_pv = 3000. # rated power (kW) +power_rated_pv = 5000. # rated power (kW) irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²) investment_price_pv = 1200. # initial investiment price ($/kW) om_price_pv = 20.# operation and maintenance price ($/kW/y) @@ -136,6 +145,6 @@ investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) lifetime_wind = 25. # lifetime (y) -X=[5000., 3000., 1800., 2000., 1800., 10000.] +X=[5000., 5000., 1800., 2000., 1800., 12000.]./1000 capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] -ini_filling_state=[0.0,0.0,0.0] +ini_filling_state=[0.0,0.0,0.0]; \ No newline at end of file diff --git a/examples/images/Microgrid_h2.png b/examples/images/Microgrid_h2.png new file mode 100644 index 0000000000000000000000000000000000000000..7e944e2429a925445418748badc5c513d394a5cd GIT binary patch literal 124663 zcmYJa1zc6l_dR?Oq@+VaN?N)*M7jlOkZ$RQODIT}G)PH_#L3>m$;y<%*~-hx&Vs_#&eY5a{A*(7=V;$-AvWp76T{>VzIsj`Zxt5LX{8B;hIS(v@b*njgIJP9B6Br#VP 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PyCall\n", - "using BenchmarkTools # for timing performance, optional\n", - "np=pyimport(\"numpy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6daf95d7-90af-4daf-b642-643a803b9422", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 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a/examples/new_examples/data/Ouessant_data_2016.csv b/examples/new_examples/data/Ouessant_data_2016.csv deleted file mode 100644 index 3b3f415..0000000 --- a/examples/new_examples/data/Ouessant_data_2016.csv +++ /dev/null @@ -1,8761 +0,0 @@ -time,Load,Ppv1k,Temp,Wind -2016-01-01 00:00:00,1453.0,0.0,10.23,3.78 -2016-01-01 01:00:00,1331.0,0.0,10.69,5.28 -2016-01-01 02:00:00,1214.0,0.0,11.14,6.78 -2016-01-01 03:00:00,1146.0,0.0,11.6,8.28 -2016-01-01 04:00:00,1078.0,0.0,11.39,9.6 -2016-01-01 05:00:00,1035.0,0.0,11.18,10.91 -2016-01-01 06:00:00,1032.0,0.0,10.97,12.23 -2016-01-01 07:00:00,1072.0,0.0,11.14,12.61 -2016-01-01 08:00:00,1142.0,0.0,11.31,12.98 -2016-01-01 09:00:00,1208.0,0.07,11.49,13.35 -2016-01-01 10:00:00,1299.0,0.0,11.65,14.18 -2016-01-01 11:00:00,1352.0,0.0,11.82,15.02 -2016-01-01 12:00:00,1363.0,0.0,11.98,15.85 -2016-01-01 13:00:00,1366.0,0.0,12.09,15.83 -2016-01-01 14:00:00,1336.0,0.0,12.19,15.8 -2016-01-01 15:00:00,1336.0,0.0,12.3,15.78 -2016-01-01 16:00:00,1307.0,0.0,12.31,15.99 -2016-01-01 17:00:00,1315.0,0.0,12.32,16.19 -2016-01-01 18:00:00,1396.0,0.0,12.34,16.4 -2016-01-01 19:00:00,1432.0,0.0,12.26,16.53 -2016-01-01 20:00:00,1452.0,0.0,12.19,16.66 -2016-01-01 21:00:00,1580.0,0.0,12.11,16.79 -2016-01-01 22:00:00,1692.0,0.0,12.08,16.32 -2016-01-01 23:00:00,1521.0,0.0,12.06,15.85 -2016-01-02 00:00:00,1344.0,0.0,12.04,15.38 -2016-01-02 01:00:00,1200.0,0.0,11.82,15.69 -2016-01-02 02:00:00,1071.0,0.0,11.61,16.0 -2016-01-02 03:00:00,1036.0,0.0,11.4,16.3 -2016-01-02 04:00:00,1059.0,0.0,11.09,16.9 -2016-01-02 05:00:00,1043.0,0.0,10.77,17.5 -2016-01-02 06:00:00,1029.0,0.0,10.46,18.1 -2016-01-02 07:00:00,1070.0,0.0,10.36,18.76 -2016-01-02 08:00:00,1197.0,0.0,10.25,19.42 -2016-01-02 09:00:00,1339.0,25.91,10.15,20.08 -2016-01-02 10:00:00,1352.0,11.78,10.06,19.68 -2016-01-02 11:00:00,1314.0,9.98,9.98,19.28 -2016-01-02 12:00:00,1273.0,0.0,9.89,18.88 -2016-01-02 13:00:00,1160.0,49.17,9.92,17.97 -2016-01-02 14:00:00,1098.0,188.44,9.94,17.06 -2016-01-02 15:00:00,1075.0,17.93,9.97,16.15 -2016-01-02 16:00:00,1106.0,2.34,10.17,15.06 -2016-01-02 17:00:00,1142.0,0.0,10.37,13.97 -2016-01-02 18:00:00,1262.0,0.0,10.57,12.88 -2016-01-02 19:00:00,1257.0,0.0,10.9,12.08 -2016-01-02 20:00:00,1251.0,0.0,11.22,11.27 -2016-01-02 21:00:00,1365.0,0.0,11.55,10.47 -2016-01-02 22:00:00,1499.0,0.0,11.37,11.36 -2016-01-02 23:00:00,1324.0,0.0,11.2,12.25 -2016-01-03 00:00:00,1176.0,0.0,11.03,13.14 -2016-01-03 01:00:00,1073.0,0.0,11.0,14.22 -2016-01-03 02:00:00,946.0,0.0,10.97,15.29 -2016-01-03 03:00:00,916.0,0.0,10.94,16.36 -2016-01-03 04:00:00,932.0,0.0,11.34,16.38 -2016-01-03 05:00:00,943.0,0.0,11.73,16.4 -2016-01-03 06:00:00,926.0,0.0,12.13,16.41 -2016-01-03 07:00:00,924.0,0.0,12.11,15.58 -2016-01-03 08:00:00,960.0,0.0,12.09,14.74 -2016-01-03 09:00:00,1075.0,27.98,12.07,13.9 -2016-01-03 10:00:00,1145.0,432.06,11.85,14.1 -2016-01-03 11:00:00,1146.0,106.64,11.64,14.29 -2016-01-03 12:00:00,1129.0,0.0,11.42,14.48 -2016-01-03 13:00:00,1023.0,492.74,11.28,14.53 -2016-01-03 14:00:00,948.0,153.18,11.14,14.58 -2016-01-03 15:00:00,891.0,86.08,11.01,14.63 -2016-01-03 16:00:00,907.0,0.0,10.89,15.11 -2016-01-03 17:00:00,934.0,0.0,10.78,15.58 -2016-01-03 18:00:00,1045.0,0.0,10.67,16.06 -2016-01-03 19:00:00,1086.0,0.0,10.74,16.41 -2016-01-03 20:00:00,1073.0,0.0,10.81,16.77 -2016-01-03 21:00:00,1210.0,0.0,10.88,17.13 -2016-01-03 22:00:00,1343.0,0.0,10.88,17.24 -2016-01-03 23:00:00,1213.0,0.0,10.88,17.35 -2016-01-04 00:00:00,1088.0,0.0,10.88,17.46 -2016-01-04 01:00:00,972.0,0.0,10.9,17.55 -2016-01-04 02:00:00,880.0,0.0,10.93,17.65 -2016-01-04 03:00:00,863.0,0.0,10.96,17.74 -2016-01-04 04:00:00,857.0,0.0,10.92,17.7 -2016-01-04 05:00:00,866.0,0.0,10.89,17.66 -2016-01-04 06:00:00,884.0,0.0,10.85,17.63 -2016-01-04 07:00:00,932.0,0.0,10.71,17.49 -2016-01-04 08:00:00,978.0,0.0,10.56,17.36 -2016-01-04 09:00:00,1037.0,0.0,10.42,17.23 -2016-01-04 10:00:00,1100.0,24.59,10.36,16.91 -2016-01-04 11:00:00,1091.0,4.89,10.31,16.58 -2016-01-04 12:00:00,1101.0,113.0,10.25,16.26 -2016-01-04 13:00:00,1046.0,0.0,10.37,16.05 -2016-01-04 14:00:00,939.0,520.39,10.48,15.83 -2016-01-04 15:00:00,928.0,173.77,10.6,15.61 -2016-01-04 16:00:00,941.0,22.56,10.56,15.91 -2016-01-04 17:00:00,972.0,0.0,10.52,16.21 -2016-01-04 18:00:00,1069.0,0.0,10.48,16.51 -2016-01-04 19:00:00,1119.0,0.0,10.47,16.45 -2016-01-04 20:00:00,1115.0,0.0,10.47,16.38 -2016-01-04 21:00:00,1199.0,0.0,10.46,16.32 -2016-01-04 22:00:00,1289.0,0.0,10.41,16.24 -2016-01-04 23:00:00,1170.0,0.0,10.37,16.16 -2016-01-05 00:00:00,1065.0,0.0,10.33,16.08 -2016-01-05 01:00:00,991.0,0.0,10.33,16.0 -2016-01-05 02:00:00,922.0,0.0,10.34,15.93 -2016-01-05 03:00:00,859.0,0.0,10.35,15.85 -2016-01-05 04:00:00,871.0,0.0,10.27,15.72 -2016-01-05 05:00:00,861.0,0.0,10.2,15.6 -2016-01-05 06:00:00,878.0,0.0,10.12,15.48 -2016-01-05 07:00:00,932.0,0.0,10.16,15.08 -2016-01-05 08:00:00,1015.0,0.0,10.2,14.68 -2016-01-05 09:00:00,1063.0,2.68,10.25,14.28 -2016-01-05 10:00:00,1068.0,1.16,10.26,13.85 -2016-01-05 11:00:00,1079.0,0.0,10.27,13.42 -2016-01-05 12:00:00,1063.0,0.0,10.28,12.99 -2016-01-05 13:00:00,995.0,818.76,10.16,12.83 -2016-01-05 14:00:00,970.0,221.93,10.04,12.66 -2016-01-05 15:00:00,962.0,10.33,9.93,12.5 -2016-01-05 16:00:00,957.0,1.7,10.12,12.18 -2016-01-05 17:00:00,976.0,0.0,10.32,11.87 -2016-01-05 18:00:00,1069.0,0.0,10.52,11.56 -2016-01-05 19:00:00,1123.0,0.0,10.5,11.74 -2016-01-05 20:00:00,1121.0,0.0,10.47,11.92 -2016-01-05 21:00:00,1232.0,0.0,10.45,12.1 -2016-01-05 22:00:00,1315.0,0.0,10.57,12.29 -2016-01-05 23:00:00,1173.0,0.0,10.69,12.48 -2016-01-06 00:00:00,1045.0,0.0,10.82,12.68 -2016-01-06 01:00:00,1001.0,0.0,10.83,12.63 -2016-01-06 02:00:00,921.0,0.0,10.84,12.57 -2016-01-06 03:00:00,875.0,0.0,10.85,12.52 -2016-01-06 04:00:00,864.0,0.0,10.72,12.42 -2016-01-06 05:00:00,854.0,0.0,10.6,12.32 -2016-01-06 06:00:00,858.0,0.0,10.47,12.22 -2016-01-06 07:00:00,940.0,0.0,10.39,11.49 -2016-01-06 08:00:00,1025.0,0.0,10.31,10.77 -2016-01-06 09:00:00,1069.0,6.0,10.24,10.04 -2016-01-06 10:00:00,1050.0,8.56,10.42,9.5 -2016-01-06 11:00:00,984.0,0.0,10.61,8.97 -2016-01-06 12:00:00,991.0,0.0,10.79,8.43 -2016-01-06 13:00:00,977.0,0.0,10.69,9.12 -2016-01-06 14:00:00,979.0,0.0,10.59,9.82 -2016-01-06 15:00:00,973.0,0.0,10.49,10.51 -2016-01-06 16:00:00,1014.0,1.51,10.73,12.69 -2016-01-06 17:00:00,1019.0,0.0,10.97,14.87 -2016-01-06 18:00:00,1092.0,0.0,11.2,17.05 -2016-01-06 19:00:00,1127.0,0.0,11.83,15.55 -2016-01-06 20:00:00,1141.0,0.0,12.47,14.06 -2016-01-06 21:00:00,1253.0,0.0,13.1,12.57 -2016-01-06 22:00:00,1292.0,0.0,13.05,12.73 -2016-01-06 23:00:00,1045.0,0.0,13.01,12.9 -2016-01-07 00:00:00,947.0,0.0,12.97,13.06 -2016-01-07 01:00:00,904.0,0.0,12.82,13.75 -2016-01-07 02:00:00,839.0,0.0,12.67,14.43 -2016-01-07 03:00:00,799.0,0.0,12.52,15.12 -2016-01-07 04:00:00,792.0,0.0,12.15,15.66 -2016-01-07 05:00:00,746.0,0.0,11.79,16.21 -2016-01-07 06:00:00,758.0,0.0,11.42,16.76 -2016-01-07 07:00:00,841.0,0.0,10.98,16.39 -2016-01-07 08:00:00,964.0,0.0,10.53,16.02 -2016-01-07 09:00:00,1071.0,0.0,10.09,15.66 -2016-01-07 10:00:00,1081.0,190.33,10.0,14.96 -2016-01-07 11:00:00,993.0,232.17,9.9,14.26 -2016-01-07 12:00:00,975.0,0.0,9.81,13.56 -2016-01-07 13:00:00,931.0,631.4,9.81,12.68 -2016-01-07 14:00:00,924.0,507.84,9.8,11.81 -2016-01-07 15:00:00,914.0,52.04,9.8,10.94 -2016-01-07 16:00:00,909.0,82.32,9.82,9.76 -2016-01-07 17:00:00,931.0,0.0,9.84,8.57 -2016-01-07 18:00:00,1012.0,0.0,9.86,7.39 -2016-01-07 19:00:00,1060.0,0.0,9.9,7.5 -2016-01-07 20:00:00,1069.0,0.0,9.93,7.6 -2016-01-07 21:00:00,1198.0,0.0,9.97,7.71 -2016-01-07 22:00:00,1317.0,0.0,10.09,8.17 -2016-01-07 23:00:00,1130.0,0.0,10.22,8.63 -2016-01-08 00:00:00,1002.0,0.0,10.35,9.09 -2016-01-08 01:00:00,949.0,0.0,10.24,9.27 -2016-01-08 02:00:00,884.0,0.0,10.14,9.45 -2016-01-08 03:00:00,870.0,0.0,10.03,9.63 -2016-01-08 04:00:00,898.0,0.0,9.93,9.39 -2016-01-08 05:00:00,857.0,0.0,9.82,9.15 -2016-01-08 06:00:00,822.0,0.0,9.72,8.91 -2016-01-08 07:00:00,898.0,0.0,9.9,8.67 -2016-01-08 08:00:00,977.0,0.0,10.08,8.43 -2016-01-08 09:00:00,1023.0,83.95,10.26,8.19 -2016-01-08 10:00:00,991.0,0.0,10.44,8.18 -2016-01-08 11:00:00,923.0,205.58,10.63,8.17 -2016-01-08 12:00:00,830.0,28.39,10.81,8.15 -2016-01-08 13:00:00,749.0,28.76,10.89,8.09 -2016-01-08 14:00:00,736.0,693.81,10.96,8.02 -2016-01-08 15:00:00,762.0,370.82,11.03,7.96 -2016-01-08 16:00:00,811.0,0.0,11.2,8.28 -2016-01-08 17:00:00,829.0,0.0,11.37,8.59 -2016-01-08 18:00:00,904.0,0.0,11.54,8.91 -2016-01-08 19:00:00,975.0,0.0,11.62,9.35 -2016-01-08 20:00:00,986.0,0.0,11.7,9.79 -2016-01-08 21:00:00,1094.0,0.0,11.78,10.23 -2016-01-08 22:00:00,1232.0,0.0,11.7,10.53 -2016-01-08 23:00:00,1084.0,0.0,11.63,10.82 -2016-01-09 00:00:00,939.0,0.0,11.56,11.12 -2016-01-09 01:00:00,894.0,0.0,11.53,11.67 -2016-01-09 02:00:00,824.0,0.0,11.51,12.22 -2016-01-09 03:00:00,815.0,0.0,11.48,12.77 -2016-01-09 04:00:00,863.0,0.0,11.45,12.89 -2016-01-09 05:00:00,852.0,0.0,11.42,13.0 -2016-01-09 06:00:00,803.0,0.0,11.39,13.12 -2016-01-09 07:00:00,824.0,0.0,11.49,12.54 -2016-01-09 08:00:00,874.0,0.0,11.58,11.96 -2016-01-09 09:00:00,946.0,24.38,11.68,11.38 -2016-01-09 10:00:00,991.0,5.43,11.62,12.4 -2016-01-09 11:00:00,1029.0,56.75,11.56,13.41 -2016-01-09 12:00:00,1044.0,64.37,11.5,14.43 -2016-01-09 13:00:00,1022.0,84.9,11.08,15.15 -2016-01-09 14:00:00,965.0,450.69,10.66,15.87 -2016-01-09 15:00:00,931.0,20.08,10.24,16.59 -2016-01-09 16:00:00,979.0,96.73,10.34,16.73 -2016-01-09 17:00:00,1042.0,0.0,10.43,16.86 -2016-01-09 18:00:00,1114.0,0.0,10.52,16.99 -2016-01-09 19:00:00,1084.0,0.0,10.4,16.8 -2016-01-09 20:00:00,1072.0,0.0,10.28,16.61 -2016-01-09 21:00:00,1190.0,0.0,10.16,16.41 -2016-01-09 22:00:00,1357.0,0.0,9.94,15.92 -2016-01-09 23:00:00,1247.0,0.0,9.73,15.42 -2016-01-10 00:00:00,1099.0,0.0,9.52,14.92 -2016-01-10 01:00:00,1003.0,0.0,9.62,14.51 -2016-01-10 02:00:00,913.0,0.0,9.73,14.09 -2016-01-10 03:00:00,871.0,0.0,9.83,13.67 -2016-01-10 04:00:00,906.0,0.0,9.51,12.93 -2016-01-10 05:00:00,956.0,0.0,9.18,12.2 -2016-01-10 06:00:00,911.0,0.0,8.86,11.46 -2016-01-10 07:00:00,881.0,0.0,8.92,11.24 -2016-01-10 08:00:00,952.0,0.0,8.98,11.02 -2016-01-10 09:00:00,997.0,122.56,9.05,10.8 -2016-01-10 10:00:00,1048.0,183.57,9.04,10.9 -2016-01-10 11:00:00,1114.0,185.38,9.02,10.99 -2016-01-10 12:00:00,1111.0,577.75,9.01,11.09 -2016-01-10 13:00:00,937.0,518.42,9.56,11.2 -2016-01-10 14:00:00,822.0,204.14,10.11,11.31 -2016-01-10 15:00:00,803.0,29.48,10.66,11.42 -2016-01-10 16:00:00,743.0,3.33,10.79,11.25 -2016-01-10 17:00:00,823.0,0.0,10.91,11.08 -2016-01-10 18:00:00,987.0,0.0,11.03,10.91 -2016-01-10 19:00:00,1038.0,0.0,10.55,11.28 -2016-01-10 20:00:00,1066.0,0.0,10.07,11.65 -2016-01-10 21:00:00,1163.0,0.0,9.59,12.01 -2016-01-10 22:00:00,1297.0,0.0,9.65,12.0 -2016-01-10 23:00:00,1170.0,0.0,9.71,11.98 -2016-01-11 00:00:00,1068.0,0.0,9.78,11.96 -2016-01-11 01:00:00,971.0,0.0,9.99,11.97 -2016-01-11 02:00:00,907.0,0.0,10.2,11.99 -2016-01-11 03:00:00,874.0,0.0,10.41,12.0 -2016-01-11 04:00:00,875.0,0.0,10.09,12.42 -2016-01-11 05:00:00,871.0,0.0,9.76,12.84 -2016-01-11 06:00:00,887.0,0.0,9.44,13.26 -2016-01-11 07:00:00,941.0,0.0,9.44,13.38 -2016-01-11 08:00:00,999.0,0.0,9.43,13.5 -2016-01-11 09:00:00,1040.0,20.71,9.43,13.63 -2016-01-11 10:00:00,1045.0,5.5,9.7,13.73 -2016-01-11 11:00:00,1066.0,24.99,9.96,13.84 -2016-01-11 12:00:00,1101.0,163.31,10.23,13.94 -2016-01-11 13:00:00,1046.0,334.53,9.95,14.25 -2016-01-11 14:00:00,904.0,219.11,9.67,14.55 -2016-01-11 15:00:00,933.0,30.1,9.39,14.86 -2016-01-11 16:00:00,997.0,9.02,9.52,15.36 -2016-01-11 17:00:00,1038.0,0.0,9.66,15.86 -2016-01-11 18:00:00,1145.0,0.0,9.8,16.36 -2016-01-11 19:00:00,1187.0,0.0,9.81,16.43 -2016-01-11 20:00:00,1175.0,0.0,9.83,16.5 -2016-01-11 21:00:00,1264.0,0.0,9.84,16.57 -2016-01-11 22:00:00,1379.0,0.0,9.78,16.07 -2016-01-11 23:00:00,1245.0,0.0,9.72,15.58 -2016-01-12 00:00:00,1167.0,0.0,9.66,15.09 -2016-01-12 01:00:00,1072.0,0.0,9.46,14.55 -2016-01-12 02:00:00,953.0,0.0,9.27,14.01 -2016-01-12 03:00:00,901.0,0.0,9.07,13.48 -2016-01-12 04:00:00,908.0,0.0,8.84,13.03 -2016-01-12 05:00:00,917.0,0.0,8.62,12.59 -2016-01-12 06:00:00,959.0,0.0,8.39,12.15 -2016-01-12 07:00:00,991.0,0.0,8.29,12.46 -2016-01-12 08:00:00,1059.0,0.0,8.18,12.78 -2016-01-12 09:00:00,1175.0,47.73,8.08,13.09 -2016-01-12 10:00:00,1114.0,157.34,8.35,13.39 -2016-01-12 11:00:00,1070.0,280.26,8.61,13.7 -2016-01-12 12:00:00,1032.0,325.59,8.88,14.0 -2016-01-12 13:00:00,988.0,550.77,8.95,14.28 -2016-01-12 14:00:00,889.0,224.68,9.02,14.55 -2016-01-12 15:00:00,907.0,148.32,9.09,14.83 -2016-01-12 16:00:00,937.0,6.65,9.22,14.62 -2016-01-12 17:00:00,980.0,0.0,9.35,14.41 -2016-01-12 18:00:00,1162.0,0.0,9.48,14.21 -2016-01-12 19:00:00,1224.0,0.0,9.47,13.57 -2016-01-12 20:00:00,1177.0,0.0,9.47,12.93 -2016-01-12 21:00:00,1247.0,0.0,9.46,12.29 -2016-01-12 22:00:00,1419.0,0.0,9.44,11.55 -2016-01-12 23:00:00,1266.0,0.0,9.42,10.81 -2016-01-13 00:00:00,1143.0,0.0,9.4,10.07 -2016-01-13 01:00:00,1123.0,0.0,9.33,9.42 -2016-01-13 02:00:00,991.0,0.0,9.27,8.77 -2016-01-13 03:00:00,926.0,0.0,9.21,8.12 -2016-01-13 04:00:00,912.0,0.0,9.17,7.6 -2016-01-13 05:00:00,923.0,0.0,9.14,7.09 -2016-01-13 06:00:00,951.0,0.0,9.1,6.57 -2016-01-13 07:00:00,1035.0,0.0,9.02,6.74 -2016-01-13 08:00:00,1092.0,0.0,8.93,6.92 -2016-01-13 09:00:00,1074.0,46.17,8.85,7.1 -2016-01-13 10:00:00,1099.0,400.13,8.87,7.7 -2016-01-13 11:00:00,1071.0,554.92,8.89,8.3 -2016-01-13 12:00:00,1053.0,103.57,8.91,8.9 -2016-01-13 13:00:00,974.0,455.87,8.79,8.86 -2016-01-13 14:00:00,960.0,355.28,8.66,8.82 -2016-01-13 15:00:00,912.0,347.44,8.53,8.79 -2016-01-13 16:00:00,920.0,11.11,8.72,8.93 -2016-01-13 17:00:00,986.0,0.0,8.9,9.07 -2016-01-13 18:00:00,1104.0,0.0,9.09,9.21 -2016-01-13 19:00:00,1153.0,0.0,9.17,9.4 -2016-01-13 20:00:00,1159.0,0.0,9.24,9.59 -2016-01-13 21:00:00,1209.0,0.0,9.32,9.78 -2016-01-13 22:00:00,1345.0,0.0,9.27,10.0 -2016-01-13 23:00:00,1192.0,0.0,9.22,10.22 -2016-01-14 00:00:00,1062.0,0.0,9.18,10.44 -2016-01-14 01:00:00,1037.0,0.0,9.12,10.51 -2016-01-14 02:00:00,965.0,0.0,9.06,10.57 -2016-01-14 03:00:00,887.0,0.0,9.0,10.63 -2016-01-14 04:00:00,912.0,0.0,9.13,11.14 -2016-01-14 05:00:00,887.0,0.0,9.25,11.64 -2016-01-14 06:00:00,934.0,0.0,9.38,12.14 -2016-01-14 07:00:00,1027.0,0.0,9.41,12.26 -2016-01-14 08:00:00,1119.0,0.0,9.44,12.38 -2016-01-14 09:00:00,1117.0,58.38,9.48,12.5 -2016-01-14 10:00:00,1092.0,341.42,9.44,12.41 -2016-01-14 11:00:00,1094.0,517.61,9.41,12.33 -2016-01-14 12:00:00,1066.0,283.89,9.37,12.25 -2016-01-14 13:00:00,1013.0,460.03,9.25,11.62 -2016-01-14 14:00:00,992.0,273.01,9.13,10.99 -2016-01-14 15:00:00,957.0,155.57,9.01,10.36 -2016-01-14 16:00:00,937.0,64.1,8.94,10.19 -2016-01-14 17:00:00,970.0,0.0,8.88,10.02 -2016-01-14 18:00:00,1094.0,0.0,8.81,9.85 -2016-01-14 19:00:00,1194.0,0.0,8.78,9.52 -2016-01-14 20:00:00,1209.0,0.0,8.74,9.2 -2016-01-14 21:00:00,1326.0,0.0,8.71,8.87 -2016-01-14 22:00:00,1405.0,0.0,8.65,9.0 -2016-01-14 23:00:00,1262.0,0.0,8.6,9.13 -2016-01-15 00:00:00,1171.0,0.0,8.55,9.26 -2016-01-15 01:00:00,1127.0,0.0,8.62,10.74 -2016-01-15 02:00:00,1075.0,0.0,8.7,12.23 -2016-01-15 03:00:00,1052.0,0.0,8.78,13.71 -2016-01-15 04:00:00,1041.0,0.0,8.73,13.93 -2016-01-15 05:00:00,1021.0,0.0,8.68,14.14 -2016-01-15 06:00:00,1038.0,0.0,8.63,14.36 -2016-01-15 07:00:00,1143.0,0.0,8.57,14.18 -2016-01-15 08:00:00,1225.0,0.0,8.51,14.01 -2016-01-15 09:00:00,1215.0,107.36,8.45,13.83 -2016-01-15 10:00:00,1158.0,407.35,8.45,13.6 -2016-01-15 11:00:00,1136.0,293.12,8.45,13.36 -2016-01-15 12:00:00,1088.0,375.64,8.45,13.12 -2016-01-15 13:00:00,1040.0,331.34,8.31,13.08 -2016-01-15 14:00:00,1044.0,229.93,8.16,13.03 -2016-01-15 15:00:00,1053.0,191.53,8.02,12.99 -2016-01-15 16:00:00,1029.0,112.73,8.24,12.69 -2016-01-15 17:00:00,1022.0,0.0,8.46,12.39 -2016-01-15 18:00:00,1145.0,0.0,8.68,12.08 -2016-01-15 19:00:00,1250.0,0.0,8.75,11.58 -2016-01-15 20:00:00,1282.0,0.0,8.82,11.07 -2016-01-15 21:00:00,1397.0,0.0,8.89,10.57 -2016-01-15 22:00:00,1498.0,0.0,9.04,10.38 -2016-01-15 23:00:00,1309.0,0.0,9.19,10.2 -2016-01-16 00:00:00,1199.0,0.0,9.35,10.01 -2016-01-16 01:00:00,1109.0,0.0,9.24,9.39 -2016-01-16 02:00:00,1040.0,0.0,9.13,8.77 -2016-01-16 03:00:00,1024.0,0.0,9.02,8.15 -2016-01-16 04:00:00,1016.0,0.0,9.09,7.59 -2016-01-16 05:00:00,953.0,0.0,9.15,7.03 -2016-01-16 06:00:00,948.0,0.0,9.22,6.47 -2016-01-16 07:00:00,989.0,0.0,9.19,5.69 -2016-01-16 08:00:00,1110.0,0.0,9.16,4.91 -2016-01-16 09:00:00,1164.0,27.15,9.14,4.14 -2016-01-16 10:00:00,1136.0,217.9,9.15,2.82 -2016-01-16 11:00:00,1063.0,497.56,9.15,1.5 -2016-01-16 12:00:00,1015.0,566.02,9.16,0.18 -2016-01-16 13:00:00,972.0,445.11,9.17,1.25 -2016-01-16 14:00:00,941.0,214.47,9.18,2.32 -2016-01-16 15:00:00,964.0,30.67,9.19,3.39 -2016-01-16 16:00:00,998.0,93.34,9.11,3.51 -2016-01-16 17:00:00,995.0,0.0,9.03,3.62 -2016-01-16 18:00:00,1084.0,0.0,8.95,3.74 -2016-01-16 19:00:00,1110.0,0.0,9.02,3.0 -2016-01-16 20:00:00,1113.0,0.0,9.1,2.26 -2016-01-16 21:00:00,1240.0,0.0,9.17,1.52 -2016-01-16 22:00:00,1374.0,0.0,9.27,2.01 -2016-01-16 23:00:00,1206.0,0.0,9.38,2.5 -2016-01-17 00:00:00,1071.0,0.0,9.49,2.99 -2016-01-17 01:00:00,964.0,0.0,9.71,3.68 -2016-01-17 02:00:00,888.0,0.0,9.94,4.37 -2016-01-17 03:00:00,894.0,0.0,10.17,5.06 -2016-01-17 04:00:00,934.0,0.0,10.29,5.4 -2016-01-17 05:00:00,916.0,0.0,10.4,5.74 -2016-01-17 06:00:00,870.0,0.0,10.52,6.08 -2016-01-17 07:00:00,891.0,0.0,10.59,6.47 -2016-01-17 08:00:00,966.0,0.0,10.65,6.86 -2016-01-17 09:00:00,1067.0,6.59,10.72,7.26 -2016-01-17 10:00:00,1139.0,23.46,10.78,7.8 -2016-01-17 11:00:00,1181.0,50.36,10.84,8.34 -2016-01-17 12:00:00,1107.0,64.63,10.9,8.88 -2016-01-17 13:00:00,1024.0,56.83,10.59,9.39 -2016-01-17 14:00:00,982.0,18.01,10.28,9.9 -2016-01-17 15:00:00,995.0,29.99,9.98,10.41 -2016-01-17 16:00:00,1026.0,7.83,9.82,10.97 -2016-01-17 17:00:00,1079.0,0.0,9.66,11.54 -2016-01-17 18:00:00,1110.0,0.0,9.5,12.1 -2016-01-17 19:00:00,1127.0,0.0,9.52,13.08 -2016-01-17 20:00:00,1150.0,0.0,9.55,14.06 -2016-01-17 21:00:00,1271.0,0.0,9.57,15.03 -2016-01-17 22:00:00,1434.0,0.0,9.99,15.1 -2016-01-17 23:00:00,1310.0,0.0,10.41,15.17 -2016-01-18 00:00:00,1193.0,0.0,10.83,15.24 -2016-01-18 01:00:00,1057.0,0.0,10.85,13.27 -2016-01-18 02:00:00,964.0,0.0,10.88,11.31 -2016-01-18 03:00:00,947.0,0.0,10.9,9.34 -2016-01-18 04:00:00,921.0,0.0,10.83,8.83 -2016-01-18 05:00:00,932.0,0.0,10.75,8.32 -2016-01-18 06:00:00,919.0,0.0,10.68,7.81 -2016-01-18 07:00:00,932.0,0.0,10.58,7.59 -2016-01-18 08:00:00,977.0,0.0,10.48,7.37 -2016-01-18 09:00:00,1030.0,96.66,10.38,7.16 -2016-01-18 10:00:00,1045.0,178.86,10.39,6.83 -2016-01-18 11:00:00,1027.0,203.76,10.4,6.51 -2016-01-18 12:00:00,988.0,193.76,10.41,6.18 -2016-01-18 13:00:00,836.0,571.42,10.47,4.72 -2016-01-18 14:00:00,785.0,508.21,10.53,3.26 -2016-01-18 15:00:00,764.0,230.6,10.59,1.81 -2016-01-18 16:00:00,783.0,78.39,10.68,1.49 -2016-01-18 17:00:00,859.0,0.0,10.77,1.18 -2016-01-18 18:00:00,962.0,0.0,10.86,0.87 -2016-01-18 19:00:00,1026.0,0.0,10.77,1.76 -2016-01-18 20:00:00,1007.0,0.0,10.67,2.64 -2016-01-18 21:00:00,1095.0,0.0,10.58,3.53 -2016-01-18 22:00:00,1273.0,0.0,10.47,3.6 -2016-01-18 23:00:00,1140.0,0.0,10.36,3.67 -2016-01-19 00:00:00,1029.0,0.0,10.26,3.74 -2016-01-19 01:00:00,967.0,0.0,10.29,3.19 -2016-01-19 02:00:00,861.0,0.0,10.32,2.63 -2016-01-19 03:00:00,801.0,0.0,10.35,2.08 -2016-01-19 04:00:00,816.0,0.0,10.21,2.48 -2016-01-19 05:00:00,840.0,0.0,10.08,2.87 -2016-01-19 06:00:00,863.0,0.0,9.94,3.27 -2016-01-19 07:00:00,943.0,0.0,9.84,3.55 -2016-01-19 08:00:00,981.0,0.0,9.74,3.84 -2016-01-19 09:00:00,1005.0,7.21,9.64,4.12 -2016-01-19 10:00:00,1020.0,103.41,9.61,4.09 -2016-01-19 11:00:00,1042.0,70.17,9.57,4.05 -2016-01-19 12:00:00,1054.0,78.03,9.54,4.01 -2016-01-19 13:00:00,1043.0,88.02,9.77,4.26 -2016-01-19 14:00:00,1006.0,60.92,9.99,4.51 -2016-01-19 15:00:00,954.0,43.71,10.22,4.76 -2016-01-19 16:00:00,913.0,39.51,10.1,5.12 -2016-01-19 17:00:00,980.0,0.0,9.99,5.49 -2016-01-19 18:00:00,1069.0,0.0,9.88,5.85 -2016-01-19 19:00:00,1102.0,0.0,9.78,6.13 -2016-01-19 20:00:00,1120.0,0.0,9.67,6.42 -2016-01-19 21:00:00,1165.0,0.0,9.57,6.7 -2016-01-19 22:00:00,1298.0,0.0,9.56,6.62 -2016-01-19 23:00:00,1146.0,0.0,9.55,6.53 -2016-01-20 00:00:00,1066.0,0.0,9.55,6.44 -2016-01-20 01:00:00,1001.0,0.0,9.56,6.07 -2016-01-20 02:00:00,941.0,0.0,9.57,5.71 -2016-01-20 03:00:00,879.0,0.0,9.58,5.34 -2016-01-20 04:00:00,864.0,0.0,9.57,5.43 -2016-01-20 05:00:00,860.0,0.0,9.56,5.53 -2016-01-20 06:00:00,915.0,0.0,9.55,5.63 -2016-01-20 07:00:00,984.0,0.0,9.6,5.8 -2016-01-20 08:00:00,1123.0,0.0,9.65,5.98 -2016-01-20 09:00:00,1130.0,30.56,9.71,6.15 -2016-01-20 10:00:00,1114.0,93.37,9.83,6.46 -2016-01-20 11:00:00,1160.0,129.35,9.96,6.76 -2016-01-20 12:00:00,1152.0,132.95,10.08,7.06 -2016-01-20 13:00:00,1096.0,284.56,10.37,7.23 -2016-01-20 14:00:00,1066.0,132.86,10.66,7.4 -2016-01-20 15:00:00,1061.0,46.85,10.94,7.57 -2016-01-20 16:00:00,1070.0,33.67,11.05,7.79 -2016-01-20 17:00:00,1064.0,0.0,11.15,8.0 -2016-01-20 18:00:00,1111.0,0.0,11.26,8.22 -2016-01-20 19:00:00,1161.0,0.0,11.28,8.54 -2016-01-20 20:00:00,1154.0,0.0,11.29,8.86 -2016-01-20 21:00:00,1260.0,0.0,11.31,9.19 -2016-01-20 22:00:00,1416.0,0.0,11.35,9.51 -2016-01-20 23:00:00,1250.0,0.0,11.4,9.83 -2016-01-21 00:00:00,1124.0,0.0,11.45,10.15 -2016-01-21 01:00:00,1053.0,0.0,11.48,10.91 -2016-01-21 02:00:00,979.0,0.0,11.52,11.66 -2016-01-21 03:00:00,936.0,0.0,11.55,12.41 -2016-01-21 04:00:00,936.0,0.0,11.62,12.2 -2016-01-21 05:00:00,917.0,0.0,11.7,11.98 -2016-01-21 06:00:00,926.0,0.0,11.77,11.77 -2016-01-21 07:00:00,981.0,0.0,11.81,11.84 -2016-01-21 08:00:00,1080.0,0.0,11.85,11.91 -2016-01-21 09:00:00,1104.0,13.7,11.89,11.99 -2016-01-21 10:00:00,1114.0,43.53,11.85,12.08 -2016-01-21 11:00:00,1152.0,19.22,11.82,12.17 -2016-01-21 12:00:00,1171.0,24.75,11.78,12.26 -2016-01-21 13:00:00,1130.0,25.46,11.73,12.37 -2016-01-21 14:00:00,1104.0,13.27,11.68,12.47 -2016-01-21 15:00:00,1096.0,10.18,11.64,12.58 -2016-01-21 16:00:00,1105.0,4.61,11.75,12.29 -2016-01-21 17:00:00,1113.0,0.0,11.86,12.01 -2016-01-21 18:00:00,1147.0,0.0,11.98,11.72 -2016-01-21 19:00:00,1184.0,0.0,12.11,11.64 -2016-01-21 20:00:00,1166.0,0.0,12.25,11.56 -2016-01-21 21:00:00,1230.0,0.0,12.38,11.48 -2016-01-21 22:00:00,1344.0,0.0,12.51,11.53 -2016-01-21 23:00:00,1207.0,0.0,12.64,11.58 -2016-01-22 00:00:00,1079.0,0.0,12.77,11.63 -2016-01-22 01:00:00,997.0,0.0,12.76,11.37 -2016-01-22 02:00:00,888.0,0.0,12.76,11.12 -2016-01-22 03:00:00,861.0,0.0,12.76,10.87 -2016-01-22 04:00:00,858.0,0.0,12.85,11.2 -2016-01-22 05:00:00,828.0,0.0,12.95,11.53 -2016-01-22 06:00:00,823.0,0.0,13.04,11.86 -2016-01-22 07:00:00,886.0,0.0,13.04,11.71 -2016-01-22 08:00:00,914.0,0.0,13.03,11.55 -2016-01-22 09:00:00,931.0,216.93,13.03,11.39 -2016-01-22 10:00:00,957.0,369.68,12.89,10.92 -2016-01-22 11:00:00,963.0,529.58,12.74,10.46 -2016-01-22 12:00:00,898.0,472.13,12.6,9.99 -2016-01-22 13:00:00,806.0,387.18,12.55,9.8 -2016-01-22 14:00:00,745.0,532.68,12.5,9.62 -2016-01-22 15:00:00,704.0,376.68,12.44,9.43 -2016-01-22 16:00:00,715.0,175.47,12.35,9.14 -2016-01-22 17:00:00,742.0,0.0,12.26,8.85 -2016-01-22 18:00:00,868.0,0.0,12.17,8.55 -2016-01-22 19:00:00,935.0,0.0,12.13,8.1 -2016-01-22 20:00:00,974.0,0.0,12.08,7.65 -2016-01-22 21:00:00,1036.0,0.0,12.04,7.2 -2016-01-22 22:00:00,1175.0,0.0,12.0,6.86 -2016-01-22 23:00:00,1057.0,0.0,11.96,6.52 -2016-01-23 00:00:00,872.0,0.0,11.93,6.18 -2016-01-23 01:00:00,836.0,0.0,11.79,5.88 -2016-01-23 02:00:00,791.0,0.0,11.66,5.58 -2016-01-23 03:00:00,771.0,0.0,11.52,5.28 -2016-01-23 04:00:00,789.0,0.0,11.44,5.94 -2016-01-23 05:00:00,790.0,0.0,11.37,6.6 -2016-01-23 06:00:00,788.0,0.0,11.29,7.26 -2016-01-23 07:00:00,831.0,0.0,11.43,8.06 -2016-01-23 08:00:00,905.0,0.0,11.56,8.86 -2016-01-23 09:00:00,973.0,120.34,11.7,9.67 -2016-01-23 10:00:00,1008.0,162.51,11.91,10.21 -2016-01-23 11:00:00,1030.0,54.28,12.13,10.75 -2016-01-23 12:00:00,1041.0,21.9,12.34,11.3 -2016-01-23 13:00:00,1014.0,45.02,12.44,11.4 -2016-01-23 14:00:00,1001.0,27.47,12.54,11.51 -2016-01-23 15:00:00,976.0,6.51,12.65,11.61 -2016-01-23 16:00:00,988.0,2.32,12.79,11.25 -2016-01-23 17:00:00,984.0,0.0,12.94,10.89 -2016-01-23 18:00:00,1007.0,0.0,13.08,10.52 -2016-01-23 19:00:00,1026.0,0.0,13.06,10.31 -2016-01-23 20:00:00,1014.0,0.0,13.04,10.1 -2016-01-23 21:00:00,1096.0,0.0,13.02,9.89 -2016-01-23 22:00:00,1213.0,0.0,12.97,9.71 -2016-01-23 23:00:00,1055.0,0.0,12.93,9.53 -2016-01-24 00:00:00,920.0,0.0,12.89,9.35 -2016-01-24 01:00:00,863.0,0.0,12.92,9.6 -2016-01-24 02:00:00,784.0,0.0,12.95,9.85 -2016-01-24 03:00:00,744.0,0.0,12.98,10.1 -2016-01-24 04:00:00,753.0,0.0,12.96,9.8 -2016-01-24 05:00:00,744.0,0.0,12.94,9.51 -2016-01-24 06:00:00,731.0,0.0,12.92,9.21 -2016-01-24 07:00:00,760.0,0.0,12.99,9.09 -2016-01-24 08:00:00,820.0,0.0,13.06,8.97 -2016-01-24 09:00:00,861.0,99.1,13.14,8.84 -2016-01-24 10:00:00,900.0,39.58,13.26,8.94 -2016-01-24 11:00:00,841.0,134.94,13.38,9.04 -2016-01-24 12:00:00,761.0,571.26,13.5,9.14 -2016-01-24 13:00:00,677.0,661.25,13.53,9.59 -2016-01-24 14:00:00,624.0,472.77,13.56,10.03 -2016-01-24 15:00:00,608.0,316.35,13.59,10.47 -2016-01-24 16:00:00,614.0,148.23,13.55,10.83 -2016-01-24 17:00:00,649.0,0.0,13.52,11.2 -2016-01-24 18:00:00,752.0,0.0,13.48,11.56 -2016-01-24 19:00:00,818.0,0.0,13.42,11.49 -2016-01-24 20:00:00,848.0,0.0,13.35,11.43 -2016-01-24 21:00:00,976.0,0.0,13.29,11.37 -2016-01-24 22:00:00,1152.0,0.0,13.34,11.13 -2016-01-24 23:00:00,991.0,0.0,13.4,10.9 -2016-01-25 00:00:00,881.0,0.0,13.46,10.66 -2016-01-25 01:00:00,809.0,0.0,13.6,10.75 -2016-01-25 02:00:00,717.0,0.0,13.74,10.85 -2016-01-25 03:00:00,689.0,0.0,13.88,10.94 -2016-01-25 04:00:00,680.0,0.0,13.91,11.45 -2016-01-25 05:00:00,676.0,0.0,13.93,11.97 -2016-01-25 06:00:00,683.0,0.0,13.96,12.48 -2016-01-25 07:00:00,735.0,0.0,13.75,12.42 -2016-01-25 08:00:00,809.0,0.0,13.54,12.35 -2016-01-25 09:00:00,822.0,103.02,13.33,12.29 -2016-01-25 10:00:00,824.0,162.42,13.05,11.93 -2016-01-25 11:00:00,820.0,259.59,12.78,11.57 -2016-01-25 12:00:00,784.0,522.37,12.5,11.21 -2016-01-25 13:00:00,715.0,530.98,12.1,11.03 -2016-01-25 14:00:00,667.0,581.9,11.7,10.86 -2016-01-25 15:00:00,679.0,433.2,11.3,10.68 -2016-01-25 16:00:00,690.0,187.76,11.15,9.88 -2016-01-25 17:00:00,715.0,0.0,11.01,9.09 -2016-01-25 18:00:00,803.0,0.0,10.86,8.3 -2016-01-25 19:00:00,889.0,0.0,10.78,7.57 -2016-01-25 20:00:00,894.0,0.0,10.69,6.84 -2016-01-25 21:00:00,970.0,0.0,10.61,6.11 -2016-01-25 22:00:00,1131.0,0.0,10.54,7.08 -2016-01-25 23:00:00,989.0,0.0,10.47,8.04 -2016-01-26 00:00:00,886.0,0.0,10.4,9.01 -2016-01-26 01:00:00,828.0,0.0,10.43,10.27 -2016-01-26 02:00:00,769.0,0.0,10.47,11.54 -2016-01-26 03:00:00,757.0,0.0,10.5,12.8 -2016-01-26 04:00:00,780.0,0.0,10.68,13.72 -2016-01-26 05:00:00,773.0,0.0,10.85,14.65 -2016-01-26 06:00:00,789.0,0.0,11.03,15.57 -2016-01-26 07:00:00,856.0,0.0,11.35,15.8 -2016-01-26 08:00:00,963.0,0.0,11.66,16.03 -2016-01-26 09:00:00,991.0,1.87,11.98,16.26 -2016-01-26 10:00:00,1004.0,11.97,12.06,16.44 -2016-01-26 11:00:00,1013.0,68.07,12.14,16.62 -2016-01-26 12:00:00,1035.0,15.18,12.22,16.8 -2016-01-26 13:00:00,1033.0,24.02,12.16,16.79 -2016-01-26 14:00:00,949.0,11.34,12.1,16.78 -2016-01-26 15:00:00,924.0,24.73,12.03,16.77 -2016-01-26 16:00:00,938.0,15.85,12.12,16.65 -2016-01-26 17:00:00,934.0,0.0,12.2,16.52 -2016-01-26 18:00:00,984.0,0.0,12.28,16.4 -2016-01-26 19:00:00,1004.0,0.0,12.42,16.27 -2016-01-26 20:00:00,1005.0,0.0,12.55,16.13 -2016-01-26 21:00:00,1084.0,0.0,12.69,16.0 -2016-01-26 22:00:00,1222.0,0.0,12.71,16.16 -2016-01-26 23:00:00,1075.0,0.0,12.74,16.32 -2016-01-27 00:00:00,950.0,0.0,12.77,16.48 -2016-01-27 01:00:00,870.0,0.0,12.66,16.27 -2016-01-27 02:00:00,808.0,0.0,12.56,16.05 -2016-01-27 03:00:00,771.0,0.0,12.45,15.83 -2016-01-27 04:00:00,777.0,0.0,12.52,15.71 -2016-01-27 05:00:00,765.0,0.0,12.58,15.59 -2016-01-27 06:00:00,778.0,0.0,12.65,15.46 -2016-01-27 07:00:00,837.0,0.0,12.66,15.31 -2016-01-27 08:00:00,922.0,0.0,12.67,15.16 -2016-01-27 09:00:00,934.0,2.8,12.68,15.01 -2016-01-27 10:00:00,940.0,5.95,12.59,14.64 -2016-01-27 11:00:00,961.0,64.19,12.5,14.28 -2016-01-27 12:00:00,956.0,30.35,12.41,13.92 -2016-01-27 13:00:00,912.0,23.3,12.24,12.28 -2016-01-27 14:00:00,894.0,24.71,12.07,10.63 -2016-01-27 15:00:00,871.0,19.88,11.9,8.99 -2016-01-27 16:00:00,880.0,39.99,11.45,9.48 -2016-01-27 17:00:00,870.0,0.0,11.01,9.97 -2016-01-27 18:00:00,947.0,0.0,10.57,10.46 -2016-01-27 19:00:00,977.0,0.0,10.21,10.27 -2016-01-27 20:00:00,971.0,0.0,9.84,10.09 -2016-01-27 21:00:00,1052.0,0.0,9.48,9.9 -2016-01-27 22:00:00,1205.0,0.0,9.52,9.57 -2016-01-27 23:00:00,1076.0,0.0,9.56,9.23 -2016-01-28 00:00:00,959.0,0.0,9.61,8.9 -2016-01-28 01:00:00,902.0,0.0,9.93,8.23 -2016-01-28 02:00:00,833.0,0.0,10.26,7.57 -2016-01-28 03:00:00,810.0,0.0,10.58,6.91 -2016-01-28 04:00:00,818.0,0.0,10.39,6.53 -2016-01-28 05:00:00,814.0,0.0,10.19,6.15 -2016-01-28 06:00:00,834.0,0.0,10.0,5.77 -2016-01-28 07:00:00,903.0,0.0,9.89,5.35 -2016-01-28 08:00:00,1003.0,0.0,9.77,4.93 -2016-01-28 09:00:00,1034.0,196.99,9.66,4.51 -2016-01-28 10:00:00,987.0,325.28,9.69,5.38 -2016-01-28 11:00:00,944.0,583.87,9.72,6.25 -2016-01-28 12:00:00,900.0,338.15,9.75,7.12 -2016-01-28 13:00:00,869.0,611.9,9.84,8.06 -2016-01-28 14:00:00,801.0,283.28,9.93,9.01 -2016-01-28 15:00:00,765.0,70.49,10.02,9.96 -2016-01-28 16:00:00,764.0,144.74,10.31,10.17 -2016-01-28 17:00:00,800.0,0.0,10.61,10.37 -2016-01-28 18:00:00,900.0,0.0,10.91,10.58 -2016-01-28 19:00:00,1011.0,0.0,11.2,10.84 -2016-01-28 20:00:00,1015.0,0.0,11.48,11.09 -2016-01-28 21:00:00,1087.0,0.0,11.77,11.35 -2016-01-28 22:00:00,1226.0,0.0,11.86,11.66 -2016-01-28 23:00:00,1091.0,0.0,11.95,11.96 -2016-01-29 00:00:00,968.0,0.0,12.04,12.26 -2016-01-29 01:00:00,915.0,0.0,11.86,12.83 -2016-01-29 02:00:00,831.0,0.0,11.69,13.39 -2016-01-29 03:00:00,784.0,0.0,11.52,13.96 -2016-01-29 04:00:00,803.0,0.0,11.52,14.44 -2016-01-29 05:00:00,786.0,0.0,11.51,14.92 -2016-01-29 06:00:00,801.0,0.0,11.51,15.41 -2016-01-29 07:00:00,873.0,0.0,11.61,14.61 -2016-01-29 08:00:00,917.0,0.0,11.71,13.81 -2016-01-29 09:00:00,916.0,37.72,11.81,13.01 -2016-01-29 10:00:00,910.0,124.38,11.84,13.03 -2016-01-29 11:00:00,899.0,17.87,11.86,13.06 -2016-01-29 12:00:00,906.0,64.23,11.89,13.09 -2016-01-29 13:00:00,874.0,37.02,11.71,13.55 -2016-01-29 14:00:00,846.0,30.49,11.52,14.02 -2016-01-29 15:00:00,828.0,15.25,11.34,14.48 -2016-01-29 16:00:00,822.0,35.85,11.52,15.47 -2016-01-29 17:00:00,832.0,0.0,11.7,16.46 -2016-01-29 18:00:00,918.0,0.0,11.88,17.45 -2016-01-29 19:00:00,979.0,0.0,11.89,17.12 -2016-01-29 20:00:00,953.0,0.0,11.9,16.8 -2016-01-29 21:00:00,1073.0,0.0,11.91,16.47 -2016-01-29 22:00:00,1212.0,0.0,11.87,15.88 -2016-01-29 23:00:00,1062.0,0.0,11.83,15.29 -2016-01-30 00:00:00,948.0,0.0,11.79,14.7 -2016-01-30 01:00:00,909.0,0.0,11.74,14.45 -2016-01-30 02:00:00,816.0,0.0,11.69,14.19 -2016-01-30 03:00:00,774.0,0.0,11.64,13.93 -2016-01-30 04:00:00,777.0,0.0,11.54,13.42 -2016-01-30 05:00:00,788.0,0.0,11.45,12.91 -2016-01-30 06:00:00,762.0,0.0,11.35,12.4 -2016-01-30 07:00:00,797.0,0.0,11.01,12.34 -2016-01-30 08:00:00,846.0,0.0,10.67,12.27 -2016-01-30 09:00:00,895.0,15.96,10.34,12.21 -2016-01-30 10:00:00,935.0,34.37,10.02,11.96 -2016-01-30 11:00:00,970.0,89.6,9.71,11.71 -2016-01-30 12:00:00,996.0,105.39,9.39,11.46 -2016-01-30 13:00:00,958.0,68.77,9.55,11.28 -2016-01-30 14:00:00,944.0,35.16,9.71,11.1 -2016-01-30 15:00:00,960.0,32.19,9.88,10.92 -2016-01-30 16:00:00,958.0,25.89,9.77,10.53 -2016-01-30 17:00:00,977.0,0.0,9.66,10.14 -2016-01-30 18:00:00,1040.0,0.0,9.55,9.75 -2016-01-30 19:00:00,1098.0,0.0,9.64,9.14 -2016-01-30 20:00:00,1053.0,0.0,9.74,8.54 -2016-01-30 21:00:00,1129.0,0.0,9.83,7.93 -2016-01-30 22:00:00,1287.0,0.0,10.09,7.89 -2016-01-30 23:00:00,1125.0,0.0,10.35,7.85 -2016-01-31 00:00:00,984.0,0.0,10.61,7.81 -2016-01-31 01:00:00,932.0,0.0,10.91,9.2 -2016-01-31 02:00:00,846.0,0.0,11.21,10.59 -2016-01-31 03:00:00,802.0,0.0,11.51,11.99 -2016-01-31 04:00:00,813.0,0.0,11.8,12.7 -2016-01-31 05:00:00,800.0,0.0,12.08,13.41 -2016-01-31 06:00:00,794.0,0.0,12.37,14.12 -2016-01-31 07:00:00,816.0,0.0,12.64,14.56 -2016-01-31 08:00:00,865.0,0.0,12.91,15.0 -2016-01-31 09:00:00,896.0,35.47,13.19,15.43 -2016-01-31 10:00:00,959.0,43.71,13.09,14.9 -2016-01-31 11:00:00,989.0,47.92,12.98,14.37 -2016-01-31 12:00:00,948.0,35.34,12.88,13.83 -2016-01-31 13:00:00,894.0,51.76,12.7,13.97 -2016-01-31 14:00:00,836.0,73.46,12.51,14.1 -2016-01-31 15:00:00,833.0,50.17,12.32,14.23 -2016-01-31 16:00:00,835.0,50.85,12.29,14.1 -2016-01-31 17:00:00,859.0,0.0,12.26,13.96 -2016-01-31 18:00:00,944.0,0.0,12.22,13.82 -2016-01-31 19:00:00,982.0,0.0,12.13,13.34 -2016-01-31 20:00:00,963.0,0.0,12.03,12.86 -2016-01-31 21:00:00,1050.0,0.0,11.94,12.39 -2016-01-31 22:00:00,1188.0,0.0,11.85,11.96 -2016-01-31 23:00:00,1062.0,0.0,11.76,11.54 -2016-02-01 00:00:00,923.0,0.0,11.68,11.12 -2016-02-01 01:00:00,835.0,0.0,11.76,11.78 -2016-02-01 02:00:00,769.0,0.0,11.84,12.44 -2016-02-01 03:00:00,755.0,0.0,11.92,13.1 -2016-02-01 04:00:00,745.0,0.0,11.97,13.17 -2016-02-01 05:00:00,741.0,0.0,12.03,13.24 -2016-02-01 06:00:00,752.0,0.0,12.09,13.31 -2016-02-01 07:00:00,811.0,0.0,12.11,13.01 -2016-02-01 08:00:00,900.0,0.0,12.13,12.7 -2016-02-01 09:00:00,910.0,121.45,12.15,12.4 -2016-02-01 10:00:00,953.0,111.47,12.11,12.36 -2016-02-01 11:00:00,951.0,68.8,12.08,12.33 -2016-02-01 12:00:00,961.0,86.72,12.04,12.29 -2016-02-01 13:00:00,911.0,154.3,11.94,12.57 -2016-02-01 14:00:00,853.0,55.02,11.84,12.86 -2016-02-01 15:00:00,819.0,45.2,11.73,13.14 -2016-02-01 16:00:00,834.0,28.88,11.85,13.0 -2016-02-01 17:00:00,873.0,0.0,11.97,12.85 -2016-02-01 18:00:00,960.0,0.0,12.09,12.7 -2016-02-01 19:00:00,1000.0,0.0,12.09,12.19 -2016-02-01 20:00:00,983.0,0.0,12.09,11.68 -2016-02-01 21:00:00,1081.0,0.0,12.09,11.17 -2016-02-01 22:00:00,1224.0,0.0,12.01,10.28 -2016-02-01 23:00:00,1063.0,0.0,11.94,9.38 -2016-02-02 00:00:00,951.0,0.0,11.87,8.48 -2016-02-02 01:00:00,864.0,0.0,11.78,9.0 -2016-02-02 02:00:00,779.0,0.0,11.69,9.52 -2016-02-02 03:00:00,775.0,0.0,11.61,10.04 -2016-02-02 04:00:00,768.0,0.0,11.5,9.72 -2016-02-02 05:00:00,769.0,0.0,11.39,9.4 -2016-02-02 06:00:00,766.0,0.0,11.29,9.08 -2016-02-02 07:00:00,823.0,0.0,11.13,8.92 -2016-02-02 08:00:00,903.0,0.0,10.98,8.77 -2016-02-02 09:00:00,955.0,280.13,10.83,8.62 -2016-02-02 10:00:00,994.0,463.18,10.58,8.86 -2016-02-02 11:00:00,956.0,458.62,10.33,9.1 -2016-02-02 12:00:00,855.0,104.82,10.08,9.34 -2016-02-02 13:00:00,769.0,380.71,10.07,9.55 -2016-02-02 14:00:00,733.0,328.59,10.06,9.77 -2016-02-02 15:00:00,734.0,314.29,10.05,9.99 -2016-02-02 16:00:00,732.0,244.53,9.89,10.37 -2016-02-02 17:00:00,787.0,0.0,9.74,10.76 -2016-02-02 18:00:00,886.0,0.0,9.59,11.14 -2016-02-02 19:00:00,994.0,0.0,9.59,11.39 -2016-02-02 20:00:00,1005.0,0.0,9.58,11.64 -2016-02-02 21:00:00,1105.0,0.0,9.57,11.89 -2016-02-02 22:00:00,1283.0,0.0,9.5,12.14 -2016-02-02 23:00:00,1147.0,0.0,9.43,12.39 -2016-02-03 00:00:00,1034.0,0.0,9.36,12.63 -2016-02-03 01:00:00,959.0,0.0,9.31,12.45 -2016-02-03 02:00:00,880.0,0.0,9.26,12.26 -2016-02-03 03:00:00,855.0,0.0,9.21,12.07 -2016-02-03 04:00:00,900.0,0.0,9.12,12.5 -2016-02-03 05:00:00,908.0,0.0,9.04,12.93 -2016-02-03 06:00:00,920.0,0.0,8.96,13.37 -2016-02-03 07:00:00,1016.0,0.0,8.81,13.46 -2016-02-03 08:00:00,1117.0,0.0,8.67,13.55 -2016-02-03 09:00:00,1132.0,125.94,8.52,13.64 -2016-02-03 10:00:00,1101.0,464.69,8.84,13.2 -2016-02-03 11:00:00,995.0,577.3,9.16,12.76 -2016-02-03 12:00:00,946.0,375.84,9.48,12.32 -2016-02-03 13:00:00,879.0,112.45,9.45,11.97 -2016-02-03 14:00:00,856.0,615.56,9.43,11.62 -2016-02-03 15:00:00,829.0,74.69,9.4,11.27 -2016-02-03 16:00:00,841.0,237.56,9.53,10.92 -2016-02-03 17:00:00,889.0,0.0,9.65,10.57 -2016-02-03 18:00:00,982.0,0.0,9.77,10.22 -2016-02-03 19:00:00,1095.0,0.0,9.92,9.69 -2016-02-03 20:00:00,1079.0,0.0,10.07,9.15 -2016-02-03 21:00:00,1181.0,0.0,10.23,8.62 -2016-02-03 22:00:00,1317.0,0.0,10.45,8.89 -2016-02-03 23:00:00,1184.0,0.0,10.67,9.16 -2016-02-04 00:00:00,1032.0,0.0,10.89,9.43 -2016-02-04 01:00:00,949.0,0.0,11.06,10.48 -2016-02-04 02:00:00,876.0,0.0,11.23,11.53 -2016-02-04 03:00:00,842.0,0.0,11.4,12.58 -2016-02-04 04:00:00,853.0,0.0,11.5,12.32 -2016-02-04 05:00:00,842.0,0.0,11.61,12.06 -2016-02-04 06:00:00,832.0,0.0,11.72,11.81 -2016-02-04 07:00:00,893.0,0.0,11.69,10.6 -2016-02-04 08:00:00,981.0,0.0,11.66,9.4 -2016-02-04 09:00:00,998.0,132.06,11.63,8.19 -2016-02-04 10:00:00,996.0,42.17,11.61,7.69 -2016-02-04 11:00:00,1020.0,65.72,11.58,7.18 -2016-02-04 12:00:00,1022.0,81.26,11.56,6.68 -2016-02-04 13:00:00,936.0,72.76,11.33,6.61 -2016-02-04 14:00:00,901.0,55.86,11.1,6.54 -2016-02-04 15:00:00,888.0,47.56,10.86,6.47 -2016-02-04 16:00:00,875.0,32.14,10.87,6.38 -2016-02-04 17:00:00,890.0,0.0,10.88,6.29 -2016-02-04 18:00:00,971.0,0.0,10.89,6.19 -2016-02-04 19:00:00,1031.0,0.0,10.89,6.1 -2016-02-04 20:00:00,1029.0,0.0,10.89,6.01 -2016-02-04 21:00:00,1093.0,0.0,10.89,5.92 -2016-02-04 22:00:00,1244.0,0.0,10.91,6.01 -2016-02-04 23:00:00,1111.0,0.0,10.93,6.1 -2016-02-05 00:00:00,960.0,0.0,10.96,6.19 -2016-02-05 01:00:00,883.0,0.0,10.89,7.26 -2016-02-05 02:00:00,806.0,0.0,10.82,8.32 -2016-02-05 03:00:00,791.0,0.0,10.76,9.38 -2016-02-05 04:00:00,787.0,0.0,10.75,9.61 -2016-02-05 05:00:00,779.0,0.0,10.75,9.84 -2016-02-05 06:00:00,792.0,0.0,10.75,10.07 -2016-02-05 07:00:00,876.0,0.0,10.8,10.31 -2016-02-05 08:00:00,909.0,0.0,10.86,10.56 -2016-02-05 09:00:00,943.0,54.99,10.91,10.8 -2016-02-05 10:00:00,919.0,38.61,11.05,11.16 -2016-02-05 11:00:00,927.0,33.44,11.18,11.53 -2016-02-05 12:00:00,902.0,48.29,11.32,11.89 -2016-02-05 13:00:00,891.0,52.82,11.46,12.43 -2016-02-05 14:00:00,882.0,75.92,11.6,12.97 -2016-02-05 15:00:00,871.0,85.1,11.74,13.5 -2016-02-05 16:00:00,904.0,56.51,11.6,13.25 -2016-02-05 17:00:00,918.0,0.0,11.46,12.99 -2016-02-05 18:00:00,975.0,0.0,11.32,12.73 -2016-02-05 19:00:00,1047.0,0.0,11.28,12.71 -2016-02-05 20:00:00,1032.0,0.0,11.24,12.69 -2016-02-05 21:00:00,1116.0,0.0,11.2,12.68 -2016-02-05 22:00:00,1266.0,0.0,11.17,11.95 -2016-02-05 23:00:00,1138.0,0.0,11.13,11.22 -2016-02-06 00:00:00,1015.0,0.0,11.09,10.5 -2016-02-06 01:00:00,937.0,0.0,11.27,11.75 -2016-02-06 02:00:00,864.0,0.0,11.45,13.01 -2016-02-06 03:00:00,822.0,0.0,11.64,14.26 -2016-02-06 04:00:00,831.0,0.0,11.66,15.08 -2016-02-06 05:00:00,815.0,0.0,11.69,15.9 -2016-02-06 06:00:00,825.0,0.0,11.72,16.72 -2016-02-06 07:00:00,870.0,0.0,11.75,17.63 -2016-02-06 08:00:00,955.0,0.0,11.79,18.54 -2016-02-06 09:00:00,994.0,11.36,11.82,19.45 -2016-02-06 10:00:00,1049.0,43.0,11.66,19.65 -2016-02-06 11:00:00,1078.0,32.69,11.51,19.85 -2016-02-06 12:00:00,1112.0,40.08,11.35,20.06 -2016-02-06 13:00:00,1111.0,29.1,11.3,18.91 -2016-02-06 14:00:00,1075.0,30.55,11.25,17.76 -2016-02-06 15:00:00,1080.0,50.64,11.2,16.61 -2016-02-06 16:00:00,1074.0,31.27,10.84,15.51 -2016-02-06 17:00:00,1050.0,0.0,10.47,14.42 -2016-02-06 18:00:00,1106.0,0.0,10.1,13.32 -2016-02-06 19:00:00,1159.0,0.0,9.78,13.55 -2016-02-06 20:00:00,1137.0,0.0,9.47,13.78 -2016-02-06 21:00:00,1241.0,0.0,9.15,14.01 -2016-02-06 22:00:00,1387.0,0.0,8.79,14.24 -2016-02-06 23:00:00,1265.0,0.0,8.42,14.46 -2016-02-07 00:00:00,1139.0,0.0,8.06,14.69 -2016-02-07 01:00:00,1073.0,0.0,8.27,14.82 -2016-02-07 02:00:00,1006.0,0.0,8.48,14.96 -2016-02-07 03:00:00,973.0,0.0,8.69,15.09 -2016-02-07 04:00:00,985.0,0.0,8.63,15.25 -2016-02-07 05:00:00,988.0,0.0,8.58,15.4 -2016-02-07 06:00:00,982.0,0.0,8.53,15.56 -2016-02-07 07:00:00,1030.0,0.0,8.69,15.49 -2016-02-07 08:00:00,1070.0,1.56,8.86,15.42 -2016-02-07 09:00:00,1125.0,57.22,9.03,15.35 -2016-02-07 10:00:00,1155.0,81.1,9.25,15.25 -2016-02-07 11:00:00,1134.0,54.92,9.46,15.15 -2016-02-07 12:00:00,1152.0,80.71,9.68,15.05 -2016-02-07 13:00:00,1086.0,64.92,9.84,15.87 -2016-02-07 14:00:00,1039.0,30.01,10.0,16.69 -2016-02-07 15:00:00,1043.0,26.4,10.16,17.52 -2016-02-07 16:00:00,1059.0,55.85,10.73,17.85 -2016-02-07 17:00:00,1063.0,0.0,11.3,18.19 -2016-02-07 18:00:00,1094.0,0.0,11.86,18.52 -2016-02-07 19:00:00,1164.0,0.0,11.98,18.68 -2016-02-07 20:00:00,1144.0,0.0,12.1,18.83 -2016-02-07 21:00:00,1229.0,0.0,12.22,18.98 -2016-02-07 22:00:00,1386.0,0.0,12.15,19.05 -2016-02-07 23:00:00,1255.0,0.0,12.08,19.12 -2016-02-08 00:00:00,1123.0,0.0,12.01,19.19 -2016-02-08 01:00:00,1026.0,0.0,11.58,19.16 -2016-02-08 02:00:00,951.0,0.0,11.15,19.14 -2016-02-08 03:00:00,941.0,0.0,10.72,19.12 -2016-02-08 04:00:00,941.0,0.0,10.63,19.72 -2016-02-08 05:00:00,944.0,0.0,10.55,20.33 -2016-02-08 06:00:00,953.0,0.0,10.47,20.94 -2016-02-08 07:00:00,1016.0,0.0,10.34,20.91 -2016-02-08 08:00:00,1082.0,2.4,10.22,20.89 -2016-02-08 09:00:00,1130.0,290.31,10.1,20.87 -2016-02-08 10:00:00,1140.0,106.9,10.01,20.73 -2016-02-08 11:00:00,1118.0,529.59,9.92,20.58 -2016-02-08 12:00:00,1068.0,57.96,9.83,20.44 -2016-02-08 13:00:00,1024.0,79.9,9.95,20.28 -2016-02-08 14:00:00,1001.0,146.86,10.07,20.12 -2016-02-08 15:00:00,1000.0,497.15,10.19,19.96 -2016-02-08 16:00:00,981.0,71.73,10.13,18.92 -2016-02-08 17:00:00,964.0,0.0,10.07,17.88 -2016-02-08 18:00:00,1047.0,0.0,10.01,16.84 -2016-02-08 19:00:00,1140.0,0.0,9.91,16.01 -2016-02-08 20:00:00,1164.0,0.0,9.81,15.18 -2016-02-08 21:00:00,1254.0,0.0,9.7,14.34 -2016-02-08 22:00:00,1400.0,0.0,9.78,13.83 -2016-02-08 23:00:00,1250.0,0.0,9.85,13.31 -2016-02-09 00:00:00,1138.0,0.0,9.93,12.79 -2016-02-09 01:00:00,1052.0,0.0,10.03,11.87 -2016-02-09 02:00:00,971.0,0.0,10.14,10.95 -2016-02-09 03:00:00,920.0,0.0,10.25,10.03 -2016-02-09 04:00:00,917.0,0.0,10.08,10.43 -2016-02-09 05:00:00,898.0,0.0,9.91,10.83 -2016-02-09 06:00:00,898.0,0.0,9.75,11.23 -2016-02-09 07:00:00,982.0,0.0,9.08,12.16 -2016-02-09 08:00:00,1085.0,3.35,8.42,13.09 -2016-02-09 09:00:00,1145.0,16.9,7.76,14.03 -2016-02-09 10:00:00,1167.0,79.96,7.4,14.8 -2016-02-09 11:00:00,1193.0,183.7,7.04,15.57 -2016-02-09 12:00:00,1227.0,52.56,6.68,16.34 -2016-02-09 13:00:00,1194.0,149.49,7.02,16.67 -2016-02-09 14:00:00,1202.0,14.25,7.37,17.0 -2016-02-09 15:00:00,1209.0,199.83,7.72,17.32 -2016-02-09 16:00:00,1207.0,124.42,7.66,17.42 -2016-02-09 17:00:00,1226.0,28.43,7.61,17.51 -2016-02-09 18:00:00,1291.0,0.0,7.55,17.6 -2016-02-09 19:00:00,1406.0,0.0,7.39,17.19 -2016-02-09 20:00:00,1425.0,0.0,7.22,16.78 -2016-02-09 21:00:00,1508.0,0.0,7.05,16.37 -2016-02-09 22:00:00,1651.0,0.0,7.06,16.04 -2016-02-09 23:00:00,1489.0,0.0,7.07,15.7 -2016-02-10 00:00:00,1360.0,0.0,7.08,15.37 -2016-02-10 01:00:00,1292.0,0.0,7.15,14.56 -2016-02-10 02:00:00,1209.0,0.0,7.22,13.75 -2016-02-10 03:00:00,1161.0,0.0,7.29,12.94 -2016-02-10 04:00:00,1171.0,0.0,7.18,12.6 -2016-02-10 05:00:00,1149.0,0.0,7.07,12.27 -2016-02-10 06:00:00,1149.0,0.0,6.97,11.93 -2016-02-10 07:00:00,1215.0,0.0,7.15,11.68 -2016-02-10 08:00:00,1266.0,41.24,7.34,11.43 -2016-02-10 09:00:00,1304.0,28.18,7.53,11.17 -2016-02-10 10:00:00,1301.0,131.07,7.67,10.87 -2016-02-10 11:00:00,1231.0,181.19,7.8,10.57 -2016-02-10 12:00:00,1208.0,752.71,7.94,10.28 -2016-02-10 13:00:00,1110.0,621.37,8.16,9.92 -2016-02-10 14:00:00,1020.0,646.19,8.39,9.56 -2016-02-10 15:00:00,999.0,469.46,8.61,9.2 -2016-02-10 16:00:00,996.0,295.97,8.6,8.57 -2016-02-10 17:00:00,1025.0,26.29,8.59,7.94 -2016-02-10 18:00:00,1116.0,0.0,8.58,7.31 -2016-02-10 19:00:00,1279.0,0.0,8.64,7.17 -2016-02-10 20:00:00,1272.0,0.0,8.7,7.03 -2016-02-10 21:00:00,1375.0,0.0,8.76,6.88 -2016-02-10 22:00:00,1551.0,0.0,8.7,6.88 -2016-02-10 23:00:00,1386.0,0.0,8.64,6.88 -2016-02-11 00:00:00,1269.0,0.0,8.58,6.88 -2016-02-11 01:00:00,1169.0,0.0,8.44,7.01 -2016-02-11 02:00:00,1085.0,0.0,8.31,7.14 -2016-02-11 03:00:00,1058.0,0.0,8.18,7.27 -2016-02-11 04:00:00,1074.0,0.0,8.0,7.15 -2016-02-11 05:00:00,1062.0,0.0,7.82,7.04 -2016-02-11 06:00:00,1084.0,0.0,7.64,6.92 -2016-02-11 07:00:00,1125.0,0.0,7.66,6.77 -2016-02-11 08:00:00,1199.0,26.08,7.69,6.61 -2016-02-11 09:00:00,1223.0,162.9,7.71,6.46 -2016-02-11 10:00:00,1269.0,404.14,7.77,6.77 -2016-02-11 11:00:00,1172.0,546.23,7.84,7.09 -2016-02-11 12:00:00,1086.0,640.34,7.9,7.41 -2016-02-11 13:00:00,986.0,269.68,8.07,7.31 -2016-02-11 14:00:00,973.0,621.72,8.24,7.22 -2016-02-11 15:00:00,961.0,151.76,8.41,7.13 -2016-02-11 16:00:00,986.0,231.92,8.45,7.22 -2016-02-11 17:00:00,1051.0,0.0,8.48,7.31 -2016-02-11 18:00:00,1135.0,0.0,8.52,7.39 -2016-02-11 19:00:00,1243.0,0.0,8.86,7.46 -2016-02-11 20:00:00,1227.0,0.0,9.2,7.53 -2016-02-11 21:00:00,1326.0,0.0,9.53,7.6 -2016-02-11 22:00:00,1491.0,0.0,9.61,8.16 -2016-02-11 23:00:00,1348.0,0.0,9.69,8.71 -2016-02-12 00:00:00,1219.0,0.0,9.77,9.27 -2016-02-12 01:00:00,1155.0,0.0,10.17,9.16 -2016-02-12 02:00:00,1067.0,0.0,10.57,9.05 -2016-02-12 03:00:00,1001.0,0.0,10.97,8.94 -2016-02-12 04:00:00,1002.0,0.0,10.74,10.38 -2016-02-12 05:00:00,1004.0,0.0,10.52,11.83 -2016-02-12 06:00:00,1015.0,0.0,10.3,13.27 -2016-02-12 07:00:00,1106.0,0.0,10.35,13.7 -2016-02-12 08:00:00,1148.0,0.0,10.41,14.13 -2016-02-12 09:00:00,1189.0,250.69,10.46,14.57 -2016-02-12 10:00:00,1191.0,307.09,10.35,14.56 -2016-02-12 11:00:00,1183.0,407.5,10.23,14.56 -2016-02-12 12:00:00,1161.0,273.45,10.12,14.55 -2016-02-12 13:00:00,1056.0,406.65,10.27,13.77 -2016-02-12 14:00:00,1020.0,629.63,10.43,12.98 -2016-02-12 15:00:00,979.0,427.86,10.59,12.19 -2016-02-12 16:00:00,970.0,95.63,10.5,11.72 -2016-02-12 17:00:00,1001.0,61.52,10.41,11.26 -2016-02-12 18:00:00,1097.0,0.0,10.33,10.79 -2016-02-12 19:00:00,1250.0,0.0,10.19,9.87 -2016-02-12 20:00:00,1294.0,0.0,10.06,8.96 -2016-02-12 21:00:00,1362.0,0.0,9.93,8.04 -2016-02-12 22:00:00,1525.0,0.0,9.83,7.73 -2016-02-12 23:00:00,1359.0,0.0,9.74,7.42 -2016-02-13 00:00:00,1206.0,0.0,9.65,7.1 -2016-02-13 01:00:00,1129.0,0.0,10.2,8.81 -2016-02-13 02:00:00,1056.0,0.0,10.75,10.52 -2016-02-13 03:00:00,1021.0,0.0,11.3,12.23 -2016-02-13 04:00:00,999.0,0.0,11.1,14.99 -2016-02-13 05:00:00,997.0,0.0,10.9,17.75 -2016-02-13 06:00:00,1027.0,0.0,10.7,20.51 -2016-02-13 07:00:00,1083.0,0.0,10.52,20.39 -2016-02-13 08:00:00,1146.0,22.87,10.34,20.26 -2016-02-13 09:00:00,1199.0,40.45,10.16,20.14 -2016-02-13 10:00:00,1269.0,277.04,10.07,19.86 -2016-02-13 11:00:00,1304.0,206.02,9.98,19.58 -2016-02-13 12:00:00,1250.0,349.51,9.89,19.3 -2016-02-13 13:00:00,1163.0,244.63,9.98,17.37 -2016-02-13 14:00:00,1072.0,471.12,10.08,15.43 -2016-02-13 15:00:00,1046.0,532.06,10.18,13.5 -2016-02-13 16:00:00,1037.0,325.93,9.84,12.77 -2016-02-13 17:00:00,1052.0,21.1,9.51,12.03 -2016-02-13 18:00:00,1136.0,0.0,9.17,11.3 -2016-02-13 19:00:00,1265.0,0.0,9.09,11.31 -2016-02-13 20:00:00,1260.0,0.0,9.01,11.31 -2016-02-13 21:00:00,1393.0,0.0,8.93,11.32 -2016-02-13 22:00:00,1530.0,0.0,8.98,11.77 -2016-02-13 23:00:00,1393.0,0.0,9.03,12.21 -2016-02-14 00:00:00,1274.0,0.0,9.09,12.65 -2016-02-14 01:00:00,1199.0,0.0,9.1,13.01 -2016-02-14 02:00:00,1102.0,0.0,9.11,13.37 -2016-02-14 03:00:00,1058.0,0.0,9.13,13.72 -2016-02-14 04:00:00,1072.0,0.0,8.94,12.96 -2016-02-14 05:00:00,1059.0,0.0,8.75,12.2 -2016-02-14 06:00:00,1055.0,0.0,8.57,11.43 -2016-02-14 07:00:00,1097.0,0.0,8.66,10.54 -2016-02-14 08:00:00,1159.0,62.8,8.76,9.65 -2016-02-14 09:00:00,1209.0,305.53,8.85,8.76 -2016-02-14 10:00:00,1265.0,55.71,8.93,7.68 -2016-02-14 11:00:00,1259.0,470.44,9.01,6.6 -2016-02-14 12:00:00,1245.0,730.19,9.09,5.52 -2016-02-14 13:00:00,1205.0,719.55,8.84,5.7 -2016-02-14 14:00:00,1186.0,599.07,8.6,5.89 -2016-02-14 15:00:00,1122.0,535.26,8.35,6.07 -2016-02-14 16:00:00,1029.0,63.82,8.54,7.75 -2016-02-14 17:00:00,1048.0,1.47,8.73,9.43 -2016-02-14 18:00:00,1148.0,0.0,8.91,11.1 -2016-02-14 19:00:00,1295.0,0.0,8.64,11.3 -2016-02-14 20:00:00,1356.0,0.0,8.37,11.5 -2016-02-14 21:00:00,1469.0,0.0,8.1,11.7 -2016-02-14 22:00:00,1637.0,0.0,7.84,11.79 -2016-02-14 23:00:00,1484.0,0.0,7.58,11.89 -2016-02-15 00:00:00,1363.0,0.0,7.33,11.99 -2016-02-15 01:00:00,1290.0,0.0,7.3,11.77 -2016-02-15 02:00:00,1193.0,0.0,7.28,11.56 -2016-02-15 03:00:00,1163.0,0.0,7.26,11.35 -2016-02-15 04:00:00,1174.0,0.0,7.29,11.52 -2016-02-15 05:00:00,1152.0,0.0,7.32,11.68 -2016-02-15 06:00:00,1168.0,0.0,7.35,11.85 -2016-02-15 07:00:00,1251.0,0.0,7.34,11.95 -2016-02-15 08:00:00,1326.0,78.46,7.34,12.05 -2016-02-15 09:00:00,1437.0,298.84,7.33,12.15 -2016-02-15 10:00:00,1419.0,72.52,7.44,11.52 -2016-02-15 11:00:00,1383.0,657.23,7.55,10.88 -2016-02-15 12:00:00,1332.0,682.98,7.66,10.25 -2016-02-15 13:00:00,1265.0,714.16,7.8,9.68 -2016-02-15 14:00:00,1175.0,300.08,7.94,9.12 -2016-02-15 15:00:00,1132.0,251.42,8.07,8.55 -2016-02-15 16:00:00,1126.0,333.87,8.0,8.05 -2016-02-15 17:00:00,1146.0,21.43,7.93,7.55 -2016-02-15 18:00:00,1225.0,0.0,7.86,7.05 -2016-02-15 19:00:00,1384.0,0.0,7.74,6.65 -2016-02-15 20:00:00,1422.0,0.0,7.62,6.25 -2016-02-15 21:00:00,1529.0,0.0,7.49,5.85 -2016-02-15 22:00:00,1670.0,0.0,7.37,5.11 -2016-02-15 23:00:00,1517.0,0.0,7.24,4.37 -2016-02-16 00:00:00,1363.0,0.0,7.12,3.63 -2016-02-16 01:00:00,1251.0,0.0,7.14,2.8 -2016-02-16 02:00:00,1170.0,0.0,7.16,1.97 -2016-02-16 03:00:00,1118.0,0.0,7.19,1.14 -2016-02-16 04:00:00,1142.0,0.0,7.11,1.33 -2016-02-16 05:00:00,1137.0,0.0,7.03,1.52 -2016-02-16 06:00:00,1162.0,0.0,6.96,1.71 -2016-02-16 07:00:00,1238.0,0.0,6.95,2.39 -2016-02-16 08:00:00,1343.0,29.3,6.95,3.07 -2016-02-16 09:00:00,1413.0,36.3,6.94,3.75 -2016-02-16 10:00:00,1377.0,84.04,7.05,4.36 -2016-02-16 11:00:00,1290.0,84.48,7.15,4.97 -2016-02-16 12:00:00,1222.0,432.16,7.26,5.59 -2016-02-16 13:00:00,1119.0,315.7,7.43,6.51 -2016-02-16 14:00:00,1058.0,116.3,7.6,7.43 -2016-02-16 15:00:00,1001.0,113.92,7.78,8.34 -2016-02-16 16:00:00,997.0,48.58,7.94,8.66 -2016-02-16 17:00:00,1056.0,33.47,8.1,8.98 -2016-02-16 18:00:00,1152.0,0.0,8.26,9.3 -2016-02-16 19:00:00,1320.0,0.0,8.39,9.61 -2016-02-16 20:00:00,1347.0,0.0,8.52,9.93 -2016-02-16 21:00:00,1452.0,0.0,8.65,10.25 -2016-02-16 22:00:00,1632.0,0.0,8.82,10.48 -2016-02-16 23:00:00,1479.0,0.0,8.98,10.72 -2016-02-17 00:00:00,1349.0,0.0,9.15,10.95 -2016-02-17 01:00:00,1261.0,0.0,9.0,11.85 -2016-02-17 02:00:00,1165.0,0.0,8.85,12.75 -2016-02-17 03:00:00,1120.0,0.0,8.7,13.66 -2016-02-17 04:00:00,1125.0,0.0,8.79,13.43 -2016-02-17 05:00:00,1105.0,0.0,8.88,13.21 -2016-02-17 06:00:00,1105.0,0.0,8.98,12.99 -2016-02-17 07:00:00,1175.0,0.0,8.98,12.75 -2016-02-17 08:00:00,1276.0,7.83,8.99,12.51 -2016-02-17 09:00:00,1361.0,82.97,9.0,12.28 -2016-02-17 10:00:00,1412.0,68.25,8.92,10.43 -2016-02-17 11:00:00,1403.0,53.45,8.84,8.58 -2016-02-17 12:00:00,1445.0,76.83,8.76,6.73 -2016-02-17 13:00:00,1423.0,53.5,8.55,7.44 -2016-02-17 14:00:00,1409.0,81.02,8.34,8.16 -2016-02-17 15:00:00,1339.0,27.36,8.12,8.87 -2016-02-17 16:00:00,1312.0,49.21,8.14,9.54 -2016-02-17 17:00:00,1335.0,18.82,8.15,10.21 -2016-02-17 18:00:00,1387.0,0.0,8.17,10.88 -2016-02-17 19:00:00,1446.0,0.0,8.27,10.57 -2016-02-17 20:00:00,1411.0,0.0,8.37,10.26 -2016-02-17 21:00:00,1501.0,0.0,8.48,9.94 -2016-02-17 22:00:00,1658.0,0.0,8.5,9.83 -2016-02-17 23:00:00,1521.0,0.0,8.52,9.71 -2016-02-18 00:00:00,1383.0,0.0,8.54,9.59 -2016-02-18 01:00:00,1288.0,0.0,8.61,9.3 -2016-02-18 02:00:00,1190.0,0.0,8.68,9.01 -2016-02-18 03:00:00,1149.0,0.0,8.75,8.72 -2016-02-18 04:00:00,1157.0,0.0,8.61,8.03 -2016-02-18 05:00:00,1164.0,0.0,8.48,7.35 -2016-02-18 06:00:00,1157.0,0.0,8.35,6.66 -2016-02-18 07:00:00,1218.0,0.0,8.3,6.01 -2016-02-18 08:00:00,1325.0,107.21,8.26,5.36 -2016-02-18 09:00:00,1356.0,80.87,8.21,4.7 -2016-02-18 10:00:00,1310.0,288.1,8.21,4.7 -2016-02-18 11:00:00,1264.0,689.94,8.22,4.7 -2016-02-18 12:00:00,1260.0,687.73,8.22,4.7 -2016-02-18 13:00:00,1200.0,448.67,8.11,5.2 -2016-02-18 14:00:00,1196.0,688.15,8.01,5.7 -2016-02-18 15:00:00,1126.0,442.19,7.91,6.19 -2016-02-18 16:00:00,1057.0,266.48,7.84,6.17 -2016-02-18 17:00:00,1033.0,68.19,7.77,6.15 -2016-02-18 18:00:00,1121.0,0.0,7.7,6.12 -2016-02-18 19:00:00,1280.0,0.0,7.79,6.23 -2016-02-18 20:00:00,1324.0,0.0,7.88,6.34 -2016-02-18 21:00:00,1421.0,0.0,7.97,6.44 -2016-02-18 22:00:00,1564.0,0.0,8.13,6.42 -2016-02-18 23:00:00,1432.0,0.0,8.28,6.4 -2016-02-19 00:00:00,1304.0,0.0,8.44,6.37 -2016-02-19 01:00:00,1207.0,0.0,8.51,6.83 -2016-02-19 02:00:00,1128.0,0.0,8.58,7.29 -2016-02-19 03:00:00,1106.0,0.0,8.65,7.75 -2016-02-19 04:00:00,1091.0,0.0,8.77,8.27 -2016-02-19 05:00:00,1063.0,0.0,8.89,8.78 -2016-02-19 06:00:00,1066.0,0.0,9.02,9.3 -2016-02-19 07:00:00,1138.0,0.0,9.11,9.85 -2016-02-19 08:00:00,1197.0,31.4,9.2,10.41 -2016-02-19 09:00:00,1271.0,106.01,9.29,10.97 -2016-02-19 10:00:00,1254.0,57.94,9.57,11.78 -2016-02-19 11:00:00,1223.0,104.12,9.86,12.59 -2016-02-19 12:00:00,1211.0,101.99,10.14,13.41 -2016-02-19 13:00:00,1187.0,141.91,10.27,13.54 -2016-02-19 14:00:00,1174.0,140.34,10.4,13.66 -2016-02-19 15:00:00,1163.0,125.37,10.53,13.79 -2016-02-19 16:00:00,1158.0,142.97,10.63,13.62 -2016-02-19 17:00:00,1118.0,50.01,10.72,13.45 -2016-02-19 18:00:00,1151.0,0.0,10.81,13.28 -2016-02-19 19:00:00,1249.0,0.0,10.94,12.19 -2016-02-19 20:00:00,1276.0,0.0,11.07,11.1 -2016-02-19 21:00:00,1387.0,0.0,11.2,10.01 -2016-02-19 22:00:00,1542.0,0.0,11.25,10.05 -2016-02-19 23:00:00,1344.0,0.0,11.29,10.08 -2016-02-20 00:00:00,1174.0,0.0,11.33,10.11 -2016-02-20 01:00:00,1087.0,0.0,11.32,10.48 -2016-02-20 02:00:00,1014.0,0.0,11.31,10.85 -2016-02-20 03:00:00,978.0,0.0,11.3,11.21 -2016-02-20 04:00:00,991.0,0.0,11.36,11.4 -2016-02-20 05:00:00,966.0,0.0,11.43,11.58 -2016-02-20 06:00:00,913.0,0.0,11.5,11.77 -2016-02-20 07:00:00,966.0,0.0,11.56,11.76 -2016-02-20 08:00:00,1033.0,32.68,11.63,11.76 -2016-02-20 09:00:00,1089.0,78.38,11.69,11.75 -2016-02-20 10:00:00,1134.0,33.35,11.71,11.95 -2016-02-20 11:00:00,1108.0,158.68,11.73,12.15 -2016-02-20 12:00:00,1091.0,169.11,11.75,12.34 -2016-02-20 13:00:00,1045.0,140.89,11.55,12.83 -2016-02-20 14:00:00,1037.0,74.53,11.36,13.32 -2016-02-20 15:00:00,1029.0,76.67,11.16,13.81 -2016-02-20 16:00:00,1057.0,87.53,11.23,13.56 -2016-02-20 17:00:00,1043.0,31.59,11.3,13.32 -2016-02-20 18:00:00,1054.0,0.0,11.37,13.08 -2016-02-20 19:00:00,1144.0,0.0,11.39,12.68 -2016-02-20 20:00:00,1137.0,0.0,11.41,12.28 -2016-02-20 21:00:00,1248.0,0.0,11.43,11.89 -2016-02-20 22:00:00,1436.0,0.0,11.46,11.53 -2016-02-20 23:00:00,1256.0,0.0,11.5,11.17 -2016-02-21 00:00:00,1065.0,0.0,11.54,10.81 -2016-02-21 01:00:00,985.0,0.0,11.3,10.99 -2016-02-21 02:00:00,898.0,0.0,11.06,11.16 -2016-02-21 03:00:00,873.0,0.0,10.83,11.34 -2016-02-21 04:00:00,908.0,0.0,10.9,10.94 -2016-02-21 05:00:00,900.0,0.0,10.97,10.55 -2016-02-21 06:00:00,836.0,0.0,11.04,10.15 -2016-02-21 07:00:00,856.0,0.0,11.08,10.25 -2016-02-21 08:00:00,913.0,19.24,11.13,10.35 -2016-02-21 09:00:00,970.0,55.59,11.18,10.46 -2016-02-21 10:00:00,1068.0,147.06,11.18,10.3 -2016-02-21 11:00:00,1094.0,141.87,11.18,10.15 -2016-02-21 12:00:00,1019.0,216.86,11.18,10.0 -2016-02-21 13:00:00,924.0,260.43,11.07,9.99 -2016-02-21 14:00:00,833.0,363.23,10.97,9.98 -2016-02-21 15:00:00,811.0,507.69,10.86,9.97 -2016-02-21 16:00:00,836.0,133.08,10.87,9.84 -2016-02-21 17:00:00,857.0,47.8,10.88,9.71 -2016-02-21 18:00:00,853.0,0.0,10.89,9.57 -2016-02-21 19:00:00,920.0,0.0,10.91,9.42 -2016-02-21 20:00:00,937.0,0.0,10.93,9.27 -2016-02-21 21:00:00,1025.0,0.0,10.94,9.12 -2016-02-21 22:00:00,1234.0,0.0,10.97,8.91 -2016-02-21 23:00:00,1160.0,0.0,11.0,8.71 -2016-02-22 00:00:00,926.0,0.0,11.03,8.51 -2016-02-22 01:00:00,808.0,0.0,10.96,8.78 -2016-02-22 02:00:00,737.0,0.0,10.9,9.05 -2016-02-22 03:00:00,735.0,0.0,10.84,9.32 -2016-02-22 04:00:00,763.0,0.0,10.85,9.23 -2016-02-22 05:00:00,782.0,0.0,10.87,9.14 -2016-02-22 06:00:00,750.0,0.0,10.89,9.05 -2016-02-22 07:00:00,774.0,0.0,10.92,9.24 -2016-02-22 08:00:00,854.0,43.06,10.96,9.43 -2016-02-22 09:00:00,887.0,71.36,10.99,9.63 -2016-02-22 10:00:00,924.0,148.27,10.98,9.34 -2016-02-22 11:00:00,1029.0,89.49,10.98,9.05 -2016-02-22 12:00:00,1022.0,226.44,10.97,8.76 -2016-02-22 13:00:00,898.0,115.21,11.01,8.85 -2016-02-22 14:00:00,851.0,163.84,11.05,8.93 -2016-02-22 15:00:00,830.0,141.31,11.09,9.02 -2016-02-22 16:00:00,855.0,115.8,11.02,8.55 -2016-02-22 17:00:00,856.0,13.18,10.95,8.07 -2016-02-22 18:00:00,908.0,0.0,10.89,7.6 -2016-02-22 19:00:00,941.0,0.0,10.79,7.35 -2016-02-22 20:00:00,973.0,0.0,10.69,7.09 -2016-02-22 21:00:00,1047.0,0.0,10.6,6.84 -2016-02-22 22:00:00,1222.0,0.0,10.42,6.53 -2016-02-22 23:00:00,1098.0,0.0,10.24,6.22 -2016-02-23 00:00:00,989.0,0.0,10.06,5.9 -2016-02-23 01:00:00,848.0,0.0,9.92,5.68 -2016-02-23 02:00:00,781.0,0.0,9.78,5.46 -2016-02-23 03:00:00,761.0,0.0,9.65,5.24 -2016-02-23 04:00:00,812.0,0.0,9.43,4.72 -2016-02-23 05:00:00,826.0,0.0,9.22,4.19 -2016-02-23 06:00:00,838.0,0.0,9.01,3.67 -2016-02-23 07:00:00,866.0,0.0,8.95,3.41 -2016-02-23 08:00:00,946.0,49.16,8.89,3.14 -2016-02-23 09:00:00,1021.0,128.99,8.83,2.88 -2016-02-23 10:00:00,1037.0,253.46,8.84,2.78 -2016-02-23 11:00:00,1067.0,275.92,8.84,2.68 -2016-02-23 12:00:00,1094.0,199.63,8.85,2.58 -2016-02-23 13:00:00,930.0,202.05,8.77,2.97 -2016-02-23 14:00:00,911.0,132.56,8.69,3.37 -2016-02-23 15:00:00,947.0,110.44,8.61,3.77 -2016-02-23 16:00:00,874.0,70.59,8.67,4.38 -2016-02-23 17:00:00,950.0,11.54,8.73,4.99 -2016-02-23 18:00:00,1055.0,0.0,8.79,5.6 -2016-02-23 19:00:00,1080.0,0.0,8.87,6.28 -2016-02-23 20:00:00,1090.0,0.0,8.95,6.96 -2016-02-23 21:00:00,1192.0,0.0,9.02,7.64 -2016-02-23 22:00:00,1372.0,0.0,9.13,8.05 -2016-02-23 23:00:00,1246.0,0.0,9.24,8.45 -2016-02-24 00:00:00,1160.0,0.0,9.35,8.86 -2016-02-24 01:00:00,1064.0,0.0,9.5,9.12 -2016-02-24 02:00:00,968.0,0.0,9.65,9.38 -2016-02-24 03:00:00,933.0,0.0,9.81,9.64 -2016-02-24 04:00:00,952.0,0.0,9.78,9.58 -2016-02-24 05:00:00,974.0,0.0,9.76,9.51 -2016-02-24 06:00:00,995.0,0.0,9.74,9.45 -2016-02-24 07:00:00,1034.0,0.0,9.65,9.54 -2016-02-24 08:00:00,1090.0,47.34,9.57,9.63 -2016-02-24 09:00:00,1204.0,71.76,9.49,9.72 -2016-02-24 10:00:00,1255.0,35.94,9.42,9.93 -2016-02-24 11:00:00,1199.0,22.94,9.34,10.14 -2016-02-24 12:00:00,1213.0,124.32,9.27,10.34 -2016-02-24 13:00:00,1110.0,25.08,9.16,10.32 -2016-02-24 14:00:00,1043.0,19.5,9.05,10.29 -2016-02-24 15:00:00,1067.0,82.06,8.94,10.26 -2016-02-24 16:00:00,997.0,57.73,8.83,10.32 -2016-02-24 17:00:00,1061.0,41.52,8.71,10.37 -2016-02-24 18:00:00,1118.0,0.0,8.59,10.43 -2016-02-24 19:00:00,1233.0,0.0,8.56,10.32 -2016-02-24 20:00:00,1195.0,0.0,8.53,10.21 -2016-02-24 21:00:00,1283.0,0.0,8.49,10.1 -2016-02-24 22:00:00,1446.0,0.0,8.48,9.78 -2016-02-24 23:00:00,1304.0,0.0,8.47,9.46 -2016-02-25 00:00:00,1201.0,0.0,8.46,9.14 -2016-02-25 01:00:00,1143.0,0.0,8.31,8.55 -2016-02-25 02:00:00,1017.0,0.0,8.16,7.96 -2016-02-25 03:00:00,984.0,0.0,8.02,7.37 -2016-02-25 04:00:00,1045.0,0.0,7.91,6.79 -2016-02-25 05:00:00,1040.0,0.0,7.8,6.22 -2016-02-25 06:00:00,1053.0,0.0,7.69,5.64 -2016-02-25 07:00:00,1132.0,0.0,7.65,5.19 -2016-02-25 08:00:00,1197.0,140.74,7.62,4.73 -2016-02-25 09:00:00,1223.0,112.41,7.58,4.28 -2016-02-25 10:00:00,1166.0,186.29,7.61,4.01 -2016-02-25 11:00:00,1074.0,177.96,7.64,3.75 -2016-02-25 12:00:00,1062.0,160.89,7.67,3.49 -2016-02-25 13:00:00,981.0,200.66,7.83,2.91 -2016-02-25 14:00:00,920.0,292.26,7.99,2.32 -2016-02-25 15:00:00,941.0,206.4,8.15,1.74 -2016-02-25 16:00:00,898.0,144.63,8.16,2.1 -2016-02-25 17:00:00,875.0,41.28,8.16,2.46 -2016-02-25 18:00:00,1004.0,0.0,8.17,2.83 -2016-02-25 19:00:00,1141.0,0.0,8.15,3.47 -2016-02-25 20:00:00,1144.0,0.0,8.13,4.11 -2016-02-25 21:00:00,1208.0,0.0,8.1,4.76 -2016-02-25 22:00:00,1361.0,0.0,8.11,5.45 -2016-02-25 23:00:00,1217.0,0.0,8.11,6.14 -2016-02-26 00:00:00,1116.0,0.0,8.11,6.83 -2016-02-26 01:00:00,1110.0,0.0,8.23,7.35 -2016-02-26 02:00:00,1015.0,0.0,8.35,7.88 -2016-02-26 03:00:00,946.0,0.0,8.47,8.4 -2016-02-26 04:00:00,968.0,0.0,8.48,9.13 -2016-02-26 05:00:00,978.0,0.0,8.49,9.85 -2016-02-26 06:00:00,1018.0,0.0,8.5,10.58 -2016-02-26 07:00:00,1114.0,0.0,8.57,11.12 -2016-02-26 08:00:00,1167.0,33.14,8.65,11.66 -2016-02-26 09:00:00,1151.0,65.34,8.72,12.19 -2016-02-26 10:00:00,1154.0,67.53,8.83,12.07 -2016-02-26 11:00:00,1225.0,285.36,8.93,11.95 -2016-02-26 12:00:00,1305.0,179.81,9.04,11.83 -2016-02-26 13:00:00,1290.0,166.45,8.99,10.39 -2016-02-26 14:00:00,1252.0,469.02,8.94,8.95 -2016-02-26 15:00:00,1214.0,131.45,8.9,7.5 -2016-02-26 16:00:00,1209.0,36.01,8.8,6.15 -2016-02-26 17:00:00,1184.0,30.41,8.7,4.79 -2016-02-26 18:00:00,1265.0,0.0,8.61,3.43 -2016-02-26 19:00:00,1414.0,0.0,8.32,3.66 -2016-02-26 20:00:00,1444.0,0.0,8.03,3.89 -2016-02-26 21:00:00,1488.0,0.0,7.74,4.12 -2016-02-26 22:00:00,1669.0,0.0,7.41,4.69 -2016-02-26 23:00:00,1491.0,0.0,7.07,5.26 -2016-02-27 00:00:00,1351.0,0.0,6.74,5.83 -2016-02-27 01:00:00,1307.0,0.0,6.43,6.46 -2016-02-27 02:00:00,1203.0,0.0,6.13,7.09 -2016-02-27 03:00:00,1112.0,0.0,5.83,7.72 -2016-02-27 04:00:00,1106.0,0.0,5.8,8.09 -2016-02-27 05:00:00,1101.0,0.0,5.77,8.46 -2016-02-27 06:00:00,1128.0,0.0,5.75,8.83 -2016-02-27 07:00:00,1203.0,0.0,5.87,9.59 -2016-02-27 08:00:00,1292.0,23.24,6.0,10.34 -2016-02-27 09:00:00,1332.0,83.14,6.12,11.1 -2016-02-27 10:00:00,1353.0,44.86,6.39,11.42 -2016-02-27 11:00:00,1338.0,49.42,6.65,11.74 -2016-02-27 12:00:00,1365.0,326.28,6.92,12.06 -2016-02-27 13:00:00,1345.0,42.32,7.57,11.88 -2016-02-27 14:00:00,1298.0,53.66,8.22,11.71 -2016-02-27 15:00:00,1250.0,44.31,8.88,11.53 -2016-02-27 16:00:00,1218.0,35.32,8.81,11.67 -2016-02-27 17:00:00,1248.0,65.66,8.75,11.81 -2016-02-27 18:00:00,1337.0,0.0,8.69,11.94 -2016-02-27 19:00:00,1458.0,0.0,8.69,11.82 -2016-02-27 20:00:00,1485.0,0.0,8.68,11.7 -2016-02-27 21:00:00,1575.0,0.0,8.68,11.57 -2016-02-27 22:00:00,1707.0,0.0,8.61,11.42 -2016-02-27 23:00:00,1607.0,0.0,8.54,11.26 -2016-02-28 00:00:00,1493.0,0.0,8.48,11.1 -2016-02-28 01:00:00,1399.0,0.0,8.22,11.08 -2016-02-28 02:00:00,1278.0,0.0,7.96,11.07 -2016-02-28 03:00:00,1225.0,0.0,7.7,11.05 -2016-02-28 04:00:00,1242.0,0.0,7.58,11.08 -2016-02-28 05:00:00,1229.0,0.0,7.46,11.12 -2016-02-28 06:00:00,1219.0,0.0,7.34,11.16 -2016-02-28 07:00:00,1250.0,0.0,7.34,11.49 -2016-02-28 08:00:00,1296.0,16.93,7.35,11.83 -2016-02-28 09:00:00,1360.0,21.07,7.35,12.17 -2016-02-28 10:00:00,1428.0,26.02,7.49,11.92 -2016-02-28 11:00:00,1436.0,30.36,7.64,11.68 -2016-02-28 12:00:00,1343.0,289.61,7.78,11.43 -2016-02-28 13:00:00,1217.0,66.26,7.87,11.79 -2016-02-28 14:00:00,1123.0,52.19,7.97,12.14 -2016-02-28 15:00:00,1080.0,76.5,8.06,12.5 -2016-02-28 16:00:00,1087.0,46.0,8.15,12.5 -2016-02-28 17:00:00,1140.0,32.04,8.23,12.51 -2016-02-28 18:00:00,1198.0,0.0,8.31,12.51 -2016-02-28 19:00:00,1284.0,0.0,8.32,12.01 -2016-02-28 20:00:00,1339.0,0.0,8.33,11.52 -2016-02-28 21:00:00,1422.0,0.0,8.35,11.02 -2016-02-28 22:00:00,1610.0,0.0,8.3,10.54 -2016-02-28 23:00:00,1485.0,0.0,8.25,10.06 -2016-02-29 00:00:00,1350.0,0.0,8.2,9.59 -2016-02-29 01:00:00,1232.0,0.0,7.99,8.81 -2016-02-29 02:00:00,1135.0,0.0,7.79,8.04 -2016-02-29 03:00:00,1119.0,0.0,7.59,7.27 -2016-02-29 04:00:00,1105.0,0.0,7.52,6.77 -2016-02-29 05:00:00,1098.0,0.0,7.46,6.28 -2016-02-29 06:00:00,1091.0,0.0,7.4,5.78 -2016-02-29 07:00:00,1149.0,0.0,7.46,5.47 -2016-02-29 08:00:00,1193.0,36.5,7.53,5.15 -2016-02-29 09:00:00,1244.0,86.88,7.6,4.84 -2016-02-29 10:00:00,1263.0,308.01,7.69,4.04 -2016-02-29 11:00:00,1275.0,527.2,7.77,3.24 -2016-02-29 12:00:00,1269.0,414.19,7.86,2.44 -2016-02-29 13:00:00,1133.0,244.77,7.96,2.77 -2016-02-29 14:00:00,1065.0,292.73,8.06,3.09 -2016-02-29 15:00:00,1029.0,277.52,8.16,3.42 -2016-02-29 16:00:00,1026.0,92.39,8.37,3.81 -2016-02-29 17:00:00,1027.0,24.49,8.58,4.2 -2016-02-29 18:00:00,1090.0,0.0,8.79,4.59 -2016-02-29 19:00:00,1194.0,0.0,9.02,5.4 -2016-02-29 20:00:00,1220.0,0.0,9.24,6.21 -2016-02-29 21:00:00,1331.0,0.0,9.47,7.02 -2016-02-29 22:00:00,1488.0,0.0,9.73,7.97 -2016-02-29 23:00:00,1321.0,0.0,10.0,8.92 -2016-03-01 00:00:00,1152.0,0.0,10.27,9.88 -2016-03-01 01:00:00,1087.0,0.0,10.61,10.85 -2016-03-01 02:00:00,1041.0,0.0,10.95,11.82 -2016-03-01 03:00:00,1018.0,0.0,11.29,12.79 -2016-03-01 04:00:00,1031.0,0.0,11.45,13.09 -2016-03-01 05:00:00,978.0,0.0,11.61,13.39 -2016-03-01 06:00:00,938.0,0.0,11.77,13.7 -2016-03-01 07:00:00,1035.0,0.0,11.84,13.91 -2016-03-01 08:00:00,1101.0,10.73,11.91,14.12 -2016-03-01 09:00:00,1132.0,15.86,11.98,14.33 -2016-03-01 10:00:00,1173.0,22.64,11.95,14.36 -2016-03-01 11:00:00,1184.0,61.92,11.92,14.4 -2016-03-01 12:00:00,1201.0,61.93,11.9,14.43 -2016-03-01 13:00:00,1178.0,140.66,11.96,14.41 -2016-03-01 14:00:00,1166.0,264.33,12.02,14.39 -2016-03-01 15:00:00,1143.0,461.76,12.08,14.37 -2016-03-01 16:00:00,1105.0,323.89,12.07,13.48 -2016-03-01 17:00:00,1096.0,52.85,12.07,12.6 -2016-03-01 18:00:00,1094.0,0.0,12.07,11.71 -2016-03-01 19:00:00,1166.0,0.0,11.95,11.72 -2016-03-01 20:00:00,1191.0,0.0,11.83,11.73 -2016-03-01 21:00:00,1272.0,0.0,11.71,11.74 -2016-03-01 22:00:00,1445.0,0.0,11.4,12.1 -2016-03-01 23:00:00,1296.0,0.0,11.1,12.46 -2016-03-02 00:00:00,1183.0,0.0,10.79,12.83 -2016-03-02 01:00:00,1092.0,0.0,10.51,13.44 -2016-03-02 02:00:00,1018.0,0.0,10.23,14.06 -2016-03-02 03:00:00,1009.0,0.0,9.94,14.68 -2016-03-02 04:00:00,1016.0,0.0,9.78,14.81 -2016-03-02 05:00:00,1026.0,0.0,9.61,14.94 -2016-03-02 06:00:00,1036.0,0.0,9.45,15.08 -2016-03-02 07:00:00,1109.0,0.0,9.39,15.95 -2016-03-02 08:00:00,1198.0,11.83,9.33,16.82 -2016-03-02 09:00:00,1238.0,254.11,9.27,17.7 -2016-03-02 10:00:00,1205.0,477.65,9.34,18.32 -2016-03-02 11:00:00,1176.0,90.46,9.41,18.95 -2016-03-02 12:00:00,1115.0,736.67,9.48,19.57 -2016-03-02 13:00:00,1036.0,253.5,9.41,18.96 -2016-03-02 14:00:00,1043.0,526.49,9.35,18.35 -2016-03-02 15:00:00,1034.0,335.39,9.29,17.74 -2016-03-02 16:00:00,1048.0,174.51,9.4,17.9 -2016-03-02 17:00:00,1071.0,90.98,9.51,18.06 -2016-03-02 18:00:00,1115.0,0.0,9.62,18.22 -2016-03-02 19:00:00,1208.0,0.0,9.71,17.7 -2016-03-02 20:00:00,1272.0,0.0,9.8,17.18 -2016-03-02 21:00:00,1366.0,0.0,9.9,16.66 -2016-03-02 22:00:00,1536.0,0.0,9.72,16.32 -2016-03-02 23:00:00,1421.0,0.0,9.55,15.97 -2016-03-03 00:00:00,1286.0,0.0,9.37,15.63 -2016-03-03 01:00:00,1171.0,0.0,9.23,15.23 -2016-03-03 02:00:00,1086.0,0.0,9.09,14.84 -2016-03-03 03:00:00,1045.0,0.0,8.95,14.44 -2016-03-03 04:00:00,1068.0,0.0,9.0,14.22 -2016-03-03 05:00:00,1044.0,0.0,9.04,13.99 -2016-03-03 06:00:00,1050.0,0.0,9.09,13.77 -2016-03-03 07:00:00,1115.0,0.0,8.78,13.01 -2016-03-03 08:00:00,1214.0,49.79,8.47,12.25 -2016-03-03 09:00:00,1239.0,75.97,8.16,11.49 -2016-03-03 10:00:00,1256.0,67.63,8.38,10.11 -2016-03-03 11:00:00,1244.0,87.31,8.6,8.73 -2016-03-03 12:00:00,1242.0,84.76,8.82,7.35 -2016-03-03 13:00:00,1175.0,87.05,9.2,8.61 -2016-03-03 14:00:00,1135.0,187.01,9.58,9.86 -2016-03-03 15:00:00,1140.0,260.75,9.97,11.12 -2016-03-03 16:00:00,1085.0,90.42,10.12,11.34 -2016-03-03 17:00:00,1080.0,7.78,10.28,11.57 -2016-03-03 18:00:00,1118.0,0.0,10.44,11.79 -2016-03-03 19:00:00,1218.0,0.0,10.22,11.94 -2016-03-03 20:00:00,1226.0,0.0,10.01,12.09 -2016-03-03 21:00:00,1323.0,0.0,9.8,12.23 -2016-03-03 22:00:00,1480.0,0.0,9.39,12.35 -2016-03-03 23:00:00,1342.0,0.0,8.99,12.47 -2016-03-04 00:00:00,1215.0,0.0,8.58,12.59 -2016-03-04 01:00:00,1150.0,0.0,8.36,11.71 -2016-03-04 02:00:00,1048.0,0.0,8.14,10.83 -2016-03-04 03:00:00,1023.0,0.0,7.91,9.94 -2016-03-04 04:00:00,1054.0,0.0,7.6,9.45 -2016-03-04 05:00:00,1045.0,0.0,7.29,8.96 -2016-03-04 06:00:00,1045.0,0.0,6.98,8.47 -2016-03-04 07:00:00,1128.0,0.0,6.77,8.32 -2016-03-04 08:00:00,1133.0,41.41,6.56,8.17 -2016-03-04 09:00:00,1159.0,90.31,6.36,8.01 -2016-03-04 10:00:00,1200.0,337.63,6.34,8.06 -2016-03-04 11:00:00,1215.0,563.3,6.33,8.11 -2016-03-04 12:00:00,1164.0,193.95,6.32,8.15 -2016-03-04 13:00:00,1114.0,813.61,6.61,8.4 -2016-03-04 14:00:00,1046.0,556.71,6.91,8.65 -2016-03-04 15:00:00,1029.0,44.64,7.21,8.9 -2016-03-04 16:00:00,1028.0,48.7,7.38,9.29 -2016-03-04 17:00:00,1056.0,7.62,7.55,9.68 -2016-03-04 18:00:00,1128.0,0.0,7.72,10.07 -2016-03-04 19:00:00,1283.0,0.0,7.65,10.9 -2016-03-04 20:00:00,1338.0,0.0,7.58,11.72 -2016-03-04 21:00:00,1430.0,0.0,7.51,12.55 -2016-03-04 22:00:00,1626.0,0.0,7.68,13.47 -2016-03-04 23:00:00,1492.0,0.0,7.85,14.39 -2016-03-05 00:00:00,1371.0,0.0,8.02,15.31 -2016-03-05 01:00:00,1268.0,0.0,8.0,15.32 -2016-03-05 02:00:00,1177.0,0.0,7.99,15.34 -2016-03-05 03:00:00,1145.0,0.0,7.97,15.35 -2016-03-05 04:00:00,1165.0,0.0,7.94,15.04 -2016-03-05 05:00:00,1174.0,0.0,7.9,14.74 -2016-03-05 06:00:00,1180.0,0.0,7.87,14.43 -2016-03-05 07:00:00,1240.0,0.0,7.75,14.17 -2016-03-05 08:00:00,1284.0,58.52,7.63,13.92 -2016-03-05 09:00:00,1336.0,153.11,7.52,13.67 -2016-03-05 10:00:00,1293.0,180.13,7.43,13.15 -2016-03-05 11:00:00,1265.0,244.54,7.35,12.63 -2016-03-05 12:00:00,1188.0,592.07,7.27,12.11 -2016-03-05 13:00:00,1104.0,488.67,7.42,11.71 -2016-03-05 14:00:00,1081.0,306.82,7.58,11.32 -2016-03-05 15:00:00,1055.0,385.74,7.74,10.92 -2016-03-05 16:00:00,1064.0,197.37,7.64,10.91 -2016-03-05 17:00:00,1082.0,71.93,7.55,10.89 -2016-03-05 18:00:00,1142.0,0.0,7.46,10.87 -2016-03-05 19:00:00,1261.0,0.0,7.5,10.66 -2016-03-05 20:00:00,1300.0,0.0,7.55,10.46 -2016-03-05 21:00:00,1421.0,0.0,7.6,10.25 -2016-03-05 22:00:00,1615.0,0.0,7.57,10.04 -2016-03-05 23:00:00,1493.0,0.0,7.55,9.83 -2016-03-06 00:00:00,1351.0,0.0,7.53,9.63 -2016-03-06 01:00:00,1265.0,0.0,7.45,9.42 -2016-03-06 02:00:00,1139.0,0.0,7.37,9.21 -2016-03-06 03:00:00,1103.0,0.0,7.28,9.01 -2016-03-06 04:00:00,1112.0,0.0,7.28,8.83 -2016-03-06 05:00:00,1112.0,0.0,7.28,8.66 -2016-03-06 06:00:00,1112.0,0.0,7.28,8.48 -2016-03-06 07:00:00,1154.0,0.0,7.24,8.45 -2016-03-06 08:00:00,1200.0,114.83,7.2,8.42 -2016-03-06 09:00:00,1223.0,97.85,7.16,8.39 -2016-03-06 10:00:00,1239.0,233.75,7.15,8.01 -2016-03-06 11:00:00,1192.0,396.18,7.14,7.64 -2016-03-06 12:00:00,1160.0,253.47,7.13,7.27 -2016-03-06 13:00:00,1096.0,111.95,7.29,7.14 -2016-03-06 14:00:00,1056.0,59.99,7.46,7.0 -2016-03-06 15:00:00,1031.0,66.99,7.63,6.87 -2016-03-06 16:00:00,1010.0,142.78,7.44,6.31 -2016-03-06 17:00:00,1057.0,17.43,7.25,5.75 -2016-03-06 18:00:00,1090.0,0.0,7.07,5.19 -2016-03-06 19:00:00,1142.0,0.0,7.12,4.17 -2016-03-06 20:00:00,1170.0,0.0,7.17,3.16 -2016-03-06 21:00:00,1290.0,0.0,7.22,2.15 -2016-03-06 22:00:00,1492.0,0.0,7.21,4.18 -2016-03-06 23:00:00,1364.0,0.0,7.21,6.21 -2016-03-07 00:00:00,1245.0,0.0,7.2,8.23 -2016-03-07 01:00:00,1122.0,0.0,7.17,8.51 -2016-03-07 02:00:00,1034.0,0.0,7.14,8.8 -2016-03-07 03:00:00,1030.0,0.0,7.1,9.08 -2016-03-07 04:00:00,1014.0,0.0,7.05,9.27 -2016-03-07 05:00:00,1026.0,0.0,6.99,9.46 -2016-03-07 06:00:00,1048.0,0.0,6.94,9.66 -2016-03-07 07:00:00,1110.0,0.0,6.97,9.47 -2016-03-07 08:00:00,1187.0,77.75,7.0,9.29 -2016-03-07 09:00:00,1251.0,257.84,7.03,9.1 -2016-03-07 10:00:00,1211.0,514.69,7.11,8.84 -2016-03-07 11:00:00,1126.0,472.65,7.19,8.57 -2016-03-07 12:00:00,1076.0,114.17,7.27,8.3 -2016-03-07 13:00:00,991.0,173.88,7.37,8.16 -2016-03-07 14:00:00,964.0,398.06,7.47,8.01 -2016-03-07 15:00:00,948.0,132.04,7.58,7.86 -2016-03-07 16:00:00,928.0,203.79,7.6,8.33 -2016-03-07 17:00:00,939.0,109.04,7.62,8.8 -2016-03-07 18:00:00,1015.0,0.0,7.64,9.27 -2016-03-07 19:00:00,1135.0,0.0,7.65,9.16 -2016-03-07 20:00:00,1161.0,0.0,7.66,9.05 -2016-03-07 21:00:00,1299.0,0.0,7.67,8.94 -2016-03-07 22:00:00,1476.0,0.0,7.62,8.51 -2016-03-07 23:00:00,1338.0,0.0,7.57,8.08 -2016-03-08 00:00:00,1211.0,0.0,7.52,7.66 -2016-03-08 01:00:00,1135.0,0.0,7.56,7.22 -2016-03-08 02:00:00,1051.0,0.0,7.6,6.78 -2016-03-08 03:00:00,1012.0,0.0,7.63,6.34 -2016-03-08 04:00:00,1030.0,0.0,7.84,6.65 -2016-03-08 05:00:00,1014.0,0.0,8.05,6.95 -2016-03-08 06:00:00,1011.0,0.0,8.26,7.26 -2016-03-08 07:00:00,1069.0,0.0,8.35,7.13 -2016-03-08 08:00:00,1130.0,12.85,8.44,7.0 -2016-03-08 09:00:00,1179.0,78.51,8.53,6.87 -2016-03-08 10:00:00,1200.0,117.59,8.74,7.72 -2016-03-08 11:00:00,1193.0,251.67,8.95,8.58 -2016-03-08 12:00:00,1195.0,67.36,9.16,9.43 -2016-03-08 13:00:00,1162.0,231.29,9.48,10.08 -2016-03-08 14:00:00,1119.0,179.56,9.81,10.72 -2016-03-08 15:00:00,1116.0,112.03,10.14,11.37 -2016-03-08 16:00:00,1041.0,77.04,10.29,11.05 -2016-03-08 17:00:00,1062.0,21.82,10.44,10.73 -2016-03-08 18:00:00,1111.0,0.0,10.59,10.41 -2016-03-08 19:00:00,1193.0,0.0,10.65,11.24 -2016-03-08 20:00:00,1174.0,0.0,10.71,12.06 -2016-03-08 21:00:00,1284.0,0.0,10.78,12.88 -2016-03-08 22:00:00,1436.0,0.0,10.69,14.42 -2016-03-08 23:00:00,1297.0,0.0,10.61,15.96 -2016-03-09 00:00:00,1178.0,0.0,10.52,17.5 -2016-03-09 01:00:00,1074.0,0.0,10.09,17.66 -2016-03-09 02:00:00,1004.0,0.0,9.67,17.81 -2016-03-09 03:00:00,976.0,0.0,9.24,17.96 -2016-03-09 04:00:00,1023.0,0.0,8.8,17.89 -2016-03-09 05:00:00,1037.0,0.0,8.35,17.82 -2016-03-09 06:00:00,1055.0,0.0,7.9,17.75 -2016-03-09 07:00:00,1154.0,0.0,7.93,18.06 -2016-03-09 08:00:00,1222.0,97.19,7.95,18.36 -2016-03-09 09:00:00,1272.0,285.19,7.98,18.66 -2016-03-09 10:00:00,1253.0,368.36,8.05,18.62 -2016-03-09 11:00:00,1205.0,376.41,8.13,18.58 -2016-03-09 12:00:00,1149.0,367.96,8.21,18.54 -2016-03-09 13:00:00,1071.0,557.36,8.21,18.8 -2016-03-09 14:00:00,1064.0,498.28,8.21,19.06 -2016-03-09 15:00:00,1048.0,434.93,8.22,19.32 -2016-03-09 16:00:00,1064.0,369.25,8.43,18.97 -2016-03-09 17:00:00,1097.0,23.37,8.64,18.63 -2016-03-09 18:00:00,1170.0,0.0,8.85,18.28 -2016-03-09 19:00:00,1256.0,0.0,9.12,17.69 -2016-03-09 20:00:00,1298.0,0.0,9.4,17.11 -2016-03-09 21:00:00,1387.0,0.0,9.68,16.52 -2016-03-09 22:00:00,1576.0,0.0,9.68,16.17 -2016-03-09 23:00:00,1439.0,0.0,9.68,15.83 -2016-03-10 00:00:00,1318.0,0.0,9.68,15.48 -2016-03-10 01:00:00,1233.0,0.0,9.54,14.84 -2016-03-10 02:00:00,1110.0,0.0,9.4,14.21 -2016-03-10 03:00:00,1070.0,0.0,9.26,13.57 -2016-03-10 04:00:00,1073.0,0.0,9.26,12.98 -2016-03-10 05:00:00,1066.0,0.0,9.25,12.39 -2016-03-10 06:00:00,1057.0,0.0,9.25,11.79 -2016-03-10 07:00:00,1108.0,3.93,9.25,11.13 -2016-03-10 08:00:00,1171.0,113.65,9.25,10.46 -2016-03-10 09:00:00,1244.0,210.34,9.25,9.79 -2016-03-10 10:00:00,1204.0,523.93,9.24,9.19 -2016-03-10 11:00:00,1122.0,431.12,9.24,8.59 -2016-03-10 12:00:00,1106.0,680.36,9.24,7.99 -2016-03-10 13:00:00,1000.0,509.1,9.24,7.93 -2016-03-10 14:00:00,950.0,307.98,9.25,7.87 -2016-03-10 15:00:00,912.0,313.25,9.26,7.81 -2016-03-10 16:00:00,898.0,392.34,9.21,7.63 -2016-03-10 17:00:00,928.0,141.81,9.17,7.45 -2016-03-10 18:00:00,961.0,0.0,9.13,7.27 -2016-03-10 19:00:00,1055.0,0.0,9.09,6.96 -2016-03-10 20:00:00,1095.0,0.0,9.06,6.65 -2016-03-10 21:00:00,1194.0,0.0,9.03,6.34 -2016-03-10 22:00:00,1401.0,0.0,8.91,5.99 -2016-03-10 23:00:00,1276.0,0.0,8.8,5.63 -2016-03-11 00:00:00,1132.0,0.0,8.69,5.27 -2016-03-11 01:00:00,1041.0,0.0,8.66,4.63 -2016-03-11 02:00:00,969.0,0.0,8.64,4.0 -2016-03-11 03:00:00,937.0,0.0,8.61,3.37 -2016-03-11 04:00:00,933.0,0.0,8.55,2.98 -2016-03-11 05:00:00,931.0,0.0,8.49,2.59 -2016-03-11 06:00:00,941.0,0.0,8.43,2.21 -2016-03-11 07:00:00,1044.0,0.65,8.42,1.91 -2016-03-11 08:00:00,1060.0,143.49,8.41,1.62 -2016-03-11 09:00:00,1051.0,317.52,8.4,1.32 -2016-03-11 10:00:00,985.0,376.35,8.45,1.34 -2016-03-11 11:00:00,947.0,488.34,8.5,1.35 -2016-03-11 12:00:00,942.0,564.1,8.55,1.37 -2016-03-11 13:00:00,877.0,502.52,8.68,1.45 -2016-03-11 14:00:00,874.0,450.74,8.82,1.53 -2016-03-11 15:00:00,853.0,262.94,8.96,1.61 -2016-03-11 16:00:00,840.0,267.75,8.99,1.43 -2016-03-11 17:00:00,859.0,57.51,9.03,1.25 -2016-03-11 18:00:00,900.0,0.0,9.07,1.06 -2016-03-11 19:00:00,994.0,0.0,9.06,1.02 -2016-03-11 20:00:00,1094.0,0.0,9.05,0.97 -2016-03-11 21:00:00,1205.0,0.0,9.05,0.92 -2016-03-11 22:00:00,1424.0,0.0,8.94,0.9 -2016-03-11 23:00:00,1304.0,0.0,8.84,0.87 -2016-03-12 00:00:00,1169.0,0.0,8.74,0.84 -2016-03-12 01:00:00,1061.0,0.0,8.62,0.83 -2016-03-12 02:00:00,973.0,0.0,8.5,0.81 -2016-03-12 03:00:00,955.0,0.0,8.37,0.8 -2016-03-12 04:00:00,975.0,0.0,8.32,0.91 -2016-03-12 05:00:00,969.0,0.0,8.28,1.01 -2016-03-12 06:00:00,980.0,0.0,8.23,1.12 -2016-03-12 07:00:00,1026.0,0.0,8.22,1.3 -2016-03-12 08:00:00,1102.0,12.2,8.21,1.49 -2016-03-12 09:00:00,1120.0,73.15,8.2,1.67 -2016-03-12 10:00:00,1106.0,203.64,8.19,1.54 -2016-03-12 11:00:00,995.0,275.03,8.18,1.41 -2016-03-12 12:00:00,967.0,452.05,8.17,1.28 -2016-03-12 13:00:00,865.0,378.29,8.34,1.31 -2016-03-12 14:00:00,830.0,632.29,8.51,1.33 -2016-03-12 15:00:00,819.0,547.68,8.68,1.35 -2016-03-12 16:00:00,814.0,405.38,8.69,2.01 -2016-03-12 17:00:00,825.0,173.64,8.7,2.68 -2016-03-12 18:00:00,879.0,0.0,8.71,3.34 -2016-03-12 19:00:00,1028.0,0.0,8.61,3.73 -2016-03-12 20:00:00,1122.0,0.0,8.51,4.12 -2016-03-12 21:00:00,1204.0,0.0,8.41,4.51 -2016-03-12 22:00:00,1428.0,0.0,8.22,4.81 -2016-03-12 23:00:00,1309.0,0.0,8.04,5.12 -2016-03-13 00:00:00,1157.0,0.0,7.86,5.42 -2016-03-13 01:00:00,1049.0,0.0,7.89,5.43 -2016-03-13 02:00:00,958.0,0.0,7.93,5.45 -2016-03-13 03:00:00,941.0,0.0,7.96,5.46 -2016-03-13 04:00:00,950.0,0.0,7.94,5.59 -2016-03-13 05:00:00,955.0,0.0,7.92,5.72 -2016-03-13 06:00:00,962.0,0.0,7.9,5.85 -2016-03-13 07:00:00,982.0,7.32,8.03,6.02 -2016-03-13 08:00:00,1039.0,109.23,8.16,6.2 -2016-03-13 09:00:00,1050.0,203.37,8.3,6.37 -2016-03-13 10:00:00,1015.0,474.22,8.57,6.71 -2016-03-13 11:00:00,982.0,674.36,8.85,7.05 -2016-03-13 12:00:00,914.0,821.41,9.13,7.39 -2016-03-13 13:00:00,828.0,784.97,9.28,7.86 -2016-03-13 14:00:00,778.0,681.55,9.43,8.33 -2016-03-13 15:00:00,764.0,410.36,9.59,8.8 -2016-03-13 16:00:00,752.0,292.43,9.42,9.23 -2016-03-13 17:00:00,772.0,101.23,9.26,9.66 -2016-03-13 18:00:00,854.0,0.0,9.1,10.08 -2016-03-13 19:00:00,962.0,0.0,8.92,10.18 -2016-03-13 20:00:00,1060.0,0.0,8.74,10.28 -2016-03-13 21:00:00,1177.0,0.0,8.57,10.39 -2016-03-13 22:00:00,1397.0,0.0,8.51,10.12 -2016-03-13 23:00:00,1291.0,0.0,8.46,9.85 -2016-03-14 00:00:00,1134.0,0.0,8.4,9.59 -2016-03-14 01:00:00,1019.0,0.0,8.43,9.34 -2016-03-14 02:00:00,950.0,0.0,8.47,9.09 -2016-03-14 03:00:00,934.0,0.0,8.5,8.84 -2016-03-14 04:00:00,932.0,0.0,8.51,9.09 -2016-03-14 05:00:00,942.0,0.0,8.51,9.35 -2016-03-14 06:00:00,955.0,0.0,8.51,9.6 -2016-03-14 07:00:00,1010.0,4.22,8.53,9.94 -2016-03-14 08:00:00,1067.0,42.88,8.56,10.27 -2016-03-14 09:00:00,1101.0,36.84,8.59,10.61 -2016-03-14 10:00:00,1087.0,78.44,8.78,10.58 -2016-03-14 11:00:00,1033.0,271.25,8.97,10.56 -2016-03-14 12:00:00,993.0,450.77,9.16,10.54 -2016-03-14 13:00:00,926.0,253.63,9.46,10.62 -2016-03-14 14:00:00,882.0,455.58,9.76,10.69 -2016-03-14 15:00:00,852.0,293.84,10.06,10.77 -2016-03-14 16:00:00,856.0,221.51,10.14,10.92 -2016-03-14 17:00:00,857.0,69.25,10.23,11.07 -2016-03-14 18:00:00,935.0,0.0,10.32,11.21 -2016-03-14 19:00:00,1063.0,0.0,10.31,11.06 -2016-03-14 20:00:00,1136.0,0.0,10.3,10.9 -2016-03-14 21:00:00,1248.0,0.0,10.3,10.74 -2016-03-14 22:00:00,1442.0,0.0,10.25,10.48 -2016-03-14 23:00:00,1299.0,0.0,10.2,10.22 -2016-03-15 00:00:00,1127.0,0.0,10.15,9.96 -2016-03-15 01:00:00,1039.0,0.0,9.68,9.21 -2016-03-15 02:00:00,966.0,0.0,9.22,8.47 -2016-03-15 03:00:00,946.0,0.0,8.75,7.72 -2016-03-15 04:00:00,967.0,0.0,8.61,7.72 -2016-03-15 05:00:00,960.0,0.0,8.47,7.71 -2016-03-15 06:00:00,972.0,0.0,8.33,7.71 -2016-03-15 07:00:00,1048.0,15.0,8.31,7.83 -2016-03-15 08:00:00,1123.0,84.05,8.29,7.95 -2016-03-15 09:00:00,1145.0,66.04,8.28,8.07 -2016-03-15 10:00:00,1074.0,83.3,8.59,8.01 -2016-03-15 11:00:00,1006.0,100.71,8.9,7.95 -2016-03-15 12:00:00,967.0,81.46,9.22,7.89 -2016-03-15 13:00:00,862.0,226.11,9.56,8.04 -2016-03-15 14:00:00,823.0,374.86,9.91,8.18 -2016-03-15 15:00:00,807.0,293.8,10.26,8.33 -2016-03-15 16:00:00,801.0,170.31,10.17,8.2 -2016-03-15 17:00:00,849.0,88.84,10.08,8.07 -2016-03-15 18:00:00,898.0,0.0,9.99,7.94 -2016-03-15 19:00:00,1032.0,0.0,9.75,7.84 -2016-03-15 20:00:00,1087.0,0.0,9.51,7.73 -2016-03-15 21:00:00,1176.0,0.0,9.27,7.63 -2016-03-15 22:00:00,1398.0,0.0,9.1,7.75 -2016-03-15 23:00:00,1255.0,0.0,8.93,7.88 -2016-03-16 00:00:00,1095.0,0.0,8.76,8.0 -2016-03-16 01:00:00,1018.0,0.0,8.81,8.43 -2016-03-16 02:00:00,945.0,0.0,8.86,8.86 -2016-03-16 03:00:00,899.0,0.0,8.91,9.28 -2016-03-16 04:00:00,912.0,0.0,8.8,9.39 -2016-03-16 05:00:00,895.0,0.0,8.69,9.49 -2016-03-16 06:00:00,888.0,0.0,8.58,9.6 -2016-03-16 07:00:00,969.0,1.92,8.57,9.75 -2016-03-16 08:00:00,1052.0,16.82,8.57,9.89 -2016-03-16 09:00:00,1165.0,62.87,8.57,10.04 -2016-03-16 10:00:00,1148.0,42.08,8.59,9.86 -2016-03-16 11:00:00,1099.0,249.7,8.61,9.67 -2016-03-16 12:00:00,995.0,193.91,8.64,9.49 -2016-03-16 13:00:00,880.0,230.35,8.87,9.36 -2016-03-16 14:00:00,839.0,35.25,9.11,9.23 -2016-03-16 15:00:00,825.0,221.03,9.35,9.1 -2016-03-16 16:00:00,843.0,412.59,9.21,9.25 -2016-03-16 17:00:00,861.0,189.3,9.07,9.39 -2016-03-16 18:00:00,941.0,0.0,8.94,9.53 -2016-03-16 19:00:00,1056.0,0.0,8.78,9.47 -2016-03-16 20:00:00,1124.0,0.0,8.62,9.41 -2016-03-16 21:00:00,1220.0,0.0,8.46,9.35 -2016-03-16 22:00:00,1429.0,0.0,8.38,9.27 -2016-03-16 23:00:00,1276.0,0.0,8.31,9.19 -2016-03-17 00:00:00,1121.0,0.0,8.24,9.1 -2016-03-17 01:00:00,1051.0,0.0,8.28,8.93 -2016-03-17 02:00:00,971.0,0.0,8.33,8.76 -2016-03-17 03:00:00,952.0,0.0,8.37,8.59 -2016-03-17 04:00:00,956.0,0.0,8.29,8.66 -2016-03-17 05:00:00,972.0,0.0,8.2,8.72 -2016-03-17 06:00:00,981.0,0.0,8.12,8.79 -2016-03-17 07:00:00,1026.0,17.01,8.17,8.81 -2016-03-17 08:00:00,1077.0,225.57,8.22,8.83 -2016-03-17 09:00:00,1090.0,142.33,8.28,8.86 -2016-03-17 10:00:00,1047.0,69.86,8.48,8.85 -2016-03-17 11:00:00,980.0,89.68,8.68,8.85 -2016-03-17 12:00:00,964.0,167.03,8.88,8.84 -2016-03-17 13:00:00,878.0,575.72,9.23,9.1 -2016-03-17 14:00:00,845.0,385.77,9.59,9.36 -2016-03-17 15:00:00,855.0,279.11,9.95,9.61 -2016-03-17 16:00:00,832.0,178.64,10.11,9.35 -2016-03-17 17:00:00,854.0,91.04,10.28,9.08 -2016-03-17 18:00:00,918.0,0.0,10.45,8.81 -2016-03-17 19:00:00,1033.0,0.0,10.51,8.37 -2016-03-17 20:00:00,1089.0,0.0,10.57,7.92 -2016-03-17 21:00:00,1144.0,0.0,10.63,7.48 -2016-03-17 22:00:00,1355.0,0.0,10.6,7.09 -2016-03-17 23:00:00,1215.0,0.0,10.58,6.69 -2016-03-18 00:00:00,1074.0,0.0,10.56,6.3 -2016-03-18 01:00:00,1007.0,0.0,10.32,6.54 -2016-03-18 02:00:00,902.0,0.0,10.08,6.77 -2016-03-18 03:00:00,890.0,0.0,9.84,7.01 -2016-03-18 04:00:00,903.0,0.0,9.81,7.02 -2016-03-18 05:00:00,902.0,0.0,9.79,7.03 -2016-03-18 06:00:00,920.0,0.0,9.76,7.05 -2016-03-18 07:00:00,1005.0,24.82,9.84,7.13 -2016-03-18 08:00:00,1008.0,202.48,9.91,7.2 -2016-03-18 09:00:00,1004.0,361.53,9.99,7.28 -2016-03-18 10:00:00,937.0,458.82,10.18,7.01 -2016-03-18 11:00:00,914.0,694.77,10.37,6.74 -2016-03-18 12:00:00,863.0,787.39,10.57,6.47 -2016-03-18 13:00:00,829.0,764.53,10.34,7.16 -2016-03-18 14:00:00,778.0,669.62,10.11,7.86 -2016-03-18 15:00:00,759.0,541.4,9.88,8.55 -2016-03-18 16:00:00,778.0,288.88,9.7,8.73 -2016-03-18 17:00:00,797.0,175.93,9.53,8.9 -2016-03-18 18:00:00,846.0,4.98,9.36,9.08 -2016-03-18 19:00:00,971.0,0.0,9.05,8.9 -2016-03-18 20:00:00,1117.0,0.0,8.75,8.72 -2016-03-18 21:00:00,1229.0,0.0,8.45,8.54 -2016-03-18 22:00:00,1432.0,0.0,8.27,8.44 -2016-03-18 23:00:00,1263.0,0.0,8.1,8.34 -2016-03-19 00:00:00,1098.0,0.0,7.93,8.25 -2016-03-19 01:00:00,1032.0,0.0,7.46,8.65 -2016-03-19 02:00:00,966.0,0.0,6.99,9.05 -2016-03-19 03:00:00,939.0,0.0,6.51,9.45 -2016-03-19 04:00:00,965.0,0.0,6.61,9.96 -2016-03-19 05:00:00,982.0,0.0,6.71,10.48 -2016-03-19 06:00:00,985.0,0.0,6.81,10.99 -2016-03-19 07:00:00,1032.0,8.77,7.11,10.81 -2016-03-19 08:00:00,1070.0,26.75,7.41,10.63 -2016-03-19 09:00:00,1088.0,31.81,7.71,10.44 -2016-03-19 10:00:00,1114.0,33.2,8.16,10.25 -2016-03-19 11:00:00,1135.0,42.08,8.61,10.06 -2016-03-19 12:00:00,1191.0,48.81,9.07,9.88 -2016-03-19 13:00:00,1144.0,60.24,9.54,9.94 -2016-03-19 14:00:00,1116.0,58.55,10.02,10.0 -2016-03-19 15:00:00,1036.0,57.64,10.5,10.07 -2016-03-19 16:00:00,1023.0,59.95,10.53,10.1 -2016-03-19 17:00:00,1041.0,41.36,10.56,10.12 -2016-03-19 18:00:00,1118.0,4.46,10.59,10.15 -2016-03-19 19:00:00,1189.0,0.0,10.43,10.14 -2016-03-19 20:00:00,1241.0,0.0,10.27,10.12 -2016-03-19 21:00:00,1358.0,0.0,10.12,10.11 -2016-03-19 22:00:00,1542.0,0.0,9.98,9.94 -2016-03-19 23:00:00,1393.0,0.0,9.84,9.78 -2016-03-20 00:00:00,1243.0,0.0,9.7,9.61 -2016-03-20 01:00:00,1129.0,0.0,9.55,9.88 -2016-03-20 02:00:00,1038.0,0.0,9.4,10.14 -2016-03-20 03:00:00,999.0,0.0,9.24,10.4 -2016-03-20 04:00:00,1005.0,0.0,9.12,10.38 -2016-03-20 05:00:00,1006.0,0.0,8.99,10.35 -2016-03-20 06:00:00,1011.0,0.0,8.87,10.33 -2016-03-20 07:00:00,1027.0,7.25,8.88,10.49 -2016-03-20 08:00:00,1106.0,22.29,8.89,10.65 -2016-03-20 09:00:00,1191.0,29.43,8.9,10.81 -2016-03-20 10:00:00,1255.0,41.23,9.1,10.79 -2016-03-20 11:00:00,1245.0,51.06,9.31,10.77 -2016-03-20 12:00:00,1238.0,63.37,9.52,10.74 -2016-03-20 13:00:00,1159.0,62.63,9.37,10.57 -2016-03-20 14:00:00,1108.0,49.55,9.22,10.4 -2016-03-20 15:00:00,1087.0,84.0,9.07,10.23 -2016-03-20 16:00:00,1086.0,59.63,9.0,10.28 -2016-03-20 17:00:00,1064.0,36.36,8.93,10.32 -2016-03-20 18:00:00,1101.0,0.91,8.87,10.36 -2016-03-20 19:00:00,1159.0,0.0,8.75,10.39 -2016-03-20 20:00:00,1199.0,0.0,8.64,10.41 -2016-03-20 21:00:00,1322.0,0.0,8.53,10.44 -2016-03-20 22:00:00,1487.0,0.0,8.47,10.08 -2016-03-20 23:00:00,1364.0,0.0,8.42,9.71 -2016-03-21 00:00:00,1213.0,0.0,8.37,9.35 -2016-03-21 01:00:00,1076.0,0.0,8.06,9.14 -2016-03-21 02:00:00,1011.0,0.0,7.75,8.94 -2016-03-21 03:00:00,975.0,0.0,7.43,8.73 -2016-03-21 04:00:00,970.0,0.0,7.45,8.88 -2016-03-21 05:00:00,982.0,0.0,7.46,9.03 -2016-03-21 06:00:00,996.0,0.0,7.47,9.19 -2016-03-21 07:00:00,1045.0,36.34,7.68,9.01 -2016-03-21 08:00:00,1103.0,276.56,7.89,8.84 -2016-03-21 09:00:00,1207.0,485.65,8.1,8.66 -2016-03-21 10:00:00,1200.0,669.06,8.43,8.41 -2016-03-21 11:00:00,1191.0,808.11,8.77,8.17 -2016-03-21 12:00:00,1206.0,879.72,9.11,7.92 -2016-03-21 13:00:00,1125.0,851.54,9.28,7.86 -2016-03-21 14:00:00,1017.0,760.18,9.45,7.8 -2016-03-21 15:00:00,973.0,621.64,9.62,7.74 -2016-03-21 16:00:00,945.0,430.74,9.67,7.75 -2016-03-21 17:00:00,919.0,201.96,9.72,7.77 -2016-03-21 18:00:00,962.0,10.47,9.77,7.78 -2016-03-21 19:00:00,1027.0,0.0,9.66,7.39 -2016-03-21 20:00:00,1130.0,0.0,9.56,7.0 -2016-03-21 21:00:00,1240.0,0.0,9.46,6.61 -2016-03-21 22:00:00,1438.0,0.0,9.3,6.06 -2016-03-21 23:00:00,1294.0,0.0,9.15,5.51 -2016-03-22 00:00:00,1137.0,0.0,9.0,4.97 -2016-03-22 01:00:00,1058.0,0.0,8.71,4.63 -2016-03-22 02:00:00,999.0,0.0,8.42,4.3 -2016-03-22 03:00:00,962.0,0.0,8.12,3.97 -2016-03-22 04:00:00,971.0,0.0,8.02,3.67 -2016-03-22 05:00:00,968.0,0.0,7.91,3.37 -2016-03-22 06:00:00,961.0,0.0,7.81,3.06 -2016-03-22 07:00:00,1017.0,32.35,7.84,3.11 -2016-03-22 08:00:00,1068.0,38.89,7.87,3.15 -2016-03-22 09:00:00,1068.0,38.35,7.9,3.2 -2016-03-22 10:00:00,1079.0,49.64,8.05,3.14 -2016-03-22 11:00:00,1071.0,51.13,8.21,3.08 -2016-03-22 12:00:00,1048.0,75.14,8.37,3.02 -2016-03-22 13:00:00,932.0,272.8,8.46,3.49 -2016-03-22 14:00:00,847.0,691.46,8.55,3.95 -2016-03-22 15:00:00,820.0,435.68,8.65,4.41 -2016-03-22 16:00:00,828.0,213.76,8.68,4.52 -2016-03-22 17:00:00,837.0,44.49,8.72,4.63 -2016-03-22 18:00:00,859.0,2.65,8.76,4.73 -2016-03-22 19:00:00,949.0,0.0,8.71,4.47 -2016-03-22 20:00:00,1040.0,0.0,8.66,4.22 -2016-03-22 21:00:00,1161.0,0.0,8.61,3.96 -2016-03-22 22:00:00,1367.0,0.0,8.49,3.8 -2016-03-22 23:00:00,1206.0,0.0,8.37,3.64 -2016-03-23 00:00:00,1055.0,0.0,8.25,3.48 -2016-03-23 01:00:00,974.0,0.0,8.2,3.6 -2016-03-23 02:00:00,901.0,0.0,8.15,3.71 -2016-03-23 03:00:00,864.0,0.0,8.1,3.83 -2016-03-23 04:00:00,885.0,0.0,8.12,4.1 -2016-03-23 05:00:00,869.0,0.0,8.15,4.37 -2016-03-23 06:00:00,867.0,0.0,8.17,4.63 -2016-03-23 07:00:00,913.0,13.82,8.31,4.65 -2016-03-23 08:00:00,966.0,59.03,8.44,4.67 -2016-03-23 09:00:00,1012.0,40.49,8.58,4.69 -2016-03-23 10:00:00,1008.0,65.71,8.72,4.04 -2016-03-23 11:00:00,985.0,85.22,8.86,3.38 -2016-03-23 12:00:00,958.0,174.88,9.01,2.73 -2016-03-23 13:00:00,922.0,275.61,9.01,3.27 -2016-03-23 14:00:00,844.0,741.81,9.01,3.82 -2016-03-23 15:00:00,804.0,622.2,9.02,4.36 -2016-03-23 16:00:00,771.0,424.47,9.0,4.19 -2016-03-23 17:00:00,772.0,139.68,8.98,4.03 -2016-03-23 18:00:00,832.0,11.85,8.96,3.86 -2016-03-23 19:00:00,946.0,0.0,8.94,3.75 -2016-03-23 20:00:00,1000.0,0.0,8.92,3.63 -2016-03-23 21:00:00,1101.0,0.0,8.9,3.52 -2016-03-23 22:00:00,1290.0,0.0,8.91,3.72 -2016-03-23 23:00:00,1135.0,0.0,8.93,3.93 -2016-03-24 00:00:00,1018.0,0.0,8.95,4.14 -2016-03-24 01:00:00,933.0,0.0,8.8,4.53 -2016-03-24 02:00:00,869.0,0.0,8.65,4.92 -2016-03-24 03:00:00,838.0,0.0,8.5,5.31 -2016-03-24 04:00:00,848.0,0.0,8.65,5.84 -2016-03-24 05:00:00,839.0,0.0,8.81,6.37 -2016-03-24 06:00:00,854.0,0.0,8.96,6.9 -2016-03-24 07:00:00,914.0,19.79,9.19,7.49 -2016-03-24 08:00:00,992.0,39.64,9.43,8.09 -2016-03-24 09:00:00,1014.0,86.07,9.67,8.69 -2016-03-24 10:00:00,989.0,100.28,9.74,9.51 -2016-03-24 11:00:00,1001.0,108.43,9.81,10.33 -2016-03-24 12:00:00,1015.0,135.82,9.89,11.14 -2016-03-24 13:00:00,933.0,175.3,9.64,11.66 -2016-03-24 14:00:00,917.0,67.35,9.39,12.17 -2016-03-24 15:00:00,931.0,93.06,9.15,12.68 -2016-03-24 16:00:00,917.0,30.85,9.2,13.21 -2016-03-24 17:00:00,947.0,24.37,9.25,13.75 -2016-03-24 18:00:00,1037.0,4.88,9.31,14.29 -2016-03-24 19:00:00,1099.0,0.0,9.52,13.27 -2016-03-24 20:00:00,1123.0,0.0,9.73,12.25 -2016-03-24 21:00:00,1219.0,0.0,9.95,11.23 -2016-03-24 22:00:00,1404.0,0.0,10.05,10.12 -2016-03-24 23:00:00,1249.0,0.0,10.16,9.01 -2016-03-25 00:00:00,1088.0,0.0,10.26,7.9 -2016-03-25 01:00:00,992.0,0.0,10.3,7.72 -2016-03-25 02:00:00,909.0,0.0,10.35,7.54 -2016-03-25 03:00:00,859.0,0.0,10.39,7.35 -2016-03-25 04:00:00,874.0,0.0,10.18,7.34 -2016-03-25 05:00:00,862.0,0.0,9.96,7.32 -2016-03-25 06:00:00,863.0,0.0,9.75,7.31 -2016-03-25 07:00:00,919.0,73.0,9.45,5.87 -2016-03-25 08:00:00,943.0,298.83,9.15,4.42 -2016-03-25 09:00:00,925.0,496.92,8.86,2.98 -2016-03-25 10:00:00,864.0,663.96,8.7,2.74 -2016-03-25 11:00:00,835.0,747.76,8.55,2.51 -2016-03-25 12:00:00,829.0,777.82,8.4,2.28 -2016-03-25 13:00:00,771.0,812.51,8.88,3.08 -2016-03-25 14:00:00,717.0,728.42,9.36,3.88 -2016-03-25 15:00:00,717.0,517.65,9.85,4.68 -2016-03-25 16:00:00,711.0,350.12,9.77,5.56 -2016-03-25 17:00:00,709.0,137.75,9.69,6.45 -2016-03-25 18:00:00,821.0,13.19,9.61,7.34 -2016-03-25 19:00:00,901.0,0.0,9.75,8.03 -2016-03-25 20:00:00,1034.0,0.0,9.9,8.73 -2016-03-25 21:00:00,1197.0,0.0,10.05,9.42 -2016-03-25 22:00:00,1398.0,0.0,10.24,9.89 -2016-03-25 23:00:00,1227.0,0.0,10.43,10.37 -2016-03-26 00:00:00,1054.0,0.0,10.62,10.84 -2016-03-26 01:00:00,949.0,0.0,10.77,11.11 -2016-03-26 02:00:00,865.0,0.0,10.92,11.38 -2016-03-26 03:00:00,837.0,0.0,11.07,11.66 -2016-03-26 04:00:00,839.0,0.0,11.19,12.0 -2016-03-26 05:00:00,840.0,0.0,11.3,12.34 -2016-03-26 06:00:00,842.0,0.0,11.41,12.69 -2016-03-26 07:00:00,878.0,6.0,11.41,13.26 -2016-03-26 08:00:00,968.0,71.1,11.42,13.82 -2016-03-26 09:00:00,1041.0,66.72,11.43,14.39 -2016-03-26 10:00:00,1081.0,56.69,11.39,14.83 -2016-03-26 11:00:00,1121.0,43.04,11.36,15.27 -2016-03-26 12:00:00,1154.0,52.89,11.33,15.71 -2016-03-26 13:00:00,1117.0,66.28,10.43,15.47 -2016-03-26 14:00:00,1124.0,41.93,9.53,15.23 -2016-03-26 15:00:00,1160.0,51.28,8.64,14.99 -2016-03-26 16:00:00,1163.0,160.54,8.52,14.46 -2016-03-26 17:00:00,1216.0,182.38,8.4,13.92 -2016-03-26 18:00:00,1267.0,17.21,8.29,13.38 -2016-03-26 19:00:00,1278.0,0.0,8.68,13.6 -2016-03-26 20:00:00,1352.0,0.0,9.08,13.82 -2016-03-26 21:00:00,1412.0,0.0,9.48,14.04 -2016-03-26 22:00:00,1586.0,0.0,9.22,14.17 -2016-03-26 23:00:00,1461.0,0.0,8.97,14.29 -2016-03-27 00:00:00,1285.0,0.0,8.72,14.41 -2016-03-27 01:00:00,1140.0,0.0,8.82,14.59 -2016-03-27 02:00:00,1116.0,0.0,8.93,14.77 -2016-03-27 03:00:00,1092.0,0.0,9.03,14.95 -2016-03-27 04:00:00,1066.0,0.0,9.1,14.97 -2016-03-27 05:00:00,1057.0,0.0,9.17,15.0 -2016-03-27 06:00:00,1061.0,0.0,9.24,15.02 -2016-03-27 07:00:00,1079.0,59.6,9.38,14.84 -2016-03-27 08:00:00,1181.0,9.02,9.52,14.66 -2016-03-27 09:00:00,1321.0,518.18,9.67,14.48 -2016-03-27 10:00:00,1370.0,654.01,9.78,14.62 -2016-03-27 11:00:00,1384.0,821.6,9.89,14.76 -2016-03-27 12:00:00,1249.0,863.32,10.0,14.9 -2016-03-27 13:00:00,1062.0,344.43,9.74,14.73 -2016-03-27 14:00:00,1000.0,169.16,9.48,14.57 -2016-03-27 15:00:00,960.0,65.09,9.23,14.4 -2016-03-27 16:00:00,967.0,68.21,9.3,14.81 -2016-03-27 17:00:00,1018.0,25.78,9.37,15.23 -2016-03-27 18:00:00,1135.0,9.63,9.45,15.64 -2016-03-27 19:00:00,1223.0,0.0,9.91,17.02 -2016-03-27 20:00:00,1285.0,0.0,10.38,18.39 -2016-03-27 21:00:00,1281.0,0.0,10.85,19.77 -2016-03-27 22:00:00,1510.0,0.0,10.95,19.38 -2016-03-27 23:00:00,1477.0,0.0,11.05,19.0 -2016-03-28 00:00:00,1299.0,0.0,11.15,18.62 -2016-03-28 01:00:00,1172.0,0.0,10.19,18.64 -2016-03-28 02:00:00,1072.0,0.0,9.24,18.66 -2016-03-28 03:00:00,1001.0,0.0,8.28,18.68 -2016-03-28 04:00:00,1009.0,0.0,8.49,17.26 -2016-03-28 05:00:00,998.0,0.0,8.7,15.83 -2016-03-28 06:00:00,993.0,0.0,8.91,14.41 -2016-03-28 07:00:00,1012.0,74.3,8.95,13.59 -2016-03-28 08:00:00,1111.0,21.59,9.0,12.76 -2016-03-28 09:00:00,1193.0,271.53,9.05,11.93 -2016-03-28 10:00:00,1218.0,691.94,8.82,12.97 -2016-03-28 11:00:00,1230.0,287.74,8.59,14.0 -2016-03-28 12:00:00,1145.0,262.37,8.36,15.03 -2016-03-28 13:00:00,1060.0,644.62,8.62,15.35 -2016-03-28 14:00:00,997.0,508.71,8.88,15.66 -2016-03-28 15:00:00,949.0,257.49,9.14,15.97 -2016-03-28 16:00:00,915.0,387.41,9.13,16.08 -2016-03-28 17:00:00,884.0,215.46,9.12,16.18 -2016-03-28 18:00:00,946.0,19.72,9.12,16.29 -2016-03-28 19:00:00,985.0,0.0,9.18,15.71 -2016-03-28 20:00:00,1015.0,0.0,9.24,15.13 -2016-03-28 21:00:00,1081.0,0.0,9.31,14.55 -2016-03-28 22:00:00,1322.0,0.0,9.21,14.06 -2016-03-28 23:00:00,1362.0,0.0,9.12,13.57 -2016-03-29 00:00:00,1221.0,0.0,9.03,13.08 -2016-03-29 01:00:00,1103.0,0.0,8.98,12.83 -2016-03-29 02:00:00,1007.0,0.0,8.93,12.59 -2016-03-29 03:00:00,971.0,0.0,8.88,12.34 -2016-03-29 04:00:00,955.0,0.0,8.89,11.6 -2016-03-29 05:00:00,940.0,0.0,8.89,10.86 -2016-03-29 06:00:00,945.0,0.0,8.89,10.12 -2016-03-29 07:00:00,993.0,81.92,9.1,10.09 -2016-03-29 08:00:00,1069.0,77.55,9.32,10.05 -2016-03-29 09:00:00,1119.0,256.58,9.54,10.01 -2016-03-29 10:00:00,1066.0,68.83,9.55,10.68 -2016-03-29 11:00:00,1026.0,708.59,9.56,11.36 -2016-03-29 12:00:00,1039.0,85.44,9.58,12.03 -2016-03-29 13:00:00,961.0,736.73,9.47,12.21 -2016-03-29 14:00:00,993.0,477.62,9.36,12.39 -2016-03-29 15:00:00,946.0,285.94,9.26,12.57 -2016-03-29 16:00:00,896.0,239.71,9.27,12.16 -2016-03-29 17:00:00,863.0,77.9,9.28,11.75 -2016-03-29 18:00:00,899.0,12.76,9.3,11.34 -2016-03-29 19:00:00,957.0,0.0,9.31,11.03 -2016-03-29 20:00:00,978.0,0.0,9.32,10.71 -2016-03-29 21:00:00,1027.0,0.0,9.34,10.4 -2016-03-29 22:00:00,1268.0,0.0,9.29,10.26 -2016-03-29 23:00:00,1272.0,0.0,9.25,10.12 -2016-03-30 00:00:00,1107.0,0.0,9.21,9.99 -2016-03-30 01:00:00,1041.0,0.0,9.28,9.52 -2016-03-30 02:00:00,960.0,0.0,9.36,9.05 -2016-03-30 03:00:00,892.0,0.0,9.43,8.58 -2016-03-30 04:00:00,898.0,0.0,9.32,7.81 -2016-03-30 05:00:00,884.0,0.0,9.22,7.04 -2016-03-30 06:00:00,877.0,0.0,9.11,6.28 -2016-03-30 07:00:00,934.0,49.34,8.93,5.77 -2016-03-30 08:00:00,1016.0,142.18,8.74,5.26 -2016-03-30 09:00:00,1099.0,229.89,8.56,4.76 -2016-03-30 10:00:00,1110.0,531.77,8.49,4.4 -2016-03-30 11:00:00,1093.0,623.69,8.42,4.03 -2016-03-30 12:00:00,1082.0,534.35,8.36,3.67 -2016-03-30 13:00:00,982.0,563.01,8.38,3.88 -2016-03-30 14:00:00,950.0,563.03,8.41,4.09 -2016-03-30 15:00:00,901.0,244.97,8.44,4.3 -2016-03-30 16:00:00,856.0,165.89,8.37,4.25 -2016-03-30 17:00:00,845.0,79.7,8.3,4.2 -2016-03-30 18:00:00,890.0,15.27,8.24,4.15 -2016-03-30 19:00:00,905.0,0.0,8.26,4.46 -2016-03-30 20:00:00,963.0,0.0,8.28,4.78 -2016-03-30 21:00:00,985.0,0.0,8.3,5.09 -2016-03-30 22:00:00,1270.0,0.0,8.25,5.6 -2016-03-30 23:00:00,1278.0,0.0,8.2,6.1 -2016-03-31 00:00:00,1105.0,0.0,8.15,6.61 -2016-03-31 01:00:00,1001.0,0.0,8.24,6.93 -2016-03-31 02:00:00,924.0,0.0,8.33,7.26 -2016-03-31 03:00:00,871.0,0.0,8.41,7.59 -2016-03-31 04:00:00,893.0,0.0,8.44,7.55 -2016-03-31 05:00:00,866.0,0.0,8.47,7.52 -2016-03-31 06:00:00,874.0,0.0,8.5,7.49 -2016-03-31 07:00:00,946.0,89.28,8.6,7.44 -2016-03-31 08:00:00,1043.0,284.72,8.71,7.39 -2016-03-31 09:00:00,1057.0,406.62,8.82,7.34 -2016-03-31 10:00:00,1036.0,693.43,8.93,7.32 -2016-03-31 11:00:00,975.0,705.6,9.04,7.31 -2016-03-31 12:00:00,941.0,854.51,9.16,7.3 -2016-03-31 13:00:00,850.0,841.85,9.11,6.97 -2016-03-31 14:00:00,782.0,777.42,9.06,6.65 -2016-03-31 15:00:00,758.0,654.13,9.01,6.33 -2016-03-31 16:00:00,742.0,464.15,9.01,6.23 -2016-03-31 17:00:00,736.0,226.48,9.01,6.13 -2016-03-31 18:00:00,776.0,20.14,9.01,6.03 -2016-03-31 19:00:00,872.0,0.0,8.92,5.0 -2016-03-31 20:00:00,867.0,0.0,8.84,3.98 -2016-03-31 21:00:00,926.0,0.0,8.76,2.95 -2016-03-31 22:00:00,1159.0,0.0,8.65,2.63 -2016-03-31 23:00:00,1170.0,0.0,8.55,2.3 -2016-04-01 00:00:00,1021.0,0.0,8.45,1.97 -2016-04-01 01:00:00,921.0,0.0,8.4,3.03 -2016-04-01 02:00:00,843.0,0.0,8.35,4.09 -2016-04-01 03:00:00,795.0,0.0,8.31,5.14 -2016-04-01 04:00:00,816.0,0.0,8.41,5.98 -2016-04-01 05:00:00,813.0,0.0,8.51,6.81 -2016-04-01 06:00:00,834.0,0.0,8.61,7.64 -2016-04-01 07:00:00,891.0,12.18,8.81,8.19 -2016-04-01 08:00:00,963.0,59.59,9.0,8.74 -2016-04-01 09:00:00,1009.0,45.73,9.19,9.28 -2016-04-01 10:00:00,966.0,121.48,9.37,9.56 -2016-04-01 11:00:00,947.0,359.47,9.54,9.84 -2016-04-01 12:00:00,951.0,139.89,9.72,10.12 -2016-04-01 13:00:00,883.0,169.67,9.76,10.46 -2016-04-01 14:00:00,835.0,206.63,9.8,10.8 -2016-04-01 15:00:00,825.0,204.24,9.85,11.13 -2016-04-01 16:00:00,827.0,75.0,9.89,11.0 -2016-04-01 17:00:00,824.0,83.75,9.94,10.87 -2016-04-01 18:00:00,904.0,0.0,9.98,10.74 -2016-04-01 19:00:00,907.0,0.0,10.02,10.92 -2016-04-01 20:00:00,984.0,0.0,10.06,11.09 -2016-04-01 21:00:00,1024.0,0.0,10.09,11.27 -2016-04-01 22:00:00,1298.0,0.0,10.02,11.14 -2016-04-01 23:00:00,1294.0,0.0,9.95,11.01 -2016-04-02 00:00:00,1146.0,0.0,9.88,10.88 -2016-04-02 01:00:00,1039.0,0.0,9.74,10.57 -2016-04-02 02:00:00,918.0,0.0,9.61,10.26 -2016-04-02 03:00:00,879.0,0.0,9.48,9.94 -2016-04-02 04:00:00,873.0,0.0,9.49,10.02 -2016-04-02 05:00:00,858.0,0.0,9.5,10.09 -2016-04-02 06:00:00,877.0,0.0,9.51,10.17 -2016-04-02 07:00:00,901.0,17.41,9.52,10.37 -2016-04-02 08:00:00,990.0,30.79,9.54,10.58 -2016-04-02 09:00:00,1043.0,51.76,9.56,10.79 -2016-04-02 10:00:00,1062.0,68.02,9.61,10.33 -2016-04-02 11:00:00,1081.0,102.06,9.67,9.87 -2016-04-02 12:00:00,1040.0,131.74,9.73,9.41 -2016-04-02 13:00:00,985.0,129.28,9.72,8.97 -2016-04-02 14:00:00,976.0,80.5,9.71,8.52 -2016-04-02 15:00:00,963.0,82.86,9.71,8.08 -2016-04-02 16:00:00,980.0,48.7,9.39,7.95 -2016-04-02 17:00:00,1007.0,32.3,9.07,7.82 -2016-04-02 18:00:00,1056.0,0.27,8.74,7.68 -2016-04-02 19:00:00,1146.0,0.0,8.83,7.99 -2016-04-02 20:00:00,1177.0,0.0,8.92,8.3 -2016-04-02 21:00:00,1198.0,0.0,9.01,8.61 -2016-04-02 22:00:00,1409.0,0.0,9.21,9.25 -2016-04-02 23:00:00,1400.0,0.0,9.4,9.88 -2016-04-03 00:00:00,1227.0,0.0,9.6,10.52 -2016-04-03 01:00:00,1133.0,0.0,9.81,10.56 -2016-04-03 02:00:00,1038.0,0.0,10.02,10.59 -2016-04-03 03:00:00,953.0,0.0,10.23,10.62 -2016-04-03 04:00:00,958.0,0.0,10.27,10.18 -2016-04-03 05:00:00,941.0,0.0,10.32,9.75 -2016-04-03 06:00:00,930.0,0.0,10.36,9.31 -2016-04-03 07:00:00,959.0,48.69,10.19,9.29 -2016-04-03 08:00:00,1033.0,250.71,10.02,9.26 -2016-04-03 09:00:00,1121.0,482.05,9.85,9.24 -2016-04-03 10:00:00,1174.0,715.53,9.91,9.24 -2016-04-03 11:00:00,1179.0,504.3,9.97,9.24 -2016-04-03 12:00:00,1085.0,848.53,10.03,9.24 -2016-04-03 13:00:00,921.0,834.86,10.04,9.12 -2016-04-03 14:00:00,840.0,710.47,10.05,9.0 -2016-04-03 15:00:00,776.0,583.65,10.06,8.88 -2016-04-03 16:00:00,769.0,294.26,9.89,8.7 -2016-04-03 17:00:00,747.0,65.59,9.73,8.51 -2016-04-03 18:00:00,807.0,25.35,9.56,8.33 -2016-04-03 19:00:00,889.0,0.0,9.6,8.02 -2016-04-03 20:00:00,938.0,0.0,9.64,7.71 -2016-04-03 21:00:00,1017.0,0.0,9.67,7.41 -2016-04-03 22:00:00,1284.0,0.0,9.48,7.09 -2016-04-03 23:00:00,1296.0,0.0,9.29,6.78 -2016-04-04 00:00:00,1120.0,0.0,9.1,6.47 -2016-04-04 01:00:00,991.0,0.0,9.27,6.22 -2016-04-04 02:00:00,907.0,0.0,9.44,5.96 -2016-04-04 03:00:00,843.0,0.0,9.61,5.71 -2016-04-04 04:00:00,849.0,0.0,9.61,5.9 -2016-04-04 05:00:00,839.0,0.0,9.61,6.1 -2016-04-04 06:00:00,840.0,0.0,9.61,6.29 -2016-04-04 07:00:00,885.0,48.63,9.67,6.4 -2016-04-04 08:00:00,971.0,238.98,9.73,6.51 -2016-04-04 09:00:00,1039.0,432.46,9.8,6.62 -2016-04-04 10:00:00,1046.0,710.22,9.88,6.73 -2016-04-04 11:00:00,1038.0,808.14,9.96,6.83 -2016-04-04 12:00:00,1027.0,635.45,10.05,6.94 -2016-04-04 13:00:00,931.0,791.79,10.1,6.79 -2016-04-04 14:00:00,856.0,687.25,10.15,6.63 -2016-04-04 15:00:00,789.0,305.8,10.2,6.48 -2016-04-04 16:00:00,821.0,401.13,10.18,6.21 -2016-04-04 17:00:00,831.0,144.95,10.17,5.93 -2016-04-04 18:00:00,921.0,21.48,10.15,5.66 -2016-04-04 19:00:00,997.0,0.0,10.12,5.84 -2016-04-04 20:00:00,1001.0,0.0,10.08,6.02 -2016-04-04 21:00:00,1041.0,0.0,10.04,6.21 -2016-04-04 22:00:00,1299.0,0.0,9.95,6.06 -2016-04-04 23:00:00,1274.0,0.0,9.85,5.92 -2016-04-05 00:00:00,1074.0,0.0,9.76,5.78 -2016-04-05 01:00:00,960.0,0.0,9.83,5.68 -2016-04-05 02:00:00,882.0,0.0,9.9,5.59 -2016-04-05 03:00:00,821.0,0.0,9.97,5.49 -2016-04-05 04:00:00,837.0,0.0,9.95,5.64 -2016-04-05 05:00:00,834.0,0.0,9.94,5.79 -2016-04-05 06:00:00,835.0,0.0,9.92,5.94 -2016-04-05 07:00:00,875.0,62.3,9.95,5.86 -2016-04-05 08:00:00,967.0,275.48,9.99,5.78 -2016-04-05 09:00:00,1025.0,469.87,10.02,5.7 -2016-04-05 10:00:00,995.0,692.93,9.95,5.71 -2016-04-05 11:00:00,960.0,733.45,9.87,5.72 -2016-04-05 12:00:00,928.0,851.68,9.8,5.74 -2016-04-05 13:00:00,839.0,835.35,9.85,6.09 -2016-04-05 14:00:00,764.0,768.21,9.91,6.44 -2016-04-05 15:00:00,723.0,420.87,9.97,6.79 -2016-04-05 16:00:00,686.0,451.65,9.84,6.8 -2016-04-05 17:00:00,714.0,221.49,9.71,6.8 -2016-04-05 18:00:00,803.0,29.58,9.57,6.81 -2016-04-05 19:00:00,871.0,0.0,9.47,7.0 -2016-04-05 20:00:00,929.0,0.0,9.37,7.18 -2016-04-05 21:00:00,944.0,0.0,9.27,7.37 -2016-04-05 22:00:00,1211.0,0.0,9.3,7.74 -2016-04-05 23:00:00,1230.0,0.0,9.32,8.12 -2016-04-06 00:00:00,1059.0,0.0,9.34,8.5 -2016-04-06 01:00:00,932.0,0.0,9.6,9.13 -2016-04-06 02:00:00,823.0,0.0,9.87,9.76 -2016-04-06 03:00:00,769.0,0.0,10.14,10.39 -2016-04-06 04:00:00,768.0,0.0,10.21,10.11 -2016-04-06 05:00:00,769.0,0.0,10.28,9.83 -2016-04-06 06:00:00,779.0,0.0,10.35,9.54 -2016-04-06 07:00:00,826.0,86.12,10.24,9.69 -2016-04-06 08:00:00,920.0,213.09,10.14,9.83 -2016-04-06 09:00:00,1024.0,142.3,10.03,9.97 -2016-04-06 10:00:00,1064.0,472.25,9.84,10.26 -2016-04-06 11:00:00,1067.0,770.19,9.64,10.55 -2016-04-06 12:00:00,1051.0,427.48,9.44,10.84 -2016-04-06 13:00:00,945.0,690.97,9.51,11.13 -2016-04-06 14:00:00,906.0,698.02,9.59,11.41 -2016-04-06 15:00:00,877.0,568.75,9.67,11.7 -2016-04-06 16:00:00,870.0,202.26,9.5,11.8 -2016-04-06 17:00:00,871.0,132.22,9.33,11.9 -2016-04-06 18:00:00,890.0,30.45,9.15,12.0 -2016-04-06 19:00:00,985.0,0.0,8.93,12.02 -2016-04-06 20:00:00,1013.0,0.0,8.71,12.04 -2016-04-06 21:00:00,1067.0,0.0,8.48,12.06 -2016-04-06 22:00:00,1326.0,0.0,8.39,12.14 -2016-04-06 23:00:00,1326.0,0.0,8.29,12.22 -2016-04-07 00:00:00,1173.0,0.0,8.2,12.3 -2016-04-07 01:00:00,1062.0,0.0,8.25,12.3 -2016-04-07 02:00:00,957.0,0.0,8.3,12.3 -2016-04-07 03:00:00,903.0,0.0,8.35,12.3 -2016-04-07 04:00:00,946.0,0.0,8.35,12.18 -2016-04-07 05:00:00,934.0,0.0,8.35,12.06 -2016-04-07 06:00:00,928.0,1.57,8.35,11.94 -2016-04-07 07:00:00,994.0,102.85,8.2,11.5 -2016-04-07 08:00:00,1111.0,331.12,8.05,11.05 -2016-04-07 09:00:00,1212.0,503.6,7.91,10.61 -2016-04-07 10:00:00,1214.0,473.8,8.06,10.5 -2016-04-07 11:00:00,1163.0,692.06,8.21,10.39 -2016-04-07 12:00:00,1151.0,816.07,8.37,10.28 -2016-04-07 13:00:00,1011.0,591.1,8.45,10.33 -2016-04-07 14:00:00,990.0,766.56,8.53,10.38 -2016-04-07 15:00:00,930.0,580.55,8.62,10.43 -2016-04-07 16:00:00,875.0,400.81,8.81,10.46 -2016-04-07 17:00:00,863.0,214.69,9.0,10.49 -2016-04-07 18:00:00,921.0,31.56,9.19,10.52 -2016-04-07 19:00:00,1017.0,0.0,9.32,10.29 -2016-04-07 20:00:00,1036.0,0.0,9.45,10.06 -2016-04-07 21:00:00,1097.0,0.0,9.58,9.82 -2016-04-07 22:00:00,1377.0,0.0,9.58,9.75 -2016-04-07 23:00:00,1403.0,0.0,9.58,9.67 -2016-04-08 00:00:00,1208.0,0.0,9.58,9.6 -2016-04-08 01:00:00,1041.0,0.0,9.63,9.37 -2016-04-08 02:00:00,943.0,0.0,9.68,9.14 -2016-04-08 03:00:00,877.0,0.0,9.73,8.91 -2016-04-08 04:00:00,895.0,0.0,9.63,8.91 -2016-04-08 05:00:00,894.0,0.0,9.53,8.91 -2016-04-08 06:00:00,910.0,0.0,9.43,8.91 -2016-04-08 07:00:00,991.0,10.87,9.39,8.4 -2016-04-08 08:00:00,1051.0,43.4,9.34,7.88 -2016-04-08 09:00:00,1138.0,158.48,9.3,7.37 -2016-04-08 10:00:00,1162.0,130.95,9.27,7.0 -2016-04-08 11:00:00,1133.0,107.64,9.25,6.64 -2016-04-08 12:00:00,1185.0,181.38,9.23,6.28 -2016-04-08 13:00:00,1087.0,109.28,9.19,6.72 -2016-04-08 14:00:00,1025.0,83.03,9.15,7.16 -2016-04-08 15:00:00,998.0,70.45,9.12,7.6 -2016-04-08 16:00:00,950.0,60.27,9.34,8.83 -2016-04-08 17:00:00,924.0,54.82,9.56,10.06 -2016-04-08 18:00:00,1022.0,17.53,9.78,11.3 -2016-04-08 19:00:00,1098.0,0.0,9.88,10.62 -2016-04-08 20:00:00,1173.0,0.0,9.97,9.94 -2016-04-08 21:00:00,1214.0,0.0,10.06,9.26 -2016-04-08 22:00:00,1452.0,0.0,9.75,9.52 -2016-04-08 23:00:00,1458.0,0.0,9.44,9.79 -2016-04-09 00:00:00,1306.0,0.0,9.13,10.06 -2016-04-09 01:00:00,1119.0,0.0,8.85,9.83 -2016-04-09 02:00:00,1006.0,0.0,8.58,9.6 -2016-04-09 03:00:00,957.0,0.0,8.31,9.38 -2016-04-09 04:00:00,949.0,0.0,8.0,9.51 -2016-04-09 05:00:00,945.0,0.0,7.69,9.64 -2016-04-09 06:00:00,964.0,2.9,7.38,9.77 -2016-04-09 07:00:00,1025.0,113.3,7.26,9.69 -2016-04-09 08:00:00,1134.0,341.48,7.14,9.61 -2016-04-09 09:00:00,1256.0,501.61,7.03,9.53 -2016-04-09 10:00:00,1309.0,708.02,7.08,9.35 -2016-04-09 11:00:00,1229.0,801.8,7.14,9.16 -2016-04-09 12:00:00,1152.0,788.16,7.2,8.98 -2016-04-09 13:00:00,1090.0,672.06,7.36,9.02 -2016-04-09 14:00:00,1000.0,626.84,7.53,9.06 -2016-04-09 15:00:00,970.0,567.84,7.7,9.1 -2016-04-09 16:00:00,926.0,141.82,7.72,8.86 -2016-04-09 17:00:00,1053.0,207.77,7.75,8.63 -2016-04-09 18:00:00,1117.0,27.22,7.77,8.39 -2016-04-09 19:00:00,1191.0,0.0,8.09,8.53 -2016-04-09 20:00:00,1215.0,0.0,8.4,8.68 -2016-04-09 21:00:00,1278.0,0.0,8.71,8.83 -2016-04-09 22:00:00,1540.0,0.0,8.57,10.04 -2016-04-09 23:00:00,1597.0,0.0,8.43,11.26 -2016-04-10 00:00:00,1408.0,0.0,8.29,12.47 -2016-04-10 01:00:00,1252.0,0.0,8.54,13.4 -2016-04-10 02:00:00,1154.0,0.0,8.79,14.33 -2016-04-10 03:00:00,1129.0,0.0,9.05,15.26 -2016-04-10 04:00:00,1119.0,0.0,9.26,15.2 -2016-04-10 05:00:00,1128.0,0.0,9.48,15.14 -2016-04-10 06:00:00,1113.0,1.15,9.7,15.08 -2016-04-10 07:00:00,1140.0,12.77,9.72,14.94 -2016-04-10 08:00:00,1257.0,26.45,9.73,14.81 -2016-04-10 09:00:00,1404.0,39.63,9.74,14.68 -2016-04-10 10:00:00,1440.0,64.13,9.78,14.2 -2016-04-10 11:00:00,1463.0,122.29,9.82,13.72 -2016-04-10 12:00:00,1359.0,140.04,9.86,13.24 -2016-04-10 13:00:00,1240.0,123.96,9.66,12.84 -2016-04-10 14:00:00,1094.0,95.08,9.46,12.43 -2016-04-10 15:00:00,1017.0,42.72,9.26,12.03 -2016-04-10 16:00:00,984.0,30.87,9.04,11.23 -2016-04-10 17:00:00,1033.0,99.8,8.82,10.43 -2016-04-10 18:00:00,1123.0,29.88,8.6,9.63 -2016-04-10 19:00:00,1174.0,0.0,8.69,8.91 -2016-04-10 20:00:00,1171.0,0.0,8.78,8.18 -2016-04-10 21:00:00,1190.0,0.0,8.86,7.46 -2016-04-10 22:00:00,1505.0,0.0,8.82,7.36 -2016-04-10 23:00:00,1514.0,0.0,8.77,7.26 -2016-04-11 00:00:00,1339.0,0.0,8.72,7.16 -2016-04-11 01:00:00,1206.0,0.0,9.06,6.28 -2016-04-11 02:00:00,1075.0,0.0,9.4,5.4 -2016-04-11 03:00:00,1034.0,0.0,9.74,4.52 -2016-04-11 04:00:00,1051.0,0.0,9.76,5.03 -2016-04-11 05:00:00,1044.0,0.0,9.78,5.53 -2016-04-11 06:00:00,1058.0,1.97,9.8,6.03 -2016-04-11 07:00:00,1108.0,113.14,9.98,6.22 -2016-04-11 08:00:00,1198.0,274.94,10.15,6.4 -2016-04-11 09:00:00,1296.0,434.32,10.32,6.59 -2016-04-11 10:00:00,1280.0,592.99,10.37,6.58 -2016-04-11 11:00:00,1148.0,831.48,10.41,6.57 -2016-04-11 12:00:00,1085.0,859.86,10.45,6.55 -2016-04-11 13:00:00,1006.0,837.37,10.52,5.4 -2016-04-11 14:00:00,896.0,751.82,10.59,4.24 -2016-04-11 15:00:00,852.0,633.28,10.67,3.09 -2016-04-11 16:00:00,827.0,444.76,10.6,2.79 -2016-04-11 17:00:00,835.0,224.2,10.53,2.49 -2016-04-11 18:00:00,884.0,33.51,10.46,2.19 -2016-04-11 19:00:00,995.0,0.0,10.36,3.02 -2016-04-11 20:00:00,1035.0,0.0,10.26,3.85 -2016-04-11 21:00:00,1053.0,0.0,10.16,4.68 -2016-04-11 22:00:00,1331.0,0.0,10.07,5.02 -2016-04-11 23:00:00,1354.0,0.0,9.98,5.36 -2016-04-12 00:00:00,1165.0,0.0,9.89,5.7 -2016-04-12 01:00:00,997.0,0.0,9.93,5.39 -2016-04-12 02:00:00,886.0,0.0,9.97,5.09 -2016-04-12 03:00:00,859.0,0.0,10.02,4.79 -2016-04-12 04:00:00,853.0,0.0,9.81,4.71 -2016-04-12 05:00:00,843.0,0.0,9.61,4.63 -2016-04-12 06:00:00,862.0,4.29,9.4,4.55 -2016-04-12 07:00:00,920.0,94.03,9.56,4.32 -2016-04-12 08:00:00,1020.0,325.3,9.72,4.09 -2016-04-12 09:00:00,1081.0,464.54,9.89,3.86 -2016-04-12 10:00:00,1047.0,683.54,10.03,4.06 -2016-04-12 11:00:00,1035.0,771.41,10.18,4.27 -2016-04-12 12:00:00,997.0,813.74,10.33,4.47 -2016-04-12 13:00:00,929.0,725.9,10.5,4.23 -2016-04-12 14:00:00,837.0,371.95,10.68,3.98 -2016-04-12 15:00:00,781.0,628.98,10.86,3.74 -2016-04-12 16:00:00,731.0,356.22,10.98,3.99 -2016-04-12 17:00:00,727.0,31.2,11.1,4.24 -2016-04-12 18:00:00,845.0,36.22,11.22,4.5 -2016-04-12 19:00:00,971.0,0.0,11.2,5.14 -2016-04-12 20:00:00,963.0,0.0,11.17,5.77 -2016-04-12 21:00:00,997.0,0.0,11.14,6.41 -2016-04-12 22:00:00,1270.0,0.0,10.99,6.71 -2016-04-12 23:00:00,1289.0,0.0,10.84,7.0 -2016-04-13 00:00:00,1095.0,0.0,10.69,7.3 -2016-04-13 01:00:00,915.0,0.0,10.73,6.94 -2016-04-13 02:00:00,835.0,0.0,10.77,6.59 -2016-04-13 03:00:00,781.0,0.0,10.82,6.23 -2016-04-13 04:00:00,781.0,0.0,10.73,5.57 -2016-04-13 05:00:00,755.0,0.0,10.64,4.91 -2016-04-13 06:00:00,776.0,0.0,10.55,4.25 -2016-04-13 07:00:00,835.0,17.29,10.48,4.14 -2016-04-13 08:00:00,966.0,159.11,10.41,4.04 -2016-04-13 09:00:00,1122.0,122.25,10.34,3.93 -2016-04-13 10:00:00,1093.0,488.05,10.26,4.23 -2016-04-13 11:00:00,1070.0,754.6,10.18,4.52 -2016-04-13 12:00:00,1088.0,851.67,10.11,4.81 -2016-04-13 13:00:00,968.0,778.71,10.28,5.12 -2016-04-13 14:00:00,894.0,770.13,10.46,5.43 -2016-04-13 15:00:00,784.0,646.27,10.64,5.74 -2016-04-13 16:00:00,726.0,447.46,10.75,5.94 -2016-04-13 17:00:00,713.0,164.1,10.86,6.13 -2016-04-13 18:00:00,808.0,37.66,10.96,6.33 -2016-04-13 19:00:00,913.0,0.0,10.88,6.48 -2016-04-13 20:00:00,967.0,0.0,10.8,6.63 -2016-04-13 21:00:00,973.0,0.0,10.71,6.79 -2016-04-13 22:00:00,1228.0,0.0,10.66,6.69 -2016-04-13 23:00:00,1250.0,0.0,10.6,6.59 -2016-04-14 00:00:00,1085.0,0.0,10.55,6.5 -2016-04-14 01:00:00,875.0,0.0,10.71,6.32 -2016-04-14 02:00:00,793.0,0.0,10.87,6.14 -2016-04-14 03:00:00,766.0,0.0,11.04,5.96 -2016-04-14 04:00:00,780.0,0.0,11.04,6.21 -2016-04-14 05:00:00,753.0,0.0,11.05,6.46 -2016-04-14 06:00:00,766.0,5.76,11.06,6.72 -2016-04-14 07:00:00,832.0,54.86,10.98,7.48 -2016-04-14 08:00:00,955.0,76.89,10.9,8.23 -2016-04-14 09:00:00,1065.0,86.39,10.82,8.99 -2016-04-14 10:00:00,1122.0,56.81,10.71,9.68 -2016-04-14 11:00:00,1224.0,67.7,10.59,10.37 -2016-04-14 12:00:00,1308.0,44.71,10.47,11.06 -2016-04-14 13:00:00,1278.0,40.15,10.72,10.48 -2016-04-14 14:00:00,1241.0,35.66,10.98,9.89 -2016-04-14 15:00:00,1182.0,194.72,11.24,9.31 -2016-04-14 16:00:00,1071.0,139.81,11.25,9.54 -2016-04-14 17:00:00,1001.0,128.62,11.27,9.77 -2016-04-14 18:00:00,1051.0,31.6,11.28,10.0 -2016-04-14 19:00:00,1111.0,0.0,11.23,10.43 -2016-04-14 20:00:00,1109.0,0.0,11.17,10.86 -2016-04-14 21:00:00,1118.0,0.0,11.11,11.28 -2016-04-14 22:00:00,1407.0,0.0,10.97,11.66 -2016-04-14 23:00:00,1409.0,0.0,10.82,12.03 -2016-04-15 00:00:00,1203.0,0.0,10.67,12.4 -2016-04-15 01:00:00,1044.0,0.0,10.62,11.92 -2016-04-15 02:00:00,966.0,0.0,10.57,11.43 -2016-04-15 03:00:00,932.0,0.0,10.53,10.95 -2016-04-15 04:00:00,925.0,0.0,10.54,10.89 -2016-04-15 05:00:00,929.0,0.0,10.55,10.83 -2016-04-15 06:00:00,932.0,10.18,10.56,10.77 -2016-04-15 07:00:00,986.0,66.9,10.5,10.04 -2016-04-15 08:00:00,1049.0,252.74,10.44,9.31 -2016-04-15 09:00:00,1126.0,173.07,10.39,8.58 -2016-04-15 10:00:00,1118.0,624.49,10.4,8.11 -2016-04-15 11:00:00,1153.0,165.66,10.41,7.65 -2016-04-15 12:00:00,1129.0,189.36,10.43,7.19 -2016-04-15 13:00:00,1066.0,114.27,10.42,7.26 -2016-04-15 14:00:00,951.0,211.77,10.41,7.34 -2016-04-15 15:00:00,849.0,172.77,10.4,7.42 -2016-04-15 16:00:00,784.0,139.27,10.31,7.26 -2016-04-15 17:00:00,762.0,101.03,10.22,7.1 -2016-04-15 18:00:00,860.0,39.99,10.13,6.94 -2016-04-15 19:00:00,923.0,0.0,9.99,6.62 -2016-04-15 20:00:00,976.0,0.0,9.84,6.29 -2016-04-15 21:00:00,972.0,0.0,9.69,5.97 -2016-04-15 22:00:00,1261.0,0.0,9.47,5.89 -2016-04-15 23:00:00,1261.0,0.0,9.25,5.8 -2016-04-16 00:00:00,1056.0,0.0,9.03,5.71 -2016-04-16 01:00:00,887.0,0.0,9.34,6.09 -2016-04-16 02:00:00,825.0,0.0,9.66,6.47 -2016-04-16 03:00:00,796.0,0.0,9.98,6.86 -2016-04-16 04:00:00,804.0,0.0,9.85,7.37 -2016-04-16 05:00:00,800.0,0.0,9.73,7.88 -2016-04-16 06:00:00,819.0,13.78,9.61,8.4 -2016-04-16 07:00:00,867.0,126.52,9.37,8.29 -2016-04-16 08:00:00,1001.0,321.57,9.13,8.19 -2016-04-16 09:00:00,1040.0,238.84,8.89,8.08 -2016-04-16 10:00:00,1046.0,429.32,8.72,7.85 -2016-04-16 11:00:00,1010.0,638.77,8.55,7.62 -2016-04-16 12:00:00,997.0,672.99,8.39,7.39 -2016-04-16 13:00:00,873.0,769.47,8.6,7.42 -2016-04-16 14:00:00,790.0,714.83,8.81,7.45 -2016-04-16 15:00:00,775.0,619.04,9.03,7.48 -2016-04-16 16:00:00,767.0,409.24,8.99,7.93 -2016-04-16 17:00:00,775.0,185.56,8.96,8.38 -2016-04-16 18:00:00,867.0,40.83,8.92,8.83 -2016-04-16 19:00:00,945.0,0.0,8.88,8.68 -2016-04-16 20:00:00,1002.0,0.0,8.84,8.54 -2016-04-16 21:00:00,1033.0,0.0,8.8,8.4 -2016-04-16 22:00:00,1339.0,0.0,8.73,7.97 -2016-04-16 23:00:00,1343.0,0.0,8.65,7.54 -2016-04-17 00:00:00,1189.0,0.0,8.58,7.12 -2016-04-17 01:00:00,1007.0,0.0,8.62,6.79 -2016-04-17 02:00:00,930.0,0.0,8.67,6.46 -2016-04-17 03:00:00,891.0,0.0,8.72,6.12 -2016-04-17 04:00:00,883.0,0.0,8.71,5.89 -2016-04-17 05:00:00,889.0,0.0,8.7,5.66 -2016-04-17 06:00:00,889.0,11.45,8.69,5.42 -2016-04-17 07:00:00,937.0,124.13,8.78,5.22 -2016-04-17 08:00:00,1027.0,193.13,8.87,5.02 -2016-04-17 09:00:00,1078.0,111.58,8.95,4.81 -2016-04-17 10:00:00,1065.0,146.77,9.02,4.67 -2016-04-17 11:00:00,1027.0,668.29,9.09,4.53 -2016-04-17 12:00:00,949.0,606.42,9.16,4.39 -2016-04-17 13:00:00,820.0,86.8,9.14,4.47 -2016-04-17 14:00:00,743.0,568.41,9.12,4.55 -2016-04-17 15:00:00,706.0,615.29,9.1,4.63 -2016-04-17 16:00:00,677.0,454.83,9.15,4.43 -2016-04-17 17:00:00,684.0,230.82,9.2,4.22 -2016-04-17 18:00:00,744.0,35.16,9.25,4.01 -2016-04-17 19:00:00,851.0,0.0,9.22,3.75 -2016-04-17 20:00:00,867.0,0.0,9.18,3.49 -2016-04-17 21:00:00,888.0,0.0,9.14,3.23 -2016-04-17 22:00:00,1176.0,0.0,9.06,2.97 -2016-04-17 23:00:00,1230.0,0.0,8.97,2.7 -2016-04-18 00:00:00,1083.0,0.0,8.88,2.44 -2016-04-18 01:00:00,929.0,0.0,8.59,2.42 -2016-04-18 02:00:00,844.0,0.0,8.3,2.4 -2016-04-18 03:00:00,804.0,0.0,8.01,2.37 -2016-04-18 04:00:00,807.0,0.0,8.01,2.19 -2016-04-18 05:00:00,799.0,0.0,8.01,2.0 -2016-04-18 06:00:00,796.0,13.88,8.01,1.82 -2016-04-18 07:00:00,827.0,117.22,8.1,1.55 -2016-04-18 08:00:00,930.0,107.25,8.19,1.28 -2016-04-18 09:00:00,965.0,126.53,8.27,1.01 -2016-04-18 10:00:00,955.0,249.17,8.43,1.15 -2016-04-18 11:00:00,924.0,368.3,8.58,1.29 -2016-04-18 12:00:00,928.0,458.3,8.74,1.43 -2016-04-18 13:00:00,913.0,512.72,8.85,1.31 -2016-04-18 14:00:00,840.0,547.29,8.96,1.2 -2016-04-18 15:00:00,784.0,526.48,9.07,1.08 -2016-04-18 16:00:00,770.0,344.55,9.23,1.39 -2016-04-18 17:00:00,742.0,227.77,9.39,1.71 -2016-04-18 18:00:00,783.0,36.18,9.54,2.03 -2016-04-18 19:00:00,868.0,0.0,9.63,2.55 -2016-04-18 20:00:00,891.0,0.0,9.72,3.07 -2016-04-18 21:00:00,917.0,0.0,9.8,3.59 -2016-04-18 22:00:00,1230.0,0.0,9.74,4.01 -2016-04-18 23:00:00,1177.0,0.0,9.67,4.44 -2016-04-19 00:00:00,1005.0,0.0,9.6,4.87 -2016-04-19 01:00:00,901.0,0.0,9.31,5.81 -2016-04-19 02:00:00,808.0,0.0,9.02,6.74 -2016-04-19 03:00:00,756.0,0.0,8.74,7.68 -2016-04-19 04:00:00,778.0,0.0,8.74,7.74 -2016-04-19 05:00:00,794.0,0.0,8.75,7.79 -2016-04-19 06:00:00,805.0,13.85,8.76,7.85 -2016-04-19 07:00:00,858.0,145.57,9.11,7.94 -2016-04-19 08:00:00,929.0,377.98,9.46,8.03 -2016-04-19 09:00:00,966.0,566.18,9.81,8.12 -2016-04-19 10:00:00,919.0,677.05,10.33,8.29 -2016-04-19 11:00:00,892.0,840.79,10.85,8.46 -2016-04-19 12:00:00,868.0,866.48,11.37,8.62 -2016-04-19 13:00:00,758.0,856.75,11.54,9.26 -2016-04-19 14:00:00,711.0,775.23,11.71,9.9 -2016-04-19 15:00:00,667.0,652.78,11.89,10.54 -2016-04-19 16:00:00,663.0,458.65,12.06,10.83 -2016-04-19 17:00:00,688.0,230.54,12.24,11.12 -2016-04-19 18:00:00,767.0,41.06,12.41,11.41 -2016-04-19 19:00:00,864.0,0.0,12.43,11.71 -2016-04-19 20:00:00,889.0,0.0,12.45,12.01 -2016-04-19 21:00:00,903.0,0.0,12.46,12.32 -2016-04-19 22:00:00,1200.0,0.0,12.4,12.28 -2016-04-19 23:00:00,1190.0,0.0,12.33,12.25 -2016-04-20 00:00:00,999.0,0.0,12.27,12.22 -2016-04-20 01:00:00,908.0,0.0,12.0,12.29 -2016-04-20 02:00:00,814.0,0.0,11.73,12.37 -2016-04-20 03:00:00,762.0,0.0,11.47,12.44 -2016-04-20 04:00:00,770.0,0.0,11.39,12.37 -2016-04-20 05:00:00,758.0,0.0,11.32,12.3 -2016-04-20 06:00:00,783.0,14.88,11.24,12.23 -2016-04-20 07:00:00,846.0,73.55,11.32,12.08 -2016-04-20 08:00:00,913.0,317.08,11.4,11.93 -2016-04-20 09:00:00,963.0,450.31,11.48,11.78 -2016-04-20 10:00:00,1000.0,355.09,11.79,11.68 -2016-04-20 11:00:00,1014.0,346.34,12.11,11.59 -2016-04-20 12:00:00,1006.0,484.87,12.43,11.49 -2016-04-20 13:00:00,868.0,503.58,12.73,11.65 -2016-04-20 14:00:00,838.0,285.65,13.04,11.8 -2016-04-20 15:00:00,832.0,118.56,13.35,11.96 -2016-04-20 16:00:00,827.0,59.18,13.25,11.64 -2016-04-20 17:00:00,877.0,91.09,13.16,11.32 -2016-04-20 18:00:00,945.0,4.84,13.06,11.01 -2016-04-20 19:00:00,1038.0,0.0,13.05,10.97 -2016-04-20 20:00:00,1054.0,0.0,13.03,10.92 -2016-04-20 21:00:00,1028.0,0.0,13.01,10.88 -2016-04-20 22:00:00,1316.0,0.0,12.89,10.3 -2016-04-20 23:00:00,1281.0,0.0,12.76,9.72 -2016-04-21 00:00:00,1087.0,0.0,12.64,9.14 -2016-04-21 01:00:00,963.0,0.0,12.4,8.56 -2016-04-21 02:00:00,858.0,0.0,12.16,7.98 -2016-04-21 03:00:00,788.0,0.0,11.92,7.39 -2016-04-21 04:00:00,803.0,0.0,11.79,6.83 -2016-04-21 05:00:00,790.0,0.0,11.67,6.26 -2016-04-21 06:00:00,808.0,0.09,11.54,5.7 -2016-04-21 07:00:00,858.0,10.74,11.45,5.11 -2016-04-21 08:00:00,921.0,61.06,11.36,4.53 -2016-04-21 09:00:00,988.0,65.67,11.27,3.94 -2016-04-21 10:00:00,1019.0,205.62,11.36,3.55 -2016-04-21 11:00:00,985.0,266.31,11.46,3.16 -2016-04-21 12:00:00,1018.0,109.06,11.56,2.77 -2016-04-21 13:00:00,933.0,207.19,11.65,2.97 -2016-04-21 14:00:00,847.0,246.85,11.74,3.17 -2016-04-21 15:00:00,784.0,368.54,11.83,3.37 -2016-04-21 16:00:00,704.0,182.84,11.7,2.84 -2016-04-21 17:00:00,677.0,51.0,11.57,2.32 -2016-04-21 18:00:00,723.0,19.88,11.44,1.79 -2016-04-21 19:00:00,826.0,0.0,11.41,1.55 -2016-04-21 20:00:00,832.0,0.0,11.38,1.31 -2016-04-21 21:00:00,821.0,0.0,11.34,1.06 -2016-04-21 22:00:00,1120.0,0.0,11.24,1.5 -2016-04-21 23:00:00,1121.0,0.0,11.14,1.94 -2016-04-22 00:00:00,951.0,0.0,11.04,2.39 -2016-04-22 01:00:00,835.0,0.0,10.99,2.39 -2016-04-22 02:00:00,727.0,0.0,10.94,2.4 -2016-04-22 03:00:00,681.0,0.0,10.9,2.4 -2016-04-22 04:00:00,694.0,0.0,10.9,2.73 -2016-04-22 05:00:00,681.0,0.0,10.9,3.05 -2016-04-22 06:00:00,674.0,21.12,10.9,3.38 -2016-04-22 07:00:00,772.0,116.44,10.94,3.31 -2016-04-22 08:00:00,804.0,117.44,10.97,3.23 -2016-04-22 09:00:00,860.0,115.73,11.01,3.16 -2016-04-22 10:00:00,862.0,129.98,11.04,3.35 -2016-04-22 11:00:00,866.0,203.35,11.08,3.54 -2016-04-22 12:00:00,863.0,370.14,11.12,3.74 -2016-04-22 13:00:00,788.0,297.78,11.13,4.11 -2016-04-22 14:00:00,705.0,171.1,11.14,4.47 -2016-04-22 15:00:00,682.0,203.06,11.16,4.84 -2016-04-22 16:00:00,647.0,139.51,11.09,4.3 -2016-04-22 17:00:00,649.0,69.58,11.02,3.77 -2016-04-22 18:00:00,724.0,26.2,10.95,3.23 -2016-04-22 19:00:00,762.0,0.0,10.89,2.91 -2016-04-22 20:00:00,846.0,0.0,10.82,2.6 -2016-04-22 21:00:00,895.0,0.0,10.75,2.29 -2016-04-22 22:00:00,1207.0,0.0,10.63,2.42 -2016-04-22 23:00:00,1196.0,0.0,10.51,2.55 -2016-04-23 00:00:00,957.0,0.0,10.39,2.68 -2016-04-23 01:00:00,826.0,0.0,10.46,2.97 -2016-04-23 02:00:00,732.0,0.0,10.53,3.26 -2016-04-23 03:00:00,678.0,0.0,10.61,3.56 -2016-04-23 04:00:00,663.0,0.0,10.55,4.35 -2016-04-23 05:00:00,667.0,0.0,10.49,5.15 -2016-04-23 06:00:00,678.0,10.76,10.43,5.94 -2016-04-23 07:00:00,710.0,52.84,10.34,6.46 -2016-04-23 08:00:00,802.0,65.37,10.25,6.98 -2016-04-23 09:00:00,850.0,70.09,10.15,7.5 -2016-04-23 10:00:00,865.0,78.74,10.08,7.53 -2016-04-23 11:00:00,903.0,178.03,10.01,7.55 -2016-04-23 12:00:00,950.0,327.38,9.94,7.57 -2016-04-23 13:00:00,895.0,835.87,9.93,7.83 -2016-04-23 14:00:00,825.0,780.46,9.93,8.1 -2016-04-23 15:00:00,820.0,650.12,9.93,8.36 -2016-04-23 16:00:00,824.0,455.67,9.89,8.19 -2016-04-23 17:00:00,818.0,230.32,9.86,8.03 -2016-04-23 18:00:00,848.0,40.55,9.82,7.86 -2016-04-23 19:00:00,914.0,0.0,9.79,7.67 -2016-04-23 20:00:00,937.0,0.0,9.75,7.49 -2016-04-23 21:00:00,970.0,0.0,9.71,7.3 -2016-04-23 22:00:00,1309.0,0.0,9.67,6.95 -2016-04-23 23:00:00,1348.0,0.0,9.62,6.6 -2016-04-24 00:00:00,1110.0,0.0,9.58,6.25 -2016-04-24 01:00:00,942.0,0.0,9.51,6.18 -2016-04-24 02:00:00,886.0,0.0,9.44,6.12 -2016-04-24 03:00:00,875.0,0.0,9.37,6.06 -2016-04-24 04:00:00,837.0,0.0,9.45,6.41 -2016-04-24 05:00:00,830.0,0.0,9.54,6.77 -2016-04-24 06:00:00,824.0,20.01,9.62,7.13 -2016-04-24 07:00:00,870.0,157.57,9.77,7.18 -2016-04-24 08:00:00,948.0,350.25,9.91,7.22 -2016-04-24 09:00:00,979.0,568.18,10.06,7.27 -2016-04-24 10:00:00,1022.0,656.41,10.03,7.12 -2016-04-24 11:00:00,1002.0,834.66,10.01,6.97 -2016-04-24 12:00:00,911.0,875.03,9.99,6.81 -2016-04-24 13:00:00,820.0,857.51,9.97,6.82 -2016-04-24 14:00:00,756.0,770.94,9.96,6.83 -2016-04-24 15:00:00,699.0,653.44,9.95,6.84 -2016-04-24 16:00:00,662.0,412.73,9.77,6.82 -2016-04-24 17:00:00,640.0,227.49,9.59,6.8 -2016-04-24 18:00:00,690.0,48.31,9.4,6.77 -2016-04-24 19:00:00,766.0,0.0,9.18,6.46 -2016-04-24 20:00:00,810.0,0.0,8.95,6.16 -2016-04-24 21:00:00,834.0,0.0,8.72,5.85 -2016-04-24 22:00:00,1187.0,0.0,8.51,5.64 -2016-04-24 23:00:00,1183.0,0.0,8.29,5.43 -2016-04-25 00:00:00,1027.0,0.0,8.08,5.21 -2016-04-25 01:00:00,895.0,0.0,8.32,5.56 -2016-04-25 02:00:00,773.0,0.0,8.56,5.91 -2016-04-25 03:00:00,740.0,0.0,8.81,6.26 -2016-04-25 04:00:00,750.0,0.0,8.92,6.85 -2016-04-25 05:00:00,738.0,0.0,9.04,7.43 -2016-04-25 06:00:00,735.0,26.24,9.15,8.01 -2016-04-25 07:00:00,796.0,149.59,9.29,8.4 -2016-04-25 08:00:00,903.0,361.16,9.43,8.79 -2016-04-25 09:00:00,976.0,497.44,9.57,9.17 -2016-04-25 10:00:00,995.0,712.31,9.63,9.29 -2016-04-25 11:00:00,953.0,591.24,9.68,9.4 -2016-04-25 12:00:00,953.0,189.16,9.73,9.52 -2016-04-25 13:00:00,851.0,380.48,9.74,9.71 -2016-04-25 14:00:00,774.0,684.93,9.75,9.89 -2016-04-25 15:00:00,769.0,624.94,9.77,10.08 -2016-04-25 16:00:00,767.0,306.96,9.74,9.87 -2016-04-25 17:00:00,774.0,123.11,9.71,9.65 -2016-04-25 18:00:00,829.0,30.76,9.68,9.43 -2016-04-25 19:00:00,938.0,1.15,9.68,9.12 -2016-04-25 20:00:00,940.0,0.0,9.68,8.81 -2016-04-25 21:00:00,941.0,0.0,9.68,8.5 -2016-04-25 22:00:00,1233.0,0.0,9.66,8.1 -2016-04-25 23:00:00,1227.0,0.0,9.64,7.7 -2016-04-26 00:00:00,1055.0,0.0,9.62,7.3 -2016-04-26 01:00:00,929.0,0.0,9.74,7.05 -2016-04-26 02:00:00,841.0,0.0,9.86,6.8 -2016-04-26 03:00:00,792.0,0.0,9.98,6.55 -2016-04-26 04:00:00,781.0,0.0,9.88,7.0 -2016-04-26 05:00:00,768.0,0.0,9.79,7.45 -2016-04-26 06:00:00,801.0,25.17,9.7,7.9 -2016-04-26 07:00:00,877.0,75.16,9.45,8.35 -2016-04-26 08:00:00,948.0,163.45,9.2,8.8 -2016-04-26 09:00:00,984.0,169.63,8.94,9.24 -2016-04-26 10:00:00,1008.0,627.48,8.65,8.99 -2016-04-26 11:00:00,1017.0,571.52,8.36,8.74 -2016-04-26 12:00:00,1035.0,808.43,8.07,8.48 -2016-04-26 13:00:00,983.0,809.6,8.11,8.81 -2016-04-26 14:00:00,910.0,541.0,8.15,9.14 -2016-04-26 15:00:00,860.0,467.04,8.19,9.48 -2016-04-26 16:00:00,840.0,433.69,8.17,9.46 -2016-04-26 17:00:00,820.0,224.18,8.15,9.44 -2016-04-26 18:00:00,849.0,43.78,8.12,9.42 -2016-04-26 19:00:00,936.0,1.17,8.24,9.21 -2016-04-26 20:00:00,959.0,0.0,8.36,9.0 -2016-04-26 21:00:00,992.0,0.0,8.47,8.79 -2016-04-26 22:00:00,1319.0,0.0,8.5,8.12 -2016-04-26 23:00:00,1320.0,0.0,8.53,7.46 -2016-04-27 00:00:00,1142.0,0.0,8.56,6.8 -2016-04-27 01:00:00,998.0,0.0,8.69,6.91 -2016-04-27 02:00:00,907.0,0.0,8.82,7.01 -2016-04-27 03:00:00,849.0,0.0,8.95,7.12 -2016-04-27 04:00:00,865.0,0.0,8.88,6.98 -2016-04-27 05:00:00,872.0,0.0,8.81,6.84 -2016-04-27 06:00:00,870.0,23.73,8.74,6.7 -2016-04-27 07:00:00,967.0,129.79,8.61,6.56 -2016-04-27 08:00:00,1034.0,387.07,8.48,6.41 -2016-04-27 09:00:00,1116.0,325.01,8.35,6.26 -2016-04-27 10:00:00,1071.0,608.09,8.26,6.38 -2016-04-27 11:00:00,1018.0,830.78,8.17,6.5 -2016-04-27 12:00:00,1046.0,795.36,8.08,6.62 -2016-04-27 13:00:00,1040.0,555.96,8.12,7.0 -2016-04-27 14:00:00,961.0,759.53,8.17,7.38 -2016-04-27 15:00:00,897.0,571.84,8.22,7.77 -2016-04-27 16:00:00,877.0,449.07,8.22,8.04 -2016-04-27 17:00:00,886.0,153.22,8.22,8.31 -2016-04-27 18:00:00,961.0,47.43,8.21,8.58 -2016-04-27 19:00:00,1033.0,1.17,8.22,8.4 -2016-04-27 20:00:00,1049.0,0.0,8.22,8.23 -2016-04-27 21:00:00,1051.0,0.0,8.22,8.06 -2016-04-27 22:00:00,1376.0,0.0,8.27,7.62 -2016-04-27 23:00:00,1378.0,0.0,8.31,7.18 -2016-04-28 00:00:00,1158.0,0.0,8.36,6.74 -2016-04-28 01:00:00,1050.0,0.0,8.52,6.16 -2016-04-28 02:00:00,954.0,0.0,8.68,5.58 -2016-04-28 03:00:00,900.0,0.0,8.84,4.99 -2016-04-28 04:00:00,904.0,0.0,8.93,5.12 -2016-04-28 05:00:00,903.0,0.0,9.03,5.25 -2016-04-28 06:00:00,923.0,20.01,9.12,5.38 -2016-04-28 07:00:00,992.0,165.33,9.25,5.52 -2016-04-28 08:00:00,1089.0,392.16,9.38,5.66 -2016-04-28 09:00:00,1149.0,585.67,9.52,5.81 -2016-04-28 10:00:00,1102.0,740.66,9.61,6.08 -2016-04-28 11:00:00,1072.0,843.96,9.7,6.36 -2016-04-28 12:00:00,1019.0,784.06,9.79,6.63 -2016-04-28 13:00:00,941.0,860.55,9.56,7.23 -2016-04-28 14:00:00,832.0,275.18,9.33,7.82 -2016-04-28 15:00:00,784.0,231.82,9.1,8.41 -2016-04-28 16:00:00,758.0,322.53,9.15,8.78 -2016-04-28 17:00:00,718.0,201.57,9.21,9.14 -2016-04-28 18:00:00,800.0,49.06,9.26,9.5 -2016-04-28 19:00:00,889.0,3.33,9.43,9.65 -2016-04-28 20:00:00,905.0,0.0,9.6,9.79 -2016-04-28 21:00:00,919.0,0.0,9.77,9.93 -2016-04-28 22:00:00,1210.0,0.0,9.67,9.64 -2016-04-28 23:00:00,1223.0,0.0,9.56,9.35 -2016-04-29 00:00:00,1048.0,0.0,9.46,9.06 -2016-04-29 01:00:00,929.0,0.0,9.27,8.95 -2016-04-29 02:00:00,840.0,0.0,9.08,8.83 -2016-04-29 03:00:00,783.0,0.0,8.9,8.72 -2016-04-29 04:00:00,794.0,0.0,8.96,8.57 -2016-04-29 05:00:00,813.0,0.0,9.02,8.43 -2016-04-29 06:00:00,806.0,22.88,9.08,8.29 -2016-04-29 07:00:00,884.0,82.71,9.11,7.57 -2016-04-29 08:00:00,911.0,61.05,9.14,6.85 -2016-04-29 09:00:00,935.0,98.04,9.18,6.12 -2016-04-29 10:00:00,938.0,185.46,9.19,5.67 -2016-04-29 11:00:00,951.0,305.81,9.2,5.22 -2016-04-29 12:00:00,951.0,144.26,9.21,4.77 -2016-04-29 13:00:00,943.0,222.21,8.9,5.28 -2016-04-29 14:00:00,873.0,300.33,8.6,5.79 -2016-04-29 15:00:00,881.0,392.08,8.3,6.3 -2016-04-29 16:00:00,872.0,67.59,8.14,6.47 -2016-04-29 17:00:00,897.0,43.68,7.98,6.63 -2016-04-29 18:00:00,973.0,45.35,7.81,6.8 -2016-04-29 19:00:00,1022.0,1.58,7.91,7.26 -2016-04-29 20:00:00,1078.0,0.0,8.01,7.72 -2016-04-29 21:00:00,1090.0,0.0,8.11,8.18 -2016-04-29 22:00:00,1368.0,0.0,8.28,8.14 -2016-04-29 23:00:00,1375.0,0.0,8.45,8.11 -2016-04-30 00:00:00,1234.0,0.0,8.62,8.07 -2016-04-30 01:00:00,1061.0,0.0,8.94,8.3 -2016-04-30 02:00:00,954.0,0.0,9.27,8.54 -2016-04-30 03:00:00,927.0,0.0,9.6,8.77 -2016-04-30 04:00:00,917.0,0.0,9.52,8.46 -2016-04-30 05:00:00,938.0,0.0,9.45,8.15 -2016-04-30 06:00:00,945.0,18.8,9.38,7.83 -2016-04-30 07:00:00,991.0,155.89,9.42,7.47 -2016-04-30 08:00:00,1090.0,379.26,9.46,7.1 -2016-04-30 09:00:00,1105.0,582.84,9.49,6.73 -2016-04-30 10:00:00,1062.0,735.88,9.56,6.63 -2016-04-30 11:00:00,1014.0,795.34,9.62,6.52 -2016-04-30 12:00:00,941.0,874.35,9.68,6.41 -2016-04-30 13:00:00,878.0,834.34,9.68,6.34 -2016-04-30 14:00:00,805.0,778.14,9.68,6.28 -2016-04-30 15:00:00,760.0,613.38,9.69,6.21 -2016-04-30 16:00:00,739.0,445.17,9.66,6.47 -2016-04-30 17:00:00,746.0,238.43,9.64,6.73 -2016-04-30 18:00:00,782.0,39.41,9.61,6.99 -2016-04-30 19:00:00,852.0,4.79,9.59,6.33 -2016-04-30 20:00:00,861.0,0.0,9.56,5.67 -2016-04-30 21:00:00,871.0,0.0,9.53,5.01 -2016-04-30 22:00:00,1232.0,0.0,9.51,4.55 -2016-04-30 23:00:00,1256.0,0.0,9.49,4.1 -2016-05-01 00:00:00,1103.0,0.0,9.47,3.64 -2016-05-01 01:00:00,926.0,0.0,9.66,4.13 -2016-05-01 02:00:00,810.0,0.0,9.84,4.62 -2016-05-01 03:00:00,795.0,0.0,10.03,5.1 -2016-05-01 04:00:00,783.0,0.0,10.11,5.45 -2016-05-01 05:00:00,778.0,0.0,10.19,5.8 -2016-05-01 06:00:00,780.0,8.97,10.27,6.15 -2016-05-01 07:00:00,801.0,40.16,10.41,6.56 -2016-05-01 08:00:00,886.0,103.17,10.56,6.97 -2016-05-01 09:00:00,972.0,154.76,10.7,7.38 -2016-05-01 10:00:00,1015.0,186.99,10.69,7.34 -2016-05-01 11:00:00,1045.0,282.65,10.69,7.3 -2016-05-01 12:00:00,1069.0,224.32,10.68,7.26 -2016-05-01 13:00:00,1013.0,310.36,10.78,7.26 -2016-05-01 14:00:00,924.0,314.89,10.89,7.26 -2016-05-01 15:00:00,864.0,453.53,10.99,7.26 -2016-05-01 16:00:00,796.0,298.74,11.06,7.1 -2016-05-01 17:00:00,747.0,166.94,11.14,6.95 -2016-05-01 18:00:00,769.0,57.52,11.22,6.8 -2016-05-01 19:00:00,806.0,2.38,11.24,6.83 -2016-05-01 20:00:00,838.0,0.0,11.27,6.86 -2016-05-01 21:00:00,850.0,0.0,11.3,6.9 -2016-05-01 22:00:00,1132.0,0.0,11.29,7.14 -2016-05-01 23:00:00,1135.0,0.0,11.28,7.39 -2016-05-02 00:00:00,981.0,0.0,11.27,7.64 -2016-05-02 01:00:00,863.0,0.0,11.24,7.83 -2016-05-02 02:00:00,784.0,0.0,11.21,8.01 -2016-05-02 03:00:00,723.0,0.0,11.18,8.19 -2016-05-02 04:00:00,728.0,0.0,11.19,8.43 -2016-05-02 05:00:00,701.0,0.0,11.2,8.67 -2016-05-02 06:00:00,697.0,26.14,11.21,8.91 -2016-05-02 07:00:00,733.0,93.44,11.22,9.12 -2016-05-02 08:00:00,821.0,135.7,11.24,9.33 -2016-05-02 09:00:00,890.0,75.25,11.25,9.54 -2016-05-02 10:00:00,881.0,98.14,11.19,9.77 -2016-05-02 11:00:00,898.0,87.88,11.14,10.0 -2016-05-02 12:00:00,925.0,63.65,11.09,10.22 -2016-05-02 13:00:00,875.0,221.38,11.06,9.63 -2016-05-02 14:00:00,795.0,433.49,11.03,9.03 -2016-05-02 15:00:00,776.0,569.67,11.0,8.44 -2016-05-02 16:00:00,789.0,447.36,10.93,8.24 -2016-05-02 17:00:00,799.0,221.82,10.85,8.04 -2016-05-02 18:00:00,894.0,44.98,10.77,7.83 -2016-05-02 19:00:00,952.0,6.3,10.66,7.74 -2016-05-02 20:00:00,932.0,0.0,10.54,7.64 -2016-05-02 21:00:00,895.0,0.0,10.42,7.54 -2016-05-02 22:00:00,1202.0,0.0,10.29,7.23 -2016-05-02 23:00:00,1192.0,0.0,10.16,6.91 -2016-05-03 00:00:00,1015.0,0.0,10.03,6.59 -2016-05-03 01:00:00,899.0,0.0,10.14,6.31 -2016-05-03 02:00:00,820.0,0.0,10.24,6.02 -2016-05-03 03:00:00,795.0,0.0,10.35,5.74 -2016-05-03 04:00:00,802.0,0.0,10.27,5.42 -2016-05-03 05:00:00,803.0,0.0,10.19,5.1 -2016-05-03 06:00:00,845.0,36.27,10.11,4.79 -2016-05-03 07:00:00,896.0,118.58,10.16,4.23 -2016-05-03 08:00:00,912.0,342.19,10.21,3.66 -2016-05-03 09:00:00,920.0,488.17,10.26,3.1 -2016-05-03 10:00:00,874.0,691.57,10.24,2.69 -2016-05-03 11:00:00,815.0,769.55,10.22,2.28 -2016-05-03 12:00:00,816.0,788.36,10.2,1.86 -2016-05-03 13:00:00,760.0,767.87,10.38,1.62 -2016-05-03 14:00:00,704.0,652.28,10.56,1.37 -2016-05-03 15:00:00,685.0,602.92,10.74,1.13 -2016-05-03 16:00:00,679.0,436.23,10.79,1.1 -2016-05-03 17:00:00,643.0,227.01,10.84,1.08 -2016-05-03 18:00:00,699.0,44.76,10.89,1.05 -2016-05-03 19:00:00,742.0,6.81,10.86,1.32 -2016-05-03 20:00:00,756.0,0.0,10.84,1.59 -2016-05-03 21:00:00,761.0,0.0,10.82,1.86 -2016-05-03 22:00:00,1104.0,0.0,10.75,2.44 -2016-05-03 23:00:00,1108.0,0.0,10.67,3.01 -2016-05-04 00:00:00,928.0,0.0,10.6,3.59 -2016-05-04 01:00:00,796.0,0.0,10.42,4.06 -2016-05-04 02:00:00,716.0,0.0,10.23,4.52 -2016-05-04 03:00:00,675.0,0.0,10.05,4.99 -2016-05-04 04:00:00,680.0,0.0,10.06,5.15 -2016-05-04 05:00:00,694.0,0.0,10.08,5.31 -2016-05-04 06:00:00,708.0,23.98,10.09,5.48 -2016-05-04 07:00:00,775.0,111.87,10.4,5.4 -2016-05-04 08:00:00,815.0,381.89,10.71,5.33 -2016-05-04 09:00:00,847.0,570.9,11.02,5.26 -2016-05-04 10:00:00,832.0,697.87,11.27,4.65 -2016-05-04 11:00:00,745.0,800.33,11.53,4.04 -2016-05-04 12:00:00,738.0,824.93,11.78,3.43 -2016-05-04 13:00:00,691.0,808.63,12.03,3.22 -2016-05-04 14:00:00,643.0,751.12,12.27,3.01 -2016-05-04 15:00:00,602.0,613.36,12.52,2.8 -2016-05-04 16:00:00,591.0,441.93,12.57,2.88 -2016-05-04 17:00:00,604.0,230.78,12.63,2.96 -2016-05-04 18:00:00,648.0,42.8,12.69,3.03 -2016-05-04 19:00:00,706.0,7.28,12.69,3.17 -2016-05-04 20:00:00,741.0,0.0,12.7,3.31 -2016-05-04 21:00:00,774.0,0.0,12.71,3.45 -2016-05-04 22:00:00,1155.0,0.0,12.67,3.59 -2016-05-04 23:00:00,1188.0,0.0,12.62,3.73 -2016-05-05 00:00:00,961.0,0.0,12.58,3.88 -2016-05-05 01:00:00,767.0,0.0,12.37,3.82 -2016-05-05 02:00:00,670.0,0.0,12.17,3.77 -2016-05-05 03:00:00,632.0,0.0,11.96,3.71 -2016-05-05 04:00:00,630.0,0.0,11.84,3.78 -2016-05-05 05:00:00,606.0,0.0,11.71,3.86 -2016-05-05 06:00:00,602.0,26.69,11.58,3.93 -2016-05-05 07:00:00,653.0,170.07,11.71,4.05 -2016-05-05 08:00:00,727.0,381.19,11.83,4.16 -2016-05-05 09:00:00,799.0,548.99,11.96,4.28 -2016-05-05 10:00:00,795.0,647.55,12.05,4.51 -2016-05-05 11:00:00,773.0,759.46,12.14,4.74 -2016-05-05 12:00:00,709.0,679.34,12.23,4.98 -2016-05-05 13:00:00,663.0,691.5,12.38,5.15 -2016-05-05 14:00:00,585.0,644.3,12.54,5.32 -2016-05-05 15:00:00,572.0,438.31,12.69,5.49 -2016-05-05 16:00:00,588.0,410.39,12.69,5.47 -2016-05-05 17:00:00,568.0,225.98,12.69,5.44 -2016-05-05 18:00:00,637.0,44.07,12.69,5.42 -2016-05-05 19:00:00,727.0,8.93,12.52,5.93 -2016-05-05 20:00:00,765.0,0.0,12.34,6.44 -2016-05-05 21:00:00,813.0,0.0,12.16,6.95 -2016-05-05 22:00:00,1166.0,0.0,12.07,7.13 -2016-05-05 23:00:00,1159.0,0.0,11.97,7.3 -2016-05-06 00:00:00,966.0,0.0,11.88,7.48 -2016-05-06 01:00:00,794.0,0.0,11.85,7.32 -2016-05-06 02:00:00,682.0,0.0,11.83,7.17 -2016-05-06 03:00:00,630.0,0.0,11.8,7.02 -2016-05-06 04:00:00,590.0,0.0,11.78,6.72 -2016-05-06 05:00:00,583.0,0.0,11.76,6.41 -2016-05-06 06:00:00,604.0,32.31,11.73,6.11 -2016-05-06 07:00:00,672.0,103.1,11.83,5.93 -2016-05-06 08:00:00,760.0,231.12,11.92,5.75 -2016-05-06 09:00:00,858.0,214.02,12.02,5.57 -2016-05-06 10:00:00,900.0,279.41,12.03,5.37 -2016-05-06 11:00:00,889.0,490.72,12.05,5.18 -2016-05-06 12:00:00,850.0,155.04,12.06,4.98 -2016-05-06 13:00:00,760.0,319.92,12.14,4.71 -2016-05-06 14:00:00,706.0,262.07,12.23,4.45 -2016-05-06 15:00:00,663.0,142.0,12.31,4.18 -2016-05-06 16:00:00,635.0,162.28,12.45,3.88 -2016-05-06 17:00:00,667.0,35.28,12.59,3.58 -2016-05-06 18:00:00,812.0,53.08,12.73,3.28 -2016-05-06 19:00:00,958.0,7.28,12.63,3.14 -2016-05-06 20:00:00,949.0,0.0,12.54,3.01 -2016-05-06 21:00:00,917.0,0.0,12.45,2.87 -2016-05-06 22:00:00,1215.0,0.0,12.32,2.6 -2016-05-06 23:00:00,1209.0,0.0,12.2,2.34 -2016-05-07 00:00:00,1007.0,0.0,12.07,2.07 -2016-05-07 01:00:00,820.0,0.0,12.16,2.99 -2016-05-07 02:00:00,702.0,0.0,12.24,3.91 -2016-05-07 03:00:00,645.0,0.0,12.33,4.83 -2016-05-07 04:00:00,623.0,0.0,12.27,4.67 -2016-05-07 05:00:00,609.0,0.0,12.21,4.51 -2016-05-07 06:00:00,610.0,34.38,12.14,4.36 -2016-05-07 07:00:00,669.0,138.58,12.15,4.43 -2016-05-07 08:00:00,824.0,139.85,12.17,4.5 -2016-05-07 09:00:00,922.0,266.14,12.18,4.57 -2016-05-07 10:00:00,989.0,128.74,12.16,4.42 -2016-05-07 11:00:00,977.0,201.69,12.15,4.28 -2016-05-07 12:00:00,967.0,331.32,12.13,4.14 -2016-05-07 13:00:00,921.0,395.81,12.11,4.29 -2016-05-07 14:00:00,896.0,370.67,12.08,4.45 -2016-05-07 15:00:00,801.0,255.25,12.06,4.61 -2016-05-07 16:00:00,712.0,215.49,12.07,4.4 -2016-05-07 17:00:00,673.0,136.08,12.09,4.2 -2016-05-07 18:00:00,730.0,58.57,12.1,4.0 -2016-05-07 19:00:00,822.0,8.95,12.11,3.72 -2016-05-07 20:00:00,815.0,0.0,12.11,3.45 -2016-05-07 21:00:00,812.0,0.0,12.12,3.17 -2016-05-07 22:00:00,1155.0,0.0,12.21,3.45 -2016-05-07 23:00:00,1157.0,0.0,12.3,3.72 -2016-05-08 00:00:00,971.0,0.0,12.39,4.0 -2016-05-08 01:00:00,772.0,0.0,12.62,4.64 -2016-05-08 02:00:00,658.0,0.0,12.85,5.28 -2016-05-08 03:00:00,613.0,0.0,13.08,5.92 -2016-05-08 04:00:00,599.0,0.0,13.34,6.99 -2016-05-08 05:00:00,577.0,0.0,13.59,8.07 -2016-05-08 06:00:00,585.0,9.39,13.84,9.14 -2016-05-08 07:00:00,600.0,30.81,13.9,9.59 -2016-05-08 08:00:00,747.0,87.47,13.97,10.03 -2016-05-08 09:00:00,814.0,67.33,14.03,10.47 -2016-05-08 10:00:00,865.0,296.9,14.02,10.29 -2016-05-08 11:00:00,901.0,240.44,14.02,10.11 -2016-05-08 12:00:00,831.0,155.26,14.02,9.93 -2016-05-08 13:00:00,710.0,116.33,14.22,9.6 -2016-05-08 14:00:00,670.0,115.41,14.42,9.28 -2016-05-08 15:00:00,631.0,79.39,14.62,8.95 -2016-05-08 16:00:00,605.0,39.36,14.57,9.04 -2016-05-08 17:00:00,604.0,21.64,14.52,9.13 -2016-05-08 18:00:00,642.0,7.58,14.48,9.21 -2016-05-08 19:00:00,689.0,2.77,14.12,9.33 -2016-05-08 20:00:00,704.0,0.0,13.77,9.44 -2016-05-08 21:00:00,679.0,0.0,13.42,9.56 -2016-05-08 22:00:00,981.0,0.0,13.47,9.44 -2016-05-08 23:00:00,999.0,0.0,13.52,9.33 -2016-05-09 00:00:00,827.0,0.0,13.57,9.21 -2016-05-09 01:00:00,696.0,0.0,13.67,8.76 -2016-05-09 02:00:00,591.0,0.0,13.76,8.31 -2016-05-09 03:00:00,549.0,0.0,13.86,7.86 -2016-05-09 04:00:00,551.0,0.0,13.76,7.9 -2016-05-09 05:00:00,534.0,0.0,13.65,7.94 -2016-05-09 06:00:00,526.0,8.21,13.54,7.97 -2016-05-09 07:00:00,579.0,125.29,13.53,7.78 -2016-05-09 08:00:00,636.0,183.69,13.53,7.6 -2016-05-09 09:00:00,706.0,507.2,13.52,7.41 -2016-05-09 10:00:00,676.0,626.64,13.38,6.79 -2016-05-09 11:00:00,650.0,423.16,13.25,6.17 -2016-05-09 12:00:00,666.0,416.37,13.11,5.56 -2016-05-09 13:00:00,602.0,732.76,13.05,5.29 -2016-05-09 14:00:00,573.0,736.62,13.0,5.02 -2016-05-09 15:00:00,560.0,557.19,12.94,4.74 -2016-05-09 16:00:00,529.0,344.94,12.86,4.32 -2016-05-09 17:00:00,514.0,148.86,12.78,3.89 -2016-05-09 18:00:00,520.0,57.61,12.69,3.46 -2016-05-09 19:00:00,561.0,11.77,12.68,3.31 -2016-05-09 20:00:00,580.0,0.0,12.67,3.16 -2016-05-09 21:00:00,545.0,0.0,12.66,3.01 -2016-05-09 22:00:00,887.0,0.0,12.61,2.75 -2016-05-09 23:00:00,877.0,0.0,12.56,2.5 -2016-05-10 00:00:00,713.0,0.0,12.51,2.25 -2016-05-10 01:00:00,615.0,0.0,12.51,1.64 -2016-05-10 02:00:00,509.0,0.0,12.51,1.03 -2016-05-10 03:00:00,476.0,0.0,12.51,0.43 -2016-05-10 04:00:00,472.0,0.0,12.39,1.14 -2016-05-10 05:00:00,478.0,1.12,12.27,1.85 -2016-05-10 06:00:00,469.0,15.81,12.15,2.57 -2016-05-10 07:00:00,521.0,15.14,12.31,3.01 -2016-05-10 08:00:00,594.0,144.77,12.48,3.45 -2016-05-10 09:00:00,639.0,138.56,12.64,3.89 -2016-05-10 10:00:00,643.0,132.95,12.63,3.48 -2016-05-10 11:00:00,641.0,333.72,12.63,3.07 -2016-05-10 12:00:00,630.0,455.58,12.62,2.66 -2016-05-10 13:00:00,564.0,428.89,12.73,1.98 -2016-05-10 14:00:00,518.0,506.1,12.84,1.29 -2016-05-10 15:00:00,497.0,328.17,12.95,0.61 -2016-05-10 16:00:00,486.0,212.87,13.16,1.41 -2016-05-10 17:00:00,459.0,126.05,13.37,2.22 -2016-05-10 18:00:00,494.0,54.33,13.57,3.02 -2016-05-10 19:00:00,532.0,12.31,13.26,3.27 -2016-05-10 20:00:00,544.0,0.0,12.94,3.53 -2016-05-10 21:00:00,539.0,0.0,12.63,3.78 -2016-05-10 22:00:00,856.0,0.0,12.38,3.5 -2016-05-10 23:00:00,855.0,0.0,12.13,3.23 -2016-05-11 00:00:00,702.0,0.0,11.88,2.95 -2016-05-11 01:00:00,544.0,0.0,11.94,2.96 -2016-05-11 02:00:00,467.0,0.0,12.0,2.97 -2016-05-11 03:00:00,431.0,0.0,12.06,2.98 -2016-05-11 04:00:00,442.0,0.0,11.91,3.26 -2016-05-11 05:00:00,433.0,1.94,11.77,3.54 -2016-05-11 06:00:00,436.0,40.23,11.62,3.82 -2016-05-11 07:00:00,487.0,82.55,11.72,3.45 -2016-05-11 08:00:00,560.0,160.66,11.82,3.09 -2016-05-11 09:00:00,617.0,181.01,11.92,2.72 -2016-05-11 10:00:00,632.0,176.31,11.77,3.26 -2016-05-11 11:00:00,654.0,246.06,11.61,3.81 -2016-05-11 12:00:00,656.0,214.58,11.46,4.36 -2016-05-11 13:00:00,589.0,248.42,11.69,4.41 -2016-05-11 14:00:00,557.0,177.93,11.93,4.46 -2016-05-11 15:00:00,518.0,233.19,12.16,4.51 -2016-05-11 16:00:00,489.0,162.36,12.07,4.51 -2016-05-11 17:00:00,492.0,49.49,11.98,4.5 -2016-05-11 18:00:00,551.0,26.09,11.9,4.5 -2016-05-11 19:00:00,616.0,0.0,11.85,4.13 -2016-05-11 20:00:00,649.0,0.0,11.8,3.76 -2016-05-11 21:00:00,618.0,0.0,11.75,3.39 -2016-05-11 22:00:00,900.0,0.0,11.69,3.66 -2016-05-11 23:00:00,898.0,0.0,11.63,3.94 -2016-05-12 00:00:00,719.0,0.0,11.57,4.21 -2016-05-12 01:00:00,608.0,0.0,11.65,4.15 -2016-05-12 02:00:00,532.0,0.0,11.73,4.1 -2016-05-12 03:00:00,477.0,0.0,11.81,4.04 -2016-05-12 04:00:00,479.0,0.0,11.8,3.54 -2016-05-12 05:00:00,471.0,2.37,11.78,3.04 -2016-05-12 06:00:00,460.0,39.17,11.76,2.54 -2016-05-12 07:00:00,503.0,96.92,11.8,2.24 -2016-05-12 08:00:00,602.0,74.53,11.84,1.95 -2016-05-12 09:00:00,645.0,128.93,11.88,1.66 -2016-05-12 10:00:00,670.0,111.73,11.87,1.62 -2016-05-12 11:00:00,708.0,193.19,11.87,1.59 -2016-05-12 12:00:00,722.0,216.75,11.86,1.56 -2016-05-12 13:00:00,656.0,250.17,11.94,2.06 -2016-05-12 14:00:00,639.0,265.37,12.02,2.55 -2016-05-12 15:00:00,612.0,255.13,12.1,3.05 -2016-05-12 16:00:00,584.0,73.04,12.12,3.37 -2016-05-12 17:00:00,552.0,33.18,12.15,3.68 -2016-05-12 18:00:00,597.0,21.88,12.18,4.0 -2016-05-12 19:00:00,643.0,4.71,12.15,3.96 -2016-05-12 20:00:00,626.0,0.0,12.12,3.92 -2016-05-12 21:00:00,624.0,0.0,12.1,3.88 -2016-05-12 22:00:00,931.0,0.0,12.05,4.0 -2016-05-12 23:00:00,939.0,0.0,11.99,4.11 -2016-05-13 00:00:00,751.0,0.0,11.94,4.23 -2016-05-13 01:00:00,625.0,0.0,11.94,4.11 -2016-05-13 02:00:00,526.0,0.0,11.93,3.98 -2016-05-13 03:00:00,498.0,0.0,11.93,3.85 -2016-05-13 04:00:00,500.0,0.0,11.92,4.0 -2016-05-13 05:00:00,482.0,0.75,11.91,4.14 -2016-05-13 06:00:00,483.0,40.29,11.89,4.29 -2016-05-13 07:00:00,559.0,102.58,12.01,3.96 -2016-05-13 08:00:00,622.0,162.08,12.12,3.63 -2016-05-13 09:00:00,661.0,286.88,12.24,3.3 -2016-05-13 10:00:00,663.0,269.52,12.26,3.23 -2016-05-13 11:00:00,694.0,358.59,12.29,3.17 -2016-05-13 12:00:00,716.0,364.76,12.31,3.1 -2016-05-13 13:00:00,668.0,374.91,12.46,3.29 -2016-05-13 14:00:00,632.0,701.84,12.62,3.48 -2016-05-13 15:00:00,590.0,580.01,12.77,3.67 -2016-05-13 16:00:00,558.0,408.81,12.76,3.66 -2016-05-13 17:00:00,546.0,221.58,12.75,3.64 -2016-05-13 18:00:00,590.0,66.38,12.73,3.63 -2016-05-13 19:00:00,640.0,15.34,12.7,3.7 -2016-05-13 20:00:00,650.0,0.0,12.66,3.77 -2016-05-13 21:00:00,662.0,0.0,12.63,3.85 -2016-05-13 22:00:00,1025.0,0.0,12.6,3.84 -2016-05-13 23:00:00,1035.0,0.0,12.57,3.84 -2016-05-14 00:00:00,830.0,0.0,12.54,3.83 -2016-05-14 01:00:00,662.0,0.0,12.52,3.88 -2016-05-14 02:00:00,579.0,0.0,12.5,3.93 -2016-05-14 03:00:00,546.0,0.0,12.48,3.97 -2016-05-14 04:00:00,534.0,0.0,12.47,4.45 -2016-05-14 05:00:00,529.0,3.73,12.46,4.93 -2016-05-14 06:00:00,532.0,46.61,12.44,5.41 -2016-05-14 07:00:00,579.0,132.83,12.5,5.55 -2016-05-14 08:00:00,647.0,211.04,12.55,5.7 -2016-05-14 09:00:00,751.0,230.63,12.61,5.85 -2016-05-14 10:00:00,781.0,246.86,12.66,5.72 -2016-05-14 11:00:00,772.0,327.7,12.71,5.59 -2016-05-14 12:00:00,751.0,302.53,12.76,5.46 -2016-05-14 13:00:00,700.0,342.64,12.8,5.2 -2016-05-14 14:00:00,636.0,442.95,12.83,4.93 -2016-05-14 15:00:00,594.0,396.14,12.87,4.66 -2016-05-14 16:00:00,555.0,330.73,12.82,4.36 -2016-05-14 17:00:00,533.0,221.32,12.77,4.06 -2016-05-14 18:00:00,597.0,73.28,12.73,3.75 -2016-05-14 19:00:00,701.0,16.57,12.55,3.42 -2016-05-14 20:00:00,759.0,0.0,12.38,3.08 -2016-05-14 21:00:00,781.0,0.0,12.21,2.74 -2016-05-14 22:00:00,1112.0,0.0,12.05,2.34 -2016-05-14 23:00:00,1163.0,0.0,11.89,1.93 -2016-05-15 00:00:00,899.0,0.0,11.73,1.52 -2016-05-15 01:00:00,715.0,0.0,11.67,1.3 -2016-05-15 02:00:00,642.0,0.0,11.61,1.09 -2016-05-15 03:00:00,575.0,0.0,11.55,0.87 -2016-05-15 04:00:00,548.0,0.0,11.48,0.99 -2016-05-15 05:00:00,532.0,1.94,11.41,1.12 -2016-05-15 06:00:00,540.0,37.46,11.34,1.24 -2016-05-15 07:00:00,567.0,84.73,11.4,1.78 -2016-05-15 08:00:00,673.0,193.98,11.46,2.32 -2016-05-15 09:00:00,792.0,359.41,11.52,2.86 -2016-05-15 10:00:00,892.0,355.71,11.48,3.04 -2016-05-15 11:00:00,892.0,310.69,11.45,3.22 -2016-05-15 12:00:00,841.0,208.98,11.41,3.41 -2016-05-15 13:00:00,756.0,642.55,11.65,3.11 -2016-05-15 14:00:00,718.0,426.96,11.89,2.81 -2016-05-15 15:00:00,676.0,150.24,12.13,2.51 -2016-05-15 16:00:00,619.0,74.52,12.11,2.65 -2016-05-15 17:00:00,620.0,72.23,12.09,2.8 -2016-05-15 18:00:00,660.0,52.45,12.06,2.94 -2016-05-15 19:00:00,782.0,10.08,11.99,3.09 -2016-05-15 20:00:00,816.0,0.0,11.91,3.25 -2016-05-15 21:00:00,781.0,0.0,11.84,3.41 -2016-05-15 22:00:00,1112.0,0.0,11.75,3.47 -2016-05-15 23:00:00,1116.0,0.0,11.67,3.53 -2016-05-16 00:00:00,919.0,0.0,11.58,3.59 -2016-05-16 01:00:00,735.0,0.0,11.54,3.26 -2016-05-16 02:00:00,651.0,0.0,11.5,2.94 -2016-05-16 03:00:00,591.0,0.0,11.46,2.62 -2016-05-16 04:00:00,573.0,0.0,11.46,2.61 -2016-05-16 05:00:00,545.0,0.76,11.46,2.6 -2016-05-16 06:00:00,546.0,27.54,11.45,2.59 -2016-05-16 07:00:00,570.0,71.4,11.5,2.73 -2016-05-16 08:00:00,670.0,110.71,11.56,2.86 -2016-05-16 09:00:00,739.0,194.32,11.61,2.99 -2016-05-16 10:00:00,766.0,294.29,11.59,3.13 -2016-05-16 11:00:00,730.0,538.54,11.58,3.27 -2016-05-16 12:00:00,701.0,817.43,11.56,3.41 -2016-05-16 13:00:00,637.0,800.99,11.74,3.59 -2016-05-16 14:00:00,607.0,499.76,11.91,3.77 -2016-05-16 15:00:00,578.0,576.84,12.09,3.96 -2016-05-16 16:00:00,511.0,231.71,12.08,3.94 -2016-05-16 17:00:00,476.0,130.19,12.07,3.92 -2016-05-16 18:00:00,502.0,25.36,12.06,3.9 -2016-05-16 19:00:00,534.0,17.85,12.04,3.71 -2016-05-16 20:00:00,562.0,0.0,12.02,3.52 -2016-05-16 21:00:00,575.0,0.0,12.0,3.32 -2016-05-16 22:00:00,906.0,0.0,11.95,3.43 -2016-05-16 23:00:00,944.0,0.0,11.91,3.54 -2016-05-17 00:00:00,761.0,0.0,11.86,3.66 -2016-05-17 01:00:00,609.0,0.0,11.8,3.81 -2016-05-17 02:00:00,511.0,0.0,11.75,3.97 -2016-05-17 03:00:00,463.0,0.0,11.69,4.12 -2016-05-17 04:00:00,458.0,0.0,11.74,4.57 -2016-05-17 05:00:00,442.0,0.0,11.79,5.01 -2016-05-17 06:00:00,432.0,11.34,11.84,5.45 -2016-05-17 07:00:00,478.0,90.12,11.99,5.66 -2016-05-17 08:00:00,547.0,259.58,12.13,5.87 -2016-05-17 09:00:00,620.0,364.3,12.28,6.08 -2016-05-17 10:00:00,673.0,477.85,12.31,6.38 -2016-05-17 11:00:00,643.0,460.79,12.33,6.67 -2016-05-17 12:00:00,605.0,372.6,12.36,6.97 -2016-05-17 13:00:00,577.0,615.1,12.38,7.2 -2016-05-17 14:00:00,556.0,575.85,12.4,7.43 -2016-05-17 15:00:00,509.0,291.86,12.42,7.66 -2016-05-17 16:00:00,489.0,101.33,12.5,7.69 -2016-05-17 17:00:00,477.0,157.09,12.58,7.73 -2016-05-17 18:00:00,477.0,71.52,12.65,7.77 -2016-05-17 19:00:00,526.0,16.58,12.68,8.0 -2016-05-17 20:00:00,547.0,0.0,12.7,8.23 -2016-05-17 21:00:00,545.0,0.0,12.73,8.46 -2016-05-17 22:00:00,869.0,0.0,12.73,9.09 -2016-05-17 23:00:00,856.0,0.0,12.74,9.73 -2016-05-18 00:00:00,661.0,0.0,12.74,10.37 -2016-05-18 01:00:00,546.0,0.0,12.66,10.65 -2016-05-18 02:00:00,470.0,0.0,12.59,10.92 -2016-05-18 03:00:00,432.0,0.0,12.51,11.2 -2016-05-18 04:00:00,434.0,0.0,12.54,11.54 -2016-05-18 05:00:00,429.0,0.0,12.57,11.88 -2016-05-18 06:00:00,422.0,49.55,12.6,12.22 -2016-05-18 07:00:00,491.0,184.39,12.44,12.22 -2016-05-18 08:00:00,545.0,386.53,12.29,12.22 -2016-05-18 09:00:00,648.0,488.58,12.13,12.22 -2016-05-18 10:00:00,673.0,729.58,11.96,12.42 -2016-05-18 11:00:00,645.0,799.04,11.8,12.63 -2016-05-18 12:00:00,649.0,579.03,11.63,12.83 -2016-05-18 13:00:00,583.0,785.57,11.93,12.13 -2016-05-18 14:00:00,542.0,653.64,12.24,11.43 -2016-05-18 15:00:00,528.0,565.94,12.54,10.73 -2016-05-18 16:00:00,519.0,451.56,12.56,10.5 -2016-05-18 17:00:00,486.0,241.18,12.57,10.27 -2016-05-18 18:00:00,498.0,48.51,12.59,10.04 -2016-05-18 19:00:00,559.0,17.83,12.62,9.84 -2016-05-18 20:00:00,549.0,0.0,12.65,9.65 -2016-05-18 21:00:00,543.0,0.0,12.69,9.45 -2016-05-18 22:00:00,869.0,0.0,12.64,9.18 -2016-05-18 23:00:00,858.0,0.0,12.58,8.91 -2016-05-19 00:00:00,681.0,0.0,12.53,8.65 -2016-05-19 01:00:00,572.0,0.0,12.41,8.56 -2016-05-19 02:00:00,489.0,0.0,12.3,8.46 -2016-05-19 03:00:00,461.0,0.0,12.18,8.37 -2016-05-19 04:00:00,468.0,0.0,12.18,7.93 -2016-05-19 05:00:00,447.0,0.75,12.18,7.49 -2016-05-19 06:00:00,430.0,51.61,12.18,7.05 -2016-05-19 07:00:00,482.0,36.1,12.35,6.68 -2016-05-19 08:00:00,548.0,75.42,12.52,6.31 -2016-05-19 09:00:00,603.0,133.43,12.69,5.94 -2016-05-19 10:00:00,620.0,191.9,12.69,6.31 -2016-05-19 11:00:00,636.0,105.87,12.7,6.67 -2016-05-19 12:00:00,657.0,191.53,12.7,7.03 -2016-05-19 13:00:00,623.0,154.25,12.82,7.77 -2016-05-19 14:00:00,603.0,172.58,12.94,8.51 -2016-05-19 15:00:00,616.0,120.13,13.06,9.24 -2016-05-19 16:00:00,590.0,106.69,13.05,9.29 -2016-05-19 17:00:00,577.0,61.49,13.04,9.34 -2016-05-19 18:00:00,621.0,6.5,13.02,9.39 -2016-05-19 19:00:00,644.0,15.94,13.04,9.12 -2016-05-19 20:00:00,662.0,0.0,13.06,8.85 -2016-05-19 21:00:00,638.0,0.0,13.08,8.58 -2016-05-19 22:00:00,956.0,0.0,13.08,8.33 -2016-05-19 23:00:00,899.0,0.0,13.07,8.07 -2016-05-20 00:00:00,710.0,0.0,13.07,7.82 -2016-05-20 01:00:00,591.0,0.0,13.09,7.45 -2016-05-20 02:00:00,510.0,0.0,13.12,7.09 -2016-05-20 03:00:00,475.0,0.0,13.14,6.72 -2016-05-20 04:00:00,472.0,0.0,13.09,6.34 -2016-05-20 05:00:00,461.0,4.19,13.03,5.97 -2016-05-20 06:00:00,454.0,56.32,12.97,5.6 -2016-05-20 07:00:00,504.0,165.82,12.95,5.65 -2016-05-20 08:00:00,573.0,157.45,12.93,5.7 -2016-05-20 09:00:00,596.0,341.32,12.91,5.75 -2016-05-20 10:00:00,623.0,229.74,12.86,6.11 -2016-05-20 11:00:00,602.0,389.52,12.82,6.48 -2016-05-20 12:00:00,627.0,368.51,12.77,6.84 -2016-05-20 13:00:00,584.0,344.51,12.89,7.54 -2016-05-20 14:00:00,554.0,91.7,13.01,8.25 -2016-05-20 15:00:00,533.0,102.69,13.13,8.95 -2016-05-20 16:00:00,525.0,102.7,13.14,9.25 -2016-05-20 17:00:00,518.0,117.37,13.15,9.54 -2016-05-20 18:00:00,570.0,8.23,13.15,9.83 -2016-05-20 19:00:00,627.0,17.16,13.16,10.1 -2016-05-20 20:00:00,625.0,0.0,13.16,10.37 -2016-05-20 21:00:00,626.0,0.0,13.17,10.63 -2016-05-20 22:00:00,958.0,0.0,13.16,10.7 -2016-05-20 23:00:00,925.0,0.0,13.16,10.77 -2016-05-21 00:00:00,719.0,0.0,13.15,10.84 -2016-05-21 01:00:00,603.0,0.0,13.01,11.09 -2016-05-21 02:00:00,509.0,0.0,12.88,11.35 -2016-05-21 03:00:00,470.0,0.0,12.74,11.6 -2016-05-21 04:00:00,494.0,0.0,12.71,11.2 -2016-05-21 05:00:00,473.0,0.0,12.68,10.79 -2016-05-21 06:00:00,495.0,35.3,12.64,10.39 -2016-05-21 07:00:00,524.0,63.16,12.66,10.29 -2016-05-21 08:00:00,608.0,49.45,12.67,10.2 -2016-05-21 09:00:00,667.0,125.97,12.69,10.11 -2016-05-21 10:00:00,713.0,119.61,12.66,9.84 -2016-05-21 11:00:00,723.0,125.96,12.64,9.57 -2016-05-21 12:00:00,804.0,131.51,12.62,9.3 -2016-05-21 13:00:00,750.0,200.5,12.51,9.17 -2016-05-21 14:00:00,769.0,257.55,12.4,9.05 -2016-05-21 15:00:00,754.0,236.03,12.29,8.92 -2016-05-21 16:00:00,742.0,428.16,12.43,8.15 -2016-05-21 17:00:00,735.0,205.81,12.57,7.37 -2016-05-21 18:00:00,745.0,80.29,12.72,6.59 -2016-05-21 19:00:00,781.0,21.57,12.74,5.88 -2016-05-21 20:00:00,724.0,0.0,12.76,5.17 -2016-05-21 21:00:00,724.0,0.0,12.78,4.46 -2016-05-21 22:00:00,1053.0,0.0,12.63,4.04 -2016-05-21 23:00:00,1011.0,0.0,12.49,3.63 -2016-05-22 00:00:00,818.0,0.0,12.34,3.21 -2016-05-22 01:00:00,665.0,0.0,12.41,4.41 -2016-05-22 02:00:00,577.0,0.0,12.49,5.6 -2016-05-22 03:00:00,526.0,0.0,12.56,6.8 -2016-05-22 04:00:00,538.0,0.0,12.52,6.62 -2016-05-22 05:00:00,541.0,8.38,12.47,6.44 -2016-05-22 06:00:00,541.0,32.83,12.42,6.26 -2016-05-22 07:00:00,553.0,185.36,12.19,6.19 -2016-05-22 08:00:00,640.0,367.93,11.95,6.11 -2016-05-22 09:00:00,723.0,575.77,11.72,6.04 -2016-05-22 10:00:00,739.0,720.68,11.51,6.17 -2016-05-22 11:00:00,680.0,824.83,11.3,6.3 -2016-05-22 12:00:00,653.0,820.83,11.09,6.43 -2016-05-22 13:00:00,591.0,820.12,11.12,7.15 -2016-05-22 14:00:00,531.0,741.4,11.14,7.87 -2016-05-22 15:00:00,493.0,614.82,11.17,8.59 -2016-05-22 16:00:00,466.0,451.88,11.41,8.53 -2016-05-22 17:00:00,447.0,245.37,11.65,8.47 -2016-05-22 18:00:00,486.0,56.02,11.89,8.41 -2016-05-22 19:00:00,542.0,22.96,11.99,8.34 -2016-05-22 20:00:00,552.0,0.0,12.09,8.26 -2016-05-22 21:00:00,524.0,0.0,12.19,8.18 -2016-05-22 22:00:00,849.0,0.0,12.19,8.14 -2016-05-22 23:00:00,881.0,0.0,12.2,8.11 -2016-05-23 00:00:00,693.0,0.0,12.2,8.07 -2016-05-23 01:00:00,560.0,0.0,12.31,7.82 -2016-05-23 02:00:00,476.0,0.0,12.41,7.57 -2016-05-23 03:00:00,444.0,0.0,12.52,7.32 -2016-05-23 04:00:00,455.0,0.0,12.48,7.08 -2016-05-23 05:00:00,444.0,9.5,12.45,6.83 -2016-05-23 06:00:00,428.0,41.9,12.41,6.58 -2016-05-23 07:00:00,472.0,129.43,12.4,6.01 -2016-05-23 08:00:00,546.0,299.46,12.39,5.44 -2016-05-23 09:00:00,589.0,412.21,12.38,4.87 -2016-05-23 10:00:00,596.0,706.85,12.3,4.82 -2016-05-23 11:00:00,590.0,802.28,12.21,4.78 -2016-05-23 12:00:00,595.0,837.72,12.13,4.73 -2016-05-23 13:00:00,553.0,815.47,12.1,4.76 -2016-05-23 14:00:00,497.0,756.83,12.07,4.79 -2016-05-23 15:00:00,482.0,622.08,12.04,4.81 -2016-05-23 16:00:00,479.0,445.23,12.02,4.87 -2016-05-23 17:00:00,428.0,236.85,12.0,4.92 -2016-05-23 18:00:00,482.0,59.13,11.98,4.98 -2016-05-23 19:00:00,535.0,23.58,11.93,4.88 -2016-05-23 20:00:00,536.0,0.0,11.88,4.79 -2016-05-23 21:00:00,523.0,0.0,11.83,4.69 -2016-05-23 22:00:00,857.0,0.0,11.78,4.7 -2016-05-23 23:00:00,844.0,0.0,11.74,4.71 -2016-05-24 00:00:00,649.0,0.0,11.69,4.72 -2016-05-24 01:00:00,526.0,0.0,11.55,4.87 -2016-05-24 02:00:00,442.0,0.0,11.41,5.03 -2016-05-24 03:00:00,422.0,0.0,11.27,5.19 -2016-05-24 04:00:00,429.0,0.0,11.33,5.62 -2016-05-24 05:00:00,427.0,10.13,11.39,6.06 -2016-05-24 06:00:00,426.0,36.4,11.44,6.5 -2016-05-24 07:00:00,495.0,150.19,11.71,6.69 -2016-05-24 08:00:00,583.0,343.18,11.99,6.88 -2016-05-24 09:00:00,598.0,528.44,12.26,7.08 -2016-05-24 10:00:00,603.0,633.28,12.51,7.37 -2016-05-24 11:00:00,571.0,535.3,12.77,7.67 -2016-05-24 12:00:00,629.0,499.72,13.02,7.97 -2016-05-24 13:00:00,551.0,557.45,13.27,8.03 -2016-05-24 14:00:00,491.0,572.2,13.52,8.08 -2016-05-24 15:00:00,480.0,537.8,13.77,8.14 -2016-05-24 16:00:00,464.0,260.02,13.84,8.78 -2016-05-24 17:00:00,473.0,157.95,13.91,9.42 -2016-05-24 18:00:00,526.0,67.4,13.98,10.06 -2016-05-24 19:00:00,583.0,11.12,13.98,10.17 -2016-05-24 20:00:00,602.0,0.0,13.98,10.28 -2016-05-24 21:00:00,576.0,0.0,13.97,10.39 -2016-05-24 22:00:00,875.0,0.0,13.9,10.47 -2016-05-24 23:00:00,878.0,0.0,13.82,10.56 -2016-05-25 00:00:00,691.0,0.0,13.75,10.65 -2016-05-25 01:00:00,560.0,0.0,13.58,10.48 -2016-05-25 02:00:00,488.0,0.0,13.41,10.31 -2016-05-25 03:00:00,432.0,0.0,13.24,10.14 -2016-05-25 04:00:00,450.0,0.0,13.04,10.17 -2016-05-25 05:00:00,444.0,5.19,12.84,10.2 -2016-05-25 06:00:00,445.0,51.26,12.63,10.23 -2016-05-25 07:00:00,509.0,113.94,12.67,10.27 -2016-05-25 08:00:00,592.0,124.41,12.71,10.31 -2016-05-25 09:00:00,616.0,347.1,12.75,10.34 -2016-05-25 10:00:00,641.0,400.18,12.9,10.2 -2016-05-25 11:00:00,643.0,655.31,13.04,10.05 -2016-05-25 12:00:00,643.0,796.59,13.19,9.9 -2016-05-25 13:00:00,579.0,570.84,13.45,9.85 -2016-05-25 14:00:00,548.0,506.78,13.7,9.8 -2016-05-25 15:00:00,516.0,446.65,13.96,9.75 -2016-05-25 16:00:00,486.0,292.35,13.86,9.63 -2016-05-25 17:00:00,492.0,236.45,13.77,9.51 -2016-05-25 18:00:00,520.0,61.74,13.68,9.39 -2016-05-25 19:00:00,576.0,21.5,13.55,9.03 -2016-05-25 20:00:00,591.0,0.0,13.43,8.68 -2016-05-25 21:00:00,561.0,0.0,13.31,8.32 -2016-05-25 22:00:00,857.0,0.0,13.2,7.87 -2016-05-25 23:00:00,856.0,0.0,13.1,7.42 -2016-05-26 00:00:00,666.0,0.0,12.99,6.97 -2016-05-26 01:00:00,562.0,0.0,12.88,6.54 -2016-05-26 02:00:00,480.0,0.0,12.78,6.11 -2016-05-26 03:00:00,438.0,0.0,12.67,5.68 -2016-05-26 04:00:00,446.0,0.0,12.62,5.51 -2016-05-26 05:00:00,434.0,5.71,12.58,5.34 -2016-05-26 06:00:00,433.0,60.6,12.53,5.17 -2016-05-26 07:00:00,489.0,170.94,12.67,5.25 -2016-05-26 08:00:00,576.0,273.43,12.81,5.32 -2016-05-26 09:00:00,603.0,191.14,12.95,5.39 -2016-05-26 10:00:00,576.0,92.28,12.94,5.12 -2016-05-26 11:00:00,602.0,253.39,12.92,4.84 -2016-05-26 12:00:00,615.0,291.94,12.91,4.57 -2016-05-26 13:00:00,551.0,143.86,12.93,3.88 -2016-05-26 14:00:00,527.0,146.0,12.94,3.2 -2016-05-26 15:00:00,490.0,235.43,12.96,2.51 -2016-05-26 16:00:00,482.0,65.79,12.94,2.62 -2016-05-26 17:00:00,468.0,24.54,12.92,2.73 -2016-05-26 18:00:00,532.0,47.7,12.9,2.84 -2016-05-26 19:00:00,569.0,5.18,12.86,2.6 -2016-05-26 20:00:00,545.0,0.0,12.82,2.36 -2016-05-26 21:00:00,516.0,0.0,12.78,2.12 -2016-05-26 22:00:00,834.0,0.0,12.68,1.71 -2016-05-26 23:00:00,842.0,0.0,12.58,1.29 -2016-05-27 00:00:00,655.0,0.0,12.48,0.87 -2016-05-27 01:00:00,529.0,0.0,12.4,0.85 -2016-05-27 02:00:00,435.0,0.0,12.31,0.82 -2016-05-27 03:00:00,401.0,0.0,12.23,0.8 -2016-05-27 04:00:00,405.0,0.0,12.19,1.04 -2016-05-27 05:00:00,413.0,11.21,12.16,1.28 -2016-05-27 06:00:00,403.0,60.5,12.12,1.52 -2016-05-27 07:00:00,462.0,139.54,12.25,1.53 -2016-05-27 08:00:00,523.0,341.05,12.37,1.54 -2016-05-27 09:00:00,541.0,514.85,12.5,1.54 -2016-05-27 10:00:00,564.0,417.39,12.51,1.63 -2016-05-27 11:00:00,576.0,654.63,12.53,1.71 -2016-05-27 12:00:00,596.0,644.35,12.54,1.79 -2016-05-27 13:00:00,530.0,728.73,12.77,1.88 -2016-05-27 14:00:00,484.0,695.36,13.0,1.97 -2016-05-27 15:00:00,467.0,581.28,13.23,2.06 -2016-05-27 16:00:00,420.0,416.35,13.25,2.3 -2016-05-27 17:00:00,405.0,225.67,13.27,2.54 -2016-05-27 18:00:00,429.0,84.79,13.28,2.79 -2016-05-27 19:00:00,456.0,11.73,13.21,2.76 -2016-05-27 20:00:00,494.0,0.0,13.14,2.73 -2016-05-27 21:00:00,467.0,0.0,13.06,2.7 -2016-05-27 22:00:00,815.0,0.0,12.7,2.78 -2016-05-27 23:00:00,824.0,0.0,12.34,2.85 -2016-05-28 00:00:00,663.0,0.0,11.98,2.92 -2016-05-28 01:00:00,506.0,0.0,11.8,2.96 -2016-05-28 02:00:00,413.0,0.0,11.62,2.99 -2016-05-28 03:00:00,396.0,0.0,11.44,3.02 -2016-05-28 04:00:00,387.0,0.0,11.44,3.18 -2016-05-28 05:00:00,382.0,6.8,11.43,3.33 -2016-05-28 06:00:00,381.0,61.35,11.42,3.49 -2016-05-28 07:00:00,408.0,116.49,11.69,3.71 -2016-05-28 08:00:00,491.0,261.3,11.95,3.94 -2016-05-28 09:00:00,541.0,304.57,12.22,4.17 -2016-05-28 10:00:00,531.0,312.43,12.39,4.5 -2016-05-28 11:00:00,545.0,768.42,12.56,4.84 -2016-05-28 12:00:00,573.0,579.69,12.73,5.17 -2016-05-28 13:00:00,522.0,732.27,12.94,5.39 -2016-05-28 14:00:00,463.0,706.73,13.14,5.6 -2016-05-28 15:00:00,439.0,578.88,13.35,5.82 -2016-05-28 16:00:00,434.0,406.79,13.12,6.43 -2016-05-28 17:00:00,435.0,100.88,12.89,7.04 -2016-05-28 18:00:00,480.0,67.78,12.66,7.66 -2016-05-28 19:00:00,565.0,1.12,12.42,7.64 -2016-05-28 20:00:00,554.0,0.0,12.17,7.62 -2016-05-28 21:00:00,560.0,0.0,11.92,7.6 -2016-05-28 22:00:00,870.0,0.0,11.87,8.25 -2016-05-28 23:00:00,862.0,0.0,11.83,8.9 -2016-05-29 00:00:00,671.0,0.0,11.78,9.54 -2016-05-29 01:00:00,534.0,0.0,12.04,8.32 -2016-05-29 02:00:00,453.0,0.0,12.29,7.09 -2016-05-29 03:00:00,430.0,0.0,12.55,5.86 -2016-05-29 04:00:00,421.0,0.0,12.54,5.9 -2016-05-29 05:00:00,389.0,1.12,12.53,5.94 -2016-05-29 06:00:00,390.0,60.09,12.51,5.99 -2016-05-29 07:00:00,407.0,140.59,12.66,5.84 -2016-05-29 08:00:00,510.0,243.38,12.81,5.69 -2016-05-29 09:00:00,575.0,257.57,12.96,5.54 -2016-05-29 10:00:00,594.0,240.57,13.04,5.57 -2016-05-29 11:00:00,566.0,601.36,13.12,5.59 -2016-05-29 12:00:00,549.0,558.85,13.2,5.61 -2016-05-29 13:00:00,499.0,730.52,13.43,5.28 -2016-05-29 14:00:00,476.0,674.05,13.65,4.95 -2016-05-29 15:00:00,458.0,545.5,13.88,4.62 -2016-05-29 16:00:00,416.0,318.19,13.89,4.61 -2016-05-29 17:00:00,392.0,113.56,13.9,4.6 -2016-05-29 18:00:00,409.0,84.09,13.91,4.59 -2016-05-29 19:00:00,454.0,7.23,13.87,5.05 -2016-05-29 20:00:00,454.0,0.0,13.83,5.51 -2016-05-29 21:00:00,453.0,0.0,13.79,5.97 -2016-05-29 22:00:00,776.0,0.0,13.79,6.1 -2016-05-29 23:00:00,773.0,0.0,13.78,6.23 -2016-05-30 00:00:00,582.0,0.0,13.78,6.36 -2016-05-30 01:00:00,479.0,0.0,13.71,6.17 -2016-05-30 02:00:00,406.0,0.0,13.65,5.97 -2016-05-30 03:00:00,368.0,0.0,13.58,5.78 -2016-05-30 04:00:00,377.0,0.0,13.52,5.52 -2016-05-30 05:00:00,364.0,11.73,13.46,5.26 -2016-05-30 06:00:00,345.0,34.58,13.4,4.99 -2016-05-30 07:00:00,388.0,184.46,13.46,4.94 -2016-05-30 08:00:00,464.0,351.44,13.53,4.89 -2016-05-30 09:00:00,515.0,512.34,13.59,4.84 -2016-05-30 10:00:00,514.0,680.09,13.56,4.9 -2016-05-30 11:00:00,523.0,778.6,13.54,4.96 -2016-05-30 12:00:00,563.0,818.27,13.51,5.02 -2016-05-30 13:00:00,499.0,779.8,13.55,5.32 -2016-05-30 14:00:00,481.0,739.55,13.6,5.62 -2016-05-30 15:00:00,444.0,602.08,13.64,5.92 -2016-05-30 16:00:00,419.0,435.89,13.62,5.94 -2016-05-30 17:00:00,394.0,242.63,13.6,5.96 -2016-05-30 18:00:00,436.0,54.36,13.59,5.99 -2016-05-30 19:00:00,500.0,20.22,13.52,6.19 -2016-05-30 20:00:00,482.0,0.0,13.46,6.39 -2016-05-30 21:00:00,477.0,0.0,13.4,6.59 -2016-05-30 22:00:00,767.0,0.0,13.4,6.81 -2016-05-30 23:00:00,754.0,0.0,13.4,7.03 -2016-05-31 00:00:00,570.0,0.0,13.4,7.24 -2016-05-31 01:00:00,458.0,0.0,13.46,7.3 -2016-05-31 02:00:00,410.0,0.0,13.53,7.35 -2016-05-31 03:00:00,360.0,0.0,13.59,7.41 -2016-05-31 04:00:00,366.0,0.0,13.62,7.63 -2016-05-31 05:00:00,360.0,11.72,13.66,7.86 -2016-05-31 06:00:00,334.0,28.37,13.69,8.08 -2016-05-31 07:00:00,383.0,184.99,13.75,8.02 -2016-05-31 08:00:00,449.0,394.23,13.82,7.96 -2016-05-31 09:00:00,514.0,574.58,13.88,7.9 -2016-05-31 10:00:00,515.0,723.3,13.81,8.03 -2016-05-31 11:00:00,526.0,819.87,13.75,8.15 -2016-05-31 12:00:00,555.0,854.05,13.68,8.28 -2016-05-31 13:00:00,498.0,842.32,13.75,8.89 -2016-05-31 14:00:00,474.0,773.68,13.81,9.51 -2016-05-31 15:00:00,467.0,639.08,13.88,10.12 -2016-05-31 16:00:00,429.0,460.79,13.94,10.24 -2016-05-31 17:00:00,396.0,249.88,13.99,10.36 -2016-05-31 18:00:00,434.0,55.79,14.05,10.48 -2016-05-31 19:00:00,459.0,22.08,14.1,10.38 -2016-05-31 20:00:00,460.0,0.0,14.16,10.27 -2016-05-31 21:00:00,442.0,0.0,14.22,10.17 -2016-05-31 22:00:00,732.0,0.0,14.24,9.94 -2016-05-31 23:00:00,742.0,0.0,14.27,9.71 -2016-06-01 00:00:00,560.0,0.0,14.29,9.49 -2016-06-01 01:00:00,452.0,0.0,14.27,9.39 -2016-06-01 02:00:00,382.0,0.0,14.26,9.29 -2016-06-01 03:00:00,344.0,0.0,14.24,9.19 -2016-06-01 04:00:00,353.0,0.0,14.19,9.11 -2016-06-01 05:00:00,338.0,11.69,14.14,9.04 -2016-06-01 06:00:00,339.0,33.81,14.09,8.97 -2016-06-01 07:00:00,392.0,182.3,13.99,8.85 -2016-06-01 08:00:00,464.0,384.3,13.89,8.74 -2016-06-01 09:00:00,520.0,356.39,13.78,8.62 -2016-06-01 10:00:00,499.0,346.5,13.72,8.41 -2016-06-01 11:00:00,490.0,269.49,13.65,8.2 -2016-06-01 12:00:00,530.0,352.17,13.58,7.99 -2016-06-01 13:00:00,491.0,362.88,13.72,7.54 -2016-06-01 14:00:00,455.0,687.59,13.85,7.09 -2016-06-01 15:00:00,430.0,392.71,13.98,6.63 -2016-06-01 16:00:00,419.0,236.24,14.04,6.48 -2016-06-01 17:00:00,414.0,130.65,14.1,6.32 -2016-06-01 18:00:00,441.0,49.01,14.16,6.17 -2016-06-01 19:00:00,503.0,7.22,14.22,5.97 -2016-06-01 20:00:00,501.0,0.0,14.27,5.77 -2016-06-01 21:00:00,483.0,0.0,14.32,5.57 -2016-06-01 22:00:00,792.0,0.0,14.32,5.42 -2016-06-01 23:00:00,804.0,0.0,14.32,5.27 -2016-06-02 00:00:00,617.0,0.0,14.32,5.12 -2016-06-02 01:00:00,450.0,0.0,14.2,5.01 -2016-06-02 02:00:00,391.0,0.0,14.08,4.91 -2016-06-02 03:00:00,378.0,0.0,13.97,4.8 -2016-06-02 04:00:00,379.0,0.0,13.97,4.97 -2016-06-02 05:00:00,340.0,5.15,13.98,5.14 -2016-06-02 06:00:00,333.0,16.98,13.98,5.31 -2016-06-02 07:00:00,392.0,128.09,13.97,5.6 -2016-06-02 08:00:00,478.0,154.7,13.96,5.9 -2016-06-02 09:00:00,525.0,190.37,13.94,6.19 -2016-06-02 10:00:00,524.0,256.18,13.94,6.25 -2016-06-02 11:00:00,526.0,188.63,13.93,6.3 -2016-06-02 12:00:00,564.0,204.68,13.92,6.36 -2016-06-02 13:00:00,520.0,181.4,14.06,6.18 -2016-06-02 14:00:00,462.0,369.87,14.19,6.01 -2016-06-02 15:00:00,428.0,302.57,14.32,5.83 -2016-06-02 16:00:00,430.0,219.02,14.25,5.58 -2016-06-02 17:00:00,426.0,119.53,14.18,5.32 -2016-06-02 18:00:00,452.0,37.93,14.11,5.06 -2016-06-02 19:00:00,514.0,12.27,14.02,5.14 -2016-06-02 20:00:00,495.0,0.0,13.92,5.21 -2016-06-02 21:00:00,476.0,0.0,13.82,5.28 -2016-06-02 22:00:00,773.0,0.0,13.72,5.27 -2016-06-02 23:00:00,779.0,0.0,13.61,5.26 -2016-06-03 00:00:00,611.0,0.0,13.5,5.26 -2016-06-03 01:00:00,458.0,0.0,13.31,5.6 -2016-06-03 02:00:00,394.0,0.0,13.12,5.95 -2016-06-03 03:00:00,383.0,0.0,12.94,6.3 -2016-06-03 04:00:00,373.0,0.0,12.89,6.2 -2016-06-03 05:00:00,370.0,11.77,12.85,6.09 -2016-06-03 06:00:00,354.0,45.52,12.81,5.99 -2016-06-03 07:00:00,416.0,38.19,12.88,6.12 -2016-06-03 08:00:00,503.0,196.89,12.95,6.25 -2016-06-03 09:00:00,521.0,543.91,13.02,6.39 -2016-06-03 10:00:00,499.0,679.05,13.15,6.4 -2016-06-03 11:00:00,543.0,786.46,13.28,6.4 -2016-06-03 12:00:00,568.0,727.68,13.4,6.41 -2016-06-03 13:00:00,543.0,696.82,13.7,6.32 -2016-06-03 14:00:00,489.0,516.64,14.0,6.23 -2016-06-03 15:00:00,472.0,317.33,14.31,6.14 -2016-06-03 16:00:00,453.0,376.65,14.31,6.0 -2016-06-03 17:00:00,435.0,231.96,14.31,5.85 -2016-06-03 18:00:00,464.0,89.74,14.31,5.71 -2016-06-03 19:00:00,507.0,30.46,14.22,5.54 -2016-06-03 20:00:00,516.0,0.0,14.13,5.38 -2016-06-03 21:00:00,510.0,0.0,14.03,5.21 -2016-06-03 22:00:00,859.0,0.0,13.87,5.08 -2016-06-03 23:00:00,855.0,0.0,13.71,4.95 -2016-06-04 00:00:00,660.0,0.0,13.55,4.81 -2016-06-04 01:00:00,497.0,0.0,13.5,4.61 -2016-06-04 02:00:00,444.0,0.0,13.44,4.4 -2016-06-04 03:00:00,406.0,0.0,13.39,4.19 -2016-06-04 04:00:00,397.0,0.0,13.34,4.16 -2016-06-04 05:00:00,387.0,4.18,13.29,4.12 -2016-06-04 06:00:00,377.0,22.44,13.24,4.08 -2016-06-04 07:00:00,402.0,51.38,13.36,3.94 -2016-06-04 08:00:00,508.0,109.02,13.48,3.8 -2016-06-04 09:00:00,548.0,192.94,13.6,3.66 -2016-06-04 10:00:00,563.0,293.08,13.67,3.3 -2016-06-04 11:00:00,581.0,254.79,13.74,2.94 -2016-06-04 12:00:00,583.0,165.4,13.81,2.58 -2016-06-04 13:00:00,552.0,314.88,13.93,2.54 -2016-06-04 14:00:00,503.0,448.49,14.05,2.51 -2016-06-04 15:00:00,498.0,358.5,14.18,2.47 -2016-06-04 16:00:00,479.0,371.32,14.15,2.5 -2016-06-04 17:00:00,475.0,243.81,14.13,2.52 -2016-06-04 18:00:00,517.0,77.13,14.11,2.55 -2016-06-04 19:00:00,562.0,28.46,14.09,2.51 -2016-06-04 20:00:00,530.0,0.0,14.07,2.48 -2016-06-04 21:00:00,556.0,0.0,14.05,2.44 -2016-06-04 22:00:00,839.0,0.0,14.0,2.28 -2016-06-04 23:00:00,853.0,0.0,13.95,2.12 -2016-06-05 00:00:00,682.0,0.0,13.89,1.96 -2016-06-05 01:00:00,495.0,0.0,13.86,2.01 -2016-06-05 02:00:00,446.0,0.0,13.83,2.06 -2016-06-05 03:00:00,421.0,0.0,13.8,2.11 -2016-06-05 04:00:00,391.0,0.0,13.8,2.07 -2016-06-05 05:00:00,372.0,5.66,13.81,2.03 -2016-06-05 06:00:00,363.0,27.19,13.81,1.99 -2016-06-05 07:00:00,398.0,96.33,13.89,2.09 -2016-06-05 08:00:00,471.0,220.17,13.96,2.19 -2016-06-05 09:00:00,527.0,261.3,14.03,2.29 -2016-06-05 10:00:00,582.0,225.92,14.05,2.1 -2016-06-05 11:00:00,604.0,194.42,14.07,1.9 -2016-06-05 12:00:00,628.0,160.85,14.08,1.71 -2016-06-05 13:00:00,546.0,194.19,14.33,1.33 -2016-06-05 14:00:00,485.0,207.23,14.58,0.96 -2016-06-05 15:00:00,467.0,203.29,14.84,0.58 -2016-06-05 16:00:00,428.0,190.51,14.76,1.0 -2016-06-05 17:00:00,389.0,123.5,14.69,1.42 -2016-06-05 18:00:00,419.0,70.6,14.62,1.83 -2016-06-05 19:00:00,429.0,22.59,14.63,2.27 -2016-06-05 20:00:00,450.0,0.0,14.64,2.71 -2016-06-05 21:00:00,435.0,0.0,14.64,3.14 -2016-06-05 22:00:00,744.0,0.0,14.6,3.26 -2016-06-05 23:00:00,782.0,0.0,14.55,3.37 -2016-06-06 00:00:00,581.0,0.0,14.5,3.48 -2016-06-06 01:00:00,455.0,0.0,14.47,3.61 -2016-06-06 02:00:00,405.0,0.0,14.44,3.75 -2016-06-06 03:00:00,367.0,0.0,14.4,3.89 -2016-06-06 04:00:00,356.0,0.0,14.4,3.97 -2016-06-06 05:00:00,338.0,14.0,14.4,4.05 -2016-06-06 06:00:00,335.0,51.5,14.39,4.12 -2016-06-06 07:00:00,376.0,119.37,14.54,3.82 -2016-06-06 08:00:00,450.0,264.24,14.69,3.51 -2016-06-06 09:00:00,478.0,524.83,14.85,3.2 -2016-06-06 10:00:00,486.0,660.07,14.88,2.74 -2016-06-06 11:00:00,508.0,454.21,14.92,2.29 -2016-06-06 12:00:00,531.0,476.44,14.95,1.83 -2016-06-06 13:00:00,456.0,734.86,14.94,1.44 -2016-06-06 14:00:00,433.0,653.87,14.92,1.05 -2016-06-06 15:00:00,413.0,336.29,14.9,0.66 -2016-06-06 16:00:00,407.0,145.14,14.87,0.69 -2016-06-06 17:00:00,390.0,195.73,14.84,0.72 -2016-06-06 18:00:00,406.0,8.72,14.81,0.74 -2016-06-06 19:00:00,461.0,29.61,14.81,0.73 -2016-06-06 20:00:00,455.0,0.0,14.81,0.71 -2016-06-06 21:00:00,451.0,0.0,14.81,0.69 -2016-06-06 22:00:00,748.0,0.0,14.75,0.87 -2016-06-06 23:00:00,724.0,0.0,14.68,1.05 -2016-06-07 00:00:00,531.0,0.0,14.61,1.23 -2016-06-07 01:00:00,438.0,0.0,14.61,1.07 -2016-06-07 02:00:00,376.0,0.0,14.6,0.91 -2016-06-07 03:00:00,343.0,0.0,14.6,0.74 -2016-06-07 04:00:00,346.0,0.0,14.55,0.85 -2016-06-07 05:00:00,321.0,3.67,14.51,0.96 -2016-06-07 06:00:00,333.0,62.97,14.47,1.06 -2016-06-07 07:00:00,408.0,132.29,14.52,1.13 -2016-06-07 08:00:00,444.0,99.39,14.57,1.19 -2016-06-07 09:00:00,503.0,207.88,14.62,1.26 -2016-06-07 10:00:00,505.0,549.35,14.61,1.41 -2016-06-07 11:00:00,532.0,659.82,14.59,1.57 -2016-06-07 12:00:00,563.0,439.7,14.57,1.72 -2016-06-07 13:00:00,484.0,722.92,14.61,1.92 -2016-06-07 14:00:00,427.0,691.41,14.65,2.11 -2016-06-07 15:00:00,450.0,597.52,14.69,2.3 -2016-06-07 16:00:00,411.0,435.74,14.68,2.47 -2016-06-07 17:00:00,380.0,243.5,14.67,2.63 -2016-06-07 18:00:00,432.0,92.44,14.65,2.8 -2016-06-07 19:00:00,469.0,10.5,14.62,2.81 -2016-06-07 20:00:00,436.0,0.0,14.59,2.82 -2016-06-07 21:00:00,444.0,0.0,14.55,2.83 -2016-06-07 22:00:00,761.0,0.0,14.46,2.87 -2016-06-07 23:00:00,754.0,0.0,14.37,2.92 -2016-06-08 00:00:00,531.0,0.0,14.28,2.97 -2016-06-08 01:00:00,440.0,0.0,14.25,2.8 -2016-06-08 02:00:00,372.0,0.0,14.22,2.63 -2016-06-08 03:00:00,343.0,0.0,14.19,2.47 -2016-06-08 04:00:00,343.0,0.0,14.14,2.81 -2016-06-08 05:00:00,331.0,15.22,14.08,3.15 -2016-06-08 06:00:00,336.0,48.51,14.02,3.49 -2016-06-08 07:00:00,405.0,136.68,14.1,3.65 -2016-06-08 08:00:00,448.0,129.68,14.18,3.81 -2016-06-08 09:00:00,513.0,207.84,14.27,3.97 -2016-06-08 10:00:00,533.0,283.01,14.31,3.92 -2016-06-08 11:00:00,540.0,366.71,14.36,3.86 -2016-06-08 12:00:00,556.0,684.8,14.41,3.81 -2016-06-08 13:00:00,523.0,796.52,14.54,4.0 -2016-06-08 14:00:00,485.0,581.84,14.66,4.18 -2016-06-08 15:00:00,487.0,618.18,14.78,4.37 -2016-06-08 16:00:00,459.0,451.62,14.66,4.39 -2016-06-08 17:00:00,419.0,251.43,14.54,4.4 -2016-06-08 18:00:00,451.0,61.49,14.42,4.41 -2016-06-08 19:00:00,489.0,27.17,14.2,4.33 -2016-06-08 20:00:00,478.0,0.0,13.98,4.25 -2016-06-08 21:00:00,466.0,0.0,13.76,4.17 -2016-06-08 22:00:00,747.0,0.0,13.62,4.17 -2016-06-08 23:00:00,727.0,0.0,13.48,4.17 -2016-06-09 00:00:00,527.0,0.0,13.33,4.17 -2016-06-09 01:00:00,452.0,0.0,13.56,4.01 -2016-06-09 02:00:00,386.0,0.0,13.79,3.85 -2016-06-09 03:00:00,352.0,0.0,14.02,3.7 -2016-06-09 04:00:00,347.0,0.0,14.09,3.97 -2016-06-09 05:00:00,323.0,14.62,14.17,4.24 -2016-06-09 06:00:00,337.0,62.42,14.25,4.51 -2016-06-09 07:00:00,385.0,168.3,14.53,4.42 -2016-06-09 08:00:00,478.0,161.32,14.81,4.33 -2016-06-09 09:00:00,525.0,212.88,15.1,4.23 -2016-06-09 10:00:00,507.0,574.0,15.25,4.15 -2016-06-09 11:00:00,521.0,662.21,15.41,4.06 -2016-06-09 12:00:00,581.0,265.32,15.57,3.97 -2016-06-09 13:00:00,520.0,280.06,15.86,4.02 -2016-06-09 14:00:00,445.0,424.82,16.14,4.06 -2016-06-09 15:00:00,455.0,224.73,16.42,4.11 -2016-06-09 16:00:00,421.0,168.68,16.64,3.89 -2016-06-09 17:00:00,415.0,49.0,16.86,3.67 -2016-06-09 18:00:00,473.0,53.36,17.08,3.45 -2016-06-09 19:00:00,512.0,1.46,16.88,3.1 -2016-06-09 20:00:00,511.0,0.0,16.67,2.76 -2016-06-09 21:00:00,475.0,0.0,16.46,2.41 -2016-06-09 22:00:00,770.0,0.0,16.18,2.58 -2016-06-09 23:00:00,765.0,0.0,15.89,2.75 -2016-06-10 00:00:00,569.0,0.0,15.6,2.92 -2016-06-10 01:00:00,446.0,0.0,15.52,2.04 -2016-06-10 02:00:00,397.0,0.0,15.44,1.16 -2016-06-10 03:00:00,349.0,0.0,15.36,0.28 -2016-06-10 04:00:00,355.0,0.0,15.19,0.25 -2016-06-10 05:00:00,341.0,0.07,15.02,0.23 -2016-06-10 06:00:00,325.0,36.18,14.85,0.21 -2016-06-10 07:00:00,385.0,74.02,14.75,0.58 -2016-06-10 08:00:00,454.0,71.85,14.65,0.95 -2016-06-10 09:00:00,478.0,344.4,14.55,1.32 -2016-06-10 10:00:00,495.0,256.06,14.45,2.19 -2016-06-10 11:00:00,527.0,269.97,14.35,3.06 -2016-06-10 12:00:00,554.0,636.82,14.25,3.93 -2016-06-10 13:00:00,510.0,715.81,14.17,3.36 -2016-06-10 14:00:00,470.0,416.82,14.08,2.78 -2016-06-10 15:00:00,467.0,346.65,13.99,2.21 -2016-06-10 16:00:00,443.0,239.39,14.11,2.63 -2016-06-10 17:00:00,407.0,136.42,14.23,3.04 -2016-06-10 18:00:00,455.0,92.81,14.35,3.46 -2016-06-10 19:00:00,490.0,7.21,14.42,3.67 -2016-06-10 20:00:00,470.0,0.0,14.48,3.89 -2016-06-10 21:00:00,461.0,0.0,14.54,4.1 -2016-06-10 22:00:00,771.0,0.0,14.52,4.39 -2016-06-10 23:00:00,746.0,0.0,14.49,4.69 -2016-06-11 00:00:00,572.0,0.0,14.46,4.98 -2016-06-11 01:00:00,431.0,0.0,14.51,5.26 -2016-06-11 02:00:00,407.0,0.0,14.56,5.54 -2016-06-11 03:00:00,364.0,0.0,14.6,5.82 -2016-06-11 04:00:00,353.0,0.0,14.56,5.86 -2016-06-11 05:00:00,342.0,9.95,14.52,5.9 -2016-06-11 06:00:00,329.0,57.91,14.47,5.94 -2016-06-11 07:00:00,333.0,151.37,14.45,5.9 -2016-06-11 08:00:00,427.0,259.91,14.43,5.85 -2016-06-11 09:00:00,492.0,441.74,14.4,5.81 -2016-06-11 10:00:00,548.0,654.23,14.41,5.91 -2016-06-11 11:00:00,539.0,724.86,14.41,6.01 -2016-06-11 12:00:00,560.0,817.64,14.41,6.11 -2016-06-11 13:00:00,518.0,628.22,14.51,5.96 -2016-06-11 14:00:00,439.0,379.77,14.61,5.81 -2016-06-11 15:00:00,446.0,184.16,14.7,5.66 -2016-06-11 16:00:00,427.0,100.8,14.84,5.73 -2016-06-11 17:00:00,416.0,136.58,14.98,5.81 -2016-06-11 18:00:00,463.0,13.01,15.11,5.89 -2016-06-11 19:00:00,533.0,17.58,15.08,6.22 -2016-06-11 20:00:00,512.0,0.0,15.05,6.54 -2016-06-11 21:00:00,506.0,0.0,15.01,6.87 -2016-06-11 22:00:00,786.0,0.0,14.95,6.98 -2016-06-11 23:00:00,766.0,0.0,14.89,7.09 -2016-06-12 00:00:00,561.0,0.0,14.82,7.2 -2016-06-12 01:00:00,480.0,0.0,14.91,7.69 -2016-06-12 02:00:00,423.0,0.0,15.0,8.18 -2016-06-12 03:00:00,371.0,0.0,15.1,8.68 -2016-06-12 04:00:00,351.0,0.0,15.09,8.36 -2016-06-12 05:00:00,340.0,0.0,15.08,8.05 -2016-06-12 06:00:00,330.0,46.42,15.07,7.74 -2016-06-12 07:00:00,337.0,41.44,15.06,7.4 -2016-06-12 08:00:00,415.0,297.17,15.04,7.07 -2016-06-12 09:00:00,481.0,353.16,15.02,6.73 -2016-06-12 10:00:00,562.0,626.76,14.96,6.54 -2016-06-12 11:00:00,598.0,459.97,14.9,6.34 -2016-06-12 12:00:00,612.0,695.11,14.83,6.15 -2016-06-12 13:00:00,546.0,421.89,14.77,6.98 -2016-06-12 14:00:00,478.0,507.26,14.71,7.82 -2016-06-12 15:00:00,453.0,213.69,14.65,8.65 -2016-06-12 16:00:00,418.0,139.66,14.63,8.46 -2016-06-12 17:00:00,385.0,29.27,14.62,8.26 -2016-06-12 18:00:00,418.0,82.81,14.61,8.07 -2016-06-12 19:00:00,462.0,8.83,14.62,7.86 -2016-06-12 20:00:00,446.0,0.0,14.62,7.65 -2016-06-12 21:00:00,435.0,0.0,14.62,7.43 -2016-06-12 22:00:00,726.0,0.0,14.69,7.31 -2016-06-12 23:00:00,741.0,0.0,14.76,7.18 -2016-06-13 00:00:00,536.0,0.0,14.82,7.05 -2016-06-13 01:00:00,449.0,0.0,14.67,6.95 -2016-06-13 02:00:00,408.0,0.0,14.52,6.85 -2016-06-13 03:00:00,345.0,0.0,14.36,6.74 -2016-06-13 04:00:00,342.0,0.0,14.42,6.61 -2016-06-13 05:00:00,330.0,0.07,14.47,6.48 -2016-06-13 06:00:00,301.0,5.33,14.52,6.34 -2016-06-13 07:00:00,341.0,51.89,14.58,6.15 -2016-06-13 08:00:00,421.0,47.4,14.64,5.96 -2016-06-13 09:00:00,484.0,147.79,14.69,5.77 -2016-06-13 10:00:00,487.0,66.27,14.6,6.88 -2016-06-13 11:00:00,531.0,521.69,14.51,8.0 -2016-06-13 12:00:00,557.0,532.78,14.42,9.12 -2016-06-13 13:00:00,524.0,383.81,14.38,9.78 -2016-06-13 14:00:00,493.0,479.41,14.34,10.45 -2016-06-13 15:00:00,478.0,321.44,14.3,11.12 -2016-06-13 16:00:00,471.0,275.81,14.27,11.17 -2016-06-13 17:00:00,437.0,258.16,14.25,11.23 -2016-06-13 18:00:00,445.0,90.46,14.23,11.28 -2016-06-13 19:00:00,502.0,7.22,14.37,11.02 -2016-06-13 20:00:00,486.0,0.0,14.51,10.75 -2016-06-13 21:00:00,464.0,0.0,14.65,10.48 -2016-06-13 22:00:00,743.0,0.0,14.67,10.25 -2016-06-13 23:00:00,735.0,0.0,14.68,10.02 -2016-06-14 00:00:00,530.0,0.0,14.69,9.79 -2016-06-14 01:00:00,448.0,0.0,14.58,9.54 -2016-06-14 02:00:00,389.0,0.0,14.46,9.3 -2016-06-14 03:00:00,344.0,0.0,14.35,9.05 -2016-06-14 04:00:00,345.0,0.0,14.3,9.07 -2016-06-14 05:00:00,330.0,6.69,14.25,9.09 -2016-06-14 06:00:00,320.0,28.63,14.2,9.12 -2016-06-14 07:00:00,380.0,115.58,14.11,8.9 -2016-06-14 08:00:00,463.0,309.25,14.02,8.68 -2016-06-14 09:00:00,520.0,537.21,13.93,8.47 -2016-06-14 10:00:00,529.0,527.72,13.81,8.09 -2016-06-14 11:00:00,519.0,372.95,13.7,7.71 -2016-06-14 12:00:00,558.0,672.5,13.59,7.32 -2016-06-14 13:00:00,488.0,448.9,13.51,7.33 -2016-06-14 14:00:00,436.0,695.59,13.43,7.34 -2016-06-14 15:00:00,459.0,605.27,13.35,7.35 -2016-06-14 16:00:00,431.0,345.88,13.49,7.26 -2016-06-14 17:00:00,391.0,228.34,13.63,7.18 -2016-06-14 18:00:00,426.0,92.76,13.77,7.09 -2016-06-14 19:00:00,478.0,27.25,13.84,7.03 -2016-06-14 20:00:00,443.0,0.0,13.91,6.96 -2016-06-14 21:00:00,445.0,0.0,13.98,6.9 -2016-06-14 22:00:00,739.0,0.0,13.91,6.85 -2016-06-14 23:00:00,730.0,0.0,13.84,6.8 -2016-06-15 00:00:00,547.0,0.0,13.77,6.74 -2016-06-15 01:00:00,465.0,0.0,13.67,6.62 -2016-06-15 02:00:00,413.0,0.0,13.57,6.49 -2016-06-15 03:00:00,352.0,0.0,13.48,6.36 -2016-06-15 04:00:00,357.0,0.0,13.43,6.21 -2016-06-15 05:00:00,331.0,15.29,13.39,6.06 -2016-06-15 06:00:00,314.0,64.31,13.35,5.9 -2016-06-15 07:00:00,382.0,176.74,13.42,5.87 -2016-06-15 08:00:00,456.0,355.2,13.49,5.83 -2016-06-15 09:00:00,516.0,573.72,13.56,5.79 -2016-06-15 10:00:00,500.0,706.51,13.61,5.9 -2016-06-15 11:00:00,504.0,776.98,13.66,6.0 -2016-06-15 12:00:00,522.0,810.96,13.71,6.11 -2016-06-15 13:00:00,473.0,803.35,13.85,5.91 -2016-06-15 14:00:00,422.0,714.83,13.98,5.71 -2016-06-15 15:00:00,437.0,522.69,14.11,5.52 -2016-06-15 16:00:00,416.0,411.61,14.13,5.43 -2016-06-15 17:00:00,393.0,224.84,14.15,5.34 -2016-06-15 18:00:00,441.0,80.61,14.17,5.26 -2016-06-15 19:00:00,477.0,10.53,14.19,5.03 -2016-06-15 20:00:00,457.0,0.0,14.21,4.8 -2016-06-15 21:00:00,441.0,0.0,14.22,4.58 -2016-06-15 22:00:00,715.0,0.0,14.18,4.23 -2016-06-15 23:00:00,722.0,0.0,14.13,3.88 -2016-06-16 00:00:00,536.0,0.0,14.08,3.53 -2016-06-16 01:00:00,445.0,0.0,14.05,3.44 -2016-06-16 02:00:00,395.0,0.0,14.01,3.36 -2016-06-16 03:00:00,337.0,0.0,13.98,3.27 -2016-06-16 04:00:00,347.0,0.0,14.01,3.12 -2016-06-16 05:00:00,318.0,15.83,14.05,2.97 -2016-06-16 06:00:00,306.0,32.68,14.09,2.81 -2016-06-16 07:00:00,382.0,174.59,14.2,2.69 -2016-06-16 08:00:00,454.0,367.1,14.31,2.56 -2016-06-16 09:00:00,489.0,552.9,14.43,2.43 -2016-06-16 10:00:00,505.0,657.95,14.39,2.88 -2016-06-16 11:00:00,498.0,731.48,14.36,3.33 -2016-06-16 12:00:00,515.0,769.71,14.33,3.78 -2016-06-16 13:00:00,477.0,751.42,14.39,4.35 -2016-06-16 14:00:00,452.0,701.76,14.45,4.92 -2016-06-16 15:00:00,467.0,593.62,14.52,5.49 -2016-06-16 16:00:00,429.0,446.67,14.49,5.63 -2016-06-16 17:00:00,406.0,251.81,14.47,5.77 -2016-06-16 18:00:00,439.0,83.19,14.45,5.92 -2016-06-16 19:00:00,479.0,34.41,14.45,5.99 -2016-06-16 20:00:00,454.0,0.0,14.45,6.06 -2016-06-16 21:00:00,426.0,0.0,14.44,6.14 -2016-06-16 22:00:00,710.0,0.0,14.31,6.4 -2016-06-16 23:00:00,726.0,0.0,14.18,6.67 -2016-06-17 00:00:00,527.0,0.0,14.05,6.94 -2016-06-17 01:00:00,447.0,0.0,14.0,7.04 -2016-06-17 02:00:00,384.0,0.0,13.95,7.14 -2016-06-17 03:00:00,332.0,0.0,13.9,7.24 -2016-06-17 04:00:00,326.0,0.0,13.74,7.37 -2016-06-17 05:00:00,308.0,8.88,13.58,7.5 -2016-06-17 06:00:00,316.0,54.81,13.42,7.63 -2016-06-17 07:00:00,374.0,107.01,13.42,7.78 -2016-06-17 08:00:00,431.0,221.16,13.42,7.94 -2016-06-17 09:00:00,472.0,365.72,13.43,8.1 -2016-06-17 10:00:00,483.0,168.14,13.47,8.28 -2016-06-17 11:00:00,481.0,168.13,13.52,8.46 -2016-06-17 12:00:00,526.0,207.27,13.56,8.63 -2016-06-17 13:00:00,496.0,122.92,13.63,8.81 -2016-06-17 14:00:00,442.0,115.0,13.69,8.98 -2016-06-17 15:00:00,443.0,380.55,13.76,9.16 -2016-06-17 16:00:00,428.0,368.73,13.84,9.05 -2016-06-17 17:00:00,417.0,257.58,13.93,8.94 -2016-06-17 18:00:00,485.0,70.74,14.01,8.83 -2016-06-17 19:00:00,527.0,33.84,14.1,8.69 -2016-06-17 20:00:00,508.0,0.0,14.18,8.56 -2016-06-17 21:00:00,478.0,0.0,14.26,8.43 -2016-06-17 22:00:00,771.0,0.0,14.33,8.26 -2016-06-17 23:00:00,746.0,0.0,14.4,8.1 -2016-06-18 00:00:00,590.0,0.0,14.46,7.93 -2016-06-18 01:00:00,451.0,0.0,14.44,7.66 -2016-06-18 02:00:00,399.0,0.0,14.42,7.4 -2016-06-18 03:00:00,363.0,0.0,14.4,7.13 -2016-06-18 04:00:00,359.0,0.0,14.36,6.86 -2016-06-18 05:00:00,342.0,14.62,14.33,6.6 -2016-06-18 06:00:00,332.0,46.65,14.3,6.33 -2016-06-18 07:00:00,359.0,131.94,14.31,6.17 -2016-06-18 08:00:00,433.0,44.51,14.32,6.02 -2016-06-18 09:00:00,487.0,276.33,14.32,5.86 -2016-06-18 10:00:00,524.0,546.0,14.31,5.93 -2016-06-18 11:00:00,527.0,756.46,14.29,5.99 -2016-06-18 12:00:00,542.0,695.68,14.27,6.06 -2016-06-18 13:00:00,508.0,367.3,14.42,5.82 -2016-06-18 14:00:00,451.0,733.27,14.57,5.58 -2016-06-18 15:00:00,468.0,616.0,14.73,5.34 -2016-06-18 16:00:00,435.0,330.59,14.72,5.35 -2016-06-18 17:00:00,403.0,152.44,14.71,5.36 -2016-06-18 18:00:00,438.0,76.38,14.7,5.37 -2016-06-18 19:00:00,510.0,18.85,14.68,5.29 -2016-06-18 20:00:00,487.0,0.0,14.66,5.22 -2016-06-18 21:00:00,482.0,0.0,14.64,5.14 -2016-06-18 22:00:00,757.0,0.0,14.6,4.92 -2016-06-18 23:00:00,781.0,0.0,14.56,4.69 -2016-06-19 00:00:00,613.0,0.0,14.52,4.47 -2016-06-19 01:00:00,447.0,0.0,14.46,4.48 -2016-06-19 02:00:00,396.0,0.0,14.4,4.49 -2016-06-19 03:00:00,360.0,0.0,14.34,4.5 -2016-06-19 04:00:00,345.0,0.0,14.41,4.81 -2016-06-19 05:00:00,307.0,9.39,14.49,5.12 -2016-06-19 06:00:00,308.0,47.95,14.57,5.43 -2016-06-19 07:00:00,331.0,66.21,14.64,5.94 -2016-06-19 08:00:00,413.0,60.9,14.71,6.46 -2016-06-19 09:00:00,481.0,107.96,14.78,6.97 -2016-06-19 10:00:00,561.0,108.01,14.73,7.22 -2016-06-19 11:00:00,580.0,120.6,14.67,7.47 -2016-06-19 12:00:00,586.0,104.21,14.61,7.72 -2016-06-19 13:00:00,515.0,108.89,14.67,7.96 -2016-06-19 14:00:00,478.0,145.82,14.73,8.2 -2016-06-19 15:00:00,479.0,224.06,14.8,8.44 -2016-06-19 16:00:00,436.0,58.66,14.82,8.86 -2016-06-19 17:00:00,402.0,40.04,14.85,9.27 -2016-06-19 18:00:00,449.0,11.16,14.88,9.68 -2016-06-19 19:00:00,479.0,1.89,14.91,9.73 -2016-06-19 20:00:00,484.0,0.0,14.94,9.78 -2016-06-19 21:00:00,477.0,0.0,14.96,9.83 -2016-06-19 22:00:00,757.0,0.0,14.94,10.16 -2016-06-19 23:00:00,733.0,0.0,14.92,10.48 -2016-06-20 00:00:00,564.0,0.0,14.89,10.8 -2016-06-20 01:00:00,444.0,0.0,14.86,11.04 -2016-06-20 02:00:00,399.0,0.0,14.83,11.28 -2016-06-20 03:00:00,369.0,0.0,14.8,11.52 -2016-06-20 04:00:00,343.0,0.0,14.94,11.7 -2016-06-20 05:00:00,321.0,6.14,15.08,11.88 -2016-06-20 06:00:00,323.0,18.21,15.22,12.06 -2016-06-20 07:00:00,352.0,47.35,15.28,11.61 -2016-06-20 08:00:00,439.0,66.15,15.34,11.17 -2016-06-20 09:00:00,497.0,96.21,15.4,10.73 -2016-06-20 10:00:00,528.0,103.22,15.32,9.9 -2016-06-20 11:00:00,572.0,447.95,15.24,9.08 -2016-06-20 12:00:00,620.0,237.54,15.15,8.25 -2016-06-20 13:00:00,569.0,164.26,15.2,7.54 -2016-06-20 14:00:00,530.0,260.28,15.24,6.83 -2016-06-20 15:00:00,547.0,312.04,15.28,6.12 -2016-06-20 16:00:00,511.0,173.52,15.3,5.83 -2016-06-20 17:00:00,440.0,45.75,15.32,5.53 -2016-06-20 18:00:00,479.0,99.8,15.33,5.23 -2016-06-20 19:00:00,549.0,6.12,15.3,4.77 -2016-06-20 20:00:00,534.0,0.0,15.27,4.32 -2016-06-20 21:00:00,490.0,0.0,15.24,3.86 -2016-06-20 22:00:00,779.0,0.0,15.22,3.9 -2016-06-20 23:00:00,756.0,0.0,15.2,3.94 -2016-06-21 00:00:00,582.0,0.0,15.17,3.97 -2016-06-21 01:00:00,467.0,0.0,15.1,4.01 -2016-06-21 02:00:00,394.0,0.0,15.03,4.06 -2016-06-21 03:00:00,347.0,0.0,14.95,4.1 -2016-06-21 04:00:00,361.0,0.0,14.91,4.02 -2016-06-21 05:00:00,326.0,1.89,14.86,3.95 -2016-06-21 06:00:00,318.0,10.54,14.81,3.88 -2016-06-21 07:00:00,382.0,146.21,14.83,3.85 -2016-06-21 08:00:00,474.0,245.44,14.85,3.83 -2016-06-21 09:00:00,515.0,356.23,14.88,3.81 -2016-06-21 10:00:00,543.0,676.05,14.92,4.19 -2016-06-21 11:00:00,533.0,563.71,14.96,4.58 -2016-06-21 12:00:00,564.0,462.96,15.0,4.97 -2016-06-21 13:00:00,506.0,190.23,15.08,4.88 -2016-06-21 14:00:00,465.0,621.83,15.16,4.8 -2016-06-21 15:00:00,471.0,487.25,15.23,4.72 -2016-06-21 16:00:00,417.0,378.6,15.27,4.74 -2016-06-21 17:00:00,411.0,170.02,15.3,4.75 -2016-06-21 18:00:00,445.0,29.13,15.33,4.77 -2016-06-21 19:00:00,486.0,27.66,15.33,4.74 -2016-06-21 20:00:00,505.0,0.0,15.33,4.7 -2016-06-21 21:00:00,481.0,0.0,15.33,4.66 -2016-06-21 22:00:00,750.0,0.0,15.3,4.78 -2016-06-21 23:00:00,736.0,0.0,15.27,4.89 -2016-06-22 00:00:00,572.0,0.0,15.23,5.01 -2016-06-22 01:00:00,439.0,0.0,15.3,5.11 -2016-06-22 02:00:00,400.0,0.0,15.37,5.22 -2016-06-22 03:00:00,359.0,0.0,15.44,5.32 -2016-06-22 04:00:00,351.0,0.0,15.39,4.8 -2016-06-22 05:00:00,313.0,3.65,15.35,4.27 -2016-06-22 06:00:00,335.0,18.83,15.31,3.74 -2016-06-22 07:00:00,401.0,34.79,15.26,3.25 -2016-06-22 08:00:00,427.0,65.07,15.21,2.76 -2016-06-22 09:00:00,485.0,148.87,15.16,2.28 -2016-06-22 10:00:00,497.0,102.33,15.21,1.89 -2016-06-22 11:00:00,532.0,90.73,15.25,1.5 -2016-06-22 12:00:00,562.0,127.37,15.29,1.12 -2016-06-22 13:00:00,524.0,357.69,15.37,1.54 -2016-06-22 14:00:00,474.0,307.25,15.45,1.95 -2016-06-22 15:00:00,457.0,307.25,15.52,2.37 -2016-06-22 16:00:00,428.0,381.53,15.55,2.86 -2016-06-22 17:00:00,403.0,253.88,15.57,3.35 -2016-06-22 18:00:00,465.0,70.12,15.59,3.83 -2016-06-22 19:00:00,509.0,25.69,15.56,4.23 -2016-06-22 20:00:00,522.0,0.0,15.53,4.63 -2016-06-22 21:00:00,482.0,0.0,15.49,5.02 -2016-06-22 22:00:00,752.0,0.0,15.39,5.19 -2016-06-22 23:00:00,755.0,0.0,15.28,5.36 -2016-06-23 00:00:00,556.0,0.0,15.17,5.53 -2016-06-23 01:00:00,438.0,0.0,15.05,5.66 -2016-06-23 02:00:00,397.0,0.0,14.93,5.78 -2016-06-23 03:00:00,352.0,0.0,14.81,5.9 -2016-06-23 04:00:00,361.0,0.0,14.73,5.84 -2016-06-23 05:00:00,332.0,13.4,14.65,5.77 -2016-06-23 06:00:00,315.0,51.05,14.57,5.71 -2016-06-23 07:00:00,373.0,168.13,14.6,5.83 -2016-06-23 08:00:00,473.0,300.34,14.63,5.94 -2016-06-23 09:00:00,537.0,481.43,14.66,6.06 -2016-06-23 10:00:00,546.0,677.4,14.62,5.88 -2016-06-23 11:00:00,534.0,777.58,14.58,5.7 -2016-06-23 12:00:00,581.0,815.58,14.53,5.52 -2016-06-23 13:00:00,547.0,439.36,14.59,5.49 -2016-06-23 14:00:00,514.0,604.01,14.65,5.47 -2016-06-23 15:00:00,502.0,340.58,14.7,5.45 -2016-06-23 16:00:00,446.0,451.56,14.71,5.26 -2016-06-23 17:00:00,429.0,261.8,14.72,5.08 -2016-06-23 18:00:00,480.0,91.06,14.73,4.9 -2016-06-23 19:00:00,528.0,5.63,14.79,5.14 -2016-06-23 20:00:00,488.0,0.0,14.85,5.38 -2016-06-23 21:00:00,459.0,0.0,14.91,5.63 -2016-06-23 22:00:00,744.0,0.0,14.94,5.52 -2016-06-23 23:00:00,733.0,0.0,14.96,5.42 -2016-06-24 00:00:00,552.0,0.0,14.98,5.31 -2016-06-24 01:00:00,446.0,0.0,15.03,5.65 -2016-06-24 02:00:00,408.0,0.0,15.07,5.98 -2016-06-24 03:00:00,356.0,0.0,15.11,6.32 -2016-06-24 04:00:00,347.0,0.0,15.13,6.43 -2016-06-24 05:00:00,315.0,11.62,15.15,6.55 -2016-06-24 06:00:00,319.0,19.53,15.17,6.66 -2016-06-24 07:00:00,395.0,66.8,15.24,6.36 -2016-06-24 08:00:00,443.0,199.79,15.31,6.06 -2016-06-24 09:00:00,484.0,291.45,15.39,5.75 -2016-06-24 10:00:00,511.0,465.54,15.34,5.83 -2016-06-24 11:00:00,550.0,771.83,15.3,5.92 -2016-06-24 12:00:00,551.0,793.57,15.25,6.0 -2016-06-24 13:00:00,554.0,366.58,15.19,6.34 -2016-06-24 14:00:00,477.0,443.26,15.13,6.69 -2016-06-24 15:00:00,481.0,392.48,15.07,7.03 -2016-06-24 16:00:00,458.0,265.77,15.04,7.02 -2016-06-24 17:00:00,414.0,234.08,15.0,7.01 -2016-06-24 18:00:00,432.0,88.37,14.96,6.99 -2016-06-24 19:00:00,467.0,29.71,14.91,6.71 -2016-06-24 20:00:00,478.0,0.0,14.85,6.42 -2016-06-24 21:00:00,447.0,0.0,14.79,6.14 -2016-06-24 22:00:00,755.0,0.0,14.69,6.18 -2016-06-24 23:00:00,761.0,0.0,14.58,6.23 -2016-06-25 00:00:00,576.0,0.0,14.47,6.28 -2016-06-25 01:00:00,462.0,0.0,14.41,6.49 -2016-06-25 02:00:00,406.0,0.0,14.35,6.71 -2016-06-25 03:00:00,359.0,0.0,14.28,6.92 -2016-06-25 04:00:00,354.0,0.0,14.18,6.95 -2016-06-25 05:00:00,336.0,12.27,14.08,6.97 -2016-06-25 06:00:00,332.0,17.65,13.98,6.99 -2016-06-25 07:00:00,372.0,169.1,14.0,7.24 -2016-06-25 08:00:00,448.0,332.54,14.02,7.49 -2016-06-25 09:00:00,502.0,297.42,14.03,7.74 -2016-06-25 10:00:00,550.0,225.19,14.05,7.79 -2016-06-25 11:00:00,550.0,584.08,14.07,7.84 -2016-06-25 12:00:00,567.0,816.77,14.09,7.89 -2016-06-25 13:00:00,536.0,823.68,14.38,7.76 -2016-06-25 14:00:00,479.0,743.46,14.67,7.62 -2016-06-25 15:00:00,512.0,624.9,14.95,7.49 -2016-06-25 16:00:00,475.0,453.55,14.95,7.52 -2016-06-25 17:00:00,428.0,147.4,14.95,7.54 -2016-06-25 18:00:00,464.0,81.15,14.95,7.57 -2016-06-25 19:00:00,512.0,12.22,14.86,7.47 -2016-06-25 20:00:00,510.0,0.0,14.76,7.37 -2016-06-25 21:00:00,485.0,0.0,14.66,7.27 -2016-06-25 22:00:00,757.0,0.0,14.46,7.17 -2016-06-25 23:00:00,757.0,0.0,14.26,7.08 -2016-06-26 00:00:00,599.0,0.0,14.06,6.98 -2016-06-26 01:00:00,475.0,0.0,14.08,6.84 -2016-06-26 02:00:00,431.0,0.0,14.09,6.69 -2016-06-26 03:00:00,391.0,0.0,14.1,6.55 -2016-06-26 04:00:00,357.0,0.0,14.06,6.43 -2016-06-26 05:00:00,340.0,10.54,14.02,6.3 -2016-06-26 06:00:00,308.0,57.66,13.97,6.18 -2016-06-26 07:00:00,344.0,119.5,13.97,5.89 -2016-06-26 08:00:00,452.0,52.03,13.97,5.6 -2016-06-26 09:00:00,521.0,157.77,13.97,5.31 -2016-06-26 10:00:00,577.0,119.17,14.08,5.84 -2016-06-26 11:00:00,599.0,170.31,14.2,6.37 -2016-06-26 12:00:00,594.0,116.81,14.32,6.9 -2016-06-26 13:00:00,551.0,104.15,14.61,7.02 -2016-06-26 14:00:00,505.0,83.83,14.9,7.14 -2016-06-26 15:00:00,539.0,97.65,15.19,7.27 -2016-06-26 16:00:00,469.0,115.6,15.19,7.43 -2016-06-26 17:00:00,438.0,134.36,15.18,7.59 -2016-06-26 18:00:00,452.0,49.54,15.17,7.75 -2016-06-26 19:00:00,502.0,32.97,15.17,7.54 -2016-06-26 20:00:00,494.0,0.0,15.17,7.34 -2016-06-26 21:00:00,475.0,0.0,15.16,7.13 -2016-06-26 22:00:00,744.0,0.0,15.11,7.15 -2016-06-26 23:00:00,743.0,0.0,15.06,7.17 -2016-06-27 00:00:00,545.0,0.0,15.01,7.19 -2016-06-27 01:00:00,446.0,0.0,15.09,7.04 -2016-06-27 02:00:00,422.0,0.0,15.16,6.89 -2016-06-27 03:00:00,381.0,0.0,15.23,6.74 -2016-06-27 04:00:00,355.0,0.0,15.2,6.49 -2016-06-27 05:00:00,333.0,5.11,15.16,6.23 -2016-06-27 06:00:00,317.0,31.3,15.13,5.97 -2016-06-27 07:00:00,379.0,134.59,15.09,5.71 -2016-06-27 08:00:00,436.0,230.97,15.05,5.45 -2016-06-27 09:00:00,511.0,409.91,15.01,5.19 -2016-06-27 10:00:00,517.0,534.59,14.88,5.09 -2016-06-27 11:00:00,517.0,700.11,14.76,4.98 -2016-06-27 12:00:00,565.0,336.33,14.64,4.88 -2016-06-27 13:00:00,513.0,572.67,14.79,4.66 -2016-06-27 14:00:00,450.0,331.25,14.94,4.43 -2016-06-27 15:00:00,474.0,328.69,15.09,4.21 -2016-06-27 16:00:00,440.0,371.96,15.04,3.96 -2016-06-27 17:00:00,411.0,225.23,15.0,3.72 -2016-06-27 18:00:00,424.0,85.5,14.96,3.48 -2016-06-27 19:00:00,496.0,16.36,14.94,2.96 -2016-06-27 20:00:00,494.0,0.0,14.91,2.45 -2016-06-27 21:00:00,449.0,0.0,14.88,1.93 -2016-06-27 22:00:00,726.0,0.0,14.8,2.22 -2016-06-27 23:00:00,721.0,0.0,14.72,2.5 -2016-06-28 00:00:00,510.0,0.0,14.64,2.79 -2016-06-28 01:00:00,422.0,0.0,14.5,3.03 -2016-06-28 02:00:00,398.0,0.0,14.36,3.27 -2016-06-28 03:00:00,345.0,0.0,14.23,3.52 -2016-06-28 04:00:00,344.0,0.0,14.18,4.16 -2016-06-28 05:00:00,331.0,7.76,14.13,4.8 -2016-06-28 06:00:00,307.0,59.1,14.08,5.43 -2016-06-28 07:00:00,346.0,128.88,14.19,6.32 -2016-06-28 08:00:00,414.0,236.42,14.29,7.21 -2016-06-28 09:00:00,486.0,120.83,14.4,8.1 -2016-06-28 10:00:00,513.0,63.31,14.54,8.89 -2016-06-28 11:00:00,521.0,123.16,14.68,9.68 -2016-06-28 12:00:00,529.0,121.56,14.82,10.47 -2016-06-28 13:00:00,487.0,469.35,15.01,10.67 -2016-06-28 14:00:00,476.0,756.26,15.2,10.86 -2016-06-28 15:00:00,499.0,615.46,15.4,11.06 -2016-06-28 16:00:00,487.0,344.52,15.4,10.57 -2016-06-28 17:00:00,456.0,249.75,15.41,10.08 -2016-06-28 18:00:00,466.0,86.06,15.42,9.59 -2016-06-28 19:00:00,514.0,37.65,15.27,8.86 -2016-06-28 20:00:00,493.0,0.0,15.12,8.14 -2016-06-28 21:00:00,453.0,0.0,14.96,7.42 -2016-06-28 22:00:00,746.0,0.0,14.74,7.53 -2016-06-28 23:00:00,760.0,0.0,14.52,7.64 -2016-06-29 00:00:00,549.0,0.0,14.3,7.75 -2016-06-29 01:00:00,440.0,0.0,14.16,7.84 -2016-06-29 02:00:00,403.0,0.0,14.01,7.93 -2016-06-29 03:00:00,348.0,0.0,13.86,8.01 -2016-06-29 04:00:00,357.0,0.0,14.04,8.91 -2016-06-29 05:00:00,326.0,0.07,14.22,9.81 -2016-06-29 06:00:00,317.0,54.9,14.39,10.7 -2016-06-29 07:00:00,368.0,43.04,14.65,10.91 -2016-06-29 08:00:00,434.0,50.41,14.91,11.12 -2016-06-29 09:00:00,506.0,76.18,15.18,11.32 -2016-06-29 10:00:00,555.0,60.14,15.14,11.28 -2016-06-29 11:00:00,569.0,192.59,15.1,11.23 -2016-06-29 12:00:00,612.0,305.94,15.06,11.19 -2016-06-29 13:00:00,585.0,592.18,15.06,10.89 -2016-06-29 14:00:00,506.0,507.92,15.05,10.6 -2016-06-29 15:00:00,535.0,468.79,15.05,10.3 -2016-06-29 16:00:00,496.0,423.94,15.16,9.82 -2016-06-29 17:00:00,443.0,188.24,15.27,9.34 -2016-06-29 18:00:00,450.0,73.2,15.38,8.86 -2016-06-29 19:00:00,485.0,2.74,15.47,8.62 -2016-06-29 20:00:00,505.0,0.0,15.56,8.38 -2016-06-29 21:00:00,458.0,0.0,15.65,8.14 -2016-06-29 22:00:00,762.0,0.0,15.62,8.36 -2016-06-29 23:00:00,738.0,0.0,15.59,8.58 -2016-06-30 00:00:00,526.0,0.0,15.55,8.8 -2016-06-30 01:00:00,435.0,0.0,15.49,8.27 -2016-06-30 02:00:00,417.0,0.0,15.43,7.73 -2016-06-30 03:00:00,371.0,0.0,15.38,7.2 -2016-06-30 04:00:00,362.0,0.0,15.37,7.03 -2016-06-30 05:00:00,337.0,7.17,15.36,6.87 -2016-06-30 06:00:00,332.0,60.32,15.35,6.7 -2016-06-30 07:00:00,369.0,59.96,15.35,6.67 -2016-06-30 08:00:00,461.0,95.98,15.35,6.64 -2016-06-30 09:00:00,515.0,145.14,15.35,6.61 -2016-06-30 10:00:00,540.0,298.96,15.36,6.91 -2016-06-30 11:00:00,538.0,452.46,15.36,7.2 -2016-06-30 12:00:00,575.0,535.8,15.36,7.5 -2016-06-30 13:00:00,534.0,630.59,15.51,7.77 -2016-06-30 14:00:00,479.0,399.55,15.65,8.05 -2016-06-30 15:00:00,523.0,245.69,15.8,8.32 -2016-06-30 16:00:00,458.0,131.0,15.81,8.54 -2016-06-30 17:00:00,427.0,118.69,15.82,8.76 -2016-06-30 18:00:00,474.0,30.5,15.83,8.98 -2016-06-30 19:00:00,530.0,29.59,15.77,8.77 -2016-06-30 20:00:00,505.0,0.0,15.71,8.56 -2016-06-30 21:00:00,492.0,0.0,15.64,8.34 -2016-06-30 22:00:00,734.0,0.0,15.57,8.35 -2016-06-30 23:00:00,747.0,0.0,15.5,8.36 -2016-07-01 00:00:00,531.0,0.0,15.42,8.37 -2016-07-01 01:00:00,437.0,0.0,15.36,8.31 -2016-07-01 02:00:00,415.0,0.0,15.3,8.25 -2016-07-01 03:00:00,361.0,0.0,15.23,8.19 -2016-07-01 04:00:00,359.0,0.0,15.19,8.1 -2016-07-01 05:00:00,331.0,10.48,15.15,8.01 -2016-07-01 06:00:00,317.0,55.7,15.1,7.92 -2016-07-01 07:00:00,410.0,112.0,14.98,7.98 -2016-07-01 08:00:00,430.0,241.39,14.86,8.05 -2016-07-01 09:00:00,487.0,298.18,14.73,8.11 -2016-07-01 10:00:00,516.0,629.25,14.63,8.55 -2016-07-01 11:00:00,550.0,615.18,14.53,8.98 -2016-07-01 12:00:00,560.0,409.97,14.43,9.42 -2016-07-01 13:00:00,520.0,427.34,14.63,9.37 -2016-07-01 14:00:00,484.0,417.98,14.84,9.32 -2016-07-01 15:00:00,496.0,465.66,15.04,9.27 -2016-07-01 16:00:00,457.0,219.43,14.93,9.54 -2016-07-01 17:00:00,403.0,209.31,14.83,9.82 -2016-07-01 18:00:00,454.0,91.28,14.72,10.1 -2016-07-01 19:00:00,501.0,15.82,14.51,10.03 -2016-07-01 20:00:00,503.0,0.0,14.3,9.96 -2016-07-01 21:00:00,499.0,0.0,14.1,9.89 -2016-07-01 22:00:00,773.0,0.0,14.07,9.93 -2016-07-01 23:00:00,765.0,0.0,14.05,9.97 -2016-07-02 00:00:00,577.0,0.0,14.03,10.01 -2016-07-02 01:00:00,460.0,0.0,14.06,10.12 -2016-07-02 02:00:00,414.0,0.0,14.08,10.23 -2016-07-02 03:00:00,364.0,0.0,14.1,10.34 -2016-07-02 04:00:00,373.0,0.0,14.08,10.07 -2016-07-02 05:00:00,366.0,11.7,14.05,9.8 -2016-07-02 06:00:00,337.0,30.79,14.02,9.53 -2016-07-02 07:00:00,364.0,149.67,14.02,9.31 -2016-07-02 08:00:00,439.0,269.88,14.01,9.09 -2016-07-02 09:00:00,478.0,330.88,14.01,8.87 -2016-07-02 10:00:00,552.0,496.66,14.03,8.73 -2016-07-02 11:00:00,555.0,106.13,14.05,8.59 -2016-07-02 12:00:00,549.0,400.08,14.07,8.46 -2016-07-02 13:00:00,543.0,766.27,14.22,8.44 -2016-07-02 14:00:00,502.0,720.31,14.38,8.43 -2016-07-02 15:00:00,488.0,266.33,14.53,8.41 -2016-07-02 16:00:00,452.0,356.51,14.59,8.21 -2016-07-02 17:00:00,429.0,203.3,14.66,8.01 -2016-07-02 18:00:00,458.0,80.66,14.72,7.81 -2016-07-02 19:00:00,525.0,13.4,14.8,7.84 -2016-07-02 20:00:00,529.0,0.0,14.88,7.88 -2016-07-02 21:00:00,510.0,0.0,14.96,7.92 -2016-07-02 22:00:00,762.0,0.0,15.04,8.48 -2016-07-02 23:00:00,784.0,0.0,15.12,9.05 -2016-07-03 00:00:00,619.0,0.0,15.2,9.61 -2016-07-03 01:00:00,458.0,0.0,15.2,9.6 -2016-07-03 02:00:00,442.0,0.0,15.2,9.59 -2016-07-03 03:00:00,406.0,0.0,15.19,9.57 -2016-07-03 04:00:00,384.0,0.0,15.28,9.61 -2016-07-03 05:00:00,365.0,6.64,15.36,9.66 -2016-07-03 06:00:00,346.0,48.29,15.44,9.7 -2016-07-03 07:00:00,353.0,148.22,15.41,9.24 -2016-07-03 08:00:00,434.0,316.13,15.37,8.78 -2016-07-03 09:00:00,495.0,513.53,15.34,8.32 -2016-07-03 10:00:00,562.0,470.59,15.3,7.69 -2016-07-03 11:00:00,584.0,741.39,15.26,7.07 -2016-07-03 12:00:00,550.0,796.91,15.22,6.44 -2016-07-03 13:00:00,510.0,796.23,15.2,5.94 -2016-07-03 14:00:00,447.0,710.36,15.18,5.45 -2016-07-03 15:00:00,472.0,402.95,15.16,4.95 -2016-07-03 16:00:00,418.0,278.95,15.15,4.51 -2016-07-03 17:00:00,382.0,127.25,15.15,4.07 -2016-07-03 18:00:00,404.0,50.16,15.15,3.63 -2016-07-03 19:00:00,457.0,23.18,15.11,3.27 -2016-07-03 20:00:00,469.0,0.0,15.07,2.92 -2016-07-03 21:00:00,430.0,0.0,15.04,2.57 -2016-07-03 22:00:00,714.0,0.0,15.01,2.66 -2016-07-03 23:00:00,724.0,0.0,14.99,2.75 -2016-07-04 00:00:00,518.0,0.0,14.96,2.84 -2016-07-04 01:00:00,437.0,0.0,15.04,3.71 -2016-07-04 02:00:00,402.0,0.0,15.12,4.59 -2016-07-04 03:00:00,340.0,0.0,15.2,5.46 -2016-07-04 04:00:00,342.0,0.0,15.23,5.39 -2016-07-04 05:00:00,332.0,9.35,15.26,5.32 -2016-07-04 06:00:00,302.0,58.1,15.29,5.26 -2016-07-04 07:00:00,341.0,138.89,15.28,5.07 -2016-07-04 08:00:00,435.0,215.35,15.27,4.88 -2016-07-04 09:00:00,502.0,287.37,15.27,4.69 -2016-07-04 10:00:00,512.0,91.22,15.26,4.82 -2016-07-04 11:00:00,538.0,196.59,15.25,4.95 -2016-07-04 12:00:00,574.0,221.04,15.24,5.08 -2016-07-04 13:00:00,536.0,328.63,15.23,5.16 -2016-07-04 14:00:00,483.0,407.7,15.23,5.25 -2016-07-04 15:00:00,478.0,276.11,15.22,5.34 -2016-07-04 16:00:00,427.0,230.17,15.23,5.51 -2016-07-04 17:00:00,399.0,122.23,15.25,5.69 -2016-07-04 18:00:00,442.0,70.0,15.27,5.86 -2016-07-04 19:00:00,502.0,25.75,15.27,6.06 -2016-07-04 20:00:00,513.0,0.0,15.27,6.25 -2016-07-04 21:00:00,486.0,0.0,15.27,6.44 -2016-07-04 22:00:00,748.0,0.0,15.25,6.59 -2016-07-04 23:00:00,736.0,0.0,15.24,6.74 -2016-07-05 00:00:00,550.0,0.0,15.22,6.88 -2016-07-05 01:00:00,417.0,0.0,15.16,7.02 -2016-07-05 02:00:00,410.0,0.0,15.1,7.15 -2016-07-05 03:00:00,352.0,0.0,15.04,7.28 -2016-07-05 04:00:00,328.0,0.0,14.83,7.28 -2016-07-05 05:00:00,308.0,8.83,14.62,7.27 -2016-07-05 06:00:00,310.0,58.07,14.41,7.27 -2016-07-05 07:00:00,363.0,106.76,14.26,6.88 -2016-07-05 08:00:00,459.0,330.25,14.11,6.49 -2016-07-05 09:00:00,515.0,491.45,13.97,6.1 -2016-07-05 10:00:00,546.0,662.85,13.97,5.78 -2016-07-05 11:00:00,564.0,763.88,13.98,5.46 -2016-07-05 12:00:00,543.0,793.81,13.98,5.14 -2016-07-05 13:00:00,501.0,795.29,14.22,4.56 -2016-07-05 14:00:00,454.0,725.06,14.47,3.98 -2016-07-05 15:00:00,476.0,605.06,14.71,3.39 -2016-07-05 16:00:00,429.0,434.91,14.73,3.28 -2016-07-05 17:00:00,393.0,259.52,14.75,3.17 -2016-07-05 18:00:00,450.0,80.6,14.77,3.06 -2016-07-05 19:00:00,481.0,34.31,14.72,3.3 -2016-07-05 20:00:00,478.0,0.0,14.67,3.53 -2016-07-05 21:00:00,463.0,0.0,14.62,3.77 -2016-07-05 22:00:00,730.0,0.0,14.55,3.46 -2016-07-05 23:00:00,729.0,0.0,14.48,3.16 -2016-07-06 00:00:00,559.0,0.0,14.41,2.86 -2016-07-06 01:00:00,433.0,0.0,14.26,2.61 -2016-07-06 02:00:00,410.0,0.0,14.11,2.36 -2016-07-06 03:00:00,363.0,0.0,13.95,2.11 -2016-07-06 04:00:00,350.0,0.0,13.93,1.72 -2016-07-06 05:00:00,323.0,10.53,13.9,1.34 -2016-07-06 06:00:00,315.0,47.15,13.87,0.95 -2016-07-06 07:00:00,359.0,158.44,13.96,0.83 -2016-07-06 08:00:00,442.0,341.72,14.05,0.71 -2016-07-06 09:00:00,480.0,509.4,14.15,0.59 -2016-07-06 10:00:00,520.0,631.59,14.31,0.54 -2016-07-06 11:00:00,525.0,708.82,14.47,0.48 -2016-07-06 12:00:00,549.0,675.67,14.63,0.43 -2016-07-06 13:00:00,515.0,716.95,14.83,0.62 -2016-07-06 14:00:00,461.0,558.59,15.02,0.81 -2016-07-06 15:00:00,488.0,493.9,15.22,1.01 -2016-07-06 16:00:00,416.0,374.73,15.31,1.26 -2016-07-06 17:00:00,392.0,259.48,15.41,1.5 -2016-07-06 18:00:00,426.0,76.19,15.5,1.75 -2016-07-06 19:00:00,477.0,34.79,15.4,1.32 -2016-07-06 20:00:00,504.0,0.0,15.3,0.89 -2016-07-06 21:00:00,496.0,0.0,15.2,0.46 -2016-07-06 22:00:00,737.0,0.0,15.11,0.57 -2016-07-06 23:00:00,733.0,0.0,15.02,0.68 -2016-07-07 00:00:00,566.0,0.0,14.93,0.79 -2016-07-07 01:00:00,418.0,0.0,14.8,1.17 -2016-07-07 02:00:00,416.0,0.0,14.67,1.56 -2016-07-07 03:00:00,357.0,0.0,14.54,1.94 -2016-07-07 04:00:00,349.0,0.0,14.5,1.82 -2016-07-07 05:00:00,327.0,5.64,14.46,1.7 -2016-07-07 06:00:00,339.0,29.69,14.42,1.57 -2016-07-07 07:00:00,367.0,95.25,14.59,1.29 -2016-07-07 08:00:00,433.0,187.57,14.75,1.0 -2016-07-07 09:00:00,492.0,292.09,14.91,0.72 -2016-07-07 10:00:00,539.0,253.79,15.23,0.95 -2016-07-07 11:00:00,541.0,641.91,15.54,1.19 -2016-07-07 12:00:00,572.0,649.81,15.85,1.42 -2016-07-07 13:00:00,549.0,592.87,15.83,2.12 -2016-07-07 14:00:00,513.0,469.55,15.8,2.82 -2016-07-07 15:00:00,493.0,148.0,15.78,3.52 -2016-07-07 16:00:00,434.0,88.61,15.67,3.8 -2016-07-07 17:00:00,393.0,68.84,15.57,4.09 -2016-07-07 18:00:00,452.0,59.18,15.46,4.37 -2016-07-07 19:00:00,532.0,36.22,15.34,4.14 -2016-07-07 20:00:00,513.0,0.0,15.22,3.91 -2016-07-07 21:00:00,490.0,0.0,15.11,3.68 -2016-07-07 22:00:00,713.0,0.0,15.06,3.88 -2016-07-07 23:00:00,712.0,0.0,15.0,4.08 -2016-07-08 00:00:00,554.0,0.0,14.95,4.28 -2016-07-08 01:00:00,437.0,0.0,14.92,4.67 -2016-07-08 02:00:00,427.0,0.0,14.88,5.06 -2016-07-08 03:00:00,369.0,0.0,14.84,5.45 -2016-07-08 04:00:00,361.0,0.0,15.01,5.78 -2016-07-08 05:00:00,329.0,9.91,15.18,6.12 -2016-07-08 06:00:00,324.0,30.29,15.35,6.46 -2016-07-08 07:00:00,383.0,91.26,15.55,6.19 -2016-07-08 08:00:00,428.0,56.06,15.74,5.92 -2016-07-08 09:00:00,495.0,187.65,15.94,5.66 -2016-07-08 10:00:00,521.0,226.66,15.94,5.41 -2016-07-08 11:00:00,544.0,631.53,15.95,5.17 -2016-07-08 12:00:00,553.0,664.91,15.96,4.92 -2016-07-08 13:00:00,544.0,689.56,16.04,4.97 -2016-07-08 14:00:00,533.0,698.44,16.12,5.01 -2016-07-08 15:00:00,523.0,467.81,16.2,5.05 -2016-07-08 16:00:00,456.0,450.69,16.19,5.21 -2016-07-08 17:00:00,431.0,262.45,16.19,5.38 -2016-07-08 18:00:00,467.0,71.18,16.19,5.54 -2016-07-08 19:00:00,505.0,32.12,16.19,5.79 -2016-07-08 20:00:00,540.0,0.0,16.19,6.03 -2016-07-08 21:00:00,507.0,0.0,16.2,6.28 -2016-07-08 22:00:00,740.0,0.0,16.19,6.51 -2016-07-08 23:00:00,741.0,0.0,16.19,6.74 -2016-07-09 00:00:00,555.0,0.0,16.19,6.98 -2016-07-09 01:00:00,435.0,0.0,16.18,7.59 -2016-07-09 02:00:00,409.0,0.0,16.17,8.2 -2016-07-09 03:00:00,366.0,0.0,16.16,8.81 -2016-07-09 04:00:00,359.0,0.0,16.12,8.62 -2016-07-09 05:00:00,329.0,7.14,16.07,8.43 -2016-07-09 06:00:00,325.0,35.49,16.02,8.23 -2016-07-09 07:00:00,347.0,111.86,15.95,8.19 -2016-07-09 08:00:00,445.0,115.25,15.88,8.15 -2016-07-09 09:00:00,483.0,143.62,15.81,8.11 -2016-07-09 10:00:00,546.0,180.62,15.81,8.31 -2016-07-09 11:00:00,570.0,141.19,15.81,8.51 -2016-07-09 12:00:00,587.0,354.16,15.81,8.7 -2016-07-09 13:00:00,562.0,321.0,15.84,8.91 -2016-07-09 14:00:00,535.0,342.84,15.87,9.13 -2016-07-09 15:00:00,523.0,240.52,15.9,9.34 -2016-07-09 16:00:00,459.0,131.02,15.91,9.37 -2016-07-09 17:00:00,435.0,62.13,15.93,9.39 -2016-07-09 18:00:00,498.0,31.2,15.95,9.42 -2016-07-09 19:00:00,568.0,1.47,15.97,9.36 -2016-07-09 20:00:00,570.0,0.0,15.99,9.3 -2016-07-09 21:00:00,507.0,0.0,16.02,9.24 -2016-07-09 22:00:00,773.0,0.0,16.07,9.21 -2016-07-09 23:00:00,755.0,0.0,16.13,9.19 -2016-07-10 00:00:00,580.0,0.0,16.18,9.16 -2016-07-10 01:00:00,450.0,0.0,16.27,9.13 -2016-07-10 02:00:00,408.0,0.0,16.36,9.1 -2016-07-10 03:00:00,377.0,0.0,16.45,9.08 -2016-07-10 04:00:00,351.0,0.0,16.5,8.84 -2016-07-10 05:00:00,332.0,8.73,16.55,8.6 -2016-07-10 06:00:00,325.0,43.46,16.6,8.36 -2016-07-10 07:00:00,339.0,149.36,16.58,8.32 -2016-07-10 08:00:00,424.0,277.37,16.56,8.28 -2016-07-10 09:00:00,514.0,503.74,16.53,8.23 -2016-07-10 10:00:00,599.0,236.63,16.49,8.48 -2016-07-10 11:00:00,651.0,616.35,16.45,8.72 -2016-07-10 12:00:00,637.0,515.48,16.41,8.97 -2016-07-10 13:00:00,560.0,748.21,16.49,9.31 -2016-07-10 14:00:00,521.0,627.3,16.56,9.66 -2016-07-10 15:00:00,514.0,416.74,16.64,10.01 -2016-07-10 16:00:00,448.0,419.81,16.64,10.11 -2016-07-10 17:00:00,418.0,230.08,16.65,10.22 -2016-07-10 18:00:00,459.0,90.84,16.66,10.32 -2016-07-10 19:00:00,534.0,7.12,16.56,10.41 -2016-07-10 20:00:00,540.0,0.0,16.45,10.5 -2016-07-10 21:00:00,472.0,0.0,16.35,10.59 -2016-07-10 22:00:00,703.0,0.0,16.31,10.59 -2016-07-10 23:00:00,721.0,0.0,16.26,10.58 -2016-07-11 00:00:00,601.0,0.0,16.22,10.58 -2016-07-11 01:00:00,440.0,0.0,16.16,10.39 -2016-07-11 02:00:00,428.0,0.0,16.1,10.19 -2016-07-11 03:00:00,382.0,0.0,16.04,10.0 -2016-07-11 04:00:00,361.0,0.0,15.92,9.77 -2016-07-11 05:00:00,317.0,8.23,15.8,9.55 -2016-07-11 06:00:00,298.0,52.62,15.68,9.32 -2016-07-11 07:00:00,351.0,155.12,15.62,8.97 -2016-07-11 08:00:00,435.0,282.0,15.56,8.61 -2016-07-11 09:00:00,519.0,389.6,15.51,8.25 -2016-07-11 10:00:00,549.0,307.41,15.54,7.92 -2016-07-11 11:00:00,572.0,172.61,15.57,7.6 -2016-07-11 12:00:00,597.0,439.81,15.6,7.27 -2016-07-11 13:00:00,560.0,388.58,15.71,7.22 -2016-07-11 14:00:00,541.0,249.41,15.83,7.18 -2016-07-11 15:00:00,528.0,245.65,15.94,7.13 -2016-07-11 16:00:00,469.0,206.02,15.81,6.81 -2016-07-11 17:00:00,425.0,227.65,15.69,6.49 -2016-07-11 18:00:00,472.0,16.84,15.57,6.17 -2016-07-11 19:00:00,553.0,32.26,15.4,6.15 -2016-07-11 20:00:00,542.0,0.0,15.23,6.14 -2016-07-11 21:00:00,490.0,0.0,15.06,6.12 -2016-07-11 22:00:00,748.0,0.0,14.92,6.32 -2016-07-11 23:00:00,736.0,0.0,14.78,6.52 -2016-07-12 00:00:00,561.0,0.0,14.64,6.72 -2016-07-12 01:00:00,428.0,0.0,14.85,6.51 -2016-07-12 02:00:00,413.0,0.0,15.06,6.29 -2016-07-12 03:00:00,381.0,0.0,15.27,6.08 -2016-07-12 04:00:00,354.0,0.0,15.24,6.19 -2016-07-12 05:00:00,333.0,6.65,15.21,6.29 -2016-07-12 06:00:00,318.0,51.3,15.18,6.4 -2016-07-12 07:00:00,365.0,154.07,15.19,6.23 -2016-07-12 08:00:00,457.0,211.86,15.19,6.06 -2016-07-12 09:00:00,530.0,450.55,15.19,5.89 -2016-07-12 10:00:00,572.0,255.94,15.19,5.89 -2016-07-12 11:00:00,588.0,612.16,15.18,5.9 -2016-07-12 12:00:00,612.0,277.95,15.17,5.9 -2016-07-12 13:00:00,586.0,764.89,14.82,6.26 -2016-07-12 14:00:00,560.0,584.08,14.46,6.61 -2016-07-12 15:00:00,539.0,350.35,14.11,6.97 -2016-07-12 16:00:00,467.0,433.43,14.26,7.07 -2016-07-12 17:00:00,444.0,258.62,14.41,7.18 -2016-07-12 18:00:00,518.0,99.49,14.56,7.28 -2016-07-12 19:00:00,582.0,27.8,14.6,7.47 -2016-07-12 20:00:00,568.0,0.0,14.64,7.65 -2016-07-12 21:00:00,529.0,0.0,14.69,7.83 -2016-07-12 22:00:00,766.0,0.0,14.55,7.96 -2016-07-12 23:00:00,778.0,0.0,14.42,8.08 -2016-07-13 00:00:00,578.0,0.0,14.28,8.21 -2016-07-13 01:00:00,434.0,0.0,14.12,8.16 -2016-07-13 02:00:00,435.0,0.0,13.95,8.11 -2016-07-13 03:00:00,370.0,0.0,13.78,8.06 -2016-07-13 04:00:00,345.0,0.0,13.9,8.02 -2016-07-13 05:00:00,335.0,5.66,14.01,7.99 -2016-07-13 06:00:00,313.0,51.61,14.12,7.96 -2016-07-13 07:00:00,368.0,154.11,14.33,8.17 -2016-07-13 08:00:00,478.0,348.82,14.54,8.39 -2016-07-13 09:00:00,550.0,530.19,14.74,8.61 -2016-07-13 10:00:00,606.0,611.78,14.78,8.46 -2016-07-13 11:00:00,629.0,438.48,14.82,8.3 -2016-07-13 12:00:00,636.0,542.54,14.86,8.15 -2016-07-13 13:00:00,612.0,786.74,14.82,8.0 -2016-07-13 14:00:00,558.0,673.92,14.77,7.84 -2016-07-13 15:00:00,529.0,364.79,14.73,7.68 -2016-07-13 16:00:00,491.0,319.89,14.77,7.72 -2016-07-13 17:00:00,433.0,266.74,14.81,7.76 -2016-07-13 18:00:00,487.0,76.13,14.85,7.79 -2016-07-13 19:00:00,569.0,29.73,14.85,7.73 -2016-07-13 20:00:00,585.0,0.0,14.84,7.66 -2016-07-13 21:00:00,532.0,0.0,14.84,7.6 -2016-07-13 22:00:00,820.0,0.0,14.81,7.55 -2016-07-13 23:00:00,784.0,0.0,14.78,7.5 -2016-07-14 00:00:00,580.0,0.0,14.75,7.45 -2016-07-14 01:00:00,464.0,0.0,14.8,7.43 -2016-07-14 02:00:00,455.0,0.0,14.85,7.4 -2016-07-14 03:00:00,391.0,0.0,14.89,7.38 -2016-07-14 04:00:00,356.0,0.0,14.9,7.27 -2016-07-14 05:00:00,318.0,7.19,14.91,7.17 -2016-07-14 06:00:00,313.0,49.99,14.92,7.06 -2016-07-14 07:00:00,347.0,111.52,14.98,6.66 -2016-07-14 08:00:00,470.0,345.48,15.03,6.26 -2016-07-14 09:00:00,557.0,491.64,15.09,5.86 -2016-07-14 10:00:00,607.0,663.93,15.11,5.58 -2016-07-14 11:00:00,644.0,754.13,15.14,5.3 -2016-07-14 12:00:00,656.0,793.33,15.17,5.02 -2016-07-14 13:00:00,625.0,794.16,15.23,4.59 -2016-07-14 14:00:00,555.0,722.42,15.28,4.16 -2016-07-14 15:00:00,537.0,597.92,15.34,3.72 -2016-07-14 16:00:00,505.0,454.5,15.33,3.55 -2016-07-14 17:00:00,440.0,258.39,15.33,3.37 -2016-07-14 18:00:00,489.0,76.91,15.33,3.2 -2016-07-14 19:00:00,550.0,26.35,15.25,2.97 -2016-07-14 20:00:00,567.0,0.0,15.17,2.73 -2016-07-14 21:00:00,551.0,0.0,15.1,2.5 -2016-07-14 22:00:00,847.0,0.0,15.02,2.02 -2016-07-14 23:00:00,846.0,0.0,14.95,1.54 -2016-07-15 00:00:00,634.0,0.0,14.88,1.06 -2016-07-15 01:00:00,466.0,0.0,14.63,1.37 -2016-07-15 02:00:00,439.0,0.0,14.38,1.68 -2016-07-15 03:00:00,384.0,0.0,14.12,1.99 -2016-07-15 04:00:00,363.0,0.0,14.16,2.35 -2016-07-15 05:00:00,334.0,0.0,14.2,2.72 -2016-07-15 06:00:00,327.0,48.65,14.24,3.09 -2016-07-15 07:00:00,371.0,122.33,14.38,3.26 -2016-07-15 08:00:00,434.0,205.37,14.52,3.44 -2016-07-15 09:00:00,551.0,298.43,14.66,3.61 -2016-07-15 10:00:00,602.0,335.17,14.8,3.54 -2016-07-15 11:00:00,643.0,488.14,14.94,3.48 -2016-07-15 12:00:00,661.0,705.96,15.07,3.41 -2016-07-15 13:00:00,640.0,754.89,15.28,3.48 -2016-07-15 14:00:00,588.0,705.66,15.48,3.55 -2016-07-15 15:00:00,570.0,609.62,15.69,3.63 -2016-07-15 16:00:00,497.0,455.68,15.73,3.38 -2016-07-15 17:00:00,459.0,264.72,15.78,3.13 -2016-07-15 18:00:00,489.0,71.49,15.83,2.88 -2016-07-15 19:00:00,586.0,24.35,15.79,2.46 -2016-07-15 20:00:00,569.0,0.0,15.75,2.04 -2016-07-15 21:00:00,565.0,0.0,15.71,1.61 -2016-07-15 22:00:00,825.0,0.0,15.68,1.5 -2016-07-15 23:00:00,805.0,0.0,15.66,1.38 -2016-07-16 00:00:00,635.0,0.0,15.63,1.27 -2016-07-16 01:00:00,505.0,0.0,15.55,0.97 -2016-07-16 02:00:00,454.0,0.0,15.46,0.68 -2016-07-16 03:00:00,379.0,0.0,15.37,0.39 -2016-07-16 04:00:00,359.0,0.0,15.37,0.51 -2016-07-16 05:00:00,322.0,5.6,15.37,0.64 -2016-07-16 06:00:00,329.0,32.79,15.37,0.77 -2016-07-16 07:00:00,344.0,147.67,15.6,1.17 -2016-07-16 08:00:00,448.0,340.87,15.83,1.57 -2016-07-16 09:00:00,550.0,511.36,16.07,1.97 -2016-07-16 10:00:00,600.0,640.71,16.24,1.91 -2016-07-16 11:00:00,619.0,724.75,16.42,1.85 -2016-07-16 12:00:00,614.0,761.61,16.6,1.79 -2016-07-16 13:00:00,604.0,762.19,16.92,1.65 -2016-07-16 14:00:00,594.0,695.73,17.23,1.51 -2016-07-16 15:00:00,577.0,592.58,17.55,1.37 -2016-07-16 16:00:00,496.0,446.45,17.38,1.88 -2016-07-16 17:00:00,441.0,262.88,17.22,2.39 -2016-07-16 18:00:00,492.0,71.16,17.05,2.9 -2016-07-16 19:00:00,583.0,25.48,17.04,3.26 -2016-07-16 20:00:00,609.0,0.0,17.02,3.61 -2016-07-16 21:00:00,588.0,0.0,17.01,3.97 -2016-07-16 22:00:00,866.0,0.0,16.91,4.1 -2016-07-16 23:00:00,846.0,0.0,16.8,4.22 -2016-07-17 00:00:00,611.0,0.0,16.7,4.34 -2016-07-17 01:00:00,509.0,0.0,16.64,4.17 -2016-07-17 02:00:00,458.0,0.0,16.58,4.0 -2016-07-17 03:00:00,375.0,0.0,16.51,3.82 -2016-07-17 04:00:00,365.0,0.0,16.48,3.71 -2016-07-17 05:00:00,337.0,5.57,16.45,3.59 -2016-07-17 06:00:00,308.0,32.7,16.41,3.48 -2016-07-17 07:00:00,342.0,147.56,16.61,3.64 -2016-07-17 08:00:00,439.0,343.23,16.81,3.81 -2016-07-17 09:00:00,535.0,517.21,17.01,3.97 -2016-07-17 10:00:00,570.0,657.51,17.22,4.22 -2016-07-17 11:00:00,634.0,744.95,17.43,4.46 -2016-07-17 12:00:00,663.0,786.99,17.63,4.7 -2016-07-17 13:00:00,610.0,790.95,17.88,4.91 -2016-07-17 14:00:00,572.0,722.88,18.13,5.13 -2016-07-17 15:00:00,540.0,612.98,18.38,5.34 -2016-07-17 16:00:00,468.0,455.37,18.4,5.55 -2016-07-17 17:00:00,428.0,264.22,18.42,5.76 -2016-07-17 18:00:00,476.0,78.55,18.44,5.97 -2016-07-17 19:00:00,528.0,31.11,18.41,6.12 -2016-07-17 20:00:00,575.0,0.0,18.37,6.27 -2016-07-17 21:00:00,527.0,0.0,18.34,6.41 -2016-07-17 22:00:00,766.0,0.0,18.35,6.46 -2016-07-17 23:00:00,782.0,0.0,18.37,6.51 -2016-07-18 00:00:00,560.0,0.0,18.39,6.55 -2016-07-18 01:00:00,429.0,0.0,18.4,6.46 -2016-07-18 02:00:00,420.0,0.0,18.4,6.36 -2016-07-18 03:00:00,367.0,0.0,18.4,6.26 -2016-07-18 04:00:00,356.0,0.0,18.37,6.37 -2016-07-18 05:00:00,318.0,5.01,18.34,6.48 -2016-07-18 06:00:00,306.0,34.4,18.3,6.59 -2016-07-18 07:00:00,353.0,146.46,18.47,6.69 -2016-07-18 08:00:00,437.0,341.46,18.63,6.78 -2016-07-18 09:00:00,518.0,517.63,18.8,6.87 -2016-07-18 10:00:00,542.0,659.22,18.98,6.79 -2016-07-18 11:00:00,565.0,751.05,19.16,6.71 -2016-07-18 12:00:00,621.0,787.51,19.34,6.63 -2016-07-18 13:00:00,638.0,791.23,19.62,6.7 -2016-07-18 14:00:00,570.0,724.51,19.91,6.77 -2016-07-18 15:00:00,547.0,609.87,20.19,6.84 -2016-07-18 16:00:00,486.0,451.29,20.2,6.8 -2016-07-18 17:00:00,456.0,261.83,20.22,6.75 -2016-07-18 18:00:00,474.0,71.37,20.23,6.7 -2016-07-18 19:00:00,550.0,25.1,20.14,6.64 -2016-07-18 20:00:00,553.0,0.0,20.04,6.58 -2016-07-18 21:00:00,508.0,0.0,19.95,6.52 -2016-07-18 22:00:00,759.0,0.0,19.89,6.38 -2016-07-18 23:00:00,792.0,0.0,19.83,6.24 -2016-07-19 00:00:00,560.0,0.0,19.77,6.1 -2016-07-19 01:00:00,424.0,0.0,19.84,6.1 -2016-07-19 02:00:00,420.0,0.0,19.91,6.1 -2016-07-19 03:00:00,370.0,0.0,19.97,6.1 -2016-07-19 04:00:00,352.0,0.0,19.84,5.73 -2016-07-19 05:00:00,325.0,4.48,19.71,5.37 -2016-07-19 06:00:00,309.0,32.2,19.57,5.01 -2016-07-19 07:00:00,359.0,144.6,19.63,4.92 -2016-07-19 08:00:00,448.0,254.77,19.69,4.84 -2016-07-19 09:00:00,529.0,503.97,19.75,4.76 -2016-07-19 10:00:00,586.0,640.41,19.91,4.72 -2016-07-19 11:00:00,586.0,572.45,20.07,4.69 -2016-07-19 12:00:00,607.0,395.3,20.23,4.65 -2016-07-19 13:00:00,604.0,190.54,20.14,3.37 -2016-07-19 14:00:00,576.0,72.33,20.06,2.1 -2016-07-19 15:00:00,557.0,66.28,19.97,0.83 -2016-07-19 16:00:00,500.0,327.52,20.24,1.6 -2016-07-19 17:00:00,463.0,50.21,20.52,2.36 -2016-07-19 18:00:00,489.0,80.85,20.79,3.13 -2016-07-19 19:00:00,569.0,30.16,20.13,3.89 -2016-07-19 20:00:00,580.0,0.0,19.47,4.66 -2016-07-19 21:00:00,530.0,0.0,18.82,5.42 -2016-07-19 22:00:00,816.0,0.0,18.66,5.33 -2016-07-19 23:00:00,820.0,0.0,18.5,5.25 -2016-07-20 00:00:00,588.0,0.0,18.34,5.16 -2016-07-20 01:00:00,438.0,0.0,17.99,5.8 -2016-07-20 02:00:00,434.0,0.0,17.63,6.45 -2016-07-20 03:00:00,380.0,0.0,17.27,7.09 -2016-07-20 04:00:00,350.0,0.0,17.15,6.95 -2016-07-20 05:00:00,328.0,1.86,17.02,6.81 -2016-07-20 06:00:00,323.0,2.71,16.89,6.68 -2016-07-20 07:00:00,397.0,146.36,16.93,6.38 -2016-07-20 08:00:00,449.0,255.48,16.97,6.09 -2016-07-20 09:00:00,509.0,113.63,17.0,5.79 -2016-07-20 10:00:00,571.0,232.72,17.16,5.68 -2016-07-20 11:00:00,601.0,420.83,17.32,5.56 -2016-07-20 12:00:00,638.0,447.81,17.47,5.45 -2016-07-20 13:00:00,661.0,787.8,17.62,5.43 -2016-07-20 14:00:00,629.0,726.26,17.78,5.42 -2016-07-20 15:00:00,597.0,608.88,17.93,5.41 -2016-07-20 16:00:00,513.0,403.77,17.79,5.45 -2016-07-20 17:00:00,474.0,146.94,17.66,5.49 -2016-07-20 18:00:00,528.0,65.61,17.53,5.53 -2016-07-20 19:00:00,567.0,22.93,17.33,5.23 -2016-07-20 20:00:00,562.0,0.0,17.12,4.92 -2016-07-20 21:00:00,557.0,0.0,16.92,4.62 -2016-07-20 22:00:00,830.0,0.0,16.77,4.26 -2016-07-20 23:00:00,836.0,0.0,16.63,3.9 -2016-07-21 00:00:00,588.0,0.0,16.49,3.54 -2016-07-21 01:00:00,445.0,0.0,16.42,3.3 -2016-07-21 02:00:00,430.0,0.0,16.35,3.06 -2016-07-21 03:00:00,386.0,0.0,16.27,2.81 -2016-07-21 04:00:00,360.0,0.0,16.22,3.06 -2016-07-21 05:00:00,329.0,0.38,16.17,3.3 -2016-07-21 06:00:00,314.0,23.06,16.12,3.54 -2016-07-21 07:00:00,366.0,41.13,16.13,3.66 -2016-07-21 08:00:00,461.0,103.77,16.14,3.77 -2016-07-21 09:00:00,541.0,129.27,16.15,3.88 -2016-07-21 10:00:00,571.0,290.04,16.28,4.27 -2016-07-21 11:00:00,612.0,411.53,16.41,4.66 -2016-07-21 12:00:00,653.0,448.65,16.54,5.05 -2016-07-21 13:00:00,650.0,761.33,16.84,4.99 -2016-07-21 14:00:00,645.0,620.6,17.13,4.93 -2016-07-21 15:00:00,608.0,465.99,17.43,4.87 -2016-07-21 16:00:00,512.0,424.99,17.54,4.86 -2016-07-21 17:00:00,460.0,215.01,17.66,4.84 -2016-07-21 18:00:00,494.0,25.31,17.77,4.83 -2016-07-21 19:00:00,561.0,16.72,17.77,4.48 -2016-07-21 20:00:00,592.0,0.0,17.77,4.14 -2016-07-21 21:00:00,564.0,0.0,17.77,3.79 -2016-07-21 22:00:00,804.0,0.0,17.75,3.68 -2016-07-21 23:00:00,835.0,0.0,17.72,3.57 -2016-07-22 00:00:00,592.0,0.0,17.7,3.46 -2016-07-22 01:00:00,459.0,0.0,17.69,3.63 -2016-07-22 02:00:00,442.0,0.0,17.67,3.8 -2016-07-22 03:00:00,377.0,0.0,17.65,3.97 -2016-07-22 04:00:00,362.0,0.0,17.62,3.7 -2016-07-22 05:00:00,328.0,3.14,17.59,3.43 -2016-07-22 06:00:00,317.0,35.13,17.56,3.16 -2016-07-22 07:00:00,398.0,142.98,17.59,2.82 -2016-07-22 08:00:00,451.0,223.58,17.62,2.48 -2016-07-22 09:00:00,516.0,337.62,17.66,2.14 -2016-07-22 10:00:00,604.0,475.96,17.65,2.48 -2016-07-22 11:00:00,596.0,550.83,17.65,2.83 -2016-07-22 12:00:00,677.0,189.39,17.65,3.17 -2016-07-22 13:00:00,646.0,312.22,17.64,3.54 -2016-07-22 14:00:00,614.0,297.95,17.62,3.9 -2016-07-22 15:00:00,590.0,421.98,17.61,4.26 -2016-07-22 16:00:00,508.0,202.08,17.56,4.5 -2016-07-22 17:00:00,442.0,181.96,17.51,4.74 -2016-07-22 18:00:00,515.0,83.81,17.46,4.98 -2016-07-22 19:00:00,602.0,28.02,17.37,5.14 -2016-07-22 20:00:00,614.0,0.0,17.27,5.31 -2016-07-22 21:00:00,592.0,0.0,17.18,5.48 -2016-07-22 22:00:00,844.0,0.0,17.09,5.12 -2016-07-22 23:00:00,841.0,0.0,17.01,4.77 -2016-07-23 00:00:00,611.0,0.0,16.93,4.41 -2016-07-23 01:00:00,456.0,0.0,16.75,4.27 -2016-07-23 02:00:00,456.0,0.0,16.57,4.13 -2016-07-23 03:00:00,392.0,0.0,16.38,3.99 -2016-07-23 04:00:00,376.0,0.0,16.31,3.96 -2016-07-23 05:00:00,337.0,1.87,16.24,3.94 -2016-07-23 06:00:00,330.0,39.41,16.16,3.92 -2016-07-23 07:00:00,384.0,140.44,16.17,3.71 -2016-07-23 08:00:00,494.0,201.93,16.18,3.5 -2016-07-23 09:00:00,580.0,274.51,16.18,3.3 -2016-07-23 10:00:00,593.0,135.21,16.37,3.49 -2016-07-23 11:00:00,639.0,399.07,16.56,3.67 -2016-07-23 12:00:00,629.0,463.86,16.74,3.86 -2016-07-23 13:00:00,608.0,273.73,16.97,4.18 -2016-07-23 14:00:00,602.0,127.97,17.2,4.5 -2016-07-23 15:00:00,603.0,139.47,17.43,4.81 -2016-07-23 16:00:00,548.0,93.3,17.44,4.77 -2016-07-23 17:00:00,498.0,201.84,17.46,4.72 -2016-07-23 18:00:00,518.0,84.87,17.47,4.68 -2016-07-23 19:00:00,606.0,14.36,17.41,4.62 -2016-07-23 20:00:00,633.0,0.0,17.35,4.57 -2016-07-23 21:00:00,591.0,0.0,17.29,4.51 -2016-07-23 22:00:00,863.0,0.0,17.13,4.38 -2016-07-23 23:00:00,848.0,0.0,16.98,4.25 -2016-07-24 00:00:00,619.0,0.0,16.82,4.12 -2016-07-24 01:00:00,474.0,0.0,16.67,4.42 -2016-07-24 02:00:00,461.0,0.0,16.52,4.72 -2016-07-24 03:00:00,400.0,0.0,16.37,5.02 -2016-07-24 04:00:00,379.0,0.0,16.44,5.63 -2016-07-24 05:00:00,345.0,0.0,16.51,6.23 -2016-07-24 06:00:00,315.0,39.39,16.57,6.84 -2016-07-24 07:00:00,339.0,18.05,16.74,7.31 -2016-07-24 08:00:00,438.0,219.97,16.91,7.79 -2016-07-24 09:00:00,524.0,96.83,17.08,8.26 -2016-07-24 10:00:00,595.0,71.51,17.2,7.93 -2016-07-24 11:00:00,667.0,166.49,17.32,7.59 -2016-07-24 12:00:00,674.0,193.83,17.44,7.26 -2016-07-24 13:00:00,638.0,278.64,17.4,6.94 -2016-07-24 14:00:00,610.0,157.71,17.37,6.63 -2016-07-24 15:00:00,584.0,145.26,17.33,6.32 -2016-07-24 16:00:00,502.0,163.05,17.28,6.04 -2016-07-24 17:00:00,471.0,39.51,17.23,5.76 -2016-07-24 18:00:00,510.0,88.27,17.18,5.48 -2016-07-24 19:00:00,572.0,26.14,17.04,5.39 -2016-07-24 20:00:00,588.0,0.0,16.89,5.3 -2016-07-24 21:00:00,569.0,0.0,16.75,5.21 -2016-07-24 22:00:00,845.0,0.0,16.62,4.85 -2016-07-24 23:00:00,787.0,0.0,16.49,4.48 -2016-07-25 00:00:00,573.0,0.0,16.36,4.11 -2016-07-25 01:00:00,454.0,0.0,16.23,4.49 -2016-07-25 02:00:00,440.0,0.0,16.1,4.86 -2016-07-25 03:00:00,384.0,0.0,15.96,5.24 -2016-07-25 04:00:00,371.0,0.0,15.92,5.05 -2016-07-25 05:00:00,339.0,1.87,15.87,4.86 -2016-07-25 06:00:00,308.0,31.51,15.82,4.68 -2016-07-25 07:00:00,356.0,141.71,15.89,4.49 -2016-07-25 08:00:00,457.0,73.27,15.96,4.31 -2016-07-25 09:00:00,548.0,171.62,16.03,4.12 -2016-07-25 10:00:00,609.0,245.64,16.18,4.21 -2016-07-25 11:00:00,646.0,382.56,16.33,4.3 -2016-07-25 12:00:00,643.0,416.59,16.47,4.39 -2016-07-25 13:00:00,634.0,385.83,16.66,4.79 -2016-07-25 14:00:00,600.0,566.29,16.85,5.19 -2016-07-25 15:00:00,604.0,344.51,17.04,5.59 -2016-07-25 16:00:00,505.0,335.78,17.0,5.55 -2016-07-25 17:00:00,454.0,137.4,16.97,5.51 -2016-07-25 18:00:00,499.0,29.59,16.94,5.48 -2016-07-25 19:00:00,569.0,22.36,16.85,5.45 -2016-07-25 20:00:00,622.0,0.0,16.75,5.42 -2016-07-25 21:00:00,577.0,0.0,16.66,5.39 -2016-07-25 22:00:00,850.0,0.0,16.56,5.17 -2016-07-25 23:00:00,828.0,0.0,16.45,4.95 -2016-07-26 00:00:00,588.0,0.0,16.35,4.73 -2016-07-26 01:00:00,459.0,0.0,16.31,4.52 -2016-07-26 02:00:00,439.0,0.0,16.26,4.32 -2016-07-26 03:00:00,376.0,0.0,16.21,4.11 -2016-07-26 04:00:00,365.0,0.0,16.18,4.05 -2016-07-26 05:00:00,332.0,0.0,16.15,3.99 -2016-07-26 06:00:00,315.0,17.47,16.12,3.93 -2016-07-26 07:00:00,359.0,37.51,16.17,3.89 -2016-07-26 08:00:00,457.0,61.07,16.22,3.86 -2016-07-26 09:00:00,573.0,189.35,16.26,3.82 -2016-07-26 10:00:00,616.0,180.76,16.33,3.95 -2016-07-26 11:00:00,617.0,524.58,16.4,4.09 -2016-07-26 12:00:00,676.0,539.06,16.46,4.22 -2016-07-26 13:00:00,671.0,172.54,16.58,4.67 -2016-07-26 14:00:00,662.0,100.59,16.71,5.12 -2016-07-26 15:00:00,620.0,232.7,16.83,5.57 -2016-07-26 16:00:00,534.0,272.91,16.88,5.94 -2016-07-26 17:00:00,470.0,73.04,16.94,6.31 -2016-07-26 18:00:00,531.0,49.8,16.99,6.68 -2016-07-26 19:00:00,639.0,24.25,16.99,7.09 -2016-07-26 20:00:00,631.0,0.0,16.99,7.51 -2016-07-26 21:00:00,582.0,0.0,16.99,7.93 -2016-07-26 22:00:00,859.0,0.0,17.02,7.93 -2016-07-26 23:00:00,811.0,0.0,17.05,7.92 -2016-07-27 00:00:00,573.0,0.0,17.08,7.92 -2016-07-27 01:00:00,464.0,0.0,17.11,7.98 -2016-07-27 02:00:00,452.0,0.0,17.13,8.05 -2016-07-27 03:00:00,389.0,0.0,17.15,8.11 -2016-07-27 04:00:00,358.0,0.0,17.2,8.12 -2016-07-27 05:00:00,343.0,0.0,17.24,8.13 -2016-07-27 06:00:00,320.0,29.36,17.28,8.14 -2016-07-27 07:00:00,370.0,86.61,17.26,7.98 -2016-07-27 08:00:00,461.0,173.79,17.24,7.83 -2016-07-27 09:00:00,542.0,261.21,17.23,7.67 -2016-07-27 10:00:00,654.0,62.45,17.15,7.56 -2016-07-27 11:00:00,667.0,304.09,17.08,7.45 -2016-07-27 12:00:00,696.0,185.38,17.01,7.34 -2016-07-27 13:00:00,702.0,386.69,17.04,7.26 -2016-07-27 14:00:00,691.0,302.93,17.08,7.19 -2016-07-27 15:00:00,617.0,424.7,17.11,7.12 -2016-07-27 16:00:00,512.0,179.87,17.08,6.74 -2016-07-27 17:00:00,475.0,144.86,17.06,6.35 -2016-07-27 18:00:00,560.0,73.3,17.04,5.97 -2016-07-27 19:00:00,674.0,9.8,17.03,5.8 -2016-07-27 20:00:00,654.0,0.0,17.02,5.62 -2016-07-27 21:00:00,615.0,0.0,17.02,5.45 -2016-07-27 22:00:00,867.0,0.0,17.01,5.46 -2016-07-27 23:00:00,788.0,0.0,17.0,5.48 -2016-07-28 00:00:00,603.0,0.0,16.99,5.49 -2016-07-28 01:00:00,484.0,0.0,17.01,5.6 -2016-07-28 02:00:00,465.0,0.0,17.03,5.71 -2016-07-28 03:00:00,423.0,0.0,17.04,5.82 -2016-07-28 04:00:00,394.0,0.0,17.07,6.24 -2016-07-28 05:00:00,341.0,0.0,17.1,6.67 -2016-07-28 06:00:00,339.0,31.98,17.13,7.09 -2016-07-28 07:00:00,375.0,90.99,17.2,7.83 -2016-07-28 08:00:00,475.0,73.02,17.27,8.57 -2016-07-28 09:00:00,572.0,293.97,17.34,9.31 -2016-07-28 10:00:00,655.0,96.77,17.34,9.24 -2016-07-28 11:00:00,719.0,185.81,17.34,9.17 -2016-07-28 12:00:00,739.0,400.98,17.33,9.1 -2016-07-28 13:00:00,738.0,216.83,17.35,8.92 -2016-07-28 14:00:00,685.0,190.36,17.38,8.74 -2016-07-28 15:00:00,649.0,344.43,17.4,8.57 -2016-07-28 16:00:00,558.0,242.08,17.38,8.39 -2016-07-28 17:00:00,525.0,93.51,17.36,8.22 -2016-07-28 18:00:00,584.0,48.41,17.34,8.04 -2016-07-28 19:00:00,661.0,9.79,17.28,7.86 -2016-07-28 20:00:00,659.0,0.0,17.22,7.68 -2016-07-28 21:00:00,615.0,0.0,17.17,7.5 -2016-07-28 22:00:00,892.0,0.0,17.04,7.48 -2016-07-28 23:00:00,836.0,0.0,16.91,7.46 -2016-07-29 00:00:00,632.0,0.0,16.78,7.43 -2016-07-29 01:00:00,475.0,0.0,16.75,7.31 -2016-07-29 02:00:00,465.0,0.0,16.71,7.2 -2016-07-29 03:00:00,399.0,0.0,16.67,7.08 -2016-07-29 04:00:00,386.0,0.0,16.62,6.94 -2016-07-29 05:00:00,344.0,0.0,16.57,6.8 -2016-07-29 06:00:00,327.0,35.38,16.52,6.66 -2016-07-29 07:00:00,375.0,136.44,16.51,6.55 -2016-07-29 08:00:00,484.0,218.46,16.5,6.44 -2016-07-29 09:00:00,596.0,352.76,16.5,6.33 -2016-07-29 10:00:00,653.0,541.32,16.45,6.32 -2016-07-29 11:00:00,715.0,315.08,16.41,6.31 -2016-07-29 12:00:00,708.0,294.51,16.37,6.3 -2016-07-29 13:00:00,711.0,605.58,16.48,6.26 -2016-07-29 14:00:00,681.0,145.76,16.6,6.22 -2016-07-29 15:00:00,638.0,376.24,16.71,6.18 -2016-07-29 16:00:00,562.0,238.67,16.71,6.06 -2016-07-29 17:00:00,475.0,137.04,16.72,5.95 -2016-07-29 18:00:00,561.0,61.4,16.72,5.83 -2016-07-29 19:00:00,667.0,23.0,16.72,5.41 -2016-07-29 20:00:00,660.0,0.0,16.72,4.98 -2016-07-29 21:00:00,635.0,0.0,16.73,4.55 -2016-07-29 22:00:00,861.0,0.0,16.72,4.3 -2016-07-29 23:00:00,849.0,0.0,16.71,4.05 -2016-07-30 00:00:00,648.0,0.0,16.7,3.79 -2016-07-30 01:00:00,500.0,0.0,16.69,3.92 -2016-07-30 02:00:00,450.0,0.0,16.68,4.05 -2016-07-30 03:00:00,396.0,0.0,16.66,4.18 -2016-07-30 04:00:00,368.0,0.0,16.63,4.37 -2016-07-30 05:00:00,347.0,0.0,16.59,4.57 -2016-07-30 06:00:00,347.0,35.35,16.55,4.76 -2016-07-30 07:00:00,388.0,112.14,16.53,4.51 -2016-07-30 08:00:00,479.0,64.76,16.51,4.27 -2016-07-30 09:00:00,610.0,191.21,16.5,4.03 -2016-07-30 10:00:00,648.0,462.61,16.48,4.05 -2016-07-30 11:00:00,663.0,604.95,16.46,4.07 -2016-07-30 12:00:00,697.0,363.25,16.44,4.1 -2016-07-30 13:00:00,691.0,510.84,16.52,4.25 -2016-07-30 14:00:00,673.0,595.03,16.59,4.4 -2016-07-30 15:00:00,624.0,597.89,16.67,4.55 -2016-07-30 16:00:00,532.0,447.09,16.68,4.74 -2016-07-30 17:00:00,497.0,245.91,16.7,4.93 -2016-07-30 18:00:00,542.0,80.65,16.71,5.12 -2016-07-30 19:00:00,616.0,19.88,16.69,5.27 -2016-07-30 20:00:00,642.0,0.0,16.67,5.43 -2016-07-30 21:00:00,652.0,0.0,16.65,5.59 -2016-07-30 22:00:00,867.0,0.0,16.59,5.66 -2016-07-30 23:00:00,825.0,0.0,16.53,5.73 -2016-07-31 00:00:00,646.0,0.0,16.47,5.81 -2016-07-31 01:00:00,512.0,0.0,16.38,5.87 -2016-07-31 02:00:00,472.0,0.0,16.28,5.93 -2016-07-31 03:00:00,408.0,0.0,16.18,5.99 -2016-07-31 04:00:00,382.0,0.0,16.21,5.83 -2016-07-31 05:00:00,343.0,0.0,16.23,5.66 -2016-07-31 06:00:00,357.0,32.77,16.25,5.5 -2016-07-31 07:00:00,376.0,138.29,16.3,5.49 -2016-07-31 08:00:00,448.0,334.96,16.35,5.48 -2016-07-31 09:00:00,569.0,512.28,16.4,5.46 -2016-07-31 10:00:00,669.0,652.84,16.36,5.49 -2016-07-31 11:00:00,695.0,434.54,16.33,5.52 -2016-07-31 12:00:00,674.0,572.12,16.29,5.54 -2016-07-31 13:00:00,646.0,382.67,16.42,5.37 -2016-07-31 14:00:00,600.0,451.13,16.55,5.19 -2016-07-31 15:00:00,578.0,548.52,16.68,5.01 -2016-07-31 16:00:00,494.0,296.71,16.65,4.76 -2016-07-31 17:00:00,436.0,206.84,16.62,4.51 -2016-07-31 18:00:00,474.0,81.01,16.59,4.26 -2016-07-31 19:00:00,553.0,20.51,16.52,4.01 -2016-07-31 20:00:00,595.0,0.0,16.45,3.76 -2016-07-31 21:00:00,568.0,0.0,16.39,3.5 -2016-07-31 22:00:00,856.0,0.0,16.34,3.66 -2016-07-31 23:00:00,825.0,0.0,16.3,3.81 -2016-08-01 00:00:00,605.0,0.0,16.25,3.96 -2016-08-01 01:00:00,490.0,0.0,16.19,3.57 -2016-08-01 02:00:00,436.0,0.0,16.13,3.18 -2016-08-01 03:00:00,413.0,0.0,16.07,2.79 -2016-08-01 04:00:00,380.0,0.0,16.08,3.64 -2016-08-01 05:00:00,320.0,0.0,16.09,4.5 -2016-08-01 06:00:00,315.0,15.66,16.1,5.35 -2016-08-01 07:00:00,348.0,57.43,16.21,6.09 -2016-08-01 08:00:00,444.0,41.17,16.31,6.82 -2016-08-01 09:00:00,572.0,94.01,16.42,7.56 -2016-08-01 10:00:00,629.0,104.85,16.51,8.34 -2016-08-01 11:00:00,662.0,177.52,16.6,9.12 -2016-08-01 12:00:00,700.0,114.97,16.7,9.9 -2016-08-01 13:00:00,702.0,82.49,16.7,10.89 -2016-08-01 14:00:00,695.0,74.86,16.71,11.88 -2016-08-01 15:00:00,675.0,68.78,16.72,12.87 -2016-08-01 16:00:00,592.0,55.15,16.84,12.36 -2016-08-01 17:00:00,600.0,42.55,16.97,11.85 -2016-08-01 18:00:00,649.0,16.07,17.09,11.34 -2016-08-01 19:00:00,718.0,6.06,17.25,11.23 -2016-08-01 20:00:00,726.0,0.0,17.41,11.12 -2016-08-01 21:00:00,691.0,0.0,17.58,11.01 -2016-08-01 22:00:00,889.0,0.0,17.66,11.21 -2016-08-01 23:00:00,817.0,0.0,17.73,11.42 -2016-08-02 00:00:00,617.0,0.0,17.81,11.63 -2016-08-02 01:00:00,525.0,0.0,17.8,11.71 -2016-08-02 02:00:00,469.0,0.0,17.78,11.79 -2016-08-02 03:00:00,411.0,0.0,17.76,11.88 -2016-08-02 04:00:00,392.0,0.0,17.8,11.68 -2016-08-02 05:00:00,336.0,0.0,17.83,11.49 -2016-08-02 06:00:00,342.0,8.13,17.86,11.3 -2016-08-02 07:00:00,392.0,96.53,17.91,11.25 -2016-08-02 08:00:00,511.0,50.38,17.97,11.2 -2016-08-02 09:00:00,618.0,78.16,18.02,11.14 -2016-08-02 10:00:00,688.0,95.0,18.04,11.15 -2016-08-02 11:00:00,690.0,111.97,18.06,11.16 -2016-08-02 12:00:00,743.0,125.15,18.08,11.17 -2016-08-02 13:00:00,744.0,258.99,18.05,11.2 -2016-08-02 14:00:00,721.0,85.03,18.02,11.23 -2016-08-02 15:00:00,683.0,218.7,17.99,11.26 -2016-08-02 16:00:00,591.0,154.21,18.04,11.18 -2016-08-02 17:00:00,555.0,139.87,18.09,11.1 -2016-08-02 18:00:00,598.0,54.3,18.14,11.02 -2016-08-02 19:00:00,747.0,11.43,18.12,10.8 -2016-08-02 20:00:00,731.0,0.0,18.09,10.57 -2016-08-02 21:00:00,699.0,0.0,18.07,10.34 -2016-08-02 22:00:00,898.0,0.0,18.09,10.04 -2016-08-02 23:00:00,821.0,0.0,18.11,9.74 -2016-08-03 00:00:00,638.0,0.0,18.13,9.43 -2016-08-03 01:00:00,529.0,0.0,18.02,9.38 -2016-08-03 02:00:00,489.0,0.0,17.91,9.32 -2016-08-03 03:00:00,417.0,0.0,17.79,9.27 -2016-08-03 04:00:00,396.0,0.0,17.79,9.04 -2016-08-03 05:00:00,365.0,0.0,17.79,8.81 -2016-08-03 06:00:00,339.0,32.55,17.78,8.58 -2016-08-03 07:00:00,394.0,104.23,17.69,8.39 -2016-08-03 08:00:00,516.0,181.5,17.59,8.19 -2016-08-03 09:00:00,606.0,486.09,17.5,8.0 -2016-08-03 10:00:00,717.0,681.92,17.32,8.24 -2016-08-03 11:00:00,737.0,774.5,17.14,8.49 -2016-08-03 12:00:00,724.0,802.06,16.96,8.73 -2016-08-03 13:00:00,723.0,676.97,16.84,8.97 -2016-08-03 14:00:00,698.0,753.8,16.72,9.21 -2016-08-03 15:00:00,661.0,612.69,16.61,9.45 -2016-08-03 16:00:00,528.0,439.2,16.45,9.41 -2016-08-03 17:00:00,499.0,204.33,16.3,9.37 -2016-08-03 18:00:00,574.0,70.34,16.14,9.34 -2016-08-03 19:00:00,681.0,10.42,16.19,9.2 -2016-08-03 20:00:00,701.0,0.0,16.23,9.07 -2016-08-03 21:00:00,640.0,0.0,16.28,8.94 -2016-08-03 22:00:00,917.0,0.0,16.43,8.65 -2016-08-03 23:00:00,846.0,0.0,16.59,8.36 -2016-08-04 00:00:00,617.0,0.0,16.74,8.07 -2016-08-04 01:00:00,503.0,0.0,16.67,8.06 -2016-08-04 02:00:00,476.0,0.0,16.6,8.06 -2016-08-04 03:00:00,424.0,0.0,16.53,8.06 -2016-08-04 04:00:00,401.0,0.0,16.54,7.55 -2016-08-04 05:00:00,346.0,0.0,16.55,7.04 -2016-08-04 06:00:00,334.0,22.4,16.56,6.54 -2016-08-04 07:00:00,394.0,120.38,16.54,6.0 -2016-08-04 08:00:00,512.0,152.38,16.53,5.47 -2016-08-04 09:00:00,630.0,119.2,16.51,4.94 -2016-08-04 10:00:00,706.0,97.59,16.54,5.08 -2016-08-04 11:00:00,741.0,55.8,16.57,5.21 -2016-08-04 12:00:00,732.0,305.96,16.6,5.35 -2016-08-04 13:00:00,738.0,390.64,16.61,5.71 -2016-08-04 14:00:00,703.0,460.29,16.63,6.08 -2016-08-04 15:00:00,662.0,302.93,16.65,6.44 -2016-08-04 16:00:00,609.0,320.46,16.65,6.32 -2016-08-04 17:00:00,571.0,257.11,16.66,6.19 -2016-08-04 18:00:00,552.0,71.11,16.67,6.07 -2016-08-04 19:00:00,638.0,17.43,16.69,5.98 -2016-08-04 20:00:00,667.0,0.0,16.71,5.89 -2016-08-04 21:00:00,653.0,0.0,16.73,5.81 -2016-08-04 22:00:00,900.0,0.0,16.73,5.68 -2016-08-04 23:00:00,853.0,0.0,16.72,5.56 -2016-08-05 00:00:00,661.0,0.0,16.72,5.43 -2016-08-05 01:00:00,519.0,0.0,16.55,5.29 -2016-08-05 02:00:00,478.0,0.0,16.38,5.14 -2016-08-05 03:00:00,423.0,0.0,16.2,4.99 -2016-08-05 04:00:00,407.0,0.0,16.12,4.89 -2016-08-05 05:00:00,351.0,0.0,16.04,4.78 -2016-08-05 06:00:00,340.0,30.83,15.95,4.68 -2016-08-05 07:00:00,393.0,131.86,15.99,4.9 -2016-08-05 08:00:00,535.0,323.41,16.02,5.13 -2016-08-05 09:00:00,642.0,437.78,16.06,5.35 -2016-08-05 10:00:00,714.0,642.78,16.16,5.29 -2016-08-05 11:00:00,728.0,346.77,16.27,5.22 -2016-08-05 12:00:00,703.0,743.75,16.38,5.16 -2016-08-05 13:00:00,717.0,796.17,16.56,5.31 -2016-08-05 14:00:00,731.0,733.77,16.75,5.45 -2016-08-05 15:00:00,683.0,623.29,16.94,5.6 -2016-08-05 16:00:00,568.0,449.07,16.95,5.48 -2016-08-05 17:00:00,520.0,163.46,16.97,5.36 -2016-08-05 18:00:00,572.0,65.89,16.98,5.24 -2016-08-05 19:00:00,669.0,5.06,16.95,5.0 -2016-08-05 20:00:00,678.0,0.0,16.92,4.76 -2016-08-05 21:00:00,676.0,0.0,16.89,4.52 -2016-08-05 22:00:00,922.0,0.0,16.91,4.46 -2016-08-05 23:00:00,852.0,0.0,16.94,4.4 -2016-08-06 00:00:00,642.0,0.0,16.96,4.34 -2016-08-06 01:00:00,527.0,0.0,16.88,4.37 -2016-08-06 02:00:00,480.0,0.0,16.8,4.39 -2016-08-06 03:00:00,412.0,0.0,16.71,4.41 -2016-08-06 04:00:00,395.0,0.0,16.72,3.99 -2016-08-06 05:00:00,366.0,0.0,16.73,3.56 -2016-08-06 06:00:00,367.0,23.61,16.73,3.13 -2016-08-06 07:00:00,415.0,127.09,16.78,3.22 -2016-08-06 08:00:00,534.0,229.28,16.84,3.31 -2016-08-06 09:00:00,635.0,339.61,16.89,3.39 -2016-08-06 10:00:00,691.0,553.0,16.93,3.78 -2016-08-06 11:00:00,716.0,593.98,16.98,4.17 -2016-08-06 12:00:00,716.0,811.97,17.03,4.57 -2016-08-06 13:00:00,717.0,801.42,17.11,5.09 -2016-08-06 14:00:00,746.0,715.39,17.19,5.6 -2016-08-06 15:00:00,681.0,626.86,17.28,6.12 -2016-08-06 16:00:00,547.0,456.19,17.27,6.24 -2016-08-06 17:00:00,485.0,234.66,17.27,6.36 -2016-08-06 18:00:00,523.0,70.76,17.27,6.48 -2016-08-06 19:00:00,611.0,14.98,17.24,6.45 -2016-08-06 20:00:00,586.0,0.0,17.2,6.41 -2016-08-06 21:00:00,552.0,0.0,17.17,6.37 -2016-08-06 22:00:00,844.0,0.0,17.18,6.39 -2016-08-06 23:00:00,804.0,0.0,17.2,6.41 -2016-08-07 00:00:00,611.0,0.0,17.21,6.43 -2016-08-07 01:00:00,507.0,0.0,17.16,6.61 -2016-08-07 02:00:00,466.0,0.0,17.11,6.8 -2016-08-07 03:00:00,407.0,0.0,17.05,6.98 -2016-08-07 04:00:00,388.0,0.0,17.09,6.79 -2016-08-07 05:00:00,359.0,0.0,17.12,6.6 -2016-08-07 06:00:00,351.0,22.96,17.15,6.41 -2016-08-07 07:00:00,373.0,56.12,17.21,6.2 -2016-08-07 08:00:00,462.0,127.27,17.26,5.98 -2016-08-07 09:00:00,555.0,205.72,17.32,5.77 -2016-08-07 10:00:00,690.0,628.18,17.3,5.7 -2016-08-07 11:00:00,790.0,697.31,17.29,5.64 -2016-08-07 12:00:00,799.0,794.95,17.28,5.57 -2016-08-07 13:00:00,779.0,785.63,17.3,5.6 -2016-08-07 14:00:00,733.0,736.33,17.33,5.62 -2016-08-07 15:00:00,695.0,620.81,17.36,5.64 -2016-08-07 16:00:00,590.0,452.21,17.41,5.91 -2016-08-07 17:00:00,516.0,224.94,17.46,6.18 -2016-08-07 18:00:00,533.0,61.79,17.51,6.46 -2016-08-07 19:00:00,591.0,14.37,17.43,6.77 -2016-08-07 20:00:00,652.0,0.0,17.35,7.09 -2016-08-07 21:00:00,661.0,0.0,17.28,7.41 -2016-08-07 22:00:00,939.0,0.0,17.13,7.18 -2016-08-07 23:00:00,846.0,0.0,16.98,6.95 -2016-08-08 00:00:00,612.0,0.0,16.83,6.72 -2016-08-08 01:00:00,517.0,0.0,16.84,6.56 -2016-08-08 02:00:00,455.0,0.0,16.85,6.4 -2016-08-08 03:00:00,409.0,0.0,16.86,6.23 -2016-08-08 04:00:00,398.0,0.0,16.95,6.23 -2016-08-08 05:00:00,343.0,0.0,17.03,6.23 -2016-08-08 06:00:00,331.0,18.61,17.11,6.22 -2016-08-08 07:00:00,387.0,132.13,17.14,6.14 -2016-08-08 08:00:00,503.0,216.26,17.18,6.06 -2016-08-08 09:00:00,650.0,467.69,17.21,5.97 -2016-08-08 10:00:00,741.0,246.08,17.07,6.05 -2016-08-08 11:00:00,719.0,646.34,16.94,6.12 -2016-08-08 12:00:00,743.0,391.04,16.81,6.19 -2016-08-08 13:00:00,744.0,618.12,16.66,6.1 -2016-08-08 14:00:00,725.0,379.23,16.51,6.01 -2016-08-08 15:00:00,676.0,464.43,16.37,5.92 -2016-08-08 16:00:00,600.0,398.89,16.25,6.39 -2016-08-08 17:00:00,557.0,210.45,16.14,6.86 -2016-08-08 18:00:00,583.0,72.58,16.02,7.34 -2016-08-08 19:00:00,682.0,0.0,15.89,7.39 -2016-08-08 20:00:00,679.0,0.0,15.75,7.44 -2016-08-08 21:00:00,631.0,0.0,15.62,7.49 -2016-08-08 22:00:00,896.0,0.0,15.58,7.45 -2016-08-08 23:00:00,845.0,0.0,15.55,7.41 -2016-08-09 00:00:00,632.0,0.0,15.51,7.37 -2016-08-09 01:00:00,502.0,0.0,15.81,7.06 -2016-08-09 02:00:00,499.0,0.0,16.11,6.76 -2016-08-09 03:00:00,439.0,0.0,16.41,6.46 -2016-08-09 04:00:00,407.0,0.0,16.38,6.46 -2016-08-09 05:00:00,363.0,0.0,16.34,6.46 -2016-08-09 06:00:00,351.0,23.06,16.3,6.46 -2016-08-09 07:00:00,390.0,130.02,16.33,6.47 -2016-08-09 08:00:00,527.0,212.53,16.35,6.48 -2016-08-09 09:00:00,626.0,365.09,16.38,6.5 -2016-08-09 10:00:00,719.0,163.57,16.31,6.8 -2016-08-09 11:00:00,714.0,681.82,16.24,7.1 -2016-08-09 12:00:00,726.0,760.03,16.17,7.41 -2016-08-09 13:00:00,719.0,768.34,16.22,7.25 -2016-08-09 14:00:00,729.0,733.02,16.27,7.09 -2016-08-09 15:00:00,693.0,604.85,16.33,6.92 -2016-08-09 16:00:00,574.0,412.3,16.21,7.34 -2016-08-09 17:00:00,521.0,215.19,16.1,7.75 -2016-08-09 18:00:00,612.0,71.68,15.98,8.17 -2016-08-09 19:00:00,721.0,12.16,15.78,8.06 -2016-08-09 20:00:00,734.0,0.0,15.57,7.95 -2016-08-09 21:00:00,672.0,0.0,15.37,7.85 -2016-08-09 22:00:00,927.0,0.0,15.27,7.46 -2016-08-09 23:00:00,857.0,0.0,15.18,7.07 -2016-08-10 00:00:00,650.0,0.0,15.08,6.68 -2016-08-10 01:00:00,519.0,0.0,15.38,6.69 -2016-08-10 02:00:00,495.0,0.0,15.68,6.7 -2016-08-10 03:00:00,416.0,0.0,15.97,6.72 -2016-08-10 04:00:00,403.0,0.0,15.98,6.79 -2016-08-10 05:00:00,358.0,0.0,15.99,6.86 -2016-08-10 06:00:00,363.0,25.66,16.0,6.94 -2016-08-10 07:00:00,426.0,117.84,16.06,6.84 -2016-08-10 08:00:00,524.0,287.62,16.11,6.74 -2016-08-10 09:00:00,659.0,426.66,16.17,6.65 -2016-08-10 10:00:00,736.0,445.01,16.22,6.44 -2016-08-10 11:00:00,750.0,518.77,16.27,6.23 -2016-08-10 12:00:00,769.0,664.01,16.32,6.03 -2016-08-10 13:00:00,743.0,687.57,16.37,6.13 -2016-08-10 14:00:00,760.0,422.6,16.42,6.23 -2016-08-10 15:00:00,720.0,217.01,16.48,6.33 -2016-08-10 16:00:00,588.0,423.59,16.52,6.53 -2016-08-10 17:00:00,530.0,246.02,16.57,6.74 -2016-08-10 18:00:00,576.0,70.27,16.62,6.94 -2016-08-10 19:00:00,732.0,11.52,16.61,6.86 -2016-08-10 20:00:00,745.0,0.0,16.6,6.78 -2016-08-10 21:00:00,700.0,0.0,16.59,6.7 -2016-08-10 22:00:00,923.0,0.0,16.53,6.52 -2016-08-10 23:00:00,877.0,0.0,16.47,6.34 -2016-08-11 00:00:00,631.0,0.0,16.41,6.17 -2016-08-11 01:00:00,525.0,0.0,16.38,5.97 -2016-08-11 02:00:00,481.0,0.0,16.35,5.78 -2016-08-11 03:00:00,417.0,0.0,16.31,5.59 -2016-08-11 04:00:00,403.0,0.0,16.29,5.67 -2016-08-11 05:00:00,360.0,0.0,16.26,5.75 -2016-08-11 06:00:00,365.0,24.34,16.23,5.83 -2016-08-11 07:00:00,398.0,124.41,16.26,5.98 -2016-08-11 08:00:00,517.0,327.44,16.28,6.12 -2016-08-11 09:00:00,665.0,520.29,16.31,6.26 -2016-08-11 10:00:00,709.0,609.27,16.25,6.26 -2016-08-11 11:00:00,705.0,717.81,16.19,6.25 -2016-08-11 12:00:00,730.0,778.49,16.13,6.25 -2016-08-11 13:00:00,725.0,789.79,16.07,6.35 -2016-08-11 14:00:00,750.0,735.36,16.01,6.45 -2016-08-11 15:00:00,670.0,614.09,15.96,6.55 -2016-08-11 16:00:00,578.0,361.1,15.89,6.57 -2016-08-11 17:00:00,539.0,218.12,15.82,6.58 -2016-08-11 18:00:00,583.0,68.87,15.75,6.59 -2016-08-11 19:00:00,699.0,8.23,15.7,6.38 -2016-08-11 20:00:00,705.0,0.0,15.64,6.16 -2016-08-11 21:00:00,637.0,0.0,15.59,5.94 -2016-08-11 22:00:00,930.0,0.0,15.62,5.53 -2016-08-11 23:00:00,856.0,0.0,15.65,5.12 -2016-08-12 00:00:00,638.0,0.0,15.68,4.7 -2016-08-12 01:00:00,502.0,0.0,15.68,4.17 -2016-08-12 02:00:00,470.0,0.0,15.67,3.64 -2016-08-12 03:00:00,428.0,0.0,15.66,3.1 -2016-08-12 04:00:00,408.0,0.0,15.58,2.88 -2016-08-12 05:00:00,360.0,0.0,15.5,2.65 -2016-08-12 06:00:00,360.0,18.75,15.41,2.43 -2016-08-12 07:00:00,390.0,81.04,15.33,2.34 -2016-08-12 08:00:00,506.0,136.31,15.24,2.24 -2016-08-12 09:00:00,662.0,198.2,15.16,2.15 -2016-08-12 10:00:00,706.0,145.48,15.24,1.99 -2016-08-12 11:00:00,739.0,265.9,15.33,1.83 -2016-08-12 12:00:00,735.0,446.29,15.42,1.67 -2016-08-12 13:00:00,710.0,417.26,15.55,1.73 -2016-08-12 14:00:00,722.0,385.42,15.68,1.8 -2016-08-12 15:00:00,681.0,417.76,15.81,1.86 -2016-08-12 16:00:00,574.0,364.27,15.98,1.49 -2016-08-12 17:00:00,510.0,200.07,16.16,1.11 -2016-08-12 18:00:00,576.0,62.2,16.34,0.73 -2016-08-12 19:00:00,693.0,8.72,16.4,0.71 -2016-08-12 20:00:00,701.0,0.0,16.46,0.69 -2016-08-12 21:00:00,678.0,0.0,16.52,0.66 -2016-08-12 22:00:00,932.0,0.0,16.48,1.11 -2016-08-12 23:00:00,881.0,0.0,16.45,1.56 -2016-08-13 00:00:00,660.0,0.0,16.41,2.01 -2016-08-13 01:00:00,538.0,0.0,16.25,1.71 -2016-08-13 02:00:00,484.0,0.0,16.09,1.4 -2016-08-13 03:00:00,437.0,0.0,15.93,1.09 -2016-08-13 04:00:00,392.0,0.0,15.91,1.08 -2016-08-13 05:00:00,353.0,0.0,15.88,1.06 -2016-08-13 06:00:00,364.0,21.79,15.85,1.05 -2016-08-13 07:00:00,412.0,127.68,16.02,1.6 -2016-08-13 08:00:00,547.0,318.09,16.19,2.16 -2016-08-13 09:00:00,673.0,496.43,16.36,2.72 -2016-08-13 10:00:00,706.0,649.43,16.52,3.02 -2016-08-13 11:00:00,733.0,738.7,16.68,3.31 -2016-08-13 12:00:00,750.0,800.11,16.85,3.61 -2016-08-13 13:00:00,714.0,776.69,16.99,3.66 -2016-08-13 14:00:00,706.0,706.46,17.13,3.71 -2016-08-13 15:00:00,670.0,586.79,17.27,3.75 -2016-08-13 16:00:00,563.0,426.53,17.26,4.07 -2016-08-13 17:00:00,512.0,231.29,17.25,4.39 -2016-08-13 18:00:00,579.0,62.1,17.24,4.7 -2016-08-13 19:00:00,670.0,6.57,17.14,4.8 -2016-08-13 20:00:00,674.0,0.0,17.03,4.89 -2016-08-13 21:00:00,673.0,0.0,16.93,4.98 -2016-08-13 22:00:00,898.0,0.0,16.81,5.1 -2016-08-13 23:00:00,855.0,0.0,16.7,5.23 -2016-08-14 00:00:00,683.0,0.0,16.58,5.35 -2016-08-14 01:00:00,526.0,0.0,16.42,5.53 -2016-08-14 02:00:00,499.0,0.0,16.25,5.71 -2016-08-14 03:00:00,435.0,0.0,16.08,5.89 -2016-08-14 04:00:00,395.0,0.0,16.02,5.94 -2016-08-14 05:00:00,352.0,0.0,15.96,5.99 -2016-08-14 06:00:00,354.0,19.35,15.9,6.04 -2016-08-14 07:00:00,373.0,128.03,16.05,6.04 -2016-08-14 08:00:00,481.0,291.92,16.21,6.04 -2016-08-14 09:00:00,605.0,509.63,16.36,6.04 -2016-08-14 10:00:00,731.0,664.56,16.68,6.03 -2016-08-14 11:00:00,775.0,745.49,17.0,6.01 -2016-08-14 12:00:00,738.0,614.2,17.33,6.0 -2016-08-14 13:00:00,714.0,713.59,17.57,6.25 -2016-08-14 14:00:00,688.0,731.7,17.82,6.5 -2016-08-14 15:00:00,650.0,613.57,18.07,6.74 -2016-08-14 16:00:00,521.0,443.96,18.09,6.79 -2016-08-14 17:00:00,468.0,240.2,18.12,6.83 -2016-08-14 18:00:00,562.0,53.27,18.14,6.87 -2016-08-14 19:00:00,647.0,7.58,18.07,6.74 -2016-08-14 20:00:00,690.0,0.0,18.0,6.61 -2016-08-14 21:00:00,716.0,0.0,17.93,6.48 -2016-08-14 22:00:00,922.0,0.0,17.9,6.42 -2016-08-14 23:00:00,842.0,0.0,17.88,6.35 -2016-08-15 00:00:00,652.0,0.0,17.85,6.29 -2016-08-15 01:00:00,538.0,0.0,17.74,6.41 -2016-08-15 02:00:00,492.0,0.0,17.62,6.53 -2016-08-15 03:00:00,434.0,0.0,17.5,6.65 -2016-08-15 04:00:00,403.0,0.0,17.52,6.67 -2016-08-15 05:00:00,348.0,0.0,17.54,6.69 -2016-08-15 06:00:00,331.0,17.35,17.56,6.72 -2016-08-15 07:00:00,354.0,128.09,17.74,6.69 -2016-08-15 08:00:00,519.0,336.49,17.93,6.67 -2016-08-15 09:00:00,624.0,519.36,18.11,6.65 -2016-08-15 10:00:00,689.0,676.73,18.37,6.57 -2016-08-15 11:00:00,732.0,756.49,18.63,6.5 -2016-08-15 12:00:00,713.0,516.37,18.9,6.43 -2016-08-15 13:00:00,666.0,796.86,19.13,6.5 -2016-08-15 14:00:00,636.0,730.11,19.36,6.57 -2016-08-15 15:00:00,626.0,607.79,19.59,6.63 -2016-08-15 16:00:00,535.0,439.45,19.65,6.81 -2016-08-15 17:00:00,485.0,229.85,19.71,6.98 -2016-08-15 18:00:00,520.0,61.17,19.77,7.16 -2016-08-15 19:00:00,613.0,6.47,19.75,7.31 -2016-08-15 20:00:00,667.0,0.0,19.73,7.45 -2016-08-15 21:00:00,662.0,0.0,19.72,7.6 -2016-08-15 22:00:00,881.0,0.0,19.68,7.5 -2016-08-15 23:00:00,818.0,0.0,19.65,7.4 -2016-08-16 00:00:00,606.0,0.0,19.61,7.3 -2016-08-16 01:00:00,502.0,0.0,19.39,7.31 -2016-08-16 02:00:00,463.0,0.0,19.17,7.33 -2016-08-16 03:00:00,426.0,0.0,18.94,7.35 -2016-08-16 04:00:00,406.0,0.0,18.89,7.26 -2016-08-16 05:00:00,335.0,0.0,18.83,7.17 -2016-08-16 06:00:00,325.0,19.06,18.77,7.08 -2016-08-16 07:00:00,382.0,122.98,18.87,7.02 -2016-08-16 08:00:00,482.0,315.76,18.98,6.96 -2016-08-16 09:00:00,589.0,427.23,19.08,6.9 -2016-08-16 10:00:00,678.0,568.69,19.19,6.6 -2016-08-16 11:00:00,707.0,729.48,19.3,6.3 -2016-08-16 12:00:00,719.0,773.82,19.42,6.0 -2016-08-16 13:00:00,704.0,553.87,19.54,5.78 -2016-08-16 14:00:00,706.0,448.92,19.66,5.57 -2016-08-16 15:00:00,640.0,564.81,19.79,5.35 -2016-08-16 16:00:00,553.0,402.77,19.93,5.03 -2016-08-16 17:00:00,502.0,105.65,20.08,4.7 -2016-08-16 18:00:00,571.0,49.13,20.23,4.37 -2016-08-16 19:00:00,625.0,5.96,19.77,4.55 -2016-08-16 20:00:00,640.0,0.0,19.31,4.73 -2016-08-16 21:00:00,621.0,0.0,18.86,4.91 -2016-08-16 22:00:00,910.0,0.0,18.66,4.43 -2016-08-16 23:00:00,820.0,0.0,18.46,3.94 -2016-08-17 00:00:00,619.0,0.0,18.26,3.46 -2016-08-17 01:00:00,512.0,0.0,18.23,3.51 -2016-08-17 02:00:00,478.0,0.0,18.19,3.56 -2016-08-17 03:00:00,431.0,0.0,18.15,3.61 -2016-08-17 04:00:00,401.0,0.0,18.17,3.64 -2016-08-17 05:00:00,370.0,0.0,18.18,3.66 -2016-08-17 06:00:00,348.0,8.1,18.19,3.68 -2016-08-17 07:00:00,399.0,102.8,18.24,3.58 -2016-08-17 08:00:00,515.0,163.27,18.29,3.48 -2016-08-17 09:00:00,599.0,158.31,18.34,3.38 -2016-08-17 10:00:00,671.0,306.65,18.34,3.41 -2016-08-17 11:00:00,712.0,104.13,18.34,3.44 -2016-08-17 12:00:00,759.0,67.85,18.34,3.48 -2016-08-17 13:00:00,740.0,157.71,18.21,3.42 -2016-08-17 14:00:00,770.0,78.45,18.08,3.36 -2016-08-17 15:00:00,722.0,154.74,17.95,3.3 -2016-08-17 16:00:00,626.0,49.51,17.85,4.26 -2016-08-17 17:00:00,553.0,70.41,17.75,5.23 -2016-08-17 18:00:00,604.0,53.19,17.65,6.19 -2016-08-17 19:00:00,658.0,2.7,17.51,6.7 -2016-08-17 20:00:00,695.0,0.0,17.37,7.21 -2016-08-17 21:00:00,649.0,0.0,17.24,7.72 -2016-08-17 22:00:00,916.0,0.0,17.15,7.83 -2016-08-17 23:00:00,809.0,0.0,17.05,7.94 -2016-08-18 00:00:00,601.0,0.0,16.96,8.06 -2016-08-18 01:00:00,471.0,0.0,17.08,8.2 -2016-08-18 02:00:00,469.0,0.0,17.19,8.34 -2016-08-18 03:00:00,434.0,0.0,17.3,8.48 -2016-08-18 04:00:00,410.0,0.0,17.22,8.58 -2016-08-18 05:00:00,356.0,0.0,17.14,8.68 -2016-08-18 06:00:00,329.0,12.07,17.06,8.77 -2016-08-18 07:00:00,377.0,98.92,16.96,8.35 -2016-08-18 08:00:00,519.0,145.64,16.85,7.93 -2016-08-18 09:00:00,641.0,201.1,16.75,7.5 -2016-08-18 10:00:00,705.0,393.29,16.66,7.13 -2016-08-18 11:00:00,735.0,313.05,16.57,6.76 -2016-08-18 12:00:00,736.0,476.43,16.48,6.39 -2016-08-18 13:00:00,725.0,685.86,16.56,5.83 -2016-08-18 14:00:00,700.0,466.44,16.64,5.26 -2016-08-18 15:00:00,650.0,299.72,16.73,4.7 -2016-08-18 16:00:00,558.0,184.31,16.84,4.46 -2016-08-18 17:00:00,484.0,85.82,16.96,4.22 -2016-08-18 18:00:00,547.0,15.4,17.07,3.97 -2016-08-18 19:00:00,664.0,2.7,17.17,4.17 -2016-08-18 20:00:00,660.0,0.0,17.27,4.37 -2016-08-18 21:00:00,619.0,0.0,17.38,4.57 -2016-08-18 22:00:00,909.0,0.0,17.47,5.95 -2016-08-18 23:00:00,823.0,0.0,17.55,7.34 -2016-08-19 00:00:00,622.0,0.0,17.64,8.73 -2016-08-19 01:00:00,504.0,0.0,17.81,9.66 -2016-08-19 02:00:00,473.0,0.0,17.98,10.58 -2016-08-19 03:00:00,420.0,0.0,18.15,11.5 -2016-08-19 04:00:00,397.0,0.0,18.09,11.75 -2016-08-19 05:00:00,351.0,0.0,18.03,12.0 -2016-08-19 06:00:00,330.0,17.93,17.97,12.25 -2016-08-19 07:00:00,374.0,125.48,17.85,11.83 -2016-08-19 08:00:00,513.0,335.96,17.74,11.42 -2016-08-19 09:00:00,608.0,512.79,17.62,11.01 -2016-08-19 10:00:00,687.0,679.73,17.53,11.37 -2016-08-19 11:00:00,726.0,620.3,17.44,11.72 -2016-08-19 12:00:00,714.0,675.57,17.36,12.08 -2016-08-19 13:00:00,723.0,774.99,17.42,12.13 -2016-08-19 14:00:00,706.0,727.93,17.48,12.17 -2016-08-19 15:00:00,668.0,245.72,17.54,12.22 -2016-08-19 16:00:00,559.0,342.23,17.57,12.34 -2016-08-19 17:00:00,511.0,220.74,17.6,12.46 -2016-08-19 18:00:00,574.0,50.91,17.63,12.58 -2016-08-19 19:00:00,627.0,2.7,17.6,12.59 -2016-08-19 20:00:00,639.0,0.0,17.57,12.61 -2016-08-19 21:00:00,625.0,0.0,17.54,12.62 -2016-08-19 22:00:00,903.0,0.0,17.48,12.73 -2016-08-19 23:00:00,805.0,0.0,17.42,12.84 -2016-08-20 00:00:00,592.0,0.0,17.36,12.95 -2016-08-20 01:00:00,491.0,0.0,17.28,13.25 -2016-08-20 02:00:00,466.0,0.0,17.19,13.54 -2016-08-20 03:00:00,405.0,0.0,17.1,13.83 -2016-08-20 04:00:00,390.0,0.0,17.06,13.66 -2016-08-20 05:00:00,352.0,0.0,17.02,13.48 -2016-08-20 06:00:00,333.0,15.01,16.98,13.3 -2016-08-20 07:00:00,382.0,122.47,16.97,13.15 -2016-08-20 08:00:00,490.0,315.36,16.96,13.01 -2016-08-20 09:00:00,625.0,488.34,16.95,12.87 -2016-08-20 10:00:00,689.0,652.64,16.87,13.01 -2016-08-20 11:00:00,674.0,232.62,16.8,13.14 -2016-08-20 12:00:00,672.0,742.83,16.73,13.28 -2016-08-20 13:00:00,683.0,703.11,16.74,13.28 -2016-08-20 14:00:00,697.0,409.15,16.76,13.27 -2016-08-20 15:00:00,666.0,450.74,16.78,13.27 -2016-08-20 16:00:00,575.0,175.97,16.8,13.03 -2016-08-20 17:00:00,494.0,67.22,16.82,12.78 -2016-08-20 18:00:00,547.0,25.48,16.84,12.54 -2016-08-20 19:00:00,630.0,1.86,16.86,12.26 -2016-08-20 20:00:00,604.0,0.0,16.87,11.99 -2016-08-20 21:00:00,600.0,0.0,16.89,11.71 -2016-08-20 22:00:00,870.0,0.0,16.92,11.46 -2016-08-20 23:00:00,799.0,0.0,16.96,11.2 -2016-08-21 00:00:00,600.0,0.0,17.0,10.95 -2016-08-21 01:00:00,483.0,0.0,16.88,10.72 -2016-08-21 02:00:00,471.0,0.0,16.76,10.48 -2016-08-21 03:00:00,407.0,0.0,16.64,10.25 -2016-08-21 04:00:00,391.0,0.0,16.72,9.82 -2016-08-21 05:00:00,343.0,0.0,16.8,9.38 -2016-08-21 06:00:00,327.0,13.83,16.87,8.95 -2016-08-21 07:00:00,367.0,112.37,16.97,8.47 -2016-08-21 08:00:00,473.0,127.97,17.07,8.0 -2016-08-21 09:00:00,599.0,165.97,17.17,7.52 -2016-08-21 10:00:00,703.0,207.11,17.23,7.52 -2016-08-21 11:00:00,737.0,222.74,17.3,7.52 -2016-08-21 12:00:00,706.0,171.89,17.37,7.52 -2016-08-21 13:00:00,673.0,367.38,17.25,7.94 -2016-08-21 14:00:00,665.0,272.55,17.13,8.37 -2016-08-21 15:00:00,612.0,609.76,17.01,8.8 -2016-08-21 16:00:00,527.0,214.4,17.01,8.59 -2016-08-21 17:00:00,424.0,229.81,17.02,8.39 -2016-08-21 18:00:00,482.0,42.07,17.03,8.18 -2016-08-21 19:00:00,562.0,1.46,17.02,8.02 -2016-08-21 20:00:00,583.0,0.0,17.01,7.86 -2016-08-21 21:00:00,588.0,0.0,17.0,7.7 -2016-08-21 22:00:00,846.0,0.0,17.03,7.77 -2016-08-21 23:00:00,824.0,0.0,17.07,7.83 -2016-08-22 00:00:00,591.0,0.0,17.1,7.9 -2016-08-22 01:00:00,465.0,0.0,17.06,8.04 -2016-08-22 02:00:00,444.0,0.0,17.02,8.18 -2016-08-22 03:00:00,406.0,0.0,16.97,8.32 -2016-08-22 04:00:00,392.0,0.0,17.0,8.01 -2016-08-22 05:00:00,331.0,0.0,17.03,7.71 -2016-08-22 06:00:00,317.0,12.07,17.05,7.41 -2016-08-22 07:00:00,373.0,124.04,17.13,7.11 -2016-08-22 08:00:00,476.0,334.8,17.21,6.82 -2016-08-22 09:00:00,580.0,518.83,17.29,6.52 -2016-08-22 10:00:00,631.0,674.12,17.34,6.14 -2016-08-22 11:00:00,687.0,756.18,17.39,5.76 -2016-08-22 12:00:00,683.0,801.21,17.44,5.38 -2016-08-22 13:00:00,665.0,788.54,17.47,5.36 -2016-08-22 14:00:00,680.0,721.16,17.5,5.33 -2016-08-22 15:00:00,650.0,604.74,17.53,5.31 -2016-08-22 16:00:00,568.0,431.13,17.63,4.76 -2016-08-22 17:00:00,506.0,226.13,17.74,4.21 -2016-08-22 18:00:00,539.0,41.82,17.85,3.66 -2016-08-22 19:00:00,630.0,0.0,17.95,3.67 -2016-08-22 20:00:00,634.0,0.0,18.05,3.69 -2016-08-22 21:00:00,624.0,0.0,18.16,3.71 -2016-08-22 22:00:00,844.0,0.0,18.33,4.06 -2016-08-22 23:00:00,808.0,0.0,18.51,4.42 -2016-08-23 00:00:00,596.0,0.0,18.68,4.77 -2016-08-23 01:00:00,476.0,0.0,18.77,4.59 -2016-08-23 02:00:00,437.0,0.0,18.85,4.41 -2016-08-23 03:00:00,413.0,0.0,18.93,4.23 -2016-08-23 04:00:00,405.0,0.0,18.88,4.35 -2016-08-23 05:00:00,355.0,0.0,18.82,4.46 -2016-08-23 06:00:00,349.0,13.67,18.76,4.58 -2016-08-23 07:00:00,372.0,27.24,18.61,4.04 -2016-08-23 08:00:00,497.0,210.16,18.47,3.49 -2016-08-23 09:00:00,596.0,492.18,18.32,2.95 -2016-08-23 10:00:00,667.0,614.11,18.1,2.46 -2016-08-23 11:00:00,676.0,627.96,17.89,1.96 -2016-08-23 12:00:00,710.0,733.43,17.68,1.46 -2016-08-23 13:00:00,683.0,735.56,17.66,2.1 -2016-08-23 14:00:00,678.0,662.39,17.65,2.73 -2016-08-23 15:00:00,628.0,564.36,17.64,3.37 -2016-08-23 16:00:00,529.0,383.98,17.59,3.65 -2016-08-23 17:00:00,483.0,84.17,17.54,3.93 -2016-08-23 18:00:00,571.0,39.34,17.49,4.21 -2016-08-23 19:00:00,674.0,0.0,17.37,4.67 -2016-08-23 20:00:00,639.0,0.0,17.25,5.14 -2016-08-23 21:00:00,624.0,0.0,17.14,5.6 -2016-08-23 22:00:00,846.0,0.0,17.02,5.88 -2016-08-23 23:00:00,813.0,0.0,16.91,6.16 -2016-08-24 00:00:00,596.0,0.0,16.8,6.44 -2016-08-24 01:00:00,471.0,0.0,16.6,6.62 -2016-08-24 02:00:00,449.0,0.0,16.4,6.79 -2016-08-24 03:00:00,426.0,0.0,16.19,6.97 -2016-08-24 04:00:00,410.0,0.0,16.15,6.97 -2016-08-24 05:00:00,347.0,0.0,16.1,6.97 -2016-08-24 06:00:00,352.0,12.14,16.05,6.97 -2016-08-24 07:00:00,394.0,108.96,16.17,7.05 -2016-08-24 08:00:00,524.0,156.7,16.3,7.14 -2016-08-24 09:00:00,598.0,130.42,16.42,7.23 -2016-08-24 10:00:00,657.0,139.7,16.57,7.15 -2016-08-24 11:00:00,688.0,61.81,16.73,7.07 -2016-08-24 12:00:00,724.0,277.37,16.89,6.99 -2016-08-24 13:00:00,722.0,508.87,16.81,6.46 -2016-08-24 14:00:00,696.0,725.46,16.74,5.92 -2016-08-24 15:00:00,677.0,577.51,16.67,5.38 -2016-08-24 16:00:00,574.0,188.35,16.69,5.48 -2016-08-24 17:00:00,504.0,83.97,16.71,5.57 -2016-08-24 18:00:00,561.0,22.05,16.73,5.67 -2016-08-24 19:00:00,628.0,0.0,16.74,5.39 -2016-08-24 20:00:00,658.0,0.0,16.75,5.11 -2016-08-24 21:00:00,632.0,0.0,16.77,4.83 -2016-08-24 22:00:00,839.0,0.0,16.77,4.8 -2016-08-24 23:00:00,783.0,0.0,16.78,4.77 -2016-08-25 00:00:00,556.0,0.0,16.79,4.74 -2016-08-25 01:00:00,444.0,0.0,16.69,4.28 -2016-08-25 02:00:00,438.0,0.0,16.59,3.81 -2016-08-25 03:00:00,398.0,0.0,16.48,3.34 -2016-08-25 04:00:00,395.0,0.0,16.46,3.18 -2016-08-25 05:00:00,328.0,0.0,16.44,3.02 -2016-08-25 06:00:00,345.0,10.95,16.42,2.86 -2016-08-25 07:00:00,404.0,93.77,16.49,3.14 -2016-08-25 08:00:00,486.0,190.87,16.57,3.42 -2016-08-25 09:00:00,604.0,341.46,16.64,3.7 -2016-08-25 10:00:00,644.0,89.67,16.72,3.62 -2016-08-25 11:00:00,681.0,161.15,16.8,3.54 -2016-08-25 12:00:00,685.0,507.73,16.88,3.46 -2016-08-25 13:00:00,693.0,132.59,16.93,3.71 -2016-08-25 14:00:00,679.0,253.34,16.98,3.97 -2016-08-25 15:00:00,622.0,197.33,17.04,4.22 -2016-08-25 16:00:00,560.0,137.58,16.97,4.28 -2016-08-25 17:00:00,505.0,23.37,16.91,4.33 -2016-08-25 18:00:00,571.0,11.64,16.84,4.39 -2016-08-25 19:00:00,622.0,0.0,16.77,4.24 -2016-08-25 20:00:00,624.0,0.0,16.7,4.09 -2016-08-25 21:00:00,607.0,0.0,16.64,3.94 -2016-08-25 22:00:00,825.0,0.0,16.58,4.34 -2016-08-25 23:00:00,789.0,0.0,16.51,4.74 -2016-08-26 00:00:00,580.0,0.0,16.45,5.14 -2016-08-26 01:00:00,459.0,0.0,16.38,5.24 -2016-08-26 02:00:00,434.0,0.0,16.31,5.33 -2016-08-26 03:00:00,396.0,0.0,16.23,5.42 -2016-08-26 04:00:00,389.0,0.0,16.09,5.18 -2016-08-26 05:00:00,346.0,0.0,15.95,4.94 -2016-08-26 06:00:00,329.0,10.43,15.81,4.7 -2016-08-26 07:00:00,388.0,121.53,15.87,4.23 -2016-08-26 08:00:00,508.0,329.8,15.92,3.75 -2016-08-26 09:00:00,575.0,518.32,15.98,3.27 -2016-08-26 10:00:00,614.0,669.41,16.17,3.18 -2016-08-26 11:00:00,618.0,744.68,16.36,3.09 -2016-08-26 12:00:00,646.0,787.69,16.55,3.01 -2016-08-26 13:00:00,659.0,777.76,16.67,3.35 -2016-08-26 14:00:00,639.0,677.85,16.8,3.7 -2016-08-26 15:00:00,590.0,560.38,16.93,4.04 -2016-08-26 16:00:00,495.0,421.41,16.93,4.44 -2016-08-26 17:00:00,446.0,165.91,16.94,4.84 -2016-08-26 18:00:00,511.0,42.04,16.94,5.24 -2016-08-26 19:00:00,584.0,0.0,16.93,5.31 -2016-08-26 20:00:00,584.0,0.0,16.92,5.37 -2016-08-26 21:00:00,595.0,0.0,16.92,5.43 -2016-08-26 22:00:00,832.0,0.0,16.93,5.41 -2016-08-26 23:00:00,779.0,0.0,16.95,5.39 -2016-08-27 00:00:00,574.0,0.0,16.96,5.37 -2016-08-27 01:00:00,471.0,0.0,16.99,5.67 -2016-08-27 02:00:00,440.0,0.0,17.02,5.98 -2016-08-27 03:00:00,396.0,0.0,17.05,6.29 -2016-08-27 04:00:00,398.0,0.0,17.14,5.99 -2016-08-27 05:00:00,339.0,0.0,17.23,5.69 -2016-08-27 06:00:00,320.0,10.35,17.31,5.39 -2016-08-27 07:00:00,394.0,62.07,17.35,4.86 -2016-08-27 08:00:00,492.0,204.53,17.38,4.33 -2016-08-27 09:00:00,559.0,472.16,17.42,3.79 -2016-08-27 10:00:00,579.0,635.59,17.46,3.66 -2016-08-27 11:00:00,613.0,724.99,17.51,3.53 -2016-08-27 12:00:00,591.0,226.2,17.56,3.39 -2016-08-27 13:00:00,589.0,730.91,17.47,4.6 -2016-08-27 14:00:00,597.0,700.45,17.38,5.81 -2016-08-27 15:00:00,533.0,599.27,17.3,7.02 -2016-08-27 16:00:00,481.0,425.46,17.31,7.22 -2016-08-27 17:00:00,442.0,109.91,17.32,7.42 -2016-08-27 18:00:00,478.0,36.77,17.33,7.61 -2016-08-27 19:00:00,547.0,0.0,17.34,8.02 -2016-08-27 20:00:00,558.0,0.0,17.35,8.43 -2016-08-27 21:00:00,552.0,0.0,17.37,8.84 -2016-08-27 22:00:00,781.0,0.0,17.22,9.14 -2016-08-27 23:00:00,760.0,0.0,17.08,9.45 -2016-08-28 00:00:00,553.0,0.0,16.93,9.75 -2016-08-28 01:00:00,457.0,0.0,16.89,10.21 -2016-08-28 02:00:00,426.0,0.0,16.85,10.66 -2016-08-28 03:00:00,388.0,0.0,16.81,11.12 -2016-08-28 04:00:00,391.0,0.0,16.93,11.23 -2016-08-28 05:00:00,331.0,0.0,17.05,11.34 -2016-08-28 06:00:00,314.0,9.25,17.17,11.45 -2016-08-28 07:00:00,353.0,86.5,17.21,10.94 -2016-08-28 08:00:00,407.0,164.19,17.26,10.43 -2016-08-28 09:00:00,521.0,300.89,17.3,9.92 -2016-08-28 10:00:00,604.0,202.81,17.23,9.56 -2016-08-28 11:00:00,661.0,664.52,17.16,9.2 -2016-08-28 12:00:00,658.0,544.46,17.09,8.84 -2016-08-28 13:00:00,640.0,343.52,16.97,8.7 -2016-08-28 14:00:00,607.0,224.16,16.85,8.57 -2016-08-28 15:00:00,572.0,81.61,16.74,8.43 -2016-08-28 16:00:00,469.0,96.53,16.78,8.33 -2016-08-28 17:00:00,434.0,78.9,16.82,8.23 -2016-08-28 18:00:00,464.0,38.25,16.86,8.14 -2016-08-28 19:00:00,511.0,0.0,16.83,7.89 -2016-08-28 20:00:00,527.0,0.0,16.8,7.65 -2016-08-28 21:00:00,510.0,0.0,16.78,7.41 -2016-08-28 22:00:00,727.0,0.0,16.77,7.23 -2016-08-28 23:00:00,666.0,0.0,16.77,7.06 -2016-08-29 00:00:00,490.0,0.0,16.76,6.88 -2016-08-29 01:00:00,416.0,0.0,16.72,6.77 -2016-08-29 02:00:00,391.0,0.0,16.68,6.65 -2016-08-29 03:00:00,391.0,0.0,16.64,6.54 -2016-08-29 04:00:00,374.0,0.0,16.51,6.44 -2016-08-29 05:00:00,330.0,0.0,16.38,6.34 -2016-08-29 06:00:00,312.0,8.2,16.25,6.25 -2016-08-29 07:00:00,381.0,114.56,16.2,6.0 -2016-08-29 08:00:00,430.0,325.71,16.14,5.76 -2016-08-29 09:00:00,516.0,502.93,16.09,5.52 -2016-08-29 10:00:00,521.0,253.43,16.12,5.17 -2016-08-29 11:00:00,531.0,372.09,16.15,4.83 -2016-08-29 12:00:00,574.0,502.08,16.18,4.48 -2016-08-29 13:00:00,610.0,444.56,16.39,4.23 -2016-08-29 14:00:00,615.0,105.87,16.61,3.98 -2016-08-29 15:00:00,565.0,261.01,16.83,3.72 -2016-08-29 16:00:00,461.0,196.53,16.8,3.5 -2016-08-29 17:00:00,421.0,161.56,16.78,3.28 -2016-08-29 18:00:00,432.0,34.07,16.76,3.06 -2016-08-29 19:00:00,491.0,0.0,16.71,2.62 -2016-08-29 20:00:00,493.0,0.0,16.65,2.18 -2016-08-29 21:00:00,471.0,0.0,16.6,1.74 -2016-08-29 22:00:00,657.0,0.0,16.56,1.44 -2016-08-29 23:00:00,571.0,0.0,16.51,1.15 -2016-08-30 00:00:00,498.0,0.0,16.47,0.86 -2016-08-30 01:00:00,432.0,0.0,16.37,0.97 -2016-08-30 02:00:00,371.0,0.0,16.27,1.09 -2016-08-30 03:00:00,415.0,0.0,16.17,1.2 -2016-08-30 04:00:00,410.0,0.0,16.09,1.66 -2016-08-30 05:00:00,331.0,0.0,16.01,2.12 -2016-08-30 06:00:00,314.0,7.67,15.92,2.58 -2016-08-30 07:00:00,361.0,99.44,16.09,2.77 -2016-08-30 08:00:00,450.0,323.31,16.25,2.96 -2016-08-30 09:00:00,487.0,505.28,16.42,3.14 -2016-08-30 10:00:00,521.0,484.53,16.56,2.79 -2016-08-30 11:00:00,526.0,465.59,16.7,2.44 -2016-08-30 12:00:00,576.0,422.85,16.84,2.08 -2016-08-30 13:00:00,602.0,235.49,16.86,2.12 -2016-08-30 14:00:00,605.0,116.08,16.88,2.17 -2016-08-30 15:00:00,539.0,366.27,16.9,2.21 -2016-08-30 16:00:00,457.0,188.21,16.84,1.92 -2016-08-30 17:00:00,408.0,202.07,16.79,1.64 -2016-08-30 18:00:00,451.0,31.01,16.73,1.35 -2016-08-30 19:00:00,489.0,0.0,16.68,1.43 -2016-08-30 20:00:00,466.0,0.0,16.63,1.52 -2016-08-30 21:00:00,488.0,0.0,16.59,1.6 -2016-08-30 22:00:00,628.0,0.0,16.56,1.95 -2016-08-30 23:00:00,561.0,0.0,16.54,2.3 -2016-08-31 00:00:00,473.0,0.0,16.51,2.65 -2016-08-31 01:00:00,404.0,0.0,16.49,3.09 -2016-08-31 02:00:00,371.0,0.0,16.46,3.53 -2016-08-31 03:00:00,402.0,0.0,16.43,3.97 -2016-08-31 04:00:00,399.0,0.0,16.47,4.29 -2016-08-31 05:00:00,329.0,0.0,16.5,4.62 -2016-08-31 06:00:00,314.0,7.12,16.53,4.94 -2016-08-31 07:00:00,389.0,72.56,16.6,4.67 -2016-08-31 08:00:00,438.0,98.21,16.66,4.4 -2016-08-31 09:00:00,445.0,105.06,16.73,4.12 -2016-08-31 10:00:00,475.0,238.52,16.76,4.03 -2016-08-31 11:00:00,517.0,347.98,16.8,3.93 -2016-08-31 12:00:00,565.0,416.92,16.84,3.83 -2016-08-31 13:00:00,578.0,748.84,16.86,3.94 -2016-08-31 14:00:00,572.0,682.23,16.89,4.06 -2016-08-31 15:00:00,540.0,576.18,16.92,4.17 -2016-08-31 16:00:00,448.0,389.28,16.84,4.2 -2016-08-31 17:00:00,429.0,174.19,16.76,4.23 -2016-08-31 18:00:00,490.0,29.66,16.68,4.26 -2016-08-31 19:00:00,497.0,0.0,16.55,4.12 -2016-08-31 20:00:00,500.0,0.0,16.42,3.98 -2016-08-31 21:00:00,483.0,0.0,16.3,3.83 -2016-08-31 22:00:00,605.0,0.0,16.15,3.68 -2016-08-31 23:00:00,529.0,0.0,16.0,3.52 -2016-09-01 00:00:00,475.0,0.0,15.85,3.37 -2016-09-01 01:00:00,397.0,0.0,15.83,3.26 -2016-09-01 02:00:00,372.0,0.0,15.81,3.16 -2016-09-01 03:00:00,413.0,0.0,15.79,3.06 -2016-09-01 04:00:00,414.0,0.0,15.71,2.47 -2016-09-01 05:00:00,313.0,0.0,15.62,1.89 -2016-09-01 06:00:00,308.0,6.11,15.54,1.3 -2016-09-01 07:00:00,366.0,115.94,15.5,1.19 -2016-09-01 08:00:00,442.0,293.12,15.47,1.09 -2016-09-01 09:00:00,469.0,415.84,15.43,0.98 -2016-09-01 10:00:00,479.0,484.36,15.5,1.1 -2016-09-01 11:00:00,513.0,595.91,15.57,1.22 -2016-09-01 12:00:00,539.0,723.58,15.65,1.34 -2016-09-01 13:00:00,565.0,687.38,15.74,1.82 -2016-09-01 14:00:00,590.0,616.24,15.82,2.3 -2016-09-01 15:00:00,536.0,498.87,15.91,2.79 -2016-09-01 16:00:00,445.0,326.28,15.91,2.91 -2016-09-01 17:00:00,379.0,146.44,15.92,3.03 -2016-09-01 18:00:00,431.0,30.34,15.92,3.16 -2016-09-01 19:00:00,463.0,0.0,15.81,3.21 -2016-09-01 20:00:00,455.0,0.0,15.69,3.26 -2016-09-01 21:00:00,443.0,0.0,15.58,3.31 -2016-09-01 22:00:00,600.0,0.0,15.54,3.65 -2016-09-01 23:00:00,538.0,0.0,15.49,3.98 -2016-09-02 00:00:00,470.0,0.0,15.45,4.32 -2016-09-02 01:00:00,393.0,0.0,15.47,4.79 -2016-09-02 02:00:00,362.0,0.0,15.49,5.26 -2016-09-02 03:00:00,414.0,0.0,15.51,5.72 -2016-09-02 04:00:00,391.0,0.0,15.57,6.17 -2016-09-02 05:00:00,308.0,0.0,15.62,6.62 -2016-09-02 06:00:00,306.0,4.12,15.68,7.06 -2016-09-02 07:00:00,378.0,24.89,15.86,6.91 -2016-09-02 08:00:00,396.0,20.11,16.05,6.76 -2016-09-02 09:00:00,449.0,76.36,16.23,6.61 -2016-09-02 10:00:00,500.0,123.35,16.39,6.33 -2016-09-02 11:00:00,500.0,81.55,16.56,6.05 -2016-09-02 12:00:00,529.0,426.71,16.73,5.77 -2016-09-02 13:00:00,559.0,514.23,16.73,5.66 -2016-09-02 14:00:00,596.0,633.88,16.74,5.56 -2016-09-02 15:00:00,551.0,520.1,16.74,5.46 -2016-09-02 16:00:00,471.0,340.0,16.77,5.32 -2016-09-02 17:00:00,412.0,176.62,16.79,5.19 -2016-09-02 18:00:00,438.0,26.37,16.82,5.05 -2016-09-02 19:00:00,463.0,0.0,16.78,4.84 -2016-09-02 20:00:00,489.0,0.0,16.74,4.63 -2016-09-02 21:00:00,474.0,0.0,16.7,4.43 -2016-09-02 22:00:00,624.0,0.0,16.66,3.96 -2016-09-02 23:00:00,590.0,0.0,16.61,3.5 -2016-09-03 00:00:00,488.0,0.0,16.57,3.03 -2016-09-03 01:00:00,407.0,0.0,16.61,3.64 -2016-09-03 02:00:00,381.0,0.0,16.64,4.25 -2016-09-03 03:00:00,408.0,0.0,16.68,4.86 -2016-09-03 04:00:00,408.0,0.0,16.74,5.54 -2016-09-03 05:00:00,320.0,0.0,16.79,6.23 -2016-09-03 06:00:00,308.0,4.57,16.85,6.91 -2016-09-03 07:00:00,359.0,5.79,16.88,7.32 -2016-09-03 08:00:00,432.0,27.53,16.92,7.74 -2016-09-03 09:00:00,495.0,150.5,16.95,8.15 -2016-09-03 10:00:00,515.0,149.28,16.96,8.69 -2016-09-03 11:00:00,505.0,132.68,16.97,9.23 -2016-09-03 12:00:00,523.0,113.25,16.98,9.77 -2016-09-03 13:00:00,565.0,96.1,17.18,9.78 -2016-09-03 14:00:00,582.0,46.82,17.38,9.79 -2016-09-03 15:00:00,554.0,40.22,17.58,9.81 -2016-09-03 16:00:00,461.0,43.12,17.59,9.71 -2016-09-03 17:00:00,410.0,26.06,17.6,9.6 -2016-09-03 18:00:00,483.0,6.88,17.61,9.5 -2016-09-03 19:00:00,548.0,0.0,17.64,9.26 -2016-09-03 20:00:00,523.0,0.0,17.66,9.02 -2016-09-03 21:00:00,509.0,0.0,17.69,8.77 -2016-09-03 22:00:00,623.0,0.0,17.73,8.82 -2016-09-03 23:00:00,576.0,0.0,17.77,8.87 -2016-09-04 00:00:00,520.0,0.0,17.81,8.92 -2016-09-04 01:00:00,447.0,0.0,17.71,8.89 -2016-09-04 02:00:00,378.0,0.0,17.6,8.85 -2016-09-04 03:00:00,414.0,0.0,17.5,8.81 -2016-09-04 04:00:00,410.0,0.0,17.45,8.53 -2016-09-04 05:00:00,332.0,0.0,17.39,8.24 -2016-09-04 06:00:00,315.0,1.85,17.34,7.96 -2016-09-04 07:00:00,339.0,13.49,17.27,7.18 -2016-09-04 08:00:00,413.0,76.33,17.2,6.4 -2016-09-04 09:00:00,468.0,70.67,17.13,5.63 -2016-09-04 10:00:00,531.0,183.59,17.13,5.2 -2016-09-04 11:00:00,560.0,238.71,17.13,4.77 -2016-09-04 12:00:00,558.0,259.26,17.14,4.34 -2016-09-04 13:00:00,569.0,214.73,17.15,4.23 -2016-09-04 14:00:00,581.0,188.03,17.16,4.11 -2016-09-04 15:00:00,543.0,112.49,17.17,4.0 -2016-09-04 16:00:00,453.0,82.69,17.23,4.59 -2016-09-04 17:00:00,370.0,32.35,17.3,5.19 -2016-09-04 18:00:00,402.0,0.22,17.36,5.78 -2016-09-04 19:00:00,476.0,0.0,17.46,6.12 -2016-09-04 20:00:00,463.0,0.0,17.55,6.47 -2016-09-04 21:00:00,459.0,0.0,17.65,6.81 -2016-09-04 22:00:00,583.0,0.0,17.72,7.37 -2016-09-04 23:00:00,557.0,0.0,17.79,7.92 -2016-09-05 00:00:00,474.0,0.0,17.86,8.47 -2016-09-05 01:00:00,397.0,0.0,17.88,8.84 -2016-09-05 02:00:00,374.0,0.0,17.9,9.2 -2016-09-05 03:00:00,397.0,0.0,17.92,9.57 -2016-09-05 04:00:00,398.0,0.0,17.91,8.99 -2016-09-05 05:00:00,303.0,0.0,17.9,8.41 -2016-09-05 06:00:00,294.0,1.06,17.89,7.83 -2016-09-05 07:00:00,350.0,53.4,17.81,7.14 -2016-09-05 08:00:00,412.0,91.74,17.72,6.46 -2016-09-05 09:00:00,457.0,90.25,17.64,5.77 -2016-09-05 10:00:00,504.0,202.85,17.59,5.65 -2016-09-05 11:00:00,536.0,378.08,17.54,5.53 -2016-09-05 12:00:00,554.0,414.73,17.49,5.41 -2016-09-05 13:00:00,549.0,552.06,17.44,5.69 -2016-09-05 14:00:00,597.0,607.32,17.39,5.98 -2016-09-05 15:00:00,545.0,500.15,17.34,6.26 -2016-09-05 16:00:00,501.0,267.23,17.28,5.72 -2016-09-05 17:00:00,424.0,86.63,17.23,5.18 -2016-09-05 18:00:00,458.0,19.58,17.17,4.63 -2016-09-05 19:00:00,511.0,0.0,17.14,4.37 -2016-09-05 20:00:00,488.0,0.0,17.11,4.1 -2016-09-05 21:00:00,472.0,0.0,17.08,3.83 -2016-09-05 22:00:00,611.0,0.0,17.09,3.9 -2016-09-05 23:00:00,540.0,0.0,17.1,3.96 -2016-09-06 00:00:00,458.0,0.0,17.11,4.03 -2016-09-06 01:00:00,410.0,0.0,17.05,3.81 -2016-09-06 02:00:00,364.0,0.0,17.0,3.6 -2016-09-06 03:00:00,400.0,0.0,16.94,3.38 -2016-09-06 04:00:00,396.0,0.0,16.94,3.77 -2016-09-06 05:00:00,304.0,0.0,16.93,4.15 -2016-09-06 06:00:00,301.0,1.46,16.93,4.54 -2016-09-06 07:00:00,389.0,56.74,16.99,4.48 -2016-09-06 08:00:00,472.0,85.4,17.04,4.42 -2016-09-06 09:00:00,512.0,143.95,17.1,4.36 -2016-09-06 10:00:00,575.0,145.1,17.15,3.8 -2016-09-06 11:00:00,572.0,282.82,17.21,3.25 -2016-09-06 12:00:00,574.0,293.51,17.27,2.69 -2016-09-06 13:00:00,612.0,251.17,17.75,2.14 -2016-09-06 14:00:00,606.0,159.44,18.23,1.59 -2016-09-06 15:00:00,566.0,244.51,18.71,1.03 -2016-09-06 16:00:00,487.0,269.87,18.5,1.7 -2016-09-06 17:00:00,417.0,181.25,18.28,2.36 -2016-09-06 18:00:00,457.0,21.31,18.07,3.02 -2016-09-06 19:00:00,510.0,0.0,17.96,3.47 -2016-09-06 20:00:00,479.0,0.0,17.84,3.92 -2016-09-06 21:00:00,486.0,0.0,17.73,4.37 -2016-09-06 22:00:00,643.0,0.0,17.82,4.74 -2016-09-06 23:00:00,554.0,0.0,17.9,5.1 -2016-09-07 00:00:00,467.0,0.0,17.99,5.46 -2016-09-07 01:00:00,393.0,0.0,18.12,5.56 -2016-09-07 02:00:00,376.0,0.0,18.24,5.66 -2016-09-07 03:00:00,400.0,0.0,18.37,5.77 -2016-09-07 04:00:00,395.0,0.0,18.5,6.06 -2016-09-07 05:00:00,315.0,0.0,18.64,6.35 -2016-09-07 06:00:00,330.0,2.24,18.77,6.65 -2016-09-07 07:00:00,408.0,71.31,18.82,6.68 -2016-09-07 08:00:00,468.0,298.02,18.86,6.7 -2016-09-07 09:00:00,514.0,374.98,18.91,6.73 -2016-09-07 10:00:00,510.0,650.11,19.0,6.61 -2016-09-07 11:00:00,546.0,749.15,19.09,6.49 -2016-09-07 12:00:00,591.0,575.3,19.18,6.37 -2016-09-07 13:00:00,590.0,764.3,19.34,6.22 -2016-09-07 14:00:00,582.0,546.17,19.49,6.06 -2016-09-07 15:00:00,555.0,549.46,19.65,5.9 -2016-09-07 16:00:00,484.0,327.3,19.33,5.49 -2016-09-07 17:00:00,423.0,96.8,19.01,5.09 -2016-09-07 18:00:00,490.0,19.99,18.69,4.68 -2016-09-07 19:00:00,540.0,0.0,18.31,5.54 -2016-09-07 20:00:00,500.0,0.0,17.92,6.4 -2016-09-07 21:00:00,527.0,0.0,17.54,7.27 -2016-09-07 22:00:00,634.0,0.0,17.32,8.1 -2016-09-07 23:00:00,572.0,0.0,17.1,8.93 -2016-09-08 00:00:00,484.0,0.0,16.88,9.77 -2016-09-08 01:00:00,418.0,0.0,16.87,9.67 -2016-09-08 02:00:00,400.0,0.0,16.86,9.57 -2016-09-08 03:00:00,422.0,0.0,16.85,9.48 -2016-09-08 04:00:00,407.0,0.0,16.88,9.44 -2016-09-08 05:00:00,323.0,0.0,16.91,9.41 -2016-09-08 06:00:00,325.0,1.46,16.94,9.38 -2016-09-08 07:00:00,391.0,110.09,17.04,8.96 -2016-09-08 08:00:00,481.0,326.53,17.14,8.54 -2016-09-08 09:00:00,497.0,504.68,17.24,8.12 -2016-09-08 10:00:00,498.0,668.49,17.35,8.18 -2016-09-08 11:00:00,538.0,751.78,17.47,8.24 -2016-09-08 12:00:00,576.0,797.11,17.59,8.3 -2016-09-08 13:00:00,603.0,757.13,17.68,8.37 -2016-09-08 14:00:00,583.0,265.91,17.76,8.44 -2016-09-08 15:00:00,543.0,326.18,17.85,8.51 -2016-09-08 16:00:00,477.0,172.13,17.91,8.81 -2016-09-08 17:00:00,418.0,58.32,17.98,9.12 -2016-09-08 18:00:00,474.0,3.66,18.04,9.42 -2016-09-08 19:00:00,511.0,0.0,18.07,9.42 -2016-09-08 20:00:00,517.0,0.0,18.09,9.41 -2016-09-08 21:00:00,517.0,0.0,18.12,9.41 -2016-09-08 22:00:00,659.0,0.0,18.04,9.01 -2016-09-08 23:00:00,584.0,0.0,17.95,8.62 -2016-09-09 00:00:00,475.0,0.0,17.87,8.22 -2016-09-09 01:00:00,414.0,0.0,17.83,8.33 -2016-09-09 02:00:00,383.0,0.0,17.8,8.43 -2016-09-09 03:00:00,401.0,0.0,17.76,8.54 -2016-09-09 04:00:00,392.0,0.0,17.75,8.55 -2016-09-09 05:00:00,342.0,0.0,17.75,8.56 -2016-09-09 06:00:00,339.0,0.0,17.74,8.57 -2016-09-09 07:00:00,397.0,67.45,17.79,9.01 -2016-09-09 08:00:00,470.0,165.12,17.83,9.45 -2016-09-09 09:00:00,522.0,169.06,17.88,9.89 -2016-09-09 10:00:00,564.0,216.83,17.91,10.32 -2016-09-09 11:00:00,580.0,576.28,17.94,10.74 -2016-09-09 12:00:00,610.0,815.34,17.97,11.17 -2016-09-09 13:00:00,625.0,773.64,17.98,11.22 -2016-09-09 14:00:00,647.0,646.17,17.98,11.26 -2016-09-09 15:00:00,611.0,499.44,17.99,11.31 -2016-09-09 16:00:00,512.0,123.09,17.99,11.24 -2016-09-09 17:00:00,463.0,92.58,17.99,11.17 -2016-09-09 18:00:00,488.0,13.19,17.99,11.1 -2016-09-09 19:00:00,539.0,0.0,18.01,10.94 -2016-09-09 20:00:00,506.0,0.0,18.02,10.77 -2016-09-09 21:00:00,504.0,0.0,18.04,10.61 -2016-09-09 22:00:00,639.0,0.0,18.03,10.45 -2016-09-09 23:00:00,614.0,0.0,18.02,10.28 -2016-09-10 00:00:00,498.0,0.0,18.01,10.12 -2016-09-10 01:00:00,449.0,0.0,17.94,9.43 -2016-09-10 02:00:00,463.0,0.0,17.88,8.73 -2016-09-10 03:00:00,460.0,0.0,17.81,8.03 -2016-09-10 04:00:00,419.0,0.0,17.73,6.88 -2016-09-10 05:00:00,349.0,0.0,17.64,5.73 -2016-09-10 06:00:00,352.0,0.0,17.56,4.58 -2016-09-10 07:00:00,393.0,38.27,17.45,4.12 -2016-09-10 08:00:00,469.0,123.93,17.34,3.67 -2016-09-10 09:00:00,558.0,98.54,17.23,3.21 -2016-09-10 10:00:00,622.0,132.19,17.08,3.3 -2016-09-10 11:00:00,656.0,699.92,16.93,3.39 -2016-09-10 12:00:00,709.0,701.38,16.78,3.48 -2016-09-10 13:00:00,705.0,567.61,16.65,4.13 -2016-09-10 14:00:00,744.0,678.49,16.51,4.79 -2016-09-10 15:00:00,687.0,561.99,16.38,5.45 -2016-09-10 16:00:00,597.0,377.04,16.26,5.41 -2016-09-10 17:00:00,494.0,167.98,16.15,5.37 -2016-09-10 18:00:00,484.0,12.77,16.03,5.32 -2016-09-10 19:00:00,507.0,0.0,15.96,5.05 -2016-09-10 20:00:00,512.0,0.0,15.9,4.77 -2016-09-10 21:00:00,511.0,0.0,15.83,4.5 -2016-09-10 22:00:00,684.0,0.0,15.76,3.71 -2016-09-10 23:00:00,618.0,0.0,15.7,2.93 -2016-09-11 00:00:00,502.0,0.0,15.63,2.15 -2016-09-11 01:00:00,478.0,0.0,15.52,1.92 -2016-09-11 02:00:00,465.0,0.0,15.41,1.69 -2016-09-11 03:00:00,457.0,0.0,15.31,1.46 -2016-09-11 04:00:00,451.0,0.0,15.3,2.25 -2016-09-11 05:00:00,369.0,0.0,15.3,3.04 -2016-09-11 06:00:00,347.0,0.0,15.29,3.83 -2016-09-11 07:00:00,379.0,102.49,15.42,4.48 -2016-09-11 08:00:00,406.0,316.16,15.55,5.13 -2016-09-11 09:00:00,536.0,516.83,15.68,5.78 -2016-09-11 10:00:00,620.0,552.85,15.97,6.04 -2016-09-11 11:00:00,703.0,468.25,16.26,6.3 -2016-09-11 12:00:00,652.0,449.41,16.55,6.57 -2016-09-11 13:00:00,621.0,521.14,16.75,6.77 -2016-09-11 14:00:00,652.0,608.66,16.94,6.98 -2016-09-11 15:00:00,588.0,552.83,17.14,7.19 -2016-09-11 16:00:00,485.0,255.79,17.2,7.18 -2016-09-11 17:00:00,402.0,86.72,17.27,7.18 -2016-09-11 18:00:00,419.0,11.66,17.33,7.17 -2016-09-11 19:00:00,457.0,0.0,17.37,7.6 -2016-09-11 20:00:00,460.0,0.0,17.4,8.04 -2016-09-11 21:00:00,471.0,0.0,17.44,8.47 -2016-09-11 22:00:00,610.0,0.0,17.48,8.85 -2016-09-11 23:00:00,553.0,0.0,17.51,9.22 -2016-09-12 00:00:00,473.0,0.0,17.55,9.6 -2016-09-12 01:00:00,428.0,0.0,17.57,9.87 -2016-09-12 02:00:00,368.0,0.0,17.59,10.14 -2016-09-12 03:00:00,404.0,0.0,17.62,10.41 -2016-09-12 04:00:00,391.0,0.0,17.69,10.41 -2016-09-12 05:00:00,307.0,0.0,17.75,10.41 -2016-09-12 06:00:00,308.0,0.0,17.82,10.41 -2016-09-12 07:00:00,362.0,56.39,17.85,10.16 -2016-09-12 08:00:00,412.0,140.92,17.89,9.91 -2016-09-12 09:00:00,506.0,109.21,17.92,9.66 -2016-09-12 10:00:00,491.0,259.87,17.97,9.52 -2016-09-12 11:00:00,544.0,344.99,18.03,9.38 -2016-09-12 12:00:00,590.0,475.82,18.09,9.24 -2016-09-12 13:00:00,580.0,548.48,18.1,8.77 -2016-09-12 14:00:00,612.0,521.54,18.1,8.29 -2016-09-12 15:00:00,581.0,451.47,18.11,7.82 -2016-09-12 16:00:00,478.0,134.73,18.05,7.64 -2016-09-12 17:00:00,407.0,27.37,17.99,7.46 -2016-09-12 18:00:00,463.0,9.36,17.93,7.28 -2016-09-12 19:00:00,530.0,0.0,17.86,7.19 -2016-09-12 20:00:00,533.0,0.0,17.8,7.1 -2016-09-12 21:00:00,504.0,0.0,17.73,7.01 -2016-09-12 22:00:00,608.0,0.0,17.68,6.39 -2016-09-12 23:00:00,510.0,0.0,17.64,5.77 -2016-09-13 00:00:00,435.0,0.0,17.59,5.14 -2016-09-13 01:00:00,422.0,0.0,17.44,4.76 -2016-09-13 02:00:00,376.0,0.0,17.29,4.38 -2016-09-13 03:00:00,408.0,0.0,17.14,4.0 -2016-09-13 04:00:00,400.0,0.0,17.02,4.24 -2016-09-13 05:00:00,346.0,0.0,16.89,4.49 -2016-09-13 06:00:00,314.0,0.0,16.77,4.73 -2016-09-13 07:00:00,363.0,44.41,16.63,5.66 -2016-09-13 08:00:00,446.0,55.87,16.49,6.59 -2016-09-13 09:00:00,504.0,64.96,16.35,7.52 -2016-09-13 10:00:00,514.0,44.1,16.31,7.8 -2016-09-13 11:00:00,546.0,31.84,16.27,8.09 -2016-09-13 12:00:00,556.0,38.3,16.24,8.37 -2016-09-13 13:00:00,577.0,47.75,16.51,8.43 -2016-09-13 14:00:00,589.0,46.95,16.77,8.49 -2016-09-13 15:00:00,563.0,41.04,17.04,8.55 -2016-09-13 16:00:00,497.0,12.88,16.99,8.38 -2016-09-13 17:00:00,449.0,20.02,16.94,8.21 -2016-09-13 18:00:00,506.0,0.22,16.89,8.04 -2016-09-13 19:00:00,561.0,0.0,16.88,8.12 -2016-09-13 20:00:00,542.0,0.0,16.86,8.21 -2016-09-13 21:00:00,513.0,0.0,16.85,8.29 -2016-09-13 22:00:00,624.0,0.0,16.96,7.32 -2016-09-13 23:00:00,551.0,0.0,17.07,6.35 -2016-09-14 00:00:00,467.0,0.0,17.18,5.38 -2016-09-14 01:00:00,406.0,0.0,17.12,5.65 -2016-09-14 02:00:00,363.0,0.0,17.06,5.91 -2016-09-14 03:00:00,397.0,0.0,17.0,6.18 -2016-09-14 04:00:00,408.0,0.0,17.07,6.26 -2016-09-14 05:00:00,331.0,0.0,17.13,6.34 -2016-09-14 06:00:00,317.0,0.0,17.2,6.43 -2016-09-14 07:00:00,385.0,79.94,17.15,7.01 -2016-09-14 08:00:00,459.0,195.44,17.09,7.6 -2016-09-14 09:00:00,478.0,460.85,17.04,8.18 -2016-09-14 10:00:00,504.0,668.58,17.08,8.28 -2016-09-14 11:00:00,492.0,735.04,17.13,8.38 -2016-09-14 12:00:00,520.0,823.57,17.18,8.48 -2016-09-14 13:00:00,543.0,775.11,17.13,8.16 -2016-09-14 14:00:00,573.0,694.43,17.07,7.84 -2016-09-14 15:00:00,539.0,558.59,17.02,7.52 -2016-09-14 16:00:00,439.0,367.0,17.1,7.4 -2016-09-14 17:00:00,381.0,103.0,17.18,7.28 -2016-09-14 18:00:00,426.0,7.06,17.26,7.16 -2016-09-14 19:00:00,481.0,0.0,17.17,6.86 -2016-09-14 20:00:00,472.0,0.0,17.08,6.56 -2016-09-14 21:00:00,477.0,0.0,16.99,6.26 -2016-09-14 22:00:00,607.0,0.0,16.93,5.77 -2016-09-14 23:00:00,540.0,0.0,16.88,5.27 -2016-09-15 00:00:00,445.0,0.0,16.82,4.77 -2016-09-15 01:00:00,393.0,0.0,16.82,4.75 -2016-09-15 02:00:00,359.0,0.0,16.81,4.73 -2016-09-15 03:00:00,384.0,0.0,16.81,4.7 -2016-09-15 04:00:00,397.0,0.0,16.75,4.42 -2016-09-15 05:00:00,316.0,0.0,16.69,4.13 -2016-09-15 06:00:00,294.0,0.0,16.63,3.85 -2016-09-15 07:00:00,358.0,45.92,16.47,4.2 -2016-09-15 08:00:00,459.0,179.75,16.32,4.56 -2016-09-15 09:00:00,503.0,61.9,16.16,4.91 -2016-09-15 10:00:00,526.0,91.73,15.99,5.57 -2016-09-15 11:00:00,594.0,59.8,15.83,6.23 -2016-09-15 12:00:00,616.0,72.0,15.67,6.88 -2016-09-15 13:00:00,625.0,85.6,16.03,6.09 -2016-09-15 14:00:00,658.0,66.37,16.38,5.29 -2016-09-15 15:00:00,613.0,39.58,16.74,4.5 -2016-09-15 16:00:00,522.0,50.59,16.67,5.5 -2016-09-15 17:00:00,437.0,22.74,16.6,6.51 -2016-09-15 18:00:00,486.0,0.0,16.53,7.52 -2016-09-15 19:00:00,557.0,0.0,16.34,7.63 -2016-09-15 20:00:00,533.0,0.0,16.16,7.75 -2016-09-15 21:00:00,498.0,0.0,15.97,7.86 -2016-09-15 22:00:00,617.0,0.0,15.91,8.08 -2016-09-15 23:00:00,542.0,0.0,15.85,8.29 -2016-09-16 00:00:00,443.0,0.0,15.79,8.51 -2016-09-16 01:00:00,413.0,0.0,15.76,8.88 -2016-09-16 02:00:00,371.0,0.0,15.72,9.25 -2016-09-16 03:00:00,412.0,0.0,15.69,9.61 -2016-09-16 04:00:00,407.0,0.0,15.77,9.89 -2016-09-16 05:00:00,342.0,0.0,15.86,10.17 -2016-09-16 06:00:00,328.0,0.0,15.94,10.44 -2016-09-16 07:00:00,426.0,49.39,15.9,10.4 -2016-09-16 08:00:00,462.0,49.42,15.86,10.35 -2016-09-16 09:00:00,499.0,308.38,15.82,10.3 -2016-09-16 10:00:00,506.0,425.73,15.78,10.12 -2016-09-16 11:00:00,537.0,341.56,15.74,9.94 -2016-09-16 12:00:00,525.0,433.67,15.71,9.77 -2016-09-16 13:00:00,565.0,258.96,15.67,9.6 -2016-09-16 14:00:00,573.0,605.51,15.64,9.43 -2016-09-16 15:00:00,521.0,141.44,15.6,9.26 -2016-09-16 16:00:00,442.0,168.24,15.52,9.5 -2016-09-16 17:00:00,387.0,66.67,15.45,9.75 -2016-09-16 18:00:00,448.0,4.55,15.37,10.0 -2016-09-16 19:00:00,488.0,0.0,15.3,10.03 -2016-09-16 20:00:00,494.0,0.0,15.24,10.06 -2016-09-16 21:00:00,511.0,0.0,15.17,10.08 -2016-09-16 22:00:00,617.0,0.0,15.2,9.73 -2016-09-16 23:00:00,561.0,0.0,15.23,9.37 -2016-09-17 00:00:00,461.0,0.0,15.26,9.02 -2016-09-17 01:00:00,398.0,0.0,15.27,8.79 -2016-09-17 02:00:00,363.0,0.0,15.27,8.56 -2016-09-17 03:00:00,399.0,0.0,15.28,8.33 -2016-09-17 04:00:00,399.0,0.0,15.25,8.1 -2016-09-17 05:00:00,332.0,0.0,15.21,7.87 -2016-09-17 06:00:00,316.0,0.0,15.18,7.64 -2016-09-17 07:00:00,369.0,51.91,15.16,7.4 -2016-09-17 08:00:00,452.0,103.93,15.15,7.15 -2016-09-17 09:00:00,515.0,166.1,15.13,6.91 -2016-09-17 10:00:00,511.0,102.33,15.13,6.99 -2016-09-17 11:00:00,535.0,428.32,15.13,7.08 -2016-09-17 12:00:00,525.0,280.56,15.13,7.16 -2016-09-17 13:00:00,567.0,109.29,15.24,7.08 -2016-09-17 14:00:00,606.0,127.97,15.35,7.0 -2016-09-17 15:00:00,557.0,128.18,15.46,6.92 -2016-09-17 16:00:00,449.0,24.95,15.39,6.89 -2016-09-17 17:00:00,402.0,26.35,15.33,6.85 -2016-09-17 18:00:00,454.0,0.0,15.26,6.81 -2016-09-17 19:00:00,490.0,0.0,15.21,6.49 -2016-09-17 20:00:00,500.0,0.0,15.16,6.17 -2016-09-17 21:00:00,518.0,0.0,15.11,5.85 -2016-09-17 22:00:00,629.0,0.0,15.03,5.59 -2016-09-17 23:00:00,571.0,0.0,14.95,5.33 -2016-09-18 00:00:00,474.0,0.0,14.87,5.08 -2016-09-18 01:00:00,431.0,0.0,15.05,4.91 -2016-09-18 02:00:00,390.0,0.0,15.23,4.74 -2016-09-18 03:00:00,419.0,0.0,15.42,4.58 -2016-09-18 04:00:00,413.0,0.0,15.39,4.35 -2016-09-18 05:00:00,348.0,0.0,15.35,4.13 -2016-09-18 06:00:00,312.0,0.0,15.32,3.9 -2016-09-18 07:00:00,360.0,31.32,15.37,3.75 -2016-09-18 08:00:00,434.0,166.67,15.42,3.6 -2016-09-18 09:00:00,515.0,61.27,15.47,3.45 -2016-09-18 10:00:00,553.0,35.46,15.57,3.64 -2016-09-18 11:00:00,582.0,112.51,15.68,3.83 -2016-09-18 12:00:00,581.0,87.06,15.79,4.03 -2016-09-18 13:00:00,582.0,115.65,15.61,3.87 -2016-09-18 14:00:00,572.0,226.67,15.42,3.71 -2016-09-18 15:00:00,547.0,127.77,15.24,3.56 -2016-09-18 16:00:00,451.0,157.82,15.32,3.38 -2016-09-18 17:00:00,378.0,30.5,15.41,3.2 -2016-09-18 18:00:00,400.0,1.38,15.49,3.02 -2016-09-18 19:00:00,445.0,0.0,15.53,2.93 -2016-09-18 20:00:00,454.0,0.0,15.56,2.84 -2016-09-18 21:00:00,451.0,0.0,15.6,2.74 -2016-09-18 22:00:00,580.0,0.0,15.63,2.78 -2016-09-18 23:00:00,527.0,0.0,15.65,2.82 -2016-09-19 00:00:00,454.0,0.0,15.68,2.86 -2016-09-19 01:00:00,406.0,0.0,15.79,2.75 -2016-09-19 02:00:00,370.0,0.0,15.9,2.65 -2016-09-19 03:00:00,391.0,0.0,16.02,2.55 -2016-09-19 04:00:00,402.0,0.0,15.92,2.62 -2016-09-19 05:00:00,318.0,0.0,15.82,2.68 -2016-09-19 06:00:00,304.0,0.0,15.72,2.74 -2016-09-19 07:00:00,350.0,47.9,15.67,2.98 -2016-09-19 08:00:00,408.0,222.74,15.61,3.21 -2016-09-19 09:00:00,476.0,137.93,15.56,3.45 -2016-09-19 10:00:00,529.0,158.68,15.56,3.83 -2016-09-19 11:00:00,562.0,367.94,15.56,4.21 -2016-09-19 12:00:00,582.0,480.69,15.56,4.59 -2016-09-19 13:00:00,579.0,334.29,15.42,5.28 -2016-09-19 14:00:00,567.0,193.61,15.27,5.97 -2016-09-19 15:00:00,565.0,137.73,15.13,6.66 -2016-09-19 16:00:00,486.0,133.42,15.1,6.96 -2016-09-19 17:00:00,393.0,53.42,15.07,7.26 -2016-09-19 18:00:00,458.0,0.0,15.04,7.56 -2016-09-19 19:00:00,500.0,0.0,15.02,7.5 -2016-09-19 20:00:00,474.0,0.0,15.0,7.44 -2016-09-19 21:00:00,470.0,0.0,14.98,7.38 -2016-09-19 22:00:00,583.0,0.0,14.99,7.13 -2016-09-19 23:00:00,528.0,0.0,15.01,6.88 -2016-09-20 00:00:00,440.0,0.0,15.02,6.63 -2016-09-20 01:00:00,404.0,0.0,14.98,6.29 -2016-09-20 02:00:00,380.0,0.0,14.93,5.94 -2016-09-20 03:00:00,400.0,0.0,14.89,5.59 -2016-09-20 04:00:00,412.0,0.0,14.89,5.6 -2016-09-20 05:00:00,330.0,0.0,14.89,5.6 -2016-09-20 06:00:00,314.0,0.0,14.89,5.61 -2016-09-20 07:00:00,373.0,35.15,14.89,5.46 -2016-09-20 08:00:00,470.0,86.55,14.89,5.3 -2016-09-20 09:00:00,492.0,56.27,14.89,5.14 -2016-09-20 10:00:00,534.0,127.06,14.93,5.05 -2016-09-20 11:00:00,542.0,180.68,14.98,4.96 -2016-09-20 12:00:00,590.0,253.31,15.03,4.87 -2016-09-20 13:00:00,614.0,470.56,15.15,4.74 -2016-09-20 14:00:00,609.0,359.09,15.26,4.6 -2016-09-20 15:00:00,568.0,134.89,15.38,4.47 -2016-09-20 16:00:00,483.0,83.74,15.43,4.11 -2016-09-20 17:00:00,412.0,50.75,15.49,3.76 -2016-09-20 18:00:00,445.0,0.0,15.54,3.41 -2016-09-20 19:00:00,495.0,0.0,15.49,3.19 -2016-09-20 20:00:00,508.0,0.0,15.44,2.97 -2016-09-20 21:00:00,501.0,0.0,15.39,2.76 -2016-09-20 22:00:00,601.0,0.0,15.28,2.33 -2016-09-20 23:00:00,536.0,0.0,15.18,1.9 -2016-09-21 00:00:00,451.0,0.0,15.07,1.48 -2016-09-21 01:00:00,416.0,0.0,15.14,1.63 -2016-09-21 02:00:00,387.0,0.0,15.2,1.78 -2016-09-21 03:00:00,423.0,0.0,15.27,1.93 -2016-09-21 04:00:00,422.0,0.0,15.21,2.23 -2016-09-21 05:00:00,334.0,0.0,15.14,2.52 -2016-09-21 06:00:00,313.0,0.0,15.08,2.81 -2016-09-21 07:00:00,389.0,17.52,15.1,2.96 -2016-09-21 08:00:00,468.0,100.4,15.11,3.1 -2016-09-21 09:00:00,539.0,110.91,15.13,3.24 -2016-09-21 10:00:00,539.0,446.54,15.15,3.42 -2016-09-21 11:00:00,536.0,494.45,15.17,3.59 -2016-09-21 12:00:00,574.0,307.74,15.2,3.77 -2016-09-21 13:00:00,584.0,250.95,15.14,4.51 -2016-09-21 14:00:00,589.0,211.03,15.08,5.26 -2016-09-21 15:00:00,537.0,276.52,15.02,6.01 -2016-09-21 16:00:00,449.0,141.48,15.16,5.93 -2016-09-21 17:00:00,398.0,48.06,15.31,5.85 -2016-09-21 18:00:00,448.0,0.0,15.45,5.77 -2016-09-21 19:00:00,520.0,0.0,15.55,5.49 -2016-09-21 20:00:00,518.0,0.0,15.65,5.2 -2016-09-21 21:00:00,478.0,0.0,15.75,4.92 -2016-09-21 22:00:00,605.0,0.0,15.75,4.9 -2016-09-21 23:00:00,542.0,0.0,15.75,4.88 -2016-09-22 00:00:00,462.0,0.0,15.75,4.86 -2016-09-22 01:00:00,405.0,0.0,15.53,4.24 -2016-09-22 02:00:00,380.0,0.0,15.31,3.62 -2016-09-22 03:00:00,399.0,0.0,15.09,3.01 -2016-09-22 04:00:00,412.0,0.0,15.02,2.91 -2016-09-22 05:00:00,335.0,0.0,14.94,2.82 -2016-09-22 06:00:00,321.0,0.0,14.87,2.73 -2016-09-22 07:00:00,390.0,48.43,14.88,2.71 -2016-09-22 08:00:00,471.0,291.97,14.88,2.69 -2016-09-22 09:00:00,527.0,464.19,14.89,2.68 -2016-09-22 10:00:00,554.0,624.42,14.93,2.39 -2016-09-22 11:00:00,556.0,720.45,14.97,2.11 -2016-09-22 12:00:00,566.0,708.34,15.01,1.82 -2016-09-22 13:00:00,581.0,724.59,15.0,2.34 -2016-09-22 14:00:00,594.0,642.13,14.99,2.87 -2016-09-22 15:00:00,592.0,347.66,14.98,3.39 -2016-09-22 16:00:00,477.0,174.71,14.91,3.46 -2016-09-22 17:00:00,442.0,59.2,14.83,3.53 -2016-09-22 18:00:00,478.0,0.0,14.76,3.6 -2016-09-22 19:00:00,519.0,0.0,14.69,3.59 -2016-09-22 20:00:00,520.0,0.0,14.62,3.58 -2016-09-22 21:00:00,518.0,0.0,14.55,3.57 -2016-09-22 22:00:00,613.0,0.0,14.48,3.17 -2016-09-22 23:00:00,542.0,0.0,14.4,2.76 -2016-09-23 00:00:00,492.0,0.0,14.33,2.36 -2016-09-23 01:00:00,436.0,0.0,14.5,2.19 -2016-09-23 02:00:00,413.0,0.0,14.66,2.02 -2016-09-23 03:00:00,423.0,0.0,14.83,1.85 -2016-09-23 04:00:00,438.0,0.0,14.81,1.95 -2016-09-23 05:00:00,368.0,0.0,14.8,2.06 -2016-09-23 06:00:00,363.0,0.0,14.78,2.17 -2016-09-23 07:00:00,460.0,37.63,14.91,3.12 -2016-09-23 08:00:00,459.0,275.52,15.04,4.07 -2016-09-23 09:00:00,493.0,484.54,15.17,5.02 -2016-09-23 10:00:00,504.0,623.81,15.44,5.59 -2016-09-23 11:00:00,502.0,558.17,15.71,6.15 -2016-09-23 12:00:00,522.0,613.22,15.98,6.72 -2016-09-23 13:00:00,545.0,737.0,16.06,6.9 -2016-09-23 14:00:00,564.0,556.91,16.15,7.08 -2016-09-23 15:00:00,508.0,423.69,16.23,7.26 -2016-09-23 16:00:00,435.0,183.44,16.33,7.37 -2016-09-23 17:00:00,382.0,22.77,16.44,7.49 -2016-09-23 18:00:00,431.0,0.0,16.54,7.6 -2016-09-23 19:00:00,490.0,0.0,16.55,8.0 -2016-09-23 20:00:00,497.0,0.0,16.57,8.41 -2016-09-23 21:00:00,530.0,0.0,16.58,8.81 -2016-09-23 22:00:00,652.0,0.0,16.62,9.28 -2016-09-23 23:00:00,565.0,0.0,16.67,9.74 -2016-09-24 00:00:00,497.0,0.0,16.71,10.21 -2016-09-24 01:00:00,429.0,0.0,16.76,10.78 -2016-09-24 02:00:00,386.0,0.0,16.82,11.36 -2016-09-24 03:00:00,411.0,0.0,16.87,11.93 -2016-09-24 04:00:00,417.0,0.0,16.96,11.9 -2016-09-24 05:00:00,358.0,0.0,17.04,11.88 -2016-09-24 06:00:00,311.0,0.0,17.13,11.85 -2016-09-24 07:00:00,363.0,36.16,17.12,11.84 -2016-09-24 08:00:00,450.0,119.42,17.11,11.84 -2016-09-24 09:00:00,526.0,204.13,17.1,11.83 -2016-09-24 10:00:00,555.0,341.18,17.11,11.82 -2016-09-24 11:00:00,545.0,131.25,17.12,11.8 -2016-09-24 12:00:00,552.0,91.58,17.13,11.78 -2016-09-24 13:00:00,576.0,113.34,17.04,11.7 -2016-09-24 14:00:00,586.0,45.48,16.95,11.62 -2016-09-24 15:00:00,562.0,34.62,16.86,11.54 -2016-09-24 16:00:00,491.0,18.06,16.78,11.17 -2016-09-24 17:00:00,422.0,16.75,16.69,10.8 -2016-09-24 18:00:00,495.0,0.0,16.61,10.43 -2016-09-24 19:00:00,547.0,0.0,16.43,9.63 -2016-09-24 20:00:00,548.0,0.0,16.24,8.83 -2016-09-24 21:00:00,535.0,0.0,16.06,8.03 -2016-09-24 22:00:00,638.0,0.0,15.68,8.84 -2016-09-24 23:00:00,586.0,0.0,15.3,9.66 -2016-09-25 00:00:00,516.0,0.0,14.92,10.47 -2016-09-25 01:00:00,463.0,0.0,15.09,10.28 -2016-09-25 02:00:00,431.0,0.0,15.27,10.1 -2016-09-25 03:00:00,448.0,0.0,15.44,9.92 -2016-09-25 04:00:00,436.0,0.0,15.28,9.84 -2016-09-25 05:00:00,357.0,0.0,15.13,9.77 -2016-09-25 06:00:00,340.0,0.0,14.97,9.7 -2016-09-25 07:00:00,373.0,41.87,14.91,9.47 -2016-09-25 08:00:00,433.0,299.21,14.84,9.24 -2016-09-25 09:00:00,484.0,476.96,14.78,9.01 -2016-09-25 10:00:00,533.0,505.24,14.83,8.74 -2016-09-25 11:00:00,544.0,428.94,14.88,8.46 -2016-09-25 12:00:00,522.0,633.83,14.93,8.19 -2016-09-25 13:00:00,540.0,291.59,15.04,8.16 -2016-09-25 14:00:00,557.0,312.38,15.14,8.12 -2016-09-25 15:00:00,533.0,163.25,15.25,8.08 -2016-09-25 16:00:00,451.0,29.16,15.34,7.79 -2016-09-25 17:00:00,388.0,38.99,15.43,7.49 -2016-09-25 18:00:00,400.0,0.0,15.52,7.2 -2016-09-25 19:00:00,446.0,0.0,15.62,7.05 -2016-09-25 20:00:00,470.0,0.0,15.72,6.91 -2016-09-25 21:00:00,463.0,0.0,15.82,6.76 -2016-09-25 22:00:00,578.0,0.0,15.85,6.77 -2016-09-25 23:00:00,523.0,0.0,15.89,6.78 -2016-09-26 00:00:00,450.0,0.0,15.92,6.79 -2016-09-26 01:00:00,436.0,0.0,15.98,6.86 -2016-09-26 02:00:00,390.0,0.0,16.05,6.93 -2016-09-26 03:00:00,416.0,0.0,16.11,7.01 -2016-09-26 04:00:00,420.0,0.0,16.09,7.47 -2016-09-26 05:00:00,336.0,0.0,16.06,7.93 -2016-09-26 06:00:00,323.0,0.0,16.04,8.39 -2016-09-26 07:00:00,383.0,37.28,16.07,8.5 -2016-09-26 08:00:00,428.0,17.46,16.09,8.61 -2016-09-26 09:00:00,471.0,31.87,16.12,8.72 -2016-09-26 10:00:00,514.0,105.86,16.15,9.0 -2016-09-26 11:00:00,515.0,64.32,16.18,9.29 -2016-09-26 12:00:00,560.0,92.67,16.21,9.57 -2016-09-26 13:00:00,585.0,43.42,16.21,9.88 -2016-09-26 14:00:00,591.0,39.78,16.21,10.18 -2016-09-26 15:00:00,593.0,67.37,16.21,10.48 -2016-09-26 16:00:00,519.0,68.11,16.16,11.02 -2016-09-26 17:00:00,443.0,22.65,16.1,11.55 -2016-09-26 18:00:00,503.0,0.0,16.05,12.08 -2016-09-26 19:00:00,546.0,0.0,16.16,11.43 -2016-09-26 20:00:00,535.0,0.0,16.27,10.79 -2016-09-26 21:00:00,478.0,0.0,16.38,10.14 -2016-09-26 22:00:00,600.0,0.0,16.44,9.6 -2016-09-26 23:00:00,537.0,0.0,16.51,9.05 -2016-09-27 00:00:00,452.0,0.0,16.57,8.51 -2016-09-27 01:00:00,412.0,0.0,16.45,7.97 -2016-09-27 02:00:00,387.0,0.0,16.33,7.43 -2016-09-27 03:00:00,427.0,0.0,16.22,6.9 -2016-09-27 04:00:00,440.0,0.0,16.22,6.88 -2016-09-27 05:00:00,343.0,0.0,16.22,6.87 -2016-09-27 06:00:00,344.0,0.0,16.22,6.86 -2016-09-27 07:00:00,398.0,11.69,16.2,6.25 -2016-09-27 08:00:00,459.0,64.15,16.19,5.65 -2016-09-27 09:00:00,518.0,164.24,16.17,5.05 -2016-09-27 10:00:00,513.0,211.99,16.16,5.12 -2016-09-27 11:00:00,546.0,332.42,16.16,5.2 -2016-09-27 12:00:00,573.0,172.44,16.16,5.27 -2016-09-27 13:00:00,595.0,496.18,16.04,5.54 -2016-09-27 14:00:00,612.0,504.69,15.92,5.82 -2016-09-27 15:00:00,548.0,430.28,15.8,6.1 -2016-09-27 16:00:00,473.0,283.86,15.5,5.93 -2016-09-27 17:00:00,431.0,90.42,15.19,5.76 -2016-09-27 18:00:00,469.0,0.0,14.89,5.59 -2016-09-27 19:00:00,525.0,0.0,14.65,4.93 -2016-09-27 20:00:00,527.0,0.0,14.41,4.27 -2016-09-27 21:00:00,494.0,0.0,14.17,3.61 -2016-09-27 22:00:00,604.0,0.0,14.04,3.66 -2016-09-27 23:00:00,538.0,0.0,13.92,3.71 -2016-09-28 00:00:00,482.0,0.0,13.79,3.75 -2016-09-28 01:00:00,417.0,0.0,13.84,4.06 -2016-09-28 02:00:00,384.0,0.0,13.88,4.38 -2016-09-28 03:00:00,434.0,0.0,13.93,4.69 -2016-09-28 04:00:00,423.0,0.0,13.96,4.75 -2016-09-28 05:00:00,341.0,0.0,14.0,4.82 -2016-09-28 06:00:00,343.0,0.0,14.03,4.88 -2016-09-28 07:00:00,407.0,55.04,14.12,5.06 -2016-09-28 08:00:00,468.0,280.48,14.21,5.24 -2016-09-28 09:00:00,546.0,396.82,14.3,5.42 -2016-09-28 10:00:00,558.0,239.11,14.4,5.78 -2016-09-28 11:00:00,538.0,263.15,14.5,6.15 -2016-09-28 12:00:00,564.0,145.66,14.61,6.51 -2016-09-28 13:00:00,560.0,129.2,14.57,6.69 -2016-09-28 14:00:00,593.0,89.39,14.53,6.87 -2016-09-28 15:00:00,544.0,109.02,14.49,7.05 -2016-09-28 16:00:00,459.0,70.97,14.55,7.11 -2016-09-28 17:00:00,405.0,28.3,14.6,7.17 -2016-09-28 18:00:00,448.0,0.0,14.66,7.23 -2016-09-28 19:00:00,508.0,0.0,14.75,7.44 -2016-09-28 20:00:00,518.0,0.0,14.85,7.65 -2016-09-28 21:00:00,499.0,0.0,14.94,7.86 -2016-09-28 22:00:00,612.0,0.0,15.06,8.11 -2016-09-28 23:00:00,532.0,0.0,15.18,8.35 -2016-09-29 00:00:00,469.0,0.0,15.3,8.59 -2016-09-29 01:00:00,430.0,0.0,15.31,8.96 -2016-09-29 02:00:00,403.0,0.0,15.31,9.32 -2016-09-29 03:00:00,430.0,0.0,15.32,9.68 -2016-09-29 04:00:00,433.0,0.0,15.47,9.38 -2016-09-29 05:00:00,338.0,0.0,15.61,9.08 -2016-09-29 06:00:00,331.0,0.0,15.76,8.77 -2016-09-29 07:00:00,413.0,12.97,15.75,8.33 -2016-09-29 08:00:00,468.0,120.72,15.74,7.89 -2016-09-29 09:00:00,499.0,325.06,15.73,7.45 -2016-09-29 10:00:00,545.0,622.03,15.55,7.19 -2016-09-29 11:00:00,560.0,641.23,15.37,6.93 -2016-09-29 12:00:00,574.0,737.22,15.2,6.68 -2016-09-29 13:00:00,588.0,583.96,15.19,6.63 -2016-09-29 14:00:00,611.0,549.08,15.18,6.57 -2016-09-29 15:00:00,562.0,450.32,15.17,6.52 -2016-09-29 16:00:00,478.0,180.47,15.0,6.34 -2016-09-29 17:00:00,407.0,48.87,14.84,6.15 -2016-09-29 18:00:00,444.0,0.0,14.67,5.96 -2016-09-29 19:00:00,524.0,0.0,14.54,6.03 -2016-09-29 20:00:00,515.0,0.0,14.4,6.1 -2016-09-29 21:00:00,455.0,0.0,14.27,6.17 -2016-09-29 22:00:00,576.0,0.0,14.2,6.18 -2016-09-29 23:00:00,531.0,0.0,14.13,6.19 -2016-09-30 00:00:00,465.0,0.0,14.06,6.21 -2016-09-30 01:00:00,414.0,0.0,14.19,6.06 -2016-09-30 02:00:00,379.0,0.0,14.31,5.91 -2016-09-30 03:00:00,426.0,0.0,14.44,5.77 -2016-09-30 04:00:00,428.0,0.0,14.41,6.0 -2016-09-30 05:00:00,341.0,0.0,14.37,6.24 -2016-09-30 06:00:00,347.0,0.0,14.34,6.48 -2016-09-30 07:00:00,449.0,60.75,14.28,6.63 -2016-09-30 08:00:00,448.0,249.76,14.22,6.77 -2016-09-30 09:00:00,458.0,486.31,14.16,6.91 -2016-09-30 10:00:00,494.0,590.63,14.15,6.51 -2016-09-30 11:00:00,510.0,725.31,14.14,6.11 -2016-09-30 12:00:00,513.0,768.71,14.13,5.71 -2016-09-30 13:00:00,534.0,604.28,14.27,5.56 -2016-09-30 14:00:00,581.0,322.56,14.41,5.41 -2016-09-30 15:00:00,520.0,419.97,14.55,5.26 -2016-09-30 16:00:00,417.0,249.31,14.45,4.99 -2016-09-30 17:00:00,378.0,94.33,14.34,4.72 -2016-09-30 18:00:00,414.0,0.0,14.24,4.46 -2016-09-30 19:00:00,485.0,0.0,14.3,4.72 -2016-09-30 20:00:00,514.0,0.0,14.36,4.98 -2016-09-30 21:00:00,504.0,0.0,14.42,5.24 -2016-09-30 22:00:00,599.0,0.0,14.46,6.09 -2016-09-30 23:00:00,541.0,0.0,14.5,6.94 -2016-10-01 00:00:00,464.0,0.0,14.54,7.79 -2016-10-01 01:00:00,411.0,0.0,14.5,9.01 -2016-10-01 02:00:00,377.0,0.0,14.46,10.22 -2016-10-01 03:00:00,429.0,0.0,14.42,11.43 -2016-10-01 04:00:00,420.0,0.0,14.1,11.24 -2016-10-01 05:00:00,348.0,0.0,13.77,11.05 -2016-10-01 06:00:00,325.0,0.0,13.45,10.86 -2016-10-01 07:00:00,380.0,69.38,13.65,10.86 -2016-10-01 08:00:00,441.0,211.11,13.85,10.86 -2016-10-01 09:00:00,503.0,351.53,14.06,10.86 -2016-10-01 10:00:00,488.0,618.08,14.06,10.78 -2016-10-01 11:00:00,507.0,602.92,14.07,10.71 -2016-10-01 12:00:00,532.0,815.57,14.09,10.63 -2016-10-01 13:00:00,590.0,628.75,14.09,10.28 -2016-10-01 14:00:00,591.0,642.92,14.1,9.92 -2016-10-01 15:00:00,546.0,230.28,14.1,9.56 -2016-10-01 16:00:00,474.0,206.31,14.12,9.43 -2016-10-01 17:00:00,417.0,53.24,14.13,9.3 -2016-10-01 18:00:00,479.0,0.0,14.15,9.17 -2016-10-01 19:00:00,523.0,0.0,14.14,8.8 -2016-10-01 20:00:00,540.0,0.0,14.12,8.43 -2016-10-01 21:00:00,541.0,0.0,14.1,8.06 -2016-10-01 22:00:00,626.0,0.0,14.2,7.81 -2016-10-01 23:00:00,567.0,0.0,14.3,7.57 -2016-10-02 00:00:00,500.0,0.0,14.4,7.32 -2016-10-02 01:00:00,439.0,0.0,14.41,6.72 -2016-10-02 02:00:00,414.0,0.0,14.42,6.11 -2016-10-02 03:00:00,453.0,0.0,14.43,5.5 -2016-10-02 04:00:00,454.0,0.0,14.36,4.92 -2016-10-02 05:00:00,356.0,0.0,14.29,4.34 -2016-10-02 06:00:00,342.0,0.0,14.22,3.75 -2016-10-02 07:00:00,385.0,75.08,14.27,3.06 -2016-10-02 08:00:00,424.0,289.36,14.32,2.37 -2016-10-02 09:00:00,501.0,483.16,14.36,1.68 -2016-10-02 10:00:00,556.0,639.73,14.45,1.63 -2016-10-02 11:00:00,564.0,707.85,14.55,1.57 -2016-10-02 12:00:00,556.0,662.46,14.64,1.52 -2016-10-02 13:00:00,575.0,668.16,14.85,1.85 -2016-10-02 14:00:00,571.0,641.84,15.07,2.18 -2016-10-02 15:00:00,499.0,483.28,15.28,2.51 -2016-10-02 16:00:00,420.0,239.23,15.43,3.02 -2016-10-02 17:00:00,365.0,66.47,15.57,3.52 -2016-10-02 18:00:00,381.0,0.0,15.72,4.03 -2016-10-02 19:00:00,415.0,0.0,15.75,4.24 -2016-10-02 20:00:00,457.0,0.0,15.77,4.46 -2016-10-02 21:00:00,453.0,0.0,15.79,4.68 -2016-10-02 22:00:00,571.0,0.0,15.86,4.99 -2016-10-02 23:00:00,515.0,0.0,15.92,5.31 -2016-10-03 00:00:00,433.0,0.0,15.99,5.63 -2016-10-03 01:00:00,417.0,0.0,15.86,6.04 -2016-10-03 02:00:00,403.0,0.0,15.74,6.46 -2016-10-03 03:00:00,421.0,0.0,15.61,6.87 -2016-10-03 04:00:00,437.0,0.0,15.62,7.14 -2016-10-03 05:00:00,369.0,0.0,15.62,7.4 -2016-10-03 06:00:00,341.0,0.0,15.62,7.67 -2016-10-03 07:00:00,414.0,65.83,15.66,7.94 -2016-10-03 08:00:00,447.0,281.79,15.69,8.22 -2016-10-03 09:00:00,472.0,491.76,15.73,8.5 -2016-10-03 10:00:00,476.0,653.16,15.73,8.57 -2016-10-03 11:00:00,476.0,739.53,15.73,8.65 -2016-10-03 12:00:00,499.0,792.34,15.73,8.73 -2016-10-03 13:00:00,526.0,708.55,15.89,8.37 -2016-10-03 14:00:00,546.0,249.68,16.06,8.0 -2016-10-03 15:00:00,540.0,363.32,16.23,7.64 -2016-10-03 16:00:00,440.0,311.47,16.24,7.9 -2016-10-03 17:00:00,386.0,78.71,16.24,8.17 -2016-10-03 18:00:00,411.0,0.0,16.25,8.43 -2016-10-03 19:00:00,449.0,0.0,16.18,9.08 -2016-10-03 20:00:00,500.0,0.0,16.11,9.72 -2016-10-03 21:00:00,455.0,0.0,16.04,10.37 -2016-10-03 22:00:00,561.0,0.0,16.07,10.54 -2016-10-03 23:00:00,516.0,0.0,16.09,10.7 -2016-10-04 00:00:00,461.0,0.0,16.12,10.87 -2016-10-04 01:00:00,406.0,0.0,16.05,10.82 -2016-10-04 02:00:00,391.0,0.0,15.98,10.77 -2016-10-04 03:00:00,426.0,0.0,15.91,10.72 -2016-10-04 04:00:00,434.0,0.0,15.86,10.51 -2016-10-04 05:00:00,343.0,0.0,15.81,10.3 -2016-10-04 06:00:00,333.0,0.0,15.75,10.1 -2016-10-04 07:00:00,409.0,51.88,15.75,10.01 -2016-10-04 08:00:00,465.0,146.28,15.75,9.93 -2016-10-04 09:00:00,503.0,241.16,15.74,9.85 -2016-10-04 10:00:00,497.0,598.17,15.85,9.72 -2016-10-04 11:00:00,513.0,490.23,15.95,9.59 -2016-10-04 12:00:00,543.0,286.29,16.06,9.46 -2016-10-04 13:00:00,563.0,233.27,16.23,8.89 -2016-10-04 14:00:00,587.0,59.69,16.41,8.31 -2016-10-04 15:00:00,534.0,37.5,16.58,7.74 -2016-10-04 16:00:00,459.0,92.42,16.48,7.58 -2016-10-04 17:00:00,400.0,33.45,16.37,7.43 -2016-10-04 18:00:00,448.0,0.0,16.27,7.27 -2016-10-04 19:00:00,523.0,0.0,16.03,7.35 -2016-10-04 20:00:00,525.0,0.0,15.79,7.43 -2016-10-04 21:00:00,475.0,0.0,15.54,7.52 -2016-10-04 22:00:00,605.0,0.0,15.4,7.43 -2016-10-04 23:00:00,546.0,0.0,15.26,7.33 -2016-10-05 00:00:00,492.0,0.0,15.12,7.24 -2016-10-05 01:00:00,443.0,0.0,15.06,6.26 -2016-10-05 02:00:00,407.0,0.0,14.99,5.28 -2016-10-05 03:00:00,437.0,0.0,14.93,4.3 -2016-10-05 04:00:00,451.0,0.0,14.71,4.69 -2016-10-05 05:00:00,354.0,0.0,14.48,5.07 -2016-10-05 06:00:00,364.0,0.0,14.26,5.45 -2016-10-05 07:00:00,440.0,37.93,14.37,5.54 -2016-10-05 08:00:00,476.0,259.4,14.47,5.63 -2016-10-05 09:00:00,503.0,462.74,14.57,5.72 -2016-10-05 10:00:00,502.0,631.84,14.73,5.59 -2016-10-05 11:00:00,526.0,614.06,14.9,5.45 -2016-10-05 12:00:00,529.0,750.12,15.06,5.31 -2016-10-05 13:00:00,554.0,750.32,15.2,5.42 -2016-10-05 14:00:00,577.0,624.64,15.35,5.53 -2016-10-05 15:00:00,513.0,488.61,15.49,5.64 -2016-10-05 16:00:00,438.0,195.91,15.47,5.51 -2016-10-05 17:00:00,384.0,75.65,15.45,5.38 -2016-10-05 18:00:00,419.0,0.0,15.43,5.26 -2016-10-05 19:00:00,479.0,0.0,15.42,5.72 -2016-10-05 20:00:00,494.0,0.0,15.4,6.18 -2016-10-05 21:00:00,458.0,0.0,15.38,6.65 -2016-10-05 22:00:00,588.0,0.0,15.45,7.14 -2016-10-05 23:00:00,525.0,0.0,15.51,7.63 -2016-10-06 00:00:00,481.0,0.0,15.57,8.12 -2016-10-06 01:00:00,425.0,0.0,15.1,7.99 -2016-10-06 02:00:00,381.0,0.0,14.63,7.85 -2016-10-06 03:00:00,431.0,0.0,14.16,7.71 -2016-10-06 04:00:00,442.0,0.0,13.93,8.35 -2016-10-06 05:00:00,363.0,0.0,13.7,9.0 -2016-10-06 06:00:00,343.0,0.0,13.47,9.64 -2016-10-06 07:00:00,421.0,58.81,13.22,10.0 -2016-10-06 08:00:00,490.0,282.43,12.97,10.37 -2016-10-06 09:00:00,502.0,494.01,12.72,10.73 -2016-10-06 10:00:00,518.0,657.76,12.83,10.27 -2016-10-06 11:00:00,504.0,750.95,12.95,9.81 -2016-10-06 12:00:00,520.0,801.18,13.06,9.35 -2016-10-06 13:00:00,545.0,770.23,13.71,9.62 -2016-10-06 14:00:00,570.0,581.55,14.36,9.89 -2016-10-06 15:00:00,537.0,196.51,15.01,10.17 -2016-10-06 16:00:00,457.0,178.62,15.03,10.23 -2016-10-06 17:00:00,394.0,68.3,15.05,10.29 -2016-10-06 18:00:00,413.0,0.0,15.07,10.36 -2016-10-06 19:00:00,491.0,0.0,14.91,9.89 -2016-10-06 20:00:00,523.0,0.0,14.75,9.43 -2016-10-06 21:00:00,474.0,0.0,14.58,8.97 -2016-10-06 22:00:00,589.0,0.0,14.48,8.68 -2016-10-06 23:00:00,555.0,0.0,14.38,8.4 -2016-10-07 00:00:00,486.0,0.0,14.28,8.12 -2016-10-07 01:00:00,462.0,0.0,14.13,7.65 -2016-10-07 02:00:00,416.0,0.0,13.99,7.17 -2016-10-07 03:00:00,454.0,0.0,13.84,6.69 -2016-10-07 04:00:00,471.0,0.0,13.73,6.29 -2016-10-07 05:00:00,395.0,0.0,13.62,5.88 -2016-10-07 06:00:00,387.0,0.0,13.51,5.48 -2016-10-07 07:00:00,474.0,14.44,13.5,5.62 -2016-10-07 08:00:00,497.0,100.37,13.48,5.76 -2016-10-07 09:00:00,502.0,333.63,13.47,5.9 -2016-10-07 10:00:00,533.0,412.54,13.56,5.81 -2016-10-07 11:00:00,520.0,570.92,13.65,5.72 -2016-10-07 12:00:00,525.0,673.38,13.74,5.63 -2016-10-07 13:00:00,547.0,631.68,13.86,4.9 -2016-10-07 14:00:00,582.0,178.25,13.98,4.17 -2016-10-07 15:00:00,550.0,112.63,14.09,3.45 -2016-10-07 16:00:00,447.0,245.09,14.2,3.43 -2016-10-07 17:00:00,413.0,46.73,14.3,3.41 -2016-10-07 18:00:00,443.0,0.0,14.41,3.39 -2016-10-07 19:00:00,483.0,0.0,14.41,3.58 -2016-10-07 20:00:00,552.0,0.0,14.41,3.77 -2016-10-07 21:00:00,560.0,0.0,14.41,3.96 -2016-10-07 22:00:00,648.0,0.0,14.45,3.57 -2016-10-07 23:00:00,601.0,0.0,14.49,3.19 -2016-10-08 00:00:00,517.0,0.0,14.53,2.8 -2016-10-08 01:00:00,464.0,0.0,14.33,2.81 -2016-10-08 02:00:00,455.0,0.0,14.14,2.83 -2016-10-08 03:00:00,487.0,0.0,13.94,2.84 -2016-10-08 04:00:00,474.0,0.0,13.95,3.27 -2016-10-08 05:00:00,429.0,0.0,13.95,3.71 -2016-10-08 06:00:00,394.0,0.0,13.96,4.14 -2016-10-08 07:00:00,457.0,26.78,14.1,4.68 -2016-10-08 08:00:00,523.0,87.09,14.23,5.22 -2016-10-08 09:00:00,554.0,58.7,14.36,5.77 -2016-10-08 10:00:00,545.0,50.39,14.53,5.51 -2016-10-08 11:00:00,547.0,165.59,14.7,5.26 -2016-10-08 12:00:00,531.0,687.69,14.88,5.01 -2016-10-08 13:00:00,588.0,674.99,15.09,4.72 -2016-10-08 14:00:00,634.0,525.73,15.31,4.43 -2016-10-08 15:00:00,573.0,367.21,15.53,4.14 -2016-10-08 16:00:00,486.0,236.42,15.49,4.46 -2016-10-08 17:00:00,450.0,66.51,15.45,4.77 -2016-10-08 18:00:00,478.0,0.0,15.41,5.09 -2016-10-08 19:00:00,534.0,0.0,15.29,5.13 -2016-10-08 20:00:00,611.0,0.0,15.17,5.17 -2016-10-08 21:00:00,603.0,0.0,15.05,5.21 -2016-10-08 22:00:00,684.0,0.0,15.0,5.13 -2016-10-08 23:00:00,633.0,0.0,14.95,5.04 -2016-10-09 00:00:00,554.0,0.0,14.9,4.95 -2016-10-09 01:00:00,500.0,0.0,14.78,5.05 -2016-10-09 02:00:00,483.0,0.0,14.66,5.14 -2016-10-09 03:00:00,469.0,0.0,14.54,5.24 -2016-10-09 04:00:00,479.0,0.0,14.47,5.25 -2016-10-09 05:00:00,412.0,0.0,14.39,5.26 -2016-10-09 06:00:00,387.0,0.0,14.31,5.27 -2016-10-09 07:00:00,421.0,21.38,14.35,5.28 -2016-10-09 08:00:00,511.0,148.39,14.38,5.29 -2016-10-09 09:00:00,564.0,301.57,14.41,5.3 -2016-10-09 10:00:00,605.0,322.25,14.52,5.49 -2016-10-09 11:00:00,599.0,443.54,14.61,5.68 -2016-10-09 12:00:00,548.0,533.79,14.72,5.88 -2016-10-09 13:00:00,560.0,553.33,14.75,5.82 -2016-10-09 14:00:00,589.0,353.41,14.79,5.76 -2016-10-09 15:00:00,538.0,397.11,14.83,5.7 -2016-10-09 16:00:00,444.0,198.99,14.78,5.94 -2016-10-09 17:00:00,411.0,55.85,14.72,6.18 -2016-10-09 18:00:00,440.0,0.0,14.67,6.43 -2016-10-09 19:00:00,496.0,0.0,14.53,6.37 -2016-10-09 20:00:00,567.0,0.0,14.39,6.32 -2016-10-09 21:00:00,518.0,0.0,14.25,6.26 -2016-10-09 22:00:00,639.0,0.0,14.19,5.96 -2016-10-09 23:00:00,590.0,0.0,14.12,5.66 -2016-10-10 00:00:00,498.0,0.0,14.06,5.35 -2016-10-10 01:00:00,453.0,0.0,14.05,5.13 -2016-10-10 02:00:00,457.0,0.0,14.05,4.91 -2016-10-10 03:00:00,477.0,0.0,14.04,4.69 -2016-10-10 04:00:00,484.0,0.0,14.03,4.81 -2016-10-10 05:00:00,414.0,0.0,14.02,4.94 -2016-10-10 06:00:00,391.0,0.0,14.01,5.06 -2016-10-10 07:00:00,451.0,15.54,14.04,5.36 -2016-10-10 08:00:00,525.0,93.38,14.06,5.66 -2016-10-10 09:00:00,574.0,114.77,14.09,5.96 -2016-10-10 10:00:00,545.0,198.86,14.1,5.95 -2016-10-10 11:00:00,535.0,226.97,14.12,5.95 -2016-10-10 12:00:00,547.0,217.1,14.15,5.94 -2016-10-10 13:00:00,578.0,387.84,14.13,6.0 -2016-10-10 14:00:00,598.0,216.35,14.12,6.06 -2016-10-10 15:00:00,564.0,240.52,14.1,6.12 -2016-10-10 16:00:00,454.0,100.03,14.11,6.39 -2016-10-10 17:00:00,419.0,21.33,14.12,6.66 -2016-10-10 18:00:00,452.0,0.0,14.13,6.92 -2016-10-10 19:00:00,525.0,0.0,14.06,7.1 -2016-10-10 20:00:00,572.0,0.0,13.99,7.27 -2016-10-10 21:00:00,536.0,0.0,13.92,7.45 -2016-10-10 22:00:00,639.0,0.0,13.91,7.66 -2016-10-10 23:00:00,582.0,0.0,13.9,7.88 -2016-10-11 00:00:00,509.0,0.0,13.89,8.1 -2016-10-11 01:00:00,465.0,0.0,13.96,8.44 -2016-10-11 02:00:00,481.0,0.0,14.03,8.78 -2016-10-11 03:00:00,469.0,0.0,14.1,9.12 -2016-10-11 04:00:00,487.0,0.0,14.04,9.2 -2016-10-11 05:00:00,427.0,0.0,13.97,9.27 -2016-10-11 06:00:00,412.0,0.0,13.9,9.35 -2016-10-11 07:00:00,471.0,10.36,13.94,9.32 -2016-10-11 08:00:00,549.0,13.74,13.97,9.3 -2016-10-11 09:00:00,606.0,109.47,14.01,9.27 -2016-10-11 10:00:00,606.0,243.88,14.03,9.24 -2016-10-11 11:00:00,597.0,301.56,14.06,9.2 -2016-10-11 12:00:00,618.0,535.55,14.1,9.17 -2016-10-11 13:00:00,620.0,654.12,14.22,9.01 -2016-10-11 14:00:00,613.0,548.63,14.35,8.85 -2016-10-11 15:00:00,577.0,383.03,14.47,8.69 -2016-10-11 16:00:00,494.0,149.57,14.5,9.0 -2016-10-11 17:00:00,438.0,30.1,14.53,9.31 -2016-10-11 18:00:00,471.0,0.0,14.56,9.61 -2016-10-11 19:00:00,569.0,0.0,14.4,9.83 -2016-10-11 20:00:00,597.0,0.0,14.24,10.06 -2016-10-11 21:00:00,545.0,0.0,14.08,10.28 -2016-10-11 22:00:00,668.0,0.0,13.92,10.46 -2016-10-11 23:00:00,603.0,0.0,13.75,10.65 -2016-10-12 00:00:00,522.0,0.0,13.58,10.84 -2016-10-12 01:00:00,508.0,0.0,13.43,10.64 -2016-10-12 02:00:00,511.0,0.0,13.29,10.45 -2016-10-12 03:00:00,516.0,0.0,13.14,10.25 -2016-10-12 04:00:00,522.0,0.0,12.94,10.16 -2016-10-12 05:00:00,468.0,0.0,12.73,10.06 -2016-10-12 06:00:00,442.0,0.0,12.52,9.97 -2016-10-12 07:00:00,528.0,29.28,12.47,10.33 -2016-10-12 08:00:00,615.0,74.08,12.41,10.68 -2016-10-12 09:00:00,637.0,231.72,12.35,11.03 -2016-10-12 10:00:00,632.0,481.55,12.61,11.36 -2016-10-12 11:00:00,622.0,718.64,12.87,11.69 -2016-10-12 12:00:00,626.0,739.38,13.12,12.01 -2016-10-12 13:00:00,628.0,731.5,13.54,12.03 -2016-10-12 14:00:00,659.0,633.38,13.96,12.05 -2016-10-12 15:00:00,602.0,497.03,14.38,12.07 -2016-10-12 16:00:00,526.0,278.48,14.41,11.92 -2016-10-12 17:00:00,479.0,40.6,14.44,11.77 -2016-10-12 18:00:00,521.0,0.0,14.47,11.61 -2016-10-12 19:00:00,595.0,0.0,14.28,11.6 -2016-10-12 20:00:00,630.0,0.0,14.09,11.59 -2016-10-12 21:00:00,600.0,0.0,13.9,11.57 -2016-10-12 22:00:00,711.0,0.0,13.79,11.28 -2016-10-12 23:00:00,648.0,0.0,13.67,10.99 -2016-10-13 00:00:00,561.0,0.0,13.55,10.7 -2016-10-13 01:00:00,530.0,0.0,13.58,10.15 -2016-10-13 02:00:00,534.0,0.0,13.62,9.6 -2016-10-13 03:00:00,547.0,0.0,13.65,9.05 -2016-10-13 04:00:00,555.0,0.0,13.5,9.31 -2016-10-13 05:00:00,480.0,0.0,13.35,9.58 -2016-10-13 06:00:00,462.0,0.0,13.19,9.85 -2016-10-13 07:00:00,529.0,18.11,13.15,10.12 -2016-10-13 08:00:00,593.0,56.2,13.11,10.4 -2016-10-13 09:00:00,617.0,104.51,13.07,10.68 -2016-10-13 10:00:00,610.0,95.93,13.11,10.36 -2016-10-13 11:00:00,579.0,215.2,13.15,10.04 -2016-10-13 12:00:00,575.0,439.72,13.19,9.72 -2016-10-13 13:00:00,600.0,200.39,13.5,8.89 -2016-10-13 14:00:00,612.0,409.49,13.82,8.06 -2016-10-13 15:00:00,585.0,151.18,14.13,7.23 -2016-10-13 16:00:00,501.0,219.25,14.02,7.18 -2016-10-13 17:00:00,457.0,19.99,13.91,7.14 -2016-10-13 18:00:00,507.0,0.0,13.8,7.09 -2016-10-13 19:00:00,600.0,0.0,13.63,6.69 -2016-10-13 20:00:00,666.0,0.0,13.46,6.28 -2016-10-13 21:00:00,581.0,0.0,13.28,5.88 -2016-10-13 22:00:00,684.0,0.0,13.23,5.17 -2016-10-13 23:00:00,618.0,0.0,13.17,4.47 -2016-10-14 00:00:00,537.0,0.0,13.11,3.77 -2016-10-14 01:00:00,502.0,0.0,13.33,3.13 -2016-10-14 02:00:00,507.0,0.0,13.54,2.49 -2016-10-14 03:00:00,540.0,0.0,13.76,1.85 -2016-10-14 04:00:00,532.0,0.0,13.7,1.54 -2016-10-14 05:00:00,484.0,0.0,13.63,1.22 -2016-10-14 06:00:00,479.0,0.0,13.56,0.91 -2016-10-14 07:00:00,579.0,22.37,13.59,1.43 -2016-10-14 08:00:00,605.0,213.17,13.62,1.95 -2016-10-14 09:00:00,631.0,308.08,13.65,2.47 -2016-10-14 10:00:00,594.0,599.63,13.84,3.23 -2016-10-14 11:00:00,575.0,682.57,14.02,3.99 -2016-10-14 12:00:00,553.0,236.71,14.2,4.74 -2016-10-14 13:00:00,569.0,173.56,14.34,5.52 -2016-10-14 14:00:00,604.0,49.68,14.47,6.3 -2016-10-14 15:00:00,567.0,54.16,14.6,7.08 -2016-10-14 16:00:00,494.0,94.79,14.43,7.24 -2016-10-14 17:00:00,433.0,5.8,14.26,7.4 -2016-10-14 18:00:00,494.0,0.0,14.09,7.56 -2016-10-14 19:00:00,582.0,0.0,13.89,7.18 -2016-10-14 20:00:00,663.0,0.0,13.69,6.8 -2016-10-14 21:00:00,615.0,0.0,13.48,6.41 -2016-10-14 22:00:00,738.0,0.0,13.59,6.43 -2016-10-14 23:00:00,686.0,0.0,13.69,6.45 -2016-10-15 00:00:00,583.0,0.0,13.79,6.47 -2016-10-15 01:00:00,563.0,0.0,13.52,6.74 -2016-10-15 02:00:00,529.0,0.0,13.24,7.02 -2016-10-15 03:00:00,536.0,0.0,12.97,7.3 -2016-10-15 04:00:00,563.0,0.0,13.13,7.34 -2016-10-15 05:00:00,486.0,0.0,13.28,7.38 -2016-10-15 06:00:00,474.0,0.0,13.43,7.42 -2016-10-15 07:00:00,511.0,2.31,13.25,7.87 -2016-10-15 08:00:00,591.0,8.83,13.07,8.32 -2016-10-15 09:00:00,642.0,30.25,12.9,8.77 -2016-10-15 10:00:00,695.0,44.12,12.99,9.08 -2016-10-15 11:00:00,724.0,672.67,13.08,9.4 -2016-10-15 12:00:00,767.0,730.06,13.18,9.71 -2016-10-15 13:00:00,714.0,398.0,13.91,9.53 -2016-10-15 14:00:00,707.0,343.96,14.65,9.34 -2016-10-15 15:00:00,665.0,246.79,15.39,9.16 -2016-10-15 16:00:00,585.0,127.95,15.57,9.51 -2016-10-15 17:00:00,519.0,11.79,15.76,9.87 -2016-10-15 18:00:00,564.0,0.0,15.94,10.22 -2016-10-15 19:00:00,616.0,0.0,15.95,10.57 -2016-10-15 20:00:00,688.0,0.0,15.96,10.93 -2016-10-15 21:00:00,644.0,0.0,15.96,11.28 -2016-10-15 22:00:00,734.0,0.0,15.62,11.37 -2016-10-15 23:00:00,690.0,0.0,15.28,11.46 -2016-10-16 00:00:00,635.0,0.0,14.94,11.54 -2016-10-16 01:00:00,558.0,0.0,14.99,12.03 -2016-10-16 02:00:00,522.0,0.0,15.05,12.52 -2016-10-16 03:00:00,551.0,0.0,15.1,13.01 -2016-10-16 04:00:00,573.0,0.0,15.04,12.54 -2016-10-16 05:00:00,499.0,0.0,14.98,12.08 -2016-10-16 06:00:00,478.0,0.0,14.91,11.61 -2016-10-16 07:00:00,506.0,5.17,14.95,11.19 -2016-10-16 08:00:00,570.0,111.78,14.99,10.76 -2016-10-16 09:00:00,604.0,226.6,15.03,10.33 -2016-10-16 10:00:00,633.0,308.04,15.12,10.11 -2016-10-16 11:00:00,597.0,440.83,15.22,9.9 -2016-10-16 12:00:00,597.0,399.47,15.31,9.68 -2016-10-16 13:00:00,601.0,425.97,15.09,10.03 -2016-10-16 14:00:00,609.0,362.96,14.88,10.38 -2016-10-16 15:00:00,594.0,244.57,14.66,10.73 -2016-10-16 16:00:00,482.0,128.44,14.71,10.54 -2016-10-16 17:00:00,425.0,3.1,14.76,10.34 -2016-10-16 18:00:00,439.0,0.0,14.81,10.15 -2016-10-16 19:00:00,516.0,0.0,14.82,9.7 -2016-10-16 20:00:00,595.0,0.0,14.83,9.25 -2016-10-16 21:00:00,554.0,0.0,14.84,8.8 -2016-10-16 22:00:00,655.0,0.0,14.85,8.53 -2016-10-16 23:00:00,611.0,0.0,14.85,8.26 -2016-10-17 00:00:00,529.0,0.0,14.86,7.99 -2016-10-17 01:00:00,516.0,0.0,14.82,8.0 -2016-10-17 02:00:00,471.0,0.0,14.79,8.02 -2016-10-17 03:00:00,494.0,0.0,14.75,8.04 -2016-10-17 04:00:00,506.0,0.0,14.77,7.64 -2016-10-17 05:00:00,450.0,0.0,14.78,7.23 -2016-10-17 06:00:00,411.0,0.0,14.8,6.83 -2016-10-17 07:00:00,472.0,2.86,14.82,6.75 -2016-10-17 08:00:00,531.0,108.22,14.84,6.68 -2016-10-17 09:00:00,556.0,192.04,14.86,6.61 -2016-10-17 10:00:00,547.0,321.01,14.83,6.63 -2016-10-17 11:00:00,556.0,403.92,14.79,6.64 -2016-10-17 12:00:00,558.0,387.9,14.76,6.66 -2016-10-17 13:00:00,557.0,706.57,14.78,6.86 -2016-10-17 14:00:00,578.0,600.94,14.8,7.05 -2016-10-17 15:00:00,553.0,420.26,14.82,7.24 -2016-10-17 16:00:00,484.0,67.86,14.81,7.07 -2016-10-17 17:00:00,415.0,2.28,14.81,6.9 -2016-10-17 18:00:00,457.0,0.0,14.8,6.73 -2016-10-17 19:00:00,543.0,0.0,14.75,6.65 -2016-10-17 20:00:00,600.0,0.0,14.69,6.57 -2016-10-17 21:00:00,562.0,0.0,14.63,6.48 -2016-10-17 22:00:00,656.0,0.0,14.63,6.61 -2016-10-17 23:00:00,593.0,0.0,14.62,6.74 -2016-10-18 00:00:00,514.0,0.0,14.62,6.87 -2016-10-18 01:00:00,508.0,0.0,14.59,7.6 -2016-10-18 02:00:00,445.0,0.0,14.55,8.32 -2016-10-18 03:00:00,495.0,0.0,14.52,9.05 -2016-10-18 04:00:00,503.0,0.0,14.47,9.35 -2016-10-18 05:00:00,423.0,0.0,14.41,9.65 -2016-10-18 06:00:00,411.0,0.0,14.36,9.94 -2016-10-18 07:00:00,478.0,0.24,14.19,9.7 -2016-10-18 08:00:00,547.0,81.22,14.02,9.46 -2016-10-18 09:00:00,566.0,66.24,13.85,9.21 -2016-10-18 10:00:00,576.0,290.06,13.69,8.67 -2016-10-18 11:00:00,599.0,189.42,13.52,8.12 -2016-10-18 12:00:00,637.0,503.33,13.36,7.57 -2016-10-18 13:00:00,631.0,638.81,13.37,7.48 -2016-10-18 14:00:00,647.0,181.98,13.38,7.39 -2016-10-18 15:00:00,612.0,441.35,13.38,7.3 -2016-10-18 16:00:00,524.0,219.49,13.28,7.23 -2016-10-18 17:00:00,478.0,0.0,13.18,7.16 -2016-10-18 18:00:00,537.0,0.0,13.08,7.09 -2016-10-18 19:00:00,608.0,0.0,12.98,6.95 -2016-10-18 20:00:00,627.0,0.0,12.88,6.81 -2016-10-18 21:00:00,591.0,0.0,12.77,6.68 -2016-10-18 22:00:00,697.0,0.0,12.78,6.44 -2016-10-18 23:00:00,650.0,0.0,12.79,6.21 -2016-10-19 00:00:00,596.0,0.0,12.8,5.97 -2016-10-19 01:00:00,542.0,0.0,12.99,5.61 -2016-10-19 02:00:00,504.0,0.0,13.18,5.25 -2016-10-19 03:00:00,534.0,0.0,13.37,4.88 -2016-10-19 04:00:00,548.0,0.0,13.27,4.83 -2016-10-19 05:00:00,478.0,0.0,13.17,4.77 -2016-10-19 06:00:00,450.0,0.0,13.07,4.72 -2016-10-19 07:00:00,529.0,0.0,13.03,4.79 -2016-10-19 08:00:00,589.0,49.77,12.99,4.86 -2016-10-19 09:00:00,646.0,183.67,12.94,4.94 -2016-10-19 10:00:00,629.0,172.28,12.88,4.91 -2016-10-19 11:00:00,607.0,165.25,12.82,4.87 -2016-10-19 12:00:00,623.0,118.52,12.76,4.84 -2016-10-19 13:00:00,635.0,86.13,12.76,5.22 -2016-10-19 14:00:00,657.0,76.84,12.77,5.6 -2016-10-19 15:00:00,615.0,82.07,12.77,5.97 -2016-10-19 16:00:00,552.0,17.1,12.75,6.11 -2016-10-19 17:00:00,513.0,0.0,12.72,6.26 -2016-10-19 18:00:00,571.0,0.0,12.7,6.4 -2016-10-19 19:00:00,712.0,0.0,12.67,6.34 -2016-10-19 20:00:00,702.0,0.0,12.64,6.29 -2016-10-19 21:00:00,628.0,0.0,12.61,6.23 -2016-10-19 22:00:00,726.0,0.0,12.69,5.9 -2016-10-19 23:00:00,691.0,0.0,12.76,5.56 -2016-10-20 00:00:00,608.0,0.0,12.84,5.23 -2016-10-20 01:00:00,580.0,0.0,12.97,4.88 -2016-10-20 02:00:00,523.0,0.0,13.11,4.53 -2016-10-20 03:00:00,554.0,0.0,13.24,4.18 -2016-10-20 04:00:00,571.0,0.0,13.22,4.38 -2016-10-20 05:00:00,488.0,0.0,13.19,4.58 -2016-10-20 06:00:00,466.0,0.0,13.17,4.79 -2016-10-20 07:00:00,543.0,0.0,13.22,4.75 -2016-10-20 08:00:00,618.0,222.05,13.27,4.72 -2016-10-20 09:00:00,622.0,324.01,13.32,4.69 -2016-10-20 10:00:00,619.0,502.28,13.39,4.67 -2016-10-20 11:00:00,620.0,674.06,13.45,4.65 -2016-10-20 12:00:00,628.0,709.96,13.52,4.63 -2016-10-20 13:00:00,644.0,695.65,13.52,4.73 -2016-10-20 14:00:00,659.0,580.85,13.53,4.82 -2016-10-20 15:00:00,625.0,441.27,13.54,4.91 -2016-10-20 16:00:00,536.0,213.51,13.51,4.96 -2016-10-20 17:00:00,486.0,0.0,13.48,5.01 -2016-10-20 18:00:00,514.0,0.0,13.45,5.06 -2016-10-20 19:00:00,622.0,0.0,13.37,4.97 -2016-10-20 20:00:00,688.0,0.0,13.28,4.89 -2016-10-20 21:00:00,631.0,0.0,13.19,4.8 -2016-10-20 22:00:00,759.0,0.0,13.21,4.61 -2016-10-20 23:00:00,725.0,0.0,13.22,4.41 -2016-10-21 00:00:00,640.0,0.0,13.23,4.22 -2016-10-21 01:00:00,597.0,0.0,13.22,3.85 -2016-10-21 02:00:00,559.0,0.0,13.21,3.48 -2016-10-21 03:00:00,584.0,0.0,13.2,3.1 -2016-10-21 04:00:00,601.0,0.0,13.17,3.1 -2016-10-21 05:00:00,534.0,0.0,13.14,3.09 -2016-10-21 06:00:00,497.0,0.0,13.1,3.09 -2016-10-21 07:00:00,582.0,0.0,13.13,3.09 -2016-10-21 08:00:00,645.0,169.6,13.16,3.1 -2016-10-21 09:00:00,660.0,224.34,13.19,3.1 -2016-10-21 10:00:00,671.0,515.0,13.3,3.14 -2016-10-21 11:00:00,661.0,529.48,13.4,3.17 -2016-10-21 12:00:00,674.0,639.78,13.52,3.2 -2016-10-21 13:00:00,666.0,540.77,13.64,3.33 -2016-10-21 14:00:00,676.0,374.52,13.77,3.47 -2016-10-21 15:00:00,631.0,179.91,13.89,3.6 -2016-10-21 16:00:00,551.0,85.89,13.88,3.99 -2016-10-21 17:00:00,508.0,0.0,13.87,4.38 -2016-10-21 18:00:00,573.0,0.0,13.86,4.77 -2016-10-21 19:00:00,699.0,0.0,13.73,5.22 -2016-10-21 20:00:00,771.0,0.0,13.59,5.66 -2016-10-21 21:00:00,730.0,0.0,13.45,6.11 -2016-10-21 22:00:00,835.0,0.0,13.38,6.42 -2016-10-21 23:00:00,768.0,0.0,13.3,6.73 -2016-10-22 00:00:00,702.0,0.0,13.23,7.03 -2016-10-22 01:00:00,663.0,0.0,13.17,6.71 -2016-10-22 02:00:00,599.0,0.0,13.11,6.39 -2016-10-22 03:00:00,634.0,0.0,13.05,6.07 -2016-10-22 04:00:00,627.0,0.0,12.95,6.23 -2016-10-22 05:00:00,556.0,0.0,12.85,6.38 -2016-10-22 06:00:00,529.0,0.0,12.75,6.54 -2016-10-22 07:00:00,618.0,0.0,12.69,6.79 -2016-10-22 08:00:00,714.0,170.02,12.63,7.04 -2016-10-22 09:00:00,748.0,337.09,12.57,7.3 -2016-10-22 10:00:00,776.0,530.48,12.68,7.4 -2016-10-22 11:00:00,721.0,601.61,12.78,7.5 -2016-10-22 12:00:00,704.0,528.51,12.89,7.6 -2016-10-22 13:00:00,720.0,477.17,13.21,7.68 -2016-10-22 14:00:00,723.0,345.94,13.54,7.76 -2016-10-22 15:00:00,672.0,268.39,13.86,7.83 -2016-10-22 16:00:00,620.0,89.58,13.88,8.44 -2016-10-22 17:00:00,576.0,0.0,13.91,9.05 -2016-10-22 18:00:00,674.0,0.0,13.93,9.66 -2016-10-22 19:00:00,815.0,0.0,13.74,10.19 -2016-10-22 20:00:00,886.0,0.0,13.55,10.72 -2016-10-22 21:00:00,806.0,0.0,13.35,11.26 -2016-10-22 22:00:00,894.0,0.0,13.14,11.41 -2016-10-22 23:00:00,822.0,0.0,12.92,11.56 -2016-10-23 00:00:00,759.0,0.0,12.71,11.71 -2016-10-23 01:00:00,696.0,0.0,12.83,11.6 -2016-10-23 02:00:00,661.0,0.0,12.94,11.49 -2016-10-23 03:00:00,698.0,0.0,13.06,11.38 -2016-10-23 04:00:00,703.0,0.0,12.79,11.94 -2016-10-23 05:00:00,638.0,0.0,12.52,12.49 -2016-10-23 06:00:00,593.0,0.0,12.25,13.05 -2016-10-23 07:00:00,654.0,0.0,12.16,13.56 -2016-10-23 08:00:00,756.0,123.61,12.07,14.08 -2016-10-23 09:00:00,879.0,316.01,11.98,14.59 -2016-10-23 10:00:00,935.0,214.63,11.81,14.53 -2016-10-23 11:00:00,1007.0,67.43,11.65,14.46 -2016-10-23 12:00:00,1048.0,20.62,11.49,14.4 -2016-10-23 13:00:00,1084.0,30.37,12.34,13.64 -2016-10-23 14:00:00,1128.0,26.68,13.18,12.88 -2016-10-23 15:00:00,1129.0,10.6,14.02,12.12 -2016-10-23 16:00:00,1010.0,5.89,14.37,10.73 -2016-10-23 17:00:00,909.0,0.0,14.73,9.33 -2016-10-23 18:00:00,960.0,0.0,15.08,7.93 -2016-10-23 19:00:00,1036.0,0.0,15.17,6.8 -2016-10-23 20:00:00,1030.0,0.0,15.25,5.68 -2016-10-23 21:00:00,942.0,0.0,15.33,4.55 -2016-10-23 22:00:00,1009.0,0.0,15.39,4.78 -2016-10-23 23:00:00,915.0,0.0,15.44,5.0 -2016-10-24 00:00:00,840.0,0.0,15.49,5.23 -2016-10-24 01:00:00,787.0,0.0,15.31,5.49 -2016-10-24 02:00:00,729.0,0.0,15.12,5.75 -2016-10-24 03:00:00,747.0,0.0,14.94,6.01 -2016-10-24 04:00:00,737.0,0.0,14.78,5.93 -2016-10-24 05:00:00,672.0,0.0,14.61,5.85 -2016-10-24 06:00:00,621.0,0.0,14.44,5.77 -2016-10-24 07:00:00,684.0,0.0,14.37,5.5 -2016-10-24 08:00:00,779.0,89.06,14.3,5.24 -2016-10-24 09:00:00,850.0,131.67,14.23,4.98 -2016-10-24 10:00:00,880.0,228.98,14.19,4.86 -2016-10-24 11:00:00,857.0,504.83,14.15,4.73 -2016-10-24 12:00:00,845.0,651.03,14.1,4.61 -2016-10-24 13:00:00,827.0,638.02,14.21,4.52 -2016-10-24 14:00:00,862.0,561.43,14.31,4.44 -2016-10-24 15:00:00,846.0,389.24,14.41,4.36 -2016-10-24 16:00:00,682.0,197.79,14.43,4.41 -2016-10-24 17:00:00,609.0,0.0,14.46,4.46 -2016-10-24 18:00:00,698.0,0.0,14.48,4.51 -2016-10-24 19:00:00,855.0,0.0,14.49,4.28 -2016-10-24 20:00:00,882.0,0.0,14.5,4.04 -2016-10-24 21:00:00,822.0,0.0,14.5,3.81 -2016-10-24 22:00:00,895.0,0.0,14.52,3.68 -2016-10-24 23:00:00,806.0,0.0,14.53,3.56 -2016-10-25 00:00:00,749.0,0.0,14.54,3.43 -2016-10-25 01:00:00,693.0,0.0,14.35,3.39 -2016-10-25 02:00:00,635.0,0.0,14.15,3.34 -2016-10-25 03:00:00,670.0,0.0,13.96,3.3 -2016-10-25 04:00:00,665.0,0.0,13.75,3.37 -2016-10-25 05:00:00,591.0,0.0,13.54,3.44 -2016-10-25 06:00:00,561.0,0.0,13.33,3.52 -2016-10-25 07:00:00,632.0,0.0,13.12,3.63 -2016-10-25 08:00:00,733.0,174.85,12.91,3.75 -2016-10-25 09:00:00,827.0,386.92,12.7,3.86 -2016-10-25 10:00:00,862.0,548.99,12.66,3.2 -2016-10-25 11:00:00,847.0,646.05,12.62,2.54 -2016-10-25 12:00:00,802.0,634.67,12.59,1.88 -2016-10-25 13:00:00,751.0,475.39,12.55,1.63 -2016-10-25 14:00:00,763.0,153.83,12.52,1.39 -2016-10-25 15:00:00,739.0,148.32,12.49,1.14 -2016-10-25 16:00:00,693.0,66.51,12.51,1.42 -2016-10-25 17:00:00,613.0,0.0,12.53,1.69 -2016-10-25 18:00:00,759.0,0.0,12.55,1.96 -2016-10-25 19:00:00,879.0,0.0,12.52,2.35 -2016-10-25 20:00:00,906.0,0.0,12.48,2.74 -2016-10-25 21:00:00,790.0,0.0,12.44,3.13 -2016-10-25 22:00:00,885.0,0.0,12.49,2.95 -2016-10-25 23:00:00,819.0,0.0,12.53,2.76 -2016-10-26 00:00:00,745.0,0.0,12.57,2.58 -2016-10-26 01:00:00,700.0,0.0,12.58,2.3 -2016-10-26 02:00:00,632.0,0.0,12.59,2.02 -2016-10-26 03:00:00,646.0,0.0,12.6,1.74 -2016-10-26 04:00:00,650.0,0.0,12.56,1.74 -2016-10-26 05:00:00,571.0,0.0,12.52,1.74 -2016-10-26 06:00:00,541.0,0.0,12.47,1.74 -2016-10-26 07:00:00,608.0,0.0,12.51,1.96 -2016-10-26 08:00:00,714.0,10.05,12.55,2.18 -2016-10-26 09:00:00,815.0,65.65,12.6,2.4 -2016-10-26 10:00:00,879.0,141.41,12.59,2.38 -2016-10-26 11:00:00,870.0,209.87,12.57,2.35 -2016-10-26 12:00:00,909.0,379.29,12.56,2.33 -2016-10-26 13:00:00,937.0,507.35,12.99,2.24 -2016-10-26 14:00:00,966.0,129.25,13.41,2.16 -2016-10-26 15:00:00,893.0,84.58,13.83,2.07 -2016-10-26 16:00:00,812.0,47.91,13.89,1.76 -2016-10-26 17:00:00,753.0,0.0,13.94,1.44 -2016-10-26 18:00:00,873.0,0.0,14.0,1.13 -2016-10-26 19:00:00,1021.0,0.0,13.98,1.01 -2016-10-26 20:00:00,980.0,0.0,13.95,0.89 -2016-10-26 21:00:00,889.0,0.0,13.92,0.77 -2016-10-26 22:00:00,972.0,0.0,13.99,1.17 -2016-10-26 23:00:00,901.0,0.0,14.05,1.57 -2016-10-27 00:00:00,785.0,0.0,14.11,1.97 -2016-10-27 01:00:00,725.0,0.0,13.92,2.56 -2016-10-27 02:00:00,687.0,0.0,13.74,3.14 -2016-10-27 03:00:00,711.0,0.0,13.55,3.72 -2016-10-27 04:00:00,715.0,0.0,13.56,3.46 -2016-10-27 05:00:00,640.0,0.0,13.56,3.2 -2016-10-27 06:00:00,608.0,0.0,13.56,2.94 -2016-10-27 07:00:00,669.0,0.0,13.62,3.03 -2016-10-27 08:00:00,775.0,41.24,13.67,3.13 -2016-10-27 09:00:00,870.0,26.53,13.73,3.23 -2016-10-27 10:00:00,912.0,43.08,13.81,3.45 -2016-10-27 11:00:00,912.0,47.5,13.9,3.67 -2016-10-27 12:00:00,918.0,55.71,13.99,3.89 -2016-10-27 13:00:00,892.0,78.58,13.88,3.7 -2016-10-27 14:00:00,925.0,51.25,13.77,3.51 -2016-10-27 15:00:00,861.0,50.48,13.65,3.32 -2016-10-27 16:00:00,741.0,31.15,13.69,2.89 -2016-10-27 17:00:00,675.0,0.0,13.72,2.46 -2016-10-27 18:00:00,799.0,0.0,13.76,2.03 -2016-10-27 19:00:00,917.0,0.0,13.75,1.37 -2016-10-27 20:00:00,923.0,0.0,13.74,0.71 -2016-10-27 21:00:00,870.0,0.0,13.73,0.06 -2016-10-27 22:00:00,971.0,0.0,13.71,0.46 -2016-10-27 23:00:00,891.0,0.0,13.69,0.86 -2016-10-28 00:00:00,786.0,0.0,13.67,1.27 -2016-10-28 01:00:00,735.0,0.0,13.5,1.49 -2016-10-28 02:00:00,674.0,0.0,13.33,1.71 -2016-10-28 03:00:00,700.0,0.0,13.16,1.93 -2016-10-28 04:00:00,697.0,0.0,13.03,2.22 -2016-10-28 05:00:00,645.0,0.0,12.9,2.51 -2016-10-28 06:00:00,605.0,0.0,12.76,2.8 -2016-10-28 07:00:00,713.0,0.0,12.8,2.84 -2016-10-28 08:00:00,780.0,23.5,12.84,2.88 -2016-10-28 09:00:00,842.0,23.85,12.88,2.92 -2016-10-28 10:00:00,836.0,41.03,13.05,3.27 -2016-10-28 11:00:00,799.0,41.0,13.23,3.62 -2016-10-28 12:00:00,758.0,43.18,13.4,3.97 -2016-10-28 13:00:00,788.0,74.17,13.35,3.73 -2016-10-28 14:00:00,789.0,52.12,13.3,3.49 -2016-10-28 15:00:00,730.0,73.69,13.25,3.26 -2016-10-28 16:00:00,640.0,38.89,13.24,4.1 -2016-10-28 17:00:00,587.0,0.0,13.22,4.95 -2016-10-28 18:00:00,680.0,0.0,13.21,5.79 -2016-10-28 19:00:00,872.0,0.0,13.13,6.17 -2016-10-28 20:00:00,926.0,0.0,13.05,6.55 -2016-10-28 21:00:00,876.0,0.0,12.96,6.92 -2016-10-28 22:00:00,967.0,0.0,12.99,6.78 -2016-10-28 23:00:00,918.0,0.0,13.02,6.63 -2016-10-29 00:00:00,844.0,0.0,13.05,6.48 -2016-10-29 01:00:00,750.0,0.0,13.0,6.14 -2016-10-29 02:00:00,705.0,0.0,12.94,5.79 -2016-10-29 03:00:00,728.0,0.0,12.89,5.45 -2016-10-29 04:00:00,725.0,0.0,12.8,5.82 -2016-10-29 05:00:00,644.0,0.0,12.7,6.19 -2016-10-29 06:00:00,636.0,0.0,12.61,6.57 -2016-10-29 07:00:00,707.0,0.0,12.62,7.0 -2016-10-29 08:00:00,815.0,28.31,12.63,7.44 -2016-10-29 09:00:00,910.0,76.25,12.64,7.88 -2016-10-29 10:00:00,895.0,134.76,12.89,7.6 -2016-10-29 11:00:00,873.0,133.21,13.14,7.31 -2016-10-29 12:00:00,829.0,248.32,13.4,7.03 -2016-10-29 13:00:00,816.0,159.87,13.64,6.59 -2016-10-29 14:00:00,846.0,224.87,13.89,6.14 -2016-10-29 15:00:00,749.0,250.58,14.14,5.7 -2016-10-29 16:00:00,689.0,77.69,14.25,5.72 -2016-10-29 17:00:00,618.0,0.0,14.35,5.74 -2016-10-29 18:00:00,754.0,0.0,14.46,5.77 -2016-10-29 19:00:00,933.0,0.0,14.45,5.83 -2016-10-29 20:00:00,963.0,0.0,14.44,5.89 -2016-10-29 21:00:00,921.0,0.0,14.42,5.94 -2016-10-29 22:00:00,1010.0,0.0,14.41,5.92 -2016-10-29 23:00:00,945.0,0.0,14.4,5.9 -2016-10-30 00:00:00,854.0,0.0,14.39,5.88 -2016-10-30 01:00:00,803.0,0.0,14.0,5.47 -2016-10-30 02:00:00,751.0,0.0,13.6,5.06 -2016-10-30 03:00:00,719.0,0.0,13.21,4.65 -2016-10-30 04:00:00,726.0,0.0,13.11,5.1 -2016-10-30 05:00:00,657.0,0.0,13.01,5.56 -2016-10-30 06:00:00,651.0,0.0,12.91,6.01 -2016-10-30 07:00:00,710.0,0.0,12.87,6.29 -2016-10-30 08:00:00,831.0,30.34,12.83,6.57 -2016-10-30 09:00:00,935.0,21.83,12.78,6.84 -2016-10-30 10:00:00,1019.0,45.57,12.99,6.54 -2016-10-30 11:00:00,1044.0,36.67,13.19,6.23 -2016-10-30 12:00:00,1026.0,34.45,13.4,5.93 -2016-10-30 13:00:00,1022.0,40.99,13.54,5.83 -2016-10-30 14:00:00,1028.0,34.4,13.68,5.72 -2016-10-30 15:00:00,968.0,297.13,13.82,5.61 -2016-10-30 16:00:00,820.0,138.01,13.96,5.58 -2016-10-30 17:00:00,752.0,0.0,14.09,5.54 -2016-10-30 18:00:00,945.0,0.0,14.23,5.5 -2016-10-30 19:00:00,1061.0,0.0,14.27,5.49 -2016-10-30 20:00:00,972.0,0.0,14.31,5.48 -2016-10-30 21:00:00,929.0,0.0,14.35,5.46 -2016-10-30 22:00:00,1007.0,0.0,14.35,5.3 -2016-10-30 23:00:00,935.0,0.0,14.34,5.14 -2016-10-31 00:00:00,825.0,0.0,14.34,4.98 -2016-10-31 01:00:00,765.0,0.0,14.32,5.19 -2016-10-31 02:00:00,724.0,0.0,14.29,5.39 -2016-10-31 03:00:00,756.0,0.0,14.27,5.6 -2016-10-31 04:00:00,747.0,0.0,14.18,5.72 -2016-10-31 05:00:00,689.0,0.0,14.09,5.85 -2016-10-31 06:00:00,662.0,0.0,14.0,5.97 -2016-10-31 07:00:00,730.0,0.0,13.96,5.99 -2016-10-31 08:00:00,860.0,110.23,13.92,6.01 -2016-10-31 09:00:00,932.0,239.75,13.88,6.03 -2016-10-31 10:00:00,881.0,114.37,14.03,5.94 -2016-10-31 11:00:00,814.0,113.52,14.18,5.84 -2016-10-31 12:00:00,796.0,244.39,14.34,5.75 -2016-10-31 13:00:00,754.0,319.07,14.53,5.09 -2016-10-31 14:00:00,783.0,71.29,14.72,4.42 -2016-10-31 15:00:00,756.0,73.16,14.91,3.75 -2016-10-31 16:00:00,635.0,66.48,14.84,3.38 -2016-10-31 17:00:00,627.0,0.0,14.78,3.01 -2016-10-31 18:00:00,802.0,0.0,14.71,2.63 -2016-10-31 19:00:00,928.0,0.0,14.58,2.54 -2016-10-31 20:00:00,881.0,0.0,14.45,2.44 -2016-10-31 21:00:00,819.0,0.0,14.32,2.34 -2016-10-31 22:00:00,924.0,0.0,14.25,2.02 -2016-10-31 23:00:00,840.0,0.0,14.17,1.7 -2016-11-01 00:00:00,778.0,0.0,14.09,1.38 -2016-11-01 01:00:00,692.0,0.0,14.08,1.33 -2016-11-01 02:00:00,638.0,0.0,14.08,1.29 -2016-11-01 03:00:00,685.0,0.0,14.07,1.24 -2016-11-01 04:00:00,686.0,0.0,13.93,1.28 -2016-11-01 05:00:00,595.0,0.0,13.79,1.31 -2016-11-01 06:00:00,586.0,0.0,13.65,1.35 -2016-11-01 07:00:00,637.0,0.0,13.62,1.61 -2016-11-01 08:00:00,749.0,118.57,13.58,1.88 -2016-11-01 09:00:00,821.0,156.29,13.55,2.14 -2016-11-01 10:00:00,773.0,127.13,13.67,2.24 -2016-11-01 11:00:00,760.0,122.97,13.8,2.35 -2016-11-01 12:00:00,739.0,133.53,13.93,2.46 -2016-11-01 13:00:00,743.0,187.72,14.0,2.36 -2016-11-01 14:00:00,776.0,115.05,14.06,2.26 -2016-11-01 15:00:00,732.0,91.08,14.13,2.17 -2016-11-01 16:00:00,647.0,11.2,13.96,2.81 -2016-11-01 17:00:00,613.0,0.0,13.78,3.46 -2016-11-01 18:00:00,719.0,0.0,13.61,4.11 -2016-11-01 19:00:00,784.0,0.0,13.62,4.26 -2016-11-01 20:00:00,746.0,0.0,13.63,4.41 -2016-11-01 21:00:00,692.0,0.0,13.64,4.57 -2016-11-01 22:00:00,813.0,0.0,13.63,4.64 -2016-11-01 23:00:00,758.0,0.0,13.62,4.71 -2016-11-02 00:00:00,663.0,0.0,13.61,4.79 -2016-11-02 01:00:00,622.0,0.0,13.62,4.73 -2016-11-02 02:00:00,563.0,0.0,13.62,4.67 -2016-11-02 03:00:00,612.0,0.0,13.62,4.61 -2016-11-02 04:00:00,621.0,0.0,13.63,4.98 -2016-11-02 05:00:00,548.0,0.0,13.65,5.35 -2016-11-02 06:00:00,521.0,0.0,13.66,5.72 -2016-11-02 07:00:00,598.0,0.0,13.72,6.25 -2016-11-02 08:00:00,642.0,23.77,13.78,6.77 -2016-11-02 09:00:00,674.0,18.33,13.84,7.3 -2016-11-02 10:00:00,664.0,87.84,13.86,7.41 -2016-11-02 11:00:00,693.0,94.18,13.88,7.52 -2016-11-02 12:00:00,765.0,69.59,13.9,7.63 -2016-11-02 13:00:00,777.0,299.02,13.72,7.38 -2016-11-02 14:00:00,831.0,535.59,13.53,7.14 -2016-11-02 15:00:00,819.0,413.6,13.35,6.9 -2016-11-02 16:00:00,705.0,160.94,13.26,6.82 -2016-11-02 17:00:00,641.0,0.0,13.17,6.74 -2016-11-02 18:00:00,724.0,0.0,13.08,6.66 -2016-11-02 19:00:00,781.0,0.0,12.99,6.42 -2016-11-02 20:00:00,749.0,0.0,12.89,6.18 -2016-11-02 21:00:00,701.0,0.0,12.8,5.94 -2016-11-02 22:00:00,814.0,0.0,12.75,5.71 -2016-11-02 23:00:00,768.0,0.0,12.7,5.49 -2016-11-03 00:00:00,704.0,0.0,12.65,5.26 -2016-11-03 01:00:00,643.0,0.0,12.4,5.14 -2016-11-03 02:00:00,621.0,0.0,12.14,5.03 -2016-11-03 03:00:00,654.0,0.0,11.88,4.91 -2016-11-03 04:00:00,662.0,0.0,11.83,5.27 -2016-11-03 05:00:00,590.0,0.0,11.77,5.63 -2016-11-03 06:00:00,601.0,0.0,11.72,5.99 -2016-11-03 07:00:00,666.0,0.0,11.76,6.03 -2016-11-03 08:00:00,726.0,11.35,11.8,6.07 -2016-11-03 09:00:00,781.0,19.87,11.85,6.11 -2016-11-03 10:00:00,808.0,45.76,12.05,5.84 -2016-11-03 11:00:00,808.0,17.8,12.26,5.57 -2016-11-03 12:00:00,831.0,26.03,12.47,5.3 -2016-11-03 13:00:00,834.0,42.65,12.64,5.43 -2016-11-03 14:00:00,888.0,34.55,12.82,5.56 -2016-11-03 15:00:00,842.0,17.74,12.99,5.7 -2016-11-03 16:00:00,774.0,9.42,13.08,5.58 -2016-11-03 17:00:00,742.0,0.0,13.16,5.47 -2016-11-03 18:00:00,820.0,0.0,13.25,5.35 -2016-11-03 19:00:00,883.0,0.0,13.3,5.22 -2016-11-03 20:00:00,832.0,0.0,13.34,5.09 -2016-11-03 21:00:00,779.0,0.0,13.39,4.95 -2016-11-03 22:00:00,879.0,0.0,13.43,5.22 -2016-11-03 23:00:00,796.0,0.0,13.48,5.49 -2016-11-04 00:00:00,730.0,0.0,13.52,5.75 -2016-11-04 01:00:00,675.0,0.0,13.47,6.33 -2016-11-04 02:00:00,641.0,0.0,13.42,6.9 -2016-11-04 03:00:00,682.0,0.0,13.37,7.48 -2016-11-04 04:00:00,666.0,0.0,13.51,7.78 -2016-11-04 05:00:00,583.0,0.0,13.65,8.08 -2016-11-04 06:00:00,612.0,0.0,13.79,8.39 -2016-11-04 07:00:00,656.0,0.0,13.85,8.03 -2016-11-04 08:00:00,678.0,73.04,13.91,7.68 -2016-11-04 09:00:00,719.0,86.64,13.97,7.32 -2016-11-04 10:00:00,719.0,41.71,13.78,7.89 -2016-11-04 11:00:00,742.0,33.01,13.59,8.46 -2016-11-04 12:00:00,738.0,230.18,13.41,9.02 -2016-11-04 13:00:00,703.0,419.59,13.27,8.71 -2016-11-04 14:00:00,788.0,240.41,13.14,8.4 -2016-11-04 15:00:00,765.0,79.1,13.0,8.08 -2016-11-04 16:00:00,717.0,39.66,12.8,7.81 -2016-11-04 17:00:00,691.0,0.0,12.59,7.54 -2016-11-04 18:00:00,774.0,0.0,12.38,7.27 -2016-11-04 19:00:00,857.0,0.0,12.19,7.29 -2016-11-04 20:00:00,842.0,0.0,12.0,7.31 -2016-11-04 21:00:00,794.0,0.0,11.81,7.34 -2016-11-04 22:00:00,912.0,0.0,11.61,7.27 -2016-11-04 23:00:00,842.0,0.0,11.42,7.2 -2016-11-05 00:00:00,776.0,0.0,11.22,7.13 -2016-11-05 01:00:00,707.0,0.0,11.14,6.85 -2016-11-05 02:00:00,677.0,0.0,11.06,6.56 -2016-11-05 03:00:00,715.0,0.0,10.98,6.28 -2016-11-05 04:00:00,726.0,0.0,11.08,6.27 -2016-11-05 05:00:00,667.0,0.0,11.17,6.27 -2016-11-05 06:00:00,636.0,0.0,11.27,6.26 -2016-11-05 07:00:00,694.0,0.0,11.3,6.81 -2016-11-05 08:00:00,765.0,9.52,11.33,7.37 -2016-11-05 09:00:00,777.0,178.35,11.36,7.92 -2016-11-05 10:00:00,777.0,144.23,11.29,8.45 -2016-11-05 11:00:00,747.0,229.41,11.22,8.98 -2016-11-05 12:00:00,700.0,80.77,11.15,9.52 -2016-11-05 13:00:00,740.0,200.03,11.19,9.55 -2016-11-05 14:00:00,767.0,170.07,11.22,9.59 -2016-11-05 15:00:00,782.0,293.99,11.26,9.63 -2016-11-05 16:00:00,794.0,85.73,11.2,9.88 -2016-11-05 17:00:00,795.0,0.0,11.13,10.14 -2016-11-05 18:00:00,896.0,0.0,11.07,10.4 -2016-11-05 19:00:00,931.0,0.0,11.03,10.48 -2016-11-05 20:00:00,913.0,0.0,10.99,10.57 -2016-11-05 21:00:00,904.0,0.0,10.95,10.65 -2016-11-05 22:00:00,1003.0,0.0,10.78,10.57 -2016-11-05 23:00:00,967.0,0.0,10.62,10.49 -2016-11-06 00:00:00,872.0,0.0,10.45,10.41 -2016-11-06 01:00:00,825.0,0.0,10.22,10.25 -2016-11-06 02:00:00,787.0,0.0,9.99,10.09 -2016-11-06 03:00:00,803.0,0.0,9.75,9.93 -2016-11-06 04:00:00,817.0,0.0,9.64,10.03 -2016-11-06 05:00:00,769.0,0.0,9.52,10.12 -2016-11-06 06:00:00,756.0,0.0,9.41,10.22 -2016-11-06 07:00:00,809.0,0.0,9.32,10.17 -2016-11-06 08:00:00,886.0,135.95,9.23,10.11 -2016-11-06 09:00:00,942.0,163.86,9.14,10.06 -2016-11-06 10:00:00,985.0,464.76,9.27,9.99 -2016-11-06 11:00:00,1000.0,439.19,9.4,9.93 -2016-11-06 12:00:00,935.0,124.75,9.54,9.86 -2016-11-06 13:00:00,940.0,314.61,9.58,9.91 -2016-11-06 14:00:00,959.0,359.45,9.63,9.96 -2016-11-06 15:00:00,934.0,350.01,9.67,10.01 -2016-11-06 16:00:00,874.0,38.87,9.7,10.95 -2016-11-06 17:00:00,836.0,0.0,9.72,11.89 -2016-11-06 18:00:00,935.0,0.0,9.74,12.83 -2016-11-06 19:00:00,998.0,0.0,9.79,12.54 -2016-11-06 20:00:00,999.0,0.0,9.83,12.25 -2016-11-06 21:00:00,957.0,0.0,9.88,11.96 -2016-11-06 22:00:00,1055.0,0.0,9.85,11.66 -2016-11-06 23:00:00,1005.0,0.0,9.82,11.35 -2016-11-07 00:00:00,900.0,0.0,9.78,11.05 -2016-11-07 01:00:00,849.0,0.0,9.91,10.91 -2016-11-07 02:00:00,838.0,0.0,10.04,10.78 -2016-11-07 03:00:00,854.0,0.0,10.17,10.65 -2016-11-07 04:00:00,865.0,0.0,10.11,10.84 -2016-11-07 05:00:00,814.0,0.0,10.05,11.03 -2016-11-07 06:00:00,796.0,0.0,9.99,11.23 -2016-11-07 07:00:00,879.0,0.0,10.09,11.24 -2016-11-07 08:00:00,939.0,139.78,10.18,11.25 -2016-11-07 09:00:00,964.0,348.22,10.28,11.26 -2016-11-07 10:00:00,939.0,500.87,10.33,10.78 -2016-11-07 11:00:00,902.0,605.67,10.38,10.3 -2016-11-07 12:00:00,884.0,631.39,10.43,9.82 -2016-11-07 13:00:00,855.0,450.54,10.66,9.36 -2016-11-07 14:00:00,915.0,46.09,10.9,8.9 -2016-11-07 15:00:00,895.0,149.73,11.13,8.44 -2016-11-07 16:00:00,831.0,5.45,11.14,8.63 -2016-11-07 17:00:00,834.0,0.0,11.14,8.82 -2016-11-07 18:00:00,960.0,0.0,11.14,9.01 -2016-11-07 19:00:00,1049.0,0.0,10.97,8.97 -2016-11-07 20:00:00,993.0,0.0,10.79,8.94 -2016-11-07 21:00:00,934.0,0.0,10.62,8.91 -2016-11-07 22:00:00,1010.0,0.0,10.55,8.74 -2016-11-07 23:00:00,958.0,0.0,10.49,8.56 -2016-11-08 00:00:00,907.0,0.0,10.42,8.39 -2016-11-08 01:00:00,851.0,0.0,10.37,7.87 -2016-11-08 02:00:00,827.0,0.0,10.32,7.36 -2016-11-08 03:00:00,869.0,0.0,10.26,6.84 -2016-11-08 04:00:00,871.0,0.0,10.38,6.76 -2016-11-08 05:00:00,794.0,0.0,10.49,6.69 -2016-11-08 06:00:00,784.0,0.0,10.61,6.61 -2016-11-08 07:00:00,858.0,0.0,10.74,6.9 -2016-11-08 08:00:00,939.0,6.58,10.87,7.2 -2016-11-08 09:00:00,955.0,4.9,11.0,7.49 -2016-11-08 10:00:00,957.0,55.08,11.24,7.51 -2016-11-08 11:00:00,958.0,75.87,11.48,7.53 -2016-11-08 12:00:00,958.0,58.77,11.72,7.54 -2016-11-08 13:00:00,995.0,54.26,11.55,8.47 -2016-11-08 14:00:00,1037.0,76.17,11.39,9.39 -2016-11-08 15:00:00,1032.0,24.82,11.22,10.32 -2016-11-08 16:00:00,1032.0,6.56,11.38,11.62 -2016-11-08 17:00:00,972.0,0.0,11.53,12.92 -2016-11-08 18:00:00,1053.0,0.0,11.68,14.22 -2016-11-08 19:00:00,1080.0,0.0,12.12,13.55 -2016-11-08 20:00:00,1023.0,0.0,12.55,12.89 -2016-11-08 21:00:00,977.0,0.0,12.99,12.22 -2016-11-08 22:00:00,1065.0,0.0,12.86,13.54 -2016-11-08 23:00:00,977.0,0.0,12.73,14.86 -2016-11-09 00:00:00,921.0,0.0,12.59,16.18 -2016-11-09 01:00:00,867.0,0.0,12.35,16.13 -2016-11-09 02:00:00,858.0,0.0,12.1,16.08 -2016-11-09 03:00:00,920.0,0.0,11.85,16.03 -2016-11-09 04:00:00,904.0,0.0,12.06,15.99 -2016-11-09 05:00:00,847.0,0.0,12.26,15.94 -2016-11-09 06:00:00,846.0,0.0,12.47,15.9 -2016-11-09 07:00:00,906.0,0.0,12.49,15.75 -2016-11-09 08:00:00,992.0,22.44,12.5,15.59 -2016-11-09 09:00:00,1028.0,292.7,12.52,15.43 -2016-11-09 10:00:00,977.0,327.86,12.25,14.99 -2016-11-09 11:00:00,928.0,556.13,11.99,14.55 -2016-11-09 12:00:00,914.0,477.47,11.73,14.11 -2016-11-09 13:00:00,909.0,213.37,11.71,14.08 -2016-11-09 14:00:00,959.0,166.97,11.68,14.06 -2016-11-09 15:00:00,971.0,81.38,11.66,14.03 -2016-11-09 16:00:00,941.0,50.53,11.97,14.0 -2016-11-09 17:00:00,931.0,0.0,12.27,13.96 -2016-11-09 18:00:00,1025.0,0.0,12.58,13.93 -2016-11-09 19:00:00,1052.0,0.0,12.6,13.43 -2016-11-09 20:00:00,980.0,0.0,12.63,12.92 -2016-11-09 21:00:00,930.0,0.0,12.65,12.41 -2016-11-09 22:00:00,1054.0,0.0,12.72,12.2 -2016-11-09 23:00:00,976.0,0.0,12.8,11.98 -2016-11-10 00:00:00,899.0,0.0,12.87,11.77 -2016-11-10 01:00:00,890.0,0.0,12.82,11.96 -2016-11-10 02:00:00,856.0,0.0,12.77,12.16 -2016-11-10 03:00:00,863.0,0.0,12.72,12.36 -2016-11-10 04:00:00,876.0,0.0,12.68,12.41 -2016-11-10 05:00:00,808.0,0.0,12.64,12.47 -2016-11-10 06:00:00,794.0,0.0,12.6,12.52 -2016-11-10 07:00:00,870.0,0.0,12.55,12.43 -2016-11-10 08:00:00,906.0,30.88,12.49,12.33 -2016-11-10 09:00:00,917.0,179.89,12.44,12.23 -2016-11-10 10:00:00,897.0,109.55,12.22,12.07 -2016-11-10 11:00:00,894.0,346.4,12.01,11.9 -2016-11-10 12:00:00,893.0,226.33,11.8,11.74 -2016-11-10 13:00:00,893.0,290.1,12.13,11.69 -2016-11-10 14:00:00,954.0,267.45,12.45,11.64 -2016-11-10 15:00:00,967.0,253.05,12.78,11.59 -2016-11-10 16:00:00,907.0,26.96,12.71,11.26 -2016-11-10 17:00:00,873.0,0.0,12.63,10.92 -2016-11-10 18:00:00,954.0,0.0,12.55,10.59 -2016-11-10 19:00:00,1052.0,0.0,12.45,10.17 -2016-11-10 20:00:00,1024.0,0.0,12.36,9.75 -2016-11-10 21:00:00,980.0,0.0,12.26,9.32 -2016-11-10 22:00:00,1080.0,0.0,12.17,8.88 -2016-11-10 23:00:00,1022.0,0.0,12.09,8.43 -2016-11-11 00:00:00,949.0,0.0,12.0,7.99 -2016-11-11 01:00:00,890.0,0.0,12.25,7.41 -2016-11-11 02:00:00,847.0,0.0,12.5,6.84 -2016-11-11 03:00:00,881.0,0.0,12.74,6.26 -2016-11-11 04:00:00,881.0,0.0,12.67,5.49 -2016-11-11 05:00:00,806.0,0.0,12.61,4.73 -2016-11-11 06:00:00,782.0,0.0,12.54,3.96 -2016-11-11 07:00:00,822.0,0.0,12.53,3.21 -2016-11-11 08:00:00,891.0,21.73,12.52,2.46 -2016-11-11 09:00:00,927.0,208.46,12.52,1.71 -2016-11-11 10:00:00,891.0,128.69,12.57,2.08 -2016-11-11 11:00:00,840.0,104.58,12.63,2.46 -2016-11-11 12:00:00,841.0,81.96,12.69,2.83 -2016-11-11 13:00:00,898.0,108.95,12.71,4.07 -2016-11-11 14:00:00,970.0,104.77,12.72,5.32 -2016-11-11 15:00:00,1005.0,51.27,12.74,6.57 -2016-11-11 16:00:00,942.0,16.07,12.94,6.9 -2016-11-11 17:00:00,946.0,0.0,13.14,7.23 -2016-11-11 18:00:00,1066.0,0.0,13.34,7.56 -2016-11-11 19:00:00,1136.0,0.0,13.55,8.23 -2016-11-11 20:00:00,1082.0,0.0,13.75,8.89 -2016-11-11 21:00:00,1018.0,0.0,13.96,9.56 -2016-11-11 22:00:00,1121.0,0.0,14.05,10.03 -2016-11-11 23:00:00,1041.0,0.0,14.14,10.51 -2016-11-12 00:00:00,975.0,0.0,14.22,10.98 -2016-11-12 01:00:00,922.0,0.0,14.32,10.27 -2016-11-12 02:00:00,860.0,0.0,14.42,9.56 -2016-11-12 03:00:00,886.0,0.0,14.51,8.86 -2016-11-12 04:00:00,870.0,0.0,14.61,8.58 -2016-11-12 05:00:00,786.0,0.0,14.7,8.3 -2016-11-12 06:00:00,739.0,0.0,14.8,8.03 -2016-11-12 07:00:00,772.0,0.0,14.65,8.32 -2016-11-12 08:00:00,827.0,59.16,14.5,8.61 -2016-11-12 09:00:00,904.0,75.36,14.36,8.9 -2016-11-12 10:00:00,953.0,244.83,14.13,8.91 -2016-11-12 11:00:00,970.0,406.9,13.9,8.92 -2016-11-12 12:00:00,948.0,218.86,13.68,8.94 -2016-11-12 13:00:00,983.0,223.59,13.5,9.15 -2016-11-12 14:00:00,1014.0,163.67,13.31,9.37 -2016-11-12 15:00:00,1004.0,309.92,13.13,9.59 -2016-11-12 16:00:00,943.0,31.61,12.93,9.45 -2016-11-12 17:00:00,911.0,0.0,12.72,9.31 -2016-11-12 18:00:00,994.0,0.0,12.51,9.17 -2016-11-12 19:00:00,1043.0,0.0,12.34,9.1 -2016-11-12 20:00:00,990.0,0.0,12.18,9.03 -2016-11-12 21:00:00,928.0,0.0,12.01,8.97 -2016-11-12 22:00:00,1054.0,0.0,12.05,8.81 -2016-11-12 23:00:00,1031.0,0.0,12.09,8.66 -2016-11-13 00:00:00,963.0,0.0,12.13,8.51 -2016-11-13 01:00:00,880.0,0.0,12.55,8.5 -2016-11-13 02:00:00,835.0,0.0,12.97,8.49 -2016-11-13 03:00:00,853.0,0.0,13.38,8.48 -2016-11-13 04:00:00,841.0,0.0,13.37,8.34 -2016-11-13 05:00:00,771.0,0.0,13.36,8.2 -2016-11-13 06:00:00,735.0,0.0,13.35,8.06 -2016-11-13 07:00:00,776.0,0.0,13.27,7.74 -2016-11-13 08:00:00,846.0,20.13,13.19,7.43 -2016-11-13 09:00:00,925.0,30.92,13.12,7.12 -2016-11-13 10:00:00,906.0,128.07,12.94,6.88 -2016-11-13 11:00:00,832.0,79.24,12.76,6.64 -2016-11-13 12:00:00,780.0,382.44,12.59,6.4 -2016-11-13 13:00:00,723.0,421.07,12.54,6.28 -2016-11-13 14:00:00,765.0,242.86,12.48,6.15 -2016-11-13 15:00:00,753.0,350.6,12.43,6.03 -2016-11-13 16:00:00,674.0,31.94,12.35,5.92 -2016-11-13 17:00:00,670.0,0.0,12.26,5.82 -2016-11-13 18:00:00,737.0,0.0,12.18,5.71 -2016-11-13 19:00:00,783.0,0.0,12.12,5.35 -2016-11-13 20:00:00,760.0,0.0,12.07,4.99 -2016-11-13 21:00:00,742.0,0.0,12.01,4.63 -2016-11-13 22:00:00,867.0,0.0,11.93,4.03 -2016-11-13 23:00:00,840.0,0.0,11.86,3.43 -2016-11-14 00:00:00,756.0,0.0,11.78,2.83 -2016-11-14 01:00:00,681.0,0.0,11.82,2.8 -2016-11-14 02:00:00,666.0,0.0,11.85,2.76 -2016-11-14 03:00:00,700.0,0.0,11.88,2.73 -2016-11-14 04:00:00,695.0,0.0,11.95,2.71 -2016-11-14 05:00:00,635.0,0.0,12.02,2.69 -2016-11-14 06:00:00,628.0,0.0,12.09,2.66 -2016-11-14 07:00:00,696.0,0.0,12.15,2.89 -2016-11-14 08:00:00,777.0,3.81,12.21,3.11 -2016-11-14 09:00:00,782.0,189.44,12.28,3.34 -2016-11-14 10:00:00,749.0,279.73,12.38,3.76 -2016-11-14 11:00:00,716.0,191.7,12.49,4.18 -2016-11-14 12:00:00,682.0,246.73,12.6,4.61 -2016-11-14 13:00:00,659.0,117.12,12.37,4.86 -2016-11-14 14:00:00,726.0,69.83,12.14,5.11 -2016-11-14 15:00:00,707.0,66.84,11.91,5.37 -2016-11-14 16:00:00,656.0,10.11,11.91,5.76 -2016-11-14 17:00:00,636.0,0.0,11.9,6.15 -2016-11-14 18:00:00,734.0,0.0,11.89,6.54 -2016-11-14 19:00:00,777.0,0.0,12.04,6.34 -2016-11-14 20:00:00,734.0,0.0,12.2,6.15 -2016-11-14 21:00:00,701.0,0.0,12.35,5.96 -2016-11-14 22:00:00,820.0,0.0,12.51,6.05 -2016-11-14 23:00:00,759.0,0.0,12.67,6.13 -2016-11-15 00:00:00,690.0,0.0,12.83,6.22 -2016-11-15 01:00:00,673.0,0.0,12.95,6.51 -2016-11-15 02:00:00,635.0,0.0,13.06,6.79 -2016-11-15 03:00:00,658.0,0.0,13.17,7.08 -2016-11-15 04:00:00,663.0,0.0,13.22,7.61 -2016-11-15 05:00:00,586.0,0.0,13.28,8.14 -2016-11-15 06:00:00,570.0,0.0,13.33,8.68 -2016-11-15 07:00:00,633.0,0.0,13.37,8.14 -2016-11-15 08:00:00,700.0,27.75,13.41,7.6 -2016-11-15 09:00:00,715.0,133.05,13.46,7.06 -2016-11-15 10:00:00,733.0,87.56,13.39,6.68 -2016-11-15 11:00:00,701.0,189.98,13.33,6.3 -2016-11-15 12:00:00,715.0,200.08,13.27,5.92 -2016-11-15 13:00:00,721.0,150.61,13.33,6.09 -2016-11-15 14:00:00,794.0,67.21,13.38,6.27 -2016-11-15 15:00:00,794.0,110.4,13.44,6.44 -2016-11-15 16:00:00,737.0,14.18,13.47,6.72 -2016-11-15 17:00:00,691.0,0.0,13.49,6.99 -2016-11-15 18:00:00,752.0,0.0,13.52,7.27 -2016-11-15 19:00:00,803.0,0.0,13.53,7.59 -2016-11-15 20:00:00,768.0,0.0,13.54,7.91 -2016-11-15 21:00:00,732.0,0.0,13.55,8.23 -2016-11-15 22:00:00,825.0,0.0,13.53,8.68 -2016-11-15 23:00:00,765.0,0.0,13.51,9.13 -2016-11-16 00:00:00,704.0,0.0,13.48,9.57 -2016-11-16 01:00:00,672.0,0.0,13.48,9.83 -2016-11-16 02:00:00,647.0,0.0,13.48,10.1 -2016-11-16 03:00:00,680.0,0.0,13.47,10.36 -2016-11-16 04:00:00,677.0,0.0,13.47,10.15 -2016-11-16 05:00:00,607.0,0.0,13.46,9.94 -2016-11-16 06:00:00,591.0,0.0,13.46,9.72 -2016-11-16 07:00:00,658.0,0.0,13.49,9.23 -2016-11-16 08:00:00,707.0,0.0,13.51,8.73 -2016-11-16 09:00:00,727.0,199.66,13.54,8.23 -2016-11-16 10:00:00,745.0,252.94,13.49,8.12 -2016-11-16 11:00:00,725.0,133.89,13.45,8.01 -2016-11-16 12:00:00,748.0,597.12,13.41,7.9 -2016-11-16 13:00:00,758.0,583.97,13.4,8.29 -2016-11-16 14:00:00,813.0,71.55,13.38,8.68 -2016-11-16 15:00:00,793.0,88.06,13.37,9.08 -2016-11-16 16:00:00,699.0,60.99,13.41,8.82 -2016-11-16 17:00:00,672.0,0.0,13.44,8.57 -2016-11-16 18:00:00,776.0,0.0,13.47,8.32 -2016-11-16 19:00:00,825.0,0.0,13.43,8.29 -2016-11-16 20:00:00,779.0,0.0,13.39,8.26 -2016-11-16 21:00:00,739.0,0.0,13.35,8.23 -2016-11-16 22:00:00,843.0,0.0,13.22,8.43 -2016-11-16 23:00:00,766.0,0.0,13.09,8.63 -2016-11-17 00:00:00,710.0,0.0,12.95,8.83 -2016-11-17 01:00:00,678.0,0.0,12.86,9.34 -2016-11-17 02:00:00,664.0,0.0,12.76,9.86 -2016-11-17 03:00:00,715.0,0.0,12.66,10.37 -2016-11-17 04:00:00,737.0,0.0,12.66,11.0 -2016-11-17 05:00:00,648.0,0.0,12.66,11.62 -2016-11-17 06:00:00,632.0,0.0,12.66,12.25 -2016-11-17 07:00:00,711.0,0.0,12.71,12.6 -2016-11-17 08:00:00,747.0,50.63,12.76,12.96 -2016-11-17 09:00:00,803.0,7.66,12.82,13.31 -2016-11-17 10:00:00,788.0,41.25,12.77,13.86 -2016-11-17 11:00:00,783.0,31.03,12.72,14.4 -2016-11-17 12:00:00,739.0,138.65,12.68,14.95 -2016-11-17 13:00:00,755.0,110.12,12.63,14.57 -2016-11-17 14:00:00,822.0,29.61,12.58,14.2 -2016-11-17 15:00:00,842.0,124.96,12.53,13.82 -2016-11-17 16:00:00,790.0,45.76,12.52,13.62 -2016-11-17 17:00:00,773.0,0.0,12.51,13.43 -2016-11-17 18:00:00,834.0,0.0,12.5,13.23 -2016-11-17 19:00:00,860.0,0.0,12.32,13.51 -2016-11-17 20:00:00,836.0,0.0,12.13,13.8 -2016-11-17 21:00:00,787.0,0.0,11.95,14.08 -2016-11-17 22:00:00,908.0,0.0,11.54,14.48 -2016-11-17 23:00:00,861.0,0.0,11.14,14.88 -2016-11-18 00:00:00,787.0,0.0,10.73,15.28 -2016-11-18 01:00:00,796.0,0.0,10.33,15.2 -2016-11-18 02:00:00,764.0,0.0,9.93,15.13 -2016-11-18 03:00:00,814.0,0.0,9.52,15.05 -2016-11-18 04:00:00,829.0,0.0,9.36,15.18 -2016-11-18 05:00:00,752.0,0.0,9.21,15.31 -2016-11-18 06:00:00,753.0,0.0,9.05,15.45 -2016-11-18 07:00:00,851.0,0.0,9.18,15.17 -2016-11-18 08:00:00,892.0,4.3,9.31,14.89 -2016-11-18 09:00:00,891.0,2.86,9.45,14.61 -2016-11-18 10:00:00,914.0,122.37,9.25,14.15 -2016-11-18 11:00:00,871.0,316.35,9.06,13.69 -2016-11-18 12:00:00,834.0,31.64,8.87,13.23 -2016-11-18 13:00:00,865.0,460.87,8.82,13.08 -2016-11-18 14:00:00,907.0,81.51,8.77,12.93 -2016-11-18 15:00:00,926.0,38.67,8.72,12.79 -2016-11-18 16:00:00,866.0,13.94,8.86,12.87 -2016-11-18 17:00:00,820.0,0.0,9.0,12.96 -2016-11-18 18:00:00,906.0,0.0,9.14,13.05 -2016-11-18 19:00:00,974.0,0.0,8.97,13.01 -2016-11-18 20:00:00,955.0,0.0,8.8,12.97 -2016-11-18 21:00:00,917.0,0.0,8.63,12.94 -2016-11-18 22:00:00,1015.0,0.0,8.99,12.54 -2016-11-18 23:00:00,945.0,0.0,9.35,12.14 -2016-11-19 00:00:00,904.0,0.0,9.7,11.74 -2016-11-19 01:00:00,860.0,0.0,9.61,11.37 -2016-11-19 02:00:00,849.0,0.0,9.51,11.0 -2016-11-19 03:00:00,905.0,0.0,9.41,10.63 -2016-11-19 04:00:00,904.0,0.0,9.71,10.14 -2016-11-19 05:00:00,827.0,0.0,10.01,9.65 -2016-11-19 06:00:00,814.0,0.0,10.31,9.16 -2016-11-19 07:00:00,866.0,0.0,10.45,9.29 -2016-11-19 08:00:00,905.0,0.77,10.59,9.42 -2016-11-19 09:00:00,914.0,44.09,10.74,9.54 -2016-11-19 10:00:00,912.0,75.39,10.58,9.85 -2016-11-19 11:00:00,924.0,47.73,10.42,10.16 -2016-11-19 12:00:00,960.0,90.12,10.26,10.47 -2016-11-19 13:00:00,983.0,69.42,10.56,10.58 -2016-11-19 14:00:00,1063.0,62.96,10.85,10.7 -2016-11-19 15:00:00,1082.0,7.73,11.15,10.81 -2016-11-19 16:00:00,1041.0,6.95,11.22,12.06 -2016-11-19 17:00:00,1018.0,0.0,11.28,13.31 -2016-11-19 18:00:00,1077.0,0.0,11.35,14.57 -2016-11-19 19:00:00,1115.0,0.0,12.07,14.63 -2016-11-19 20:00:00,1057.0,0.0,12.79,14.7 -2016-11-19 21:00:00,970.0,0.0,13.51,14.77 -2016-11-19 22:00:00,1046.0,0.0,13.42,15.75 -2016-11-19 23:00:00,978.0,0.0,13.33,16.72 -2016-11-20 00:00:00,940.0,0.0,13.23,17.7 -2016-11-20 01:00:00,907.0,0.0,12.65,16.97 -2016-11-20 02:00:00,884.0,0.0,12.06,16.25 -2016-11-20 03:00:00,926.0,0.0,11.47,15.53 -2016-11-20 04:00:00,924.0,0.0,11.46,13.77 -2016-11-20 05:00:00,837.0,0.0,11.46,12.02 -2016-11-20 06:00:00,801.0,0.0,11.45,10.26 -2016-11-20 07:00:00,818.0,0.0,11.38,8.27 -2016-11-20 08:00:00,858.0,2.82,11.31,6.28 -2016-11-20 09:00:00,866.0,64.81,11.25,4.29 -2016-11-20 10:00:00,849.0,131.84,11.33,4.0 -2016-11-20 11:00:00,845.0,61.0,11.42,3.7 -2016-11-20 12:00:00,845.0,92.4,11.51,3.41 -2016-11-20 13:00:00,878.0,18.5,11.88,4.0 -2016-11-20 14:00:00,918.0,29.59,12.24,4.58 -2016-11-20 15:00:00,963.0,25.3,12.61,5.17 -2016-11-20 16:00:00,842.0,2.32,12.73,5.61 -2016-11-20 17:00:00,812.0,0.0,12.84,6.06 -2016-11-20 18:00:00,860.0,0.0,12.96,6.5 -2016-11-20 19:00:00,859.0,0.0,12.75,7.32 -2016-11-20 20:00:00,831.0,0.0,12.54,8.14 -2016-11-20 21:00:00,805.0,0.0,12.33,8.97 -2016-11-20 22:00:00,898.0,0.0,12.11,9.22 -2016-11-20 23:00:00,856.0,0.0,11.9,9.48 -2016-11-21 00:00:00,801.0,0.0,11.68,9.74 -2016-11-21 01:00:00,753.0,0.0,11.9,9.02 -2016-11-21 02:00:00,741.0,0.0,12.12,8.29 -2016-11-21 03:00:00,773.0,0.0,12.34,7.57 -2016-11-21 04:00:00,760.0,0.0,12.34,6.86 -2016-11-21 05:00:00,698.0,0.0,12.35,6.16 -2016-11-21 06:00:00,681.0,0.0,12.35,5.45 -2016-11-21 07:00:00,739.0,0.0,12.02,5.58 -2016-11-21 08:00:00,791.0,0.0,11.69,5.71 -2016-11-21 09:00:00,838.0,5.99,11.37,5.83 -2016-11-21 10:00:00,824.0,15.23,11.43,6.77 -2016-11-21 11:00:00,841.0,26.18,11.49,7.7 -2016-11-21 12:00:00,835.0,32.63,11.56,8.63 -2016-11-21 13:00:00,813.0,43.0,11.28,8.57 -2016-11-21 14:00:00,872.0,41.56,11.01,8.51 -2016-11-21 15:00:00,919.0,17.29,10.73,8.44 -2016-11-21 16:00:00,885.0,14.91,10.55,8.8 -2016-11-21 17:00:00,858.0,0.0,10.36,9.16 -2016-11-21 18:00:00,935.0,0.0,10.18,9.52 -2016-11-21 19:00:00,945.0,0.0,10.21,10.76 -2016-11-21 20:00:00,943.0,0.0,10.23,12.0 -2016-11-21 21:00:00,917.0,0.0,10.26,13.24 -2016-11-21 22:00:00,1032.0,0.0,10.12,14.46 -2016-11-21 23:00:00,972.0,0.0,9.98,15.67 -2016-11-22 00:00:00,939.0,0.0,9.83,16.88 -2016-11-22 01:00:00,916.0,0.0,9.98,15.87 -2016-11-22 02:00:00,878.0,0.0,10.13,14.86 -2016-11-22 03:00:00,914.0,0.0,10.28,13.85 -2016-11-22 04:00:00,905.0,0.0,10.32,13.22 -2016-11-22 05:00:00,826.0,0.0,10.37,12.59 -2016-11-22 06:00:00,819.0,0.0,10.41,11.96 -2016-11-22 07:00:00,872.0,0.0,10.39,11.31 -2016-11-22 08:00:00,947.0,1.54,10.36,10.65 -2016-11-22 09:00:00,973.0,41.64,10.34,10.0 -2016-11-22 10:00:00,930.0,8.37,10.23,9.14 -2016-11-22 11:00:00,923.0,32.86,10.13,8.27 -2016-11-22 12:00:00,915.0,51.58,10.03,7.41 -2016-11-22 13:00:00,892.0,50.08,9.96,7.69 -2016-11-22 14:00:00,945.0,50.52,9.89,7.98 -2016-11-22 15:00:00,930.0,115.5,9.82,8.26 -2016-11-22 16:00:00,887.0,0.0,9.84,8.13 -2016-11-22 17:00:00,876.0,0.0,9.86,8.0 -2016-11-22 18:00:00,920.0,0.0,9.88,7.86 -2016-11-22 19:00:00,960.0,0.0,9.86,7.67 -2016-11-22 20:00:00,926.0,0.0,9.84,7.49 -2016-11-22 21:00:00,899.0,0.0,9.82,7.3 -2016-11-22 22:00:00,992.0,0.0,9.78,6.99 -2016-11-22 23:00:00,951.0,0.0,9.74,6.69 -2016-11-23 00:00:00,890.0,0.0,9.7,6.39 -2016-11-23 01:00:00,862.0,0.0,9.7,6.13 -2016-11-23 02:00:00,836.0,0.0,9.69,5.87 -2016-11-23 03:00:00,899.0,0.0,9.68,5.61 -2016-11-23 04:00:00,877.0,0.0,9.77,5.68 -2016-11-23 05:00:00,788.0,0.0,9.85,5.74 -2016-11-23 06:00:00,781.0,0.0,9.94,5.81 -2016-11-23 07:00:00,851.0,0.0,10.04,6.34 -2016-11-23 08:00:00,915.0,0.0,10.14,6.86 -2016-11-23 09:00:00,929.0,284.18,10.25,7.39 -2016-11-23 10:00:00,940.0,159.49,10.38,7.66 -2016-11-23 11:00:00,918.0,467.07,10.51,7.92 -2016-11-23 12:00:00,952.0,138.23,10.65,8.18 -2016-11-23 13:00:00,966.0,57.5,10.9,8.53 -2016-11-23 14:00:00,1033.0,51.02,11.14,8.89 -2016-11-23 15:00:00,1051.0,167.37,11.39,9.24 -2016-11-23 16:00:00,989.0,1.44,11.44,10.03 -2016-11-23 17:00:00,953.0,0.0,11.48,10.81 -2016-11-23 18:00:00,1015.0,0.0,11.52,11.6 -2016-11-23 19:00:00,1009.0,0.0,11.53,12.06 -2016-11-23 20:00:00,992.0,0.0,11.53,12.52 -2016-11-23 21:00:00,936.0,0.0,11.54,12.98 -2016-11-23 22:00:00,1043.0,0.0,11.99,12.84 -2016-11-23 23:00:00,970.0,0.0,12.45,12.69 -2016-11-24 00:00:00,871.0,0.0,12.9,12.55 -2016-11-24 01:00:00,851.0,0.0,13.32,12.77 -2016-11-24 02:00:00,827.0,0.0,13.73,12.99 -2016-11-24 03:00:00,865.0,0.0,14.14,13.21 -2016-11-24 04:00:00,866.0,0.0,14.12,13.72 -2016-11-24 05:00:00,770.0,0.0,14.09,14.23 -2016-11-24 06:00:00,766.0,0.0,14.07,14.73 -2016-11-24 07:00:00,813.0,0.0,13.89,14.52 -2016-11-24 08:00:00,873.0,0.0,13.71,14.31 -2016-11-24 09:00:00,910.0,2.78,13.54,14.1 -2016-11-24 10:00:00,897.0,64.45,13.43,14.17 -2016-11-24 11:00:00,890.0,457.53,13.33,14.23 -2016-11-24 12:00:00,887.0,204.33,13.23,14.3 -2016-11-24 13:00:00,942.0,91.87,13.25,14.35 -2016-11-24 14:00:00,1014.0,184.45,13.26,14.4 -2016-11-24 15:00:00,1007.0,51.38,13.28,14.46 -2016-11-24 16:00:00,937.0,1.0,13.3,14.18 -2016-11-24 17:00:00,903.0,0.0,13.32,13.9 -2016-11-24 18:00:00,934.0,0.0,13.34,13.63 -2016-11-24 19:00:00,937.0,0.0,13.27,13.22 -2016-11-24 20:00:00,895.0,0.0,13.21,12.82 -2016-11-24 21:00:00,837.0,0.0,13.14,12.41 -2016-11-24 22:00:00,921.0,0.0,13.04,12.03 -2016-11-24 23:00:00,873.0,0.0,12.95,11.65 -2016-11-25 00:00:00,807.0,0.0,12.85,11.27 -2016-11-25 01:00:00,784.0,0.0,12.69,11.6 -2016-11-25 02:00:00,767.0,0.0,12.53,11.92 -2016-11-25 03:00:00,795.0,0.0,12.37,12.25 -2016-11-25 04:00:00,801.0,0.0,12.21,12.38 -2016-11-25 05:00:00,720.0,0.0,12.06,12.51 -2016-11-25 06:00:00,734.0,0.0,11.9,12.65 -2016-11-25 07:00:00,799.0,0.0,11.7,12.67 -2016-11-25 08:00:00,837.0,0.0,11.5,12.69 -2016-11-25 09:00:00,848.0,243.34,11.31,12.72 -2016-11-25 10:00:00,878.0,135.58,11.24,12.86 -2016-11-25 11:00:00,881.0,244.64,11.18,13.01 -2016-11-25 12:00:00,854.0,342.08,11.12,13.16 -2016-11-25 13:00:00,886.0,370.35,11.08,13.14 -2016-11-25 14:00:00,942.0,345.97,11.03,13.11 -2016-11-25 15:00:00,932.0,259.85,10.99,13.09 -2016-11-25 16:00:00,863.0,1.02,11.0,13.68 -2016-11-25 17:00:00,834.0,0.0,11.0,14.27 -2016-11-25 18:00:00,868.0,0.0,11.01,14.86 -2016-11-25 19:00:00,898.0,0.0,11.04,13.79 -2016-11-25 20:00:00,881.0,0.0,11.06,12.73 -2016-11-25 21:00:00,827.0,0.0,11.09,11.67 -2016-11-25 22:00:00,939.0,0.0,11.16,11.13 -2016-11-25 23:00:00,869.0,0.0,11.23,10.58 -2016-11-26 00:00:00,843.0,0.0,11.3,10.04 -2016-11-26 01:00:00,797.0,0.0,11.3,9.36 -2016-11-26 02:00:00,778.0,0.0,11.3,8.67 -2016-11-26 03:00:00,817.0,0.0,11.29,7.99 -2016-11-26 04:00:00,816.0,0.0,11.35,7.89 -2016-11-26 05:00:00,747.0,0.0,11.41,7.8 -2016-11-26 06:00:00,744.0,0.0,11.47,7.71 -2016-11-26 07:00:00,771.0,0.0,11.44,7.6 -2016-11-26 08:00:00,800.0,0.0,11.41,7.5 -2016-11-26 09:00:00,836.0,6.56,11.39,7.39 -2016-11-26 10:00:00,870.0,15.23,11.43,7.22 -2016-11-26 11:00:00,859.0,20.6,11.47,7.05 -2016-11-26 12:00:00,856.0,21.97,11.51,6.88 -2016-11-26 13:00:00,869.0,26.16,11.6,6.61 -2016-11-26 14:00:00,913.0,17.86,11.69,6.33 -2016-11-26 15:00:00,949.0,28.77,11.78,6.06 -2016-11-26 16:00:00,911.0,0.0,11.85,6.62 -2016-11-26 17:00:00,879.0,0.0,11.92,7.19 -2016-11-26 18:00:00,923.0,0.0,11.99,7.75 -2016-11-26 19:00:00,959.0,0.0,11.93,7.99 -2016-11-26 20:00:00,932.0,0.0,11.87,8.22 -2016-11-26 21:00:00,877.0,0.0,11.81,8.46 -2016-11-26 22:00:00,949.0,0.0,11.67,8.37 -2016-11-26 23:00:00,897.0,0.0,11.53,8.29 -2016-11-27 00:00:00,861.0,0.0,11.38,8.21 -2016-11-27 01:00:00,822.0,0.0,11.39,8.0 -2016-11-27 02:00:00,811.0,0.0,11.39,7.78 -2016-11-27 03:00:00,874.0,0.0,11.39,7.57 -2016-11-27 04:00:00,863.0,0.0,11.35,7.8 -2016-11-27 05:00:00,792.0,0.0,11.3,8.02 -2016-11-27 06:00:00,794.0,0.0,11.26,8.25 -2016-11-27 07:00:00,813.0,0.0,11.3,8.5 -2016-11-27 08:00:00,862.0,0.0,11.33,8.75 -2016-11-27 09:00:00,935.0,265.07,11.37,9.01 -2016-11-27 10:00:00,952.0,435.47,11.53,9.19 -2016-11-27 11:00:00,911.0,539.88,11.7,9.37 -2016-11-27 12:00:00,841.0,579.41,11.87,9.54 -2016-11-27 13:00:00,786.0,380.09,11.91,9.68 -2016-11-27 14:00:00,851.0,22.63,11.94,9.82 -2016-11-27 15:00:00,855.0,177.89,11.98,9.96 -2016-11-27 16:00:00,772.0,0.0,11.85,10.07 -2016-11-27 17:00:00,772.0,0.0,11.71,10.18 -2016-11-27 18:00:00,865.0,0.0,11.57,10.29 -2016-11-27 19:00:00,888.0,0.0,11.41,10.49 -2016-11-27 20:00:00,890.0,0.0,11.26,10.69 -2016-11-27 21:00:00,876.0,0.0,11.1,10.9 -2016-11-27 22:00:00,967.0,0.0,10.93,10.93 -2016-11-27 23:00:00,904.0,0.0,10.77,10.96 -2016-11-28 00:00:00,831.0,0.0,10.6,10.99 -2016-11-28 01:00:00,784.0,0.0,10.45,10.83 -2016-11-28 02:00:00,785.0,0.0,10.3,10.67 -2016-11-28 03:00:00,833.0,0.0,10.15,10.51 -2016-11-28 04:00:00,848.0,0.0,10.06,10.65 -2016-11-28 05:00:00,795.0,0.0,9.96,10.8 -2016-11-28 06:00:00,806.0,0.0,9.87,10.94 -2016-11-28 07:00:00,863.0,0.0,9.79,11.03 -2016-11-28 08:00:00,944.0,0.0,9.71,11.12 -2016-11-28 09:00:00,967.0,11.49,9.63,11.21 -2016-11-28 10:00:00,944.0,30.79,9.55,10.92 -2016-11-28 11:00:00,904.0,78.6,9.47,10.63 -2016-11-28 12:00:00,859.0,126.85,9.39,10.34 -2016-11-28 13:00:00,857.0,54.06,9.56,10.45 -2016-11-28 14:00:00,861.0,259.96,9.73,10.56 -2016-11-28 15:00:00,853.0,278.12,9.9,10.66 -2016-11-28 16:00:00,828.0,0.0,9.94,10.91 -2016-11-28 17:00:00,815.0,0.0,9.97,11.16 -2016-11-28 18:00:00,946.0,0.0,10.0,11.41 -2016-11-28 19:00:00,993.0,0.0,9.76,11.3 -2016-11-28 20:00:00,994.0,0.0,9.52,11.19 -2016-11-28 21:00:00,950.0,0.0,9.28,11.08 -2016-11-28 22:00:00,1054.0,0.0,8.98,10.8 -2016-11-28 23:00:00,1002.0,0.0,8.69,10.52 -2016-11-29 00:00:00,969.0,0.0,8.39,10.25 -2016-11-29 01:00:00,955.0,0.0,8.18,9.96 -2016-11-29 02:00:00,958.0,0.0,7.96,9.67 -2016-11-29 03:00:00,1021.0,0.0,7.74,9.38 -2016-11-29 04:00:00,1023.0,0.0,7.64,9.37 -2016-11-29 05:00:00,966.0,0.0,7.53,9.37 -2016-11-29 06:00:00,954.0,0.0,7.43,9.37 -2016-11-29 07:00:00,1016.0,0.0,7.3,9.35 -2016-11-29 08:00:00,1095.0,0.0,7.17,9.34 -2016-11-29 09:00:00,1122.0,267.83,7.04,9.32 -2016-11-29 10:00:00,1051.0,478.53,7.11,9.36 -2016-11-29 11:00:00,978.0,587.38,7.18,9.39 -2016-11-29 12:00:00,932.0,653.42,7.26,9.42 -2016-11-29 13:00:00,921.0,573.98,7.42,9.43 -2016-11-29 14:00:00,989.0,466.84,7.59,9.43 -2016-11-29 15:00:00,980.0,291.29,7.75,9.43 -2016-11-29 16:00:00,932.0,0.0,7.79,9.47 -2016-11-29 17:00:00,918.0,0.0,7.82,9.51 -2016-11-29 18:00:00,1029.0,0.0,7.85,9.54 -2016-11-29 19:00:00,1090.0,0.0,7.71,9.37 -2016-11-29 20:00:00,1086.0,0.0,7.57,9.2 -2016-11-29 21:00:00,1077.0,0.0,7.43,9.03 -2016-11-29 22:00:00,1180.0,0.0,7.39,8.79 -2016-11-29 23:00:00,1106.0,0.0,7.35,8.54 -2016-11-30 00:00:00,1065.0,0.0,7.3,8.29 -2016-11-30 01:00:00,1034.0,0.0,7.42,8.16 -2016-11-30 02:00:00,1030.0,0.0,7.54,8.03 -2016-11-30 03:00:00,1081.0,0.0,7.65,7.9 -2016-11-30 04:00:00,1082.0,0.0,7.84,7.61 -2016-11-30 05:00:00,1014.0,0.0,8.03,7.31 -2016-11-30 06:00:00,1008.0,0.0,8.22,7.02 -2016-11-30 07:00:00,1085.0,0.0,8.46,6.61 -2016-11-30 08:00:00,1145.0,0.0,8.7,6.2 -2016-11-30 09:00:00,1141.0,155.42,8.94,5.79 -2016-11-30 10:00:00,1066.0,140.21,9.13,6.0 -2016-11-30 11:00:00,1024.0,553.02,9.32,6.2 -2016-11-30 12:00:00,958.0,596.0,9.52,6.4 -2016-11-30 13:00:00,976.0,363.36,9.84,6.45 -2016-11-30 14:00:00,1063.0,388.13,10.16,6.5 -2016-11-30 15:00:00,1073.0,115.8,10.48,6.55 -2016-11-30 16:00:00,997.0,0.0,10.57,6.66 -2016-11-30 17:00:00,965.0,0.0,10.66,6.77 -2016-11-30 18:00:00,1068.0,0.0,10.75,6.88 -2016-11-30 19:00:00,1119.0,0.0,10.78,6.88 -2016-11-30 20:00:00,1072.0,0.0,10.8,6.87 -2016-11-30 21:00:00,1039.0,0.0,10.83,6.87 -2016-11-30 22:00:00,1140.0,0.0,10.85,6.77 -2016-11-30 23:00:00,1062.0,0.0,10.87,6.68 -2016-12-01 00:00:00,1018.0,0.0,10.88,6.58 -2016-12-01 01:00:00,988.0,0.0,10.79,6.6 -2016-12-01 02:00:00,987.0,0.0,10.71,6.62 -2016-12-01 03:00:00,1019.0,0.0,10.62,6.63 -2016-12-01 04:00:00,1026.0,0.0,10.57,6.56 -2016-12-01 05:00:00,933.0,0.0,10.53,6.48 -2016-12-01 06:00:00,920.0,0.0,10.48,6.4 -2016-12-01 07:00:00,987.0,0.0,10.47,6.4 -2016-12-01 08:00:00,1039.0,0.0,10.46,6.4 -2016-12-01 09:00:00,1057.0,250.6,10.45,6.4 -2016-12-01 10:00:00,1008.0,432.41,10.58,6.43 -2016-12-01 11:00:00,919.0,546.4,10.71,6.46 -2016-12-01 12:00:00,880.0,594.36,10.84,6.48 -2016-12-01 13:00:00,880.0,564.52,10.93,6.17 -2016-12-01 14:00:00,904.0,458.48,11.01,5.85 -2016-12-01 15:00:00,882.0,292.21,11.1,5.53 -2016-12-01 16:00:00,843.0,0.0,11.0,5.46 -2016-12-01 17:00:00,808.0,0.0,10.9,5.38 -2016-12-01 18:00:00,924.0,0.0,10.8,5.31 -2016-12-01 19:00:00,964.0,0.0,10.62,5.52 -2016-12-01 20:00:00,968.0,0.0,10.44,5.72 -2016-12-01 21:00:00,962.0,0.0,10.26,5.93 -2016-12-01 22:00:00,1062.0,0.0,10.12,6.25 -2016-12-01 23:00:00,996.0,0.0,9.99,6.57 -2016-12-02 00:00:00,955.0,0.0,9.85,6.88 -2016-12-02 01:00:00,937.0,0.0,9.69,6.85 -2016-12-02 02:00:00,928.0,0.0,9.53,6.81 -2016-12-02 03:00:00,991.0,0.0,9.37,6.77 -2016-12-02 04:00:00,976.0,0.0,9.32,7.0 -2016-12-02 05:00:00,932.0,0.0,9.27,7.22 -2016-12-02 06:00:00,937.0,0.0,9.22,7.45 -2016-12-02 07:00:00,1007.0,0.0,9.23,7.63 -2016-12-02 08:00:00,1054.0,0.0,9.25,7.82 -2016-12-02 09:00:00,1036.0,71.65,9.27,8.0 -2016-12-02 10:00:00,993.0,131.75,9.39,7.94 -2016-12-02 11:00:00,946.0,116.21,9.52,7.87 -2016-12-02 12:00:00,877.0,99.63,9.65,7.81 -2016-12-02 13:00:00,888.0,323.0,9.8,7.81 -2016-12-02 14:00:00,919.0,214.17,9.94,7.82 -2016-12-02 15:00:00,931.0,138.64,10.08,7.82 -2016-12-02 16:00:00,865.0,0.0,10.03,8.05 -2016-12-02 17:00:00,872.0,0.0,9.99,8.27 -2016-12-02 18:00:00,989.0,0.0,9.94,8.5 -2016-12-02 19:00:00,1065.0,0.0,9.78,8.6 -2016-12-02 20:00:00,1043.0,0.0,9.62,8.71 -2016-12-02 21:00:00,1029.0,0.0,9.46,8.81 -2016-12-02 22:00:00,1133.0,0.0,9.23,8.86 -2016-12-02 23:00:00,1079.0,0.0,9.0,8.91 -2016-12-03 00:00:00,1058.0,0.0,8.77,8.95 -2016-12-03 01:00:00,1016.0,0.0,8.56,9.11 -2016-12-03 02:00:00,1017.0,0.0,8.35,9.27 -2016-12-03 03:00:00,1042.0,0.0,8.15,9.43 -2016-12-03 04:00:00,1060.0,0.0,8.16,9.66 -2016-12-03 05:00:00,978.0,0.0,8.17,9.88 -2016-12-03 06:00:00,967.0,0.0,8.18,10.1 -2016-12-03 07:00:00,1010.0,0.0,8.25,10.39 -2016-12-03 08:00:00,1048.0,0.0,8.32,10.68 -2016-12-03 09:00:00,1088.0,44.26,8.4,10.98 -2016-12-03 10:00:00,1105.0,53.48,8.51,11.29 -2016-12-03 11:00:00,1031.0,69.25,8.63,11.6 -2016-12-03 12:00:00,951.0,53.53,8.75,11.92 -2016-12-03 13:00:00,940.0,78.31,9.12,11.67 -2016-12-03 14:00:00,988.0,310.32,9.49,11.43 -2016-12-03 15:00:00,1012.0,59.59,9.86,11.19 -2016-12-03 16:00:00,980.0,0.0,9.93,11.21 -2016-12-03 17:00:00,972.0,0.0,9.99,11.24 -2016-12-03 18:00:00,1042.0,0.0,10.06,11.27 -2016-12-03 19:00:00,1096.0,0.0,9.94,11.39 -2016-12-03 20:00:00,1078.0,0.0,9.82,11.51 -2016-12-03 21:00:00,1074.0,0.0,9.69,11.63 -2016-12-03 22:00:00,1162.0,0.0,9.49,11.48 -2016-12-03 23:00:00,1121.0,0.0,9.28,11.34 -2016-12-04 00:00:00,1055.0,0.0,9.08,11.2 -2016-12-04 01:00:00,1057.0,0.0,8.87,10.74 -2016-12-04 02:00:00,1036.0,0.0,8.67,10.27 -2016-12-04 03:00:00,1059.0,0.0,8.47,9.81 -2016-12-04 04:00:00,1072.0,0.0,8.44,9.76 -2016-12-04 05:00:00,1012.0,0.0,8.41,9.71 -2016-12-04 06:00:00,1005.0,0.0,8.38,9.66 -2016-12-04 07:00:00,1043.0,0.0,8.47,9.77 -2016-12-04 08:00:00,1092.0,0.0,8.57,9.88 -2016-12-04 09:00:00,1142.0,32.76,8.67,9.99 -2016-12-04 10:00:00,1124.0,114.26,9.01,9.67 -2016-12-04 11:00:00,1124.0,51.03,9.35,9.36 -2016-12-04 12:00:00,1108.0,281.41,9.69,9.05 -2016-12-04 13:00:00,1128.0,534.6,10.35,8.68 -2016-12-04 14:00:00,1155.0,62.14,11.0,8.3 -2016-12-04 15:00:00,1116.0,130.3,11.66,7.93 -2016-12-04 16:00:00,1023.0,0.0,11.75,7.8 -2016-12-04 17:00:00,979.0,0.0,11.84,7.67 -2016-12-04 18:00:00,1037.0,0.0,11.93,7.54 -2016-12-04 19:00:00,1075.0,0.0,11.93,7.57 -2016-12-04 20:00:00,1065.0,0.0,11.93,7.59 -2016-12-04 21:00:00,1012.0,0.0,11.92,7.61 -2016-12-04 22:00:00,1115.0,0.0,12.0,7.52 -2016-12-04 23:00:00,1072.0,0.0,12.08,7.42 -2016-12-05 00:00:00,1009.0,0.0,12.16,7.32 -2016-12-05 01:00:00,971.0,0.0,12.39,7.12 -2016-12-05 02:00:00,974.0,0.0,12.62,6.91 -2016-12-05 03:00:00,999.0,0.0,12.86,6.7 -2016-12-05 04:00:00,1005.0,0.0,12.9,6.56 -2016-12-05 05:00:00,944.0,0.0,12.94,6.42 -2016-12-05 06:00:00,923.0,0.0,12.98,6.28 -2016-12-05 07:00:00,972.0,0.0,13.0,6.3 -2016-12-05 08:00:00,1018.0,0.0,13.02,6.32 -2016-12-05 09:00:00,1056.0,13.16,13.04,6.34 -2016-12-05 10:00:00,1032.0,19.75,13.07,6.28 -2016-12-05 11:00:00,969.0,41.82,13.11,6.21 -2016-12-05 12:00:00,930.0,27.35,13.15,6.14 -2016-12-05 13:00:00,940.0,28.04,13.2,5.57 -2016-12-05 14:00:00,1070.0,18.38,13.25,5.0 -2016-12-05 15:00:00,1079.0,23.24,13.3,4.43 -2016-12-05 16:00:00,997.0,0.0,13.18,3.97 -2016-12-05 17:00:00,968.0,0.0,13.07,3.52 -2016-12-05 18:00:00,1001.0,0.0,12.95,3.06 -2016-12-05 19:00:00,1009.0,0.0,12.86,3.3 -2016-12-05 20:00:00,982.0,0.0,12.77,3.54 -2016-12-05 21:00:00,952.0,0.0,12.67,3.78 -2016-12-05 22:00:00,1042.0,0.0,12.64,3.86 -2016-12-05 23:00:00,976.0,0.0,12.61,3.94 -2016-12-06 00:00:00,919.0,0.0,12.58,4.01 -2016-12-06 01:00:00,897.0,0.0,12.52,4.02 -2016-12-06 02:00:00,861.0,0.0,12.46,4.02 -2016-12-06 03:00:00,884.0,0.0,12.41,4.03 -2016-12-06 04:00:00,892.0,0.0,12.42,4.21 -2016-12-06 05:00:00,806.0,0.0,12.44,4.4 -2016-12-06 06:00:00,790.0,0.0,12.45,4.58 -2016-12-06 07:00:00,848.0,0.0,12.49,4.97 -2016-12-06 08:00:00,905.0,0.0,12.53,5.36 -2016-12-06 09:00:00,937.0,37.15,12.57,5.75 -2016-12-06 10:00:00,919.0,59.37,12.71,5.94 -2016-12-06 11:00:00,954.0,73.72,12.86,6.14 -2016-12-06 12:00:00,934.0,132.4,13.01,6.33 -2016-12-06 13:00:00,956.0,194.85,12.71,6.81 -2016-12-06 14:00:00,1020.0,69.68,12.41,7.29 -2016-12-06 15:00:00,1008.0,99.24,12.11,7.77 -2016-12-06 16:00:00,933.0,0.0,12.3,7.79 -2016-12-06 17:00:00,879.0,0.0,12.48,7.82 -2016-12-06 18:00:00,928.0,0.0,12.67,7.85 -2016-12-06 19:00:00,981.0,0.0,12.8,7.95 -2016-12-06 20:00:00,947.0,0.0,12.94,8.06 -2016-12-06 21:00:00,901.0,0.0,13.07,8.17 -2016-12-06 22:00:00,1009.0,0.0,13.17,8.33 -2016-12-06 23:00:00,923.0,0.0,13.26,8.5 -2016-12-07 00:00:00,852.0,0.0,13.36,8.66 -2016-12-07 01:00:00,817.0,0.0,13.13,9.06 -2016-12-07 02:00:00,792.0,0.0,12.91,9.45 -2016-12-07 03:00:00,827.0,0.0,12.69,9.85 -2016-12-07 04:00:00,838.0,0.0,12.91,9.99 -2016-12-07 05:00:00,777.0,0.0,13.14,10.13 -2016-12-07 06:00:00,764.0,0.0,13.36,10.28 -2016-12-07 07:00:00,814.0,0.0,13.54,10.26 -2016-12-07 08:00:00,892.0,0.0,13.71,10.25 -2016-12-07 09:00:00,884.0,183.77,13.89,10.23 -2016-12-07 10:00:00,872.0,159.18,13.88,10.06 -2016-12-07 11:00:00,796.0,302.9,13.88,9.88 -2016-12-07 12:00:00,769.0,470.48,13.87,9.71 -2016-12-07 13:00:00,830.0,251.61,13.86,9.55 -2016-12-07 14:00:00,886.0,92.02,13.84,9.39 -2016-12-07 15:00:00,880.0,107.85,13.83,9.23 -2016-12-07 16:00:00,835.0,0.0,13.8,8.98 -2016-12-07 17:00:00,809.0,0.0,13.76,8.74 -2016-12-07 18:00:00,866.0,0.0,13.73,8.5 -2016-12-07 19:00:00,905.0,0.0,13.69,8.43 -2016-12-07 20:00:00,864.0,0.0,13.66,8.37 -2016-12-07 21:00:00,833.0,0.0,13.62,8.3 -2016-12-07 22:00:00,931.0,0.0,13.63,8.59 -2016-12-07 23:00:00,871.0,0.0,13.63,8.88 -2016-12-08 00:00:00,789.0,0.0,13.64,9.17 -2016-12-08 01:00:00,794.0,0.0,13.79,9.84 -2016-12-08 02:00:00,764.0,0.0,13.94,10.51 -2016-12-08 03:00:00,795.0,0.0,14.1,11.19 -2016-12-08 04:00:00,793.0,0.0,14.14,10.98 -2016-12-08 05:00:00,708.0,0.0,14.18,10.77 -2016-12-08 06:00:00,698.0,0.0,14.23,10.57 -2016-12-08 07:00:00,747.0,0.0,14.19,9.94 -2016-12-08 08:00:00,829.0,0.0,14.16,9.31 -2016-12-08 09:00:00,816.0,90.13,14.13,8.69 -2016-12-08 10:00:00,813.0,53.07,14.01,8.25 -2016-12-08 11:00:00,812.0,34.39,13.9,7.81 -2016-12-08 12:00:00,802.0,71.25,13.78,7.37 -2016-12-08 13:00:00,868.0,19.0,13.86,7.3 -2016-12-08 14:00:00,913.0,7.03,13.95,7.24 -2016-12-08 15:00:00,902.0,151.11,14.03,7.17 -2016-12-08 16:00:00,870.0,0.0,14.05,7.28 -2016-12-08 17:00:00,815.0,0.0,14.07,7.38 -2016-12-08 18:00:00,849.0,0.0,14.09,7.49 -2016-12-08 19:00:00,889.0,0.0,14.08,7.82 -2016-12-08 20:00:00,839.0,0.0,14.08,8.14 -2016-12-08 21:00:00,799.0,0.0,14.07,8.47 -2016-12-08 22:00:00,903.0,0.0,14.03,8.64 -2016-12-08 23:00:00,845.0,0.0,13.98,8.82 -2016-12-09 00:00:00,767.0,0.0,13.94,8.99 -2016-12-09 01:00:00,742.0,0.0,13.9,9.44 -2016-12-09 02:00:00,730.0,0.0,13.86,9.89 -2016-12-09 03:00:00,784.0,0.0,13.83,10.34 -2016-12-09 04:00:00,782.0,0.0,13.77,10.12 -2016-12-09 05:00:00,714.0,0.0,13.72,9.9 -2016-12-09 06:00:00,708.0,0.0,13.66,9.68 -2016-12-09 07:00:00,773.0,0.0,13.65,9.57 -2016-12-09 08:00:00,818.0,0.0,13.63,9.46 -2016-12-09 09:00:00,818.0,90.52,13.62,9.35 -2016-12-09 10:00:00,783.0,126.68,13.64,9.34 -2016-12-09 11:00:00,760.0,392.98,13.67,9.32 -2016-12-09 12:00:00,721.0,347.59,13.69,9.31 -2016-12-09 13:00:00,738.0,237.24,13.68,9.37 -2016-12-09 14:00:00,830.0,177.3,13.66,9.43 -2016-12-09 15:00:00,874.0,4.29,13.64,9.49 -2016-12-09 16:00:00,801.0,0.0,13.71,9.57 -2016-12-09 17:00:00,764.0,0.0,13.77,9.66 -2016-12-09 18:00:00,830.0,0.0,13.84,9.74 -2016-12-09 19:00:00,863.0,0.0,13.86,9.76 -2016-12-09 20:00:00,837.0,0.0,13.89,9.78 -2016-12-09 21:00:00,825.0,0.0,13.91,9.81 -2016-12-09 22:00:00,922.0,0.0,13.91,9.71 -2016-12-09 23:00:00,840.0,0.0,13.9,9.62 -2016-12-10 00:00:00,781.0,0.0,13.9,9.53 -2016-12-10 01:00:00,747.0,0.0,13.86,9.01 -2016-12-10 02:00:00,717.0,0.0,13.83,8.48 -2016-12-10 03:00:00,768.0,0.0,13.8,7.96 -2016-12-10 04:00:00,776.0,0.0,13.83,8.16 -2016-12-10 05:00:00,691.0,0.0,13.86,8.36 -2016-12-10 06:00:00,675.0,0.0,13.89,8.57 -2016-12-10 07:00:00,710.0,0.0,13.94,8.86 -2016-12-10 08:00:00,764.0,0.0,13.99,9.16 -2016-12-10 09:00:00,818.0,18.68,14.05,9.46 -2016-12-10 10:00:00,832.0,57.97,14.0,9.55 -2016-12-10 11:00:00,845.0,64.59,13.96,9.65 -2016-12-10 12:00:00,850.0,38.04,13.92,9.74 -2016-12-10 13:00:00,897.0,15.04,13.91,9.81 -2016-12-10 14:00:00,933.0,64.03,13.89,9.88 -2016-12-10 15:00:00,918.0,61.0,13.88,9.94 -2016-12-10 16:00:00,873.0,0.0,13.75,8.9 -2016-12-10 17:00:00,845.0,0.0,13.61,7.86 -2016-12-10 18:00:00,888.0,0.0,13.48,6.81 -2016-12-10 19:00:00,899.0,0.0,13.27,6.04 -2016-12-10 20:00:00,858.0,0.0,13.07,5.26 -2016-12-10 21:00:00,824.0,0.0,12.86,4.48 -2016-12-10 22:00:00,925.0,0.0,12.71,4.84 -2016-12-10 23:00:00,860.0,0.0,12.55,5.2 -2016-12-11 00:00:00,819.0,0.0,12.4,5.56 -2016-12-11 01:00:00,769.0,0.0,12.37,4.76 -2016-12-11 02:00:00,735.0,0.0,12.34,3.97 -2016-12-11 03:00:00,791.0,0.0,12.32,3.17 -2016-12-11 04:00:00,794.0,0.0,12.31,3.45 -2016-12-11 05:00:00,714.0,0.0,12.3,3.72 -2016-12-11 06:00:00,693.0,0.0,12.28,4.0 -2016-12-11 07:00:00,720.0,0.0,12.29,4.33 -2016-12-11 08:00:00,790.0,0.0,12.3,4.66 -2016-12-11 09:00:00,841.0,192.25,12.31,4.99 -2016-12-11 10:00:00,854.0,377.69,12.29,5.24 -2016-12-11 11:00:00,851.0,513.39,12.28,5.49 -2016-12-11 12:00:00,867.0,71.0,12.27,5.74 -2016-12-11 13:00:00,872.0,68.04,12.41,5.79 -2016-12-11 14:00:00,946.0,28.14,12.55,5.85 -2016-12-11 15:00:00,973.0,76.23,12.69,5.9 -2016-12-11 16:00:00,870.0,0.0,12.85,6.0 -2016-12-11 17:00:00,826.0,0.0,13.01,6.09 -2016-12-11 18:00:00,852.0,0.0,13.17,6.18 -2016-12-11 19:00:00,866.0,0.0,13.24,6.35 -2016-12-11 20:00:00,851.0,0.0,13.32,6.52 -2016-12-11 21:00:00,819.0,0.0,13.39,6.69 -2016-12-11 22:00:00,929.0,0.0,13.36,7.13 -2016-12-11 23:00:00,867.0,0.0,13.33,7.56 -2016-12-12 00:00:00,773.0,0.0,13.3,8.0 -2016-12-12 01:00:00,717.0,0.0,13.2,8.4 -2016-12-12 02:00:00,720.0,0.0,13.1,8.79 -2016-12-12 03:00:00,736.0,0.0,13.0,9.19 -2016-12-12 04:00:00,741.0,0.0,12.92,8.91 -2016-12-12 05:00:00,670.0,0.0,12.85,8.63 -2016-12-12 06:00:00,665.0,0.0,12.77,8.36 -2016-12-12 07:00:00,716.0,0.0,12.74,7.88 -2016-12-12 08:00:00,769.0,0.0,12.71,7.4 -2016-12-12 09:00:00,814.0,17.54,12.68,6.92 -2016-12-12 10:00:00,831.0,39.83,12.63,6.78 -2016-12-12 11:00:00,833.0,52.18,12.58,6.64 -2016-12-12 12:00:00,844.0,140.46,12.53,6.5 -2016-12-12 13:00:00,835.0,201.3,12.64,6.53 -2016-12-12 14:00:00,894.0,107.46,12.74,6.57 -2016-12-12 15:00:00,842.0,145.72,12.85,6.61 -2016-12-12 16:00:00,792.0,0.0,12.92,6.25 -2016-12-12 17:00:00,767.0,0.0,13.0,5.9 -2016-12-12 18:00:00,830.0,0.0,13.07,5.54 -2016-12-12 19:00:00,850.0,0.0,13.07,5.88 -2016-12-12 20:00:00,800.0,0.0,13.07,6.21 -2016-12-12 21:00:00,780.0,0.0,13.07,6.54 -2016-12-12 22:00:00,899.0,0.0,13.05,6.91 -2016-12-12 23:00:00,830.0,0.0,13.02,7.28 -2016-12-13 00:00:00,768.0,0.0,13.0,7.66 -2016-12-13 01:00:00,727.0,0.0,13.24,8.06 -2016-12-13 02:00:00,696.0,0.0,13.48,8.47 -2016-12-13 03:00:00,738.0,0.0,13.72,8.88 -2016-12-13 04:00:00,733.0,0.0,13.62,8.91 -2016-12-13 05:00:00,655.0,0.0,13.52,8.94 -2016-12-13 06:00:00,649.0,0.0,13.43,8.97 -2016-12-13 07:00:00,692.0,0.0,13.36,9.07 -2016-12-13 08:00:00,755.0,0.0,13.3,9.17 -2016-12-13 09:00:00,781.0,81.62,13.24,9.27 -2016-12-13 10:00:00,789.0,67.45,13.18,9.39 -2016-12-13 11:00:00,782.0,143.14,13.13,9.51 -2016-12-13 12:00:00,814.0,42.62,13.08,9.63 -2016-12-13 13:00:00,827.0,73.23,13.12,9.78 -2016-12-13 14:00:00,893.0,82.38,13.17,9.93 -2016-12-13 15:00:00,876.0,188.04,13.21,10.08 -2016-12-13 16:00:00,790.0,0.0,13.22,10.04 -2016-12-13 17:00:00,770.0,0.0,13.23,10.0 -2016-12-13 18:00:00,826.0,0.0,13.24,9.96 -2016-12-13 19:00:00,873.0,0.0,13.19,10.17 -2016-12-13 20:00:00,858.0,0.0,13.14,10.38 -2016-12-13 21:00:00,834.0,0.0,13.08,10.59 -2016-12-13 22:00:00,926.0,0.0,13.06,10.64 -2016-12-13 23:00:00,845.0,0.0,13.05,10.69 -2016-12-14 00:00:00,805.0,0.0,13.03,10.74 -2016-12-14 01:00:00,798.0,0.0,12.99,10.48 -2016-12-14 02:00:00,780.0,0.0,12.95,10.21 -2016-12-14 03:00:00,814.0,0.0,12.91,9.94 -2016-12-14 04:00:00,822.0,0.0,12.95,9.98 -2016-12-14 05:00:00,748.0,0.0,13.0,10.01 -2016-12-14 06:00:00,737.0,0.0,13.05,10.04 -2016-12-14 07:00:00,806.0,0.0,13.13,10.08 -2016-12-14 08:00:00,866.0,0.0,13.21,10.12 -2016-12-14 09:00:00,874.0,5.93,13.3,10.17 -2016-12-14 10:00:00,856.0,12.52,13.16,9.28 -2016-12-14 11:00:00,801.0,13.81,13.02,8.39 -2016-12-14 12:00:00,824.0,26.69,12.88,7.5 -2016-12-14 13:00:00,880.0,79.38,12.8,7.25 -2016-12-14 14:00:00,936.0,26.71,12.71,7.0 -2016-12-14 15:00:00,923.0,140.91,12.63,6.74 -2016-12-14 16:00:00,842.0,0.0,12.6,5.98 -2016-12-14 17:00:00,815.0,0.0,12.58,5.21 -2016-12-14 18:00:00,851.0,0.0,12.55,4.44 -2016-12-14 19:00:00,885.0,0.0,12.44,4.18 -2016-12-14 20:00:00,861.0,0.0,12.33,3.92 -2016-12-14 21:00:00,820.0,0.0,12.22,3.66 -2016-12-14 22:00:00,913.0,0.0,12.16,4.34 -2016-12-14 23:00:00,846.0,0.0,12.11,5.03 -2016-12-15 00:00:00,800.0,0.0,12.05,5.72 -2016-12-15 01:00:00,775.0,0.0,12.11,6.01 -2016-12-15 02:00:00,746.0,0.0,12.17,6.3 -2016-12-15 03:00:00,819.0,0.0,12.23,6.59 -2016-12-15 04:00:00,797.0,0.0,12.23,6.51 -2016-12-15 05:00:00,724.0,0.0,12.23,6.43 -2016-12-15 06:00:00,722.0,0.0,12.23,6.34 -2016-12-15 07:00:00,772.0,0.0,12.23,6.78 -2016-12-15 08:00:00,841.0,0.0,12.22,7.22 -2016-12-15 09:00:00,863.0,187.69,12.22,7.66 -2016-12-15 10:00:00,864.0,385.56,12.18,7.97 -2016-12-15 11:00:00,812.0,72.72,12.15,8.29 -2016-12-15 12:00:00,821.0,70.47,12.11,8.61 -2016-12-15 13:00:00,865.0,39.1,12.17,8.85 -2016-12-15 14:00:00,933.0,100.62,12.22,9.08 -2016-12-15 15:00:00,948.0,72.7,12.28,9.32 -2016-12-15 16:00:00,884.0,0.0,12.34,9.83 -2016-12-15 17:00:00,847.0,0.0,12.4,10.34 -2016-12-15 18:00:00,906.0,0.0,12.46,10.84 -2016-12-15 19:00:00,942.0,0.0,12.49,11.13 -2016-12-15 20:00:00,920.0,0.0,12.52,11.41 -2016-12-15 21:00:00,874.0,0.0,12.55,11.7 -2016-12-15 22:00:00,978.0,0.0,12.62,11.48 -2016-12-15 23:00:00,898.0,0.0,12.68,11.27 -2016-12-16 00:00:00,821.0,0.0,12.75,11.06 -2016-12-16 01:00:00,819.0,0.0,12.79,10.7 -2016-12-16 02:00:00,787.0,0.0,12.83,10.34 -2016-12-16 03:00:00,808.0,0.0,12.87,9.97 -2016-12-16 04:00:00,824.0,0.0,12.79,9.1 -2016-12-16 05:00:00,747.0,0.0,12.71,8.23 -2016-12-16 06:00:00,734.0,0.0,12.64,7.37 -2016-12-16 07:00:00,763.0,0.0,12.6,6.94 -2016-12-16 08:00:00,827.0,0.0,12.56,6.52 -2016-12-16 09:00:00,841.0,1.42,12.53,6.1 -2016-12-16 10:00:00,874.0,23.23,12.53,5.75 -2016-12-16 11:00:00,858.0,67.19,12.54,5.4 -2016-12-16 12:00:00,850.0,205.69,12.54,5.05 -2016-12-16 13:00:00,855.0,331.16,12.47,4.38 -2016-12-16 14:00:00,859.0,203.84,12.4,3.71 -2016-12-16 15:00:00,830.0,211.44,12.33,3.03 -2016-12-16 16:00:00,784.0,0.0,12.3,2.64 -2016-12-16 17:00:00,798.0,0.0,12.27,2.24 -2016-12-16 18:00:00,854.0,0.0,12.24,1.85 -2016-12-16 19:00:00,866.0,0.0,12.15,1.67 -2016-12-16 20:00:00,833.0,0.0,12.07,1.49 -2016-12-16 21:00:00,800.0,0.0,11.98,1.31 -2016-12-16 22:00:00,917.0,0.0,11.9,1.48 -2016-12-16 23:00:00,855.0,0.0,11.83,1.65 -2016-12-17 00:00:00,822.0,0.0,11.75,1.82 -2016-12-17 01:00:00,791.0,0.0,11.67,2.19 -2016-12-17 02:00:00,751.0,0.0,11.59,2.57 -2016-12-17 03:00:00,793.0,0.0,11.52,2.94 -2016-12-17 04:00:00,793.0,0.0,11.49,2.72 -2016-12-17 05:00:00,737.0,0.0,11.47,2.5 -2016-12-17 06:00:00,711.0,0.0,11.44,2.28 -2016-12-17 07:00:00,757.0,0.0,11.4,2.08 -2016-12-17 08:00:00,805.0,0.0,11.35,1.88 -2016-12-17 09:00:00,847.0,167.87,11.31,1.68 -2016-12-17 10:00:00,866.0,111.04,11.33,1.53 -2016-12-17 11:00:00,874.0,127.58,11.36,1.37 -2016-12-17 12:00:00,906.0,250.67,11.39,1.21 -2016-12-17 13:00:00,942.0,217.73,11.36,1.56 -2016-12-17 14:00:00,985.0,99.48,11.33,1.9 -2016-12-17 15:00:00,989.0,56.89,11.3,2.25 -2016-12-17 16:00:00,955.0,0.0,11.36,2.32 -2016-12-17 17:00:00,935.0,0.0,11.41,2.4 -2016-12-17 18:00:00,966.0,0.0,11.47,2.47 -2016-12-17 19:00:00,986.0,0.0,11.41,2.3 -2016-12-17 20:00:00,957.0,0.0,11.36,2.14 -2016-12-17 21:00:00,928.0,0.0,11.3,1.97 -2016-12-17 22:00:00,1037.0,0.0,11.21,1.88 -2016-12-17 23:00:00,972.0,0.0,11.12,1.78 -2016-12-18 00:00:00,908.0,0.0,11.03,1.68 -2016-12-18 01:00:00,878.0,0.0,10.85,1.78 -2016-12-18 02:00:00,848.0,0.0,10.67,1.88 -2016-12-18 03:00:00,905.0,0.0,10.49,1.97 -2016-12-18 04:00:00,912.0,0.0,10.25,2.16 -2016-12-18 05:00:00,842.0,0.0,10.01,2.35 -2016-12-18 06:00:00,831.0,0.0,9.77,2.54 -2016-12-18 07:00:00,868.0,0.0,9.55,2.7 -2016-12-18 08:00:00,904.0,0.0,9.33,2.86 -2016-12-18 09:00:00,967.0,33.41,9.11,3.02 -2016-12-18 10:00:00,995.0,46.87,8.99,2.99 -2016-12-18 11:00:00,944.0,36.67,8.88,2.96 -2016-12-18 12:00:00,875.0,69.77,8.76,2.92 -2016-12-18 13:00:00,853.0,50.21,8.82,2.98 -2016-12-18 14:00:00,900.0,317.56,8.88,3.04 -2016-12-18 15:00:00,890.0,125.56,8.94,3.1 -2016-12-18 16:00:00,810.0,0.0,8.95,3.25 -2016-12-18 17:00:00,802.0,0.0,8.95,3.39 -2016-12-18 18:00:00,897.0,0.0,8.96,3.53 -2016-12-18 19:00:00,953.0,0.0,8.91,3.85 -2016-12-18 20:00:00,953.0,0.0,8.86,4.17 -2016-12-18 21:00:00,921.0,0.0,8.8,4.5 -2016-12-18 22:00:00,1014.0,0.0,8.71,3.93 -2016-12-18 23:00:00,956.0,0.0,8.61,3.36 -2016-12-19 00:00:00,910.0,0.0,8.52,2.79 -2016-12-19 01:00:00,906.0,0.0,8.54,2.97 -2016-12-19 02:00:00,876.0,0.0,8.57,3.15 -2016-12-19 03:00:00,915.0,0.0,8.6,3.34 -2016-12-19 04:00:00,918.0,0.0,8.74,3.48 -2016-12-19 05:00:00,846.0,0.0,8.88,3.61 -2016-12-19 06:00:00,829.0,0.0,9.02,3.75 -2016-12-19 07:00:00,889.0,0.0,9.36,4.15 -2016-12-19 08:00:00,948.0,0.0,9.71,4.54 -2016-12-19 09:00:00,980.0,52.98,10.06,4.94 -2016-12-19 10:00:00,1024.0,74.19,10.19,5.46 -2016-12-19 11:00:00,1062.0,63.47,10.33,5.99 -2016-12-19 12:00:00,1100.0,49.92,10.47,6.51 -2016-12-19 13:00:00,1097.0,34.24,10.52,6.91 -2016-12-19 14:00:00,1189.0,58.82,10.57,7.32 -2016-12-19 15:00:00,1204.0,29.88,10.62,7.72 -2016-12-19 16:00:00,1119.0,0.0,10.46,7.6 -2016-12-19 17:00:00,1091.0,0.0,10.29,7.49 -2016-12-19 18:00:00,1129.0,0.0,10.13,7.37 -2016-12-19 19:00:00,1161.0,0.0,9.93,6.9 -2016-12-19 20:00:00,1100.0,0.0,9.73,6.43 -2016-12-19 21:00:00,1059.0,0.0,9.53,5.96 -2016-12-19 22:00:00,1123.0,0.0,9.39,5.32 -2016-12-19 23:00:00,1040.0,0.0,9.24,4.69 -2016-12-20 00:00:00,969.0,0.0,9.09,4.06 -2016-12-20 01:00:00,973.0,0.0,9.25,4.12 -2016-12-20 02:00:00,923.0,0.0,9.41,4.18 -2016-12-20 03:00:00,944.0,0.0,9.58,4.25 -2016-12-20 04:00:00,950.0,0.0,9.44,3.72 -2016-12-20 05:00:00,878.0,0.0,9.3,3.19 -2016-12-20 06:00:00,878.0,0.0,9.16,2.66 -2016-12-20 07:00:00,934.0,0.0,9.19,2.55 -2016-12-20 08:00:00,983.0,0.0,9.21,2.44 -2016-12-20 09:00:00,1044.0,4.41,9.23,2.33 -2016-12-20 10:00:00,1100.0,20.11,9.37,2.64 -2016-12-20 11:00:00,1092.0,32.15,9.52,2.95 -2016-12-20 12:00:00,1115.0,24.26,9.66,3.26 -2016-12-20 13:00:00,1132.0,48.5,9.81,4.25 -2016-12-20 14:00:00,1210.0,63.77,9.96,5.25 -2016-12-20 15:00:00,1227.0,58.99,10.11,6.25 -2016-12-20 16:00:00,1150.0,0.0,10.16,6.52 -2016-12-20 17:00:00,1100.0,0.0,10.22,6.79 -2016-12-20 18:00:00,1168.0,0.0,10.27,7.06 -2016-12-20 19:00:00,1168.0,0.0,10.29,7.48 -2016-12-20 20:00:00,1133.0,0.0,10.31,7.9 -2016-12-20 21:00:00,1092.0,0.0,10.32,8.32 -2016-12-20 22:00:00,1149.0,0.0,10.4,9.47 -2016-12-20 23:00:00,1084.0,0.0,10.48,10.62 -2016-12-21 00:00:00,1030.0,0.0,10.56,11.77 -2016-12-21 01:00:00,1025.0,0.0,11.14,11.58 -2016-12-21 02:00:00,978.0,0.0,11.72,11.39 -2016-12-21 03:00:00,1000.0,0.0,12.3,11.2 -2016-12-21 04:00:00,1001.0,0.0,12.37,10.25 -2016-12-21 05:00:00,919.0,0.0,12.44,9.31 -2016-12-21 06:00:00,913.0,0.0,12.52,8.36 -2016-12-21 07:00:00,937.0,0.0,12.62,8.49 -2016-12-21 08:00:00,990.0,0.0,12.72,8.63 -2016-12-21 09:00:00,1016.0,62.65,12.82,8.76 -2016-12-21 10:00:00,1052.0,30.99,12.77,8.92 -2016-12-21 11:00:00,1065.0,68.94,12.73,9.08 -2016-12-21 12:00:00,1064.0,40.48,12.68,9.24 -2016-12-21 13:00:00,1085.0,93.44,12.59,7.94 -2016-12-21 14:00:00,1111.0,63.02,12.49,6.65 -2016-12-21 15:00:00,1094.0,31.77,12.4,5.35 -2016-12-21 16:00:00,1090.0,0.0,12.36,5.54 -2016-12-21 17:00:00,1070.0,0.0,12.31,5.72 -2016-12-21 18:00:00,1116.0,0.0,12.27,5.9 -2016-12-21 19:00:00,1131.0,0.0,12.13,5.46 -2016-12-21 20:00:00,1080.0,0.0,11.99,5.02 -2016-12-21 21:00:00,1012.0,0.0,11.84,4.58 -2016-12-21 22:00:00,1106.0,0.0,11.79,4.07 -2016-12-21 23:00:00,1032.0,0.0,11.73,3.57 -2016-12-22 00:00:00,978.0,0.0,11.68,3.06 -2016-12-22 01:00:00,952.0,0.0,11.61,2.83 -2016-12-22 02:00:00,902.0,0.0,11.55,2.59 -2016-12-22 03:00:00,932.0,0.0,11.49,2.36 -2016-12-22 04:00:00,930.0,0.0,11.5,2.26 -2016-12-22 05:00:00,858.0,0.0,11.51,2.16 -2016-12-22 06:00:00,854.0,0.0,11.52,2.06 -2016-12-22 07:00:00,895.0,0.0,11.54,1.91 -2016-12-22 08:00:00,953.0,0.0,11.55,1.76 -2016-12-22 09:00:00,1009.0,160.2,11.56,1.61 -2016-12-22 10:00:00,1031.0,132.42,11.54,1.87 -2016-12-22 11:00:00,985.0,194.38,11.53,2.12 -2016-12-22 12:00:00,912.0,407.97,11.52,2.37 -2016-12-22 13:00:00,904.0,246.02,11.59,3.42 -2016-12-22 14:00:00,922.0,232.17,11.65,4.46 -2016-12-22 15:00:00,946.0,31.85,11.71,5.5 -2016-12-22 16:00:00,899.0,0.0,11.81,5.4 -2016-12-22 17:00:00,917.0,0.0,11.9,5.3 -2016-12-22 18:00:00,1004.0,0.0,12.0,5.2 -2016-12-22 19:00:00,1066.0,0.0,11.95,4.98 -2016-12-22 20:00:00,1047.0,0.0,11.9,4.77 -2016-12-22 21:00:00,983.0,0.0,11.84,4.55 -2016-12-22 22:00:00,1083.0,0.0,11.85,4.97 -2016-12-22 23:00:00,1006.0,0.0,11.85,5.38 -2016-12-23 00:00:00,949.0,0.0,11.85,5.79 -2016-12-23 01:00:00,937.0,0.0,11.83,6.88 -2016-12-23 02:00:00,890.0,0.0,11.81,7.97 -2016-12-23 03:00:00,915.0,0.0,11.8,9.06 -2016-12-23 04:00:00,935.0,0.0,11.84,9.83 -2016-12-23 05:00:00,869.0,0.0,11.89,10.6 -2016-12-23 06:00:00,853.0,0.0,11.94,11.37 -2016-12-23 07:00:00,921.0,0.0,12.01,11.7 -2016-12-23 08:00:00,969.0,0.0,12.09,12.03 -2016-12-23 09:00:00,996.0,41.4,12.17,12.36 -2016-12-23 10:00:00,1025.0,126.53,12.31,12.36 -2016-12-23 11:00:00,996.0,112.22,12.45,12.36 -2016-12-23 12:00:00,1032.0,37.58,12.59,12.36 -2016-12-23 13:00:00,1027.0,49.49,12.66,12.86 -2016-12-23 14:00:00,1071.0,36.1,12.74,13.35 -2016-12-23 15:00:00,1076.0,52.86,12.81,13.85 -2016-12-23 16:00:00,1027.0,0.0,12.9,13.25 -2016-12-23 17:00:00,978.0,0.0,12.98,12.64 -2016-12-23 18:00:00,1033.0,0.0,13.07,12.04 -2016-12-23 19:00:00,1054.0,0.0,13.02,10.95 -2016-12-23 20:00:00,1028.0,0.0,12.97,9.85 -2016-12-23 21:00:00,1013.0,0.0,12.91,8.76 -2016-12-23 22:00:00,1066.0,0.0,12.62,8.51 -2016-12-23 23:00:00,1001.0,0.0,12.34,8.27 -2016-12-24 00:00:00,947.0,0.0,12.05,8.03 -2016-12-24 01:00:00,905.0,0.0,11.9,8.03 -2016-12-24 02:00:00,913.0,0.0,11.75,8.03 -2016-12-24 03:00:00,930.0,0.0,11.6,8.03 -2016-12-24 04:00:00,960.0,0.0,11.44,8.05 -2016-12-24 05:00:00,897.0,0.0,11.29,8.07 -2016-12-24 06:00:00,867.0,0.0,11.14,8.1 -2016-12-24 07:00:00,912.0,0.0,11.07,7.99 -2016-12-24 08:00:00,969.0,0.0,11.0,7.88 -2016-12-24 09:00:00,1029.0,30.73,10.93,7.78 -2016-12-24 10:00:00,1045.0,126.08,11.0,7.91 -2016-12-24 11:00:00,1051.0,241.12,11.07,8.04 -2016-12-24 12:00:00,998.0,363.5,11.14,8.17 -2016-12-24 13:00:00,1008.0,288.41,11.28,8.76 -2016-12-24 14:00:00,1020.0,201.03,11.41,9.35 -2016-12-24 15:00:00,1017.0,185.71,11.55,9.94 -2016-12-24 16:00:00,998.0,0.0,11.65,10.04 -2016-12-24 17:00:00,992.0,0.0,11.74,10.13 -2016-12-24 18:00:00,1100.0,0.0,11.84,10.22 -2016-12-24 19:00:00,1119.0,0.0,12.01,10.11 -2016-12-24 20:00:00,1075.0,0.0,12.19,10.01 -2016-12-24 21:00:00,1024.0,0.0,12.36,9.9 -2016-12-24 22:00:00,1100.0,0.0,12.43,9.61 -2016-12-24 23:00:00,1059.0,0.0,12.49,9.31 -2016-12-25 00:00:00,999.0,0.0,12.55,9.02 -2016-12-25 01:00:00,921.0,0.0,12.48,8.93 -2016-12-25 02:00:00,899.0,0.0,12.41,8.84 -2016-12-25 03:00:00,907.0,0.0,12.35,8.74 -2016-12-25 04:00:00,901.0,0.0,12.43,8.88 -2016-12-25 05:00:00,840.0,0.0,12.52,9.02 -2016-12-25 06:00:00,807.0,0.0,12.6,9.16 -2016-12-25 07:00:00,825.0,0.0,12.68,9.1 -2016-12-25 08:00:00,843.0,0.0,12.75,9.05 -2016-12-25 09:00:00,895.0,13.95,12.82,8.99 -2016-12-25 10:00:00,951.0,63.59,12.74,9.03 -2016-12-25 11:00:00,973.0,142.34,12.67,9.07 -2016-12-25 12:00:00,961.0,222.88,12.59,9.1 -2016-12-25 13:00:00,984.0,88.83,12.46,9.18 -2016-12-25 14:00:00,988.0,129.53,12.33,9.26 -2016-12-25 15:00:00,980.0,97.03,12.2,9.34 -2016-12-25 16:00:00,910.0,0.0,12.29,9.42 -2016-12-25 17:00:00,872.0,0.0,12.38,9.49 -2016-12-25 18:00:00,917.0,0.0,12.47,9.57 -2016-12-25 19:00:00,929.0,0.0,12.5,9.8 -2016-12-25 20:00:00,930.0,0.0,12.53,10.02 -2016-12-25 21:00:00,898.0,0.0,12.55,10.25 -2016-12-25 22:00:00,981.0,0.0,12.59,10.51 -2016-12-25 23:00:00,928.0,0.0,12.63,10.76 -2016-12-26 00:00:00,868.0,0.0,12.67,11.02 -2016-12-26 01:00:00,865.0,0.0,12.5,10.08 -2016-12-26 02:00:00,828.0,0.0,12.33,9.14 -2016-12-26 03:00:00,860.0,0.0,12.16,8.21 -2016-12-26 04:00:00,874.0,0.0,11.96,8.4 -2016-12-26 05:00:00,815.0,0.0,11.76,8.58 -2016-12-26 06:00:00,804.0,0.0,11.56,8.77 -2016-12-26 07:00:00,840.0,0.0,11.48,8.36 -2016-12-26 08:00:00,888.0,0.0,11.4,7.94 -2016-12-26 09:00:00,954.0,126.86,11.33,7.53 -2016-12-26 10:00:00,954.0,289.66,11.23,7.6 -2016-12-26 11:00:00,933.0,438.63,11.13,7.67 -2016-12-26 12:00:00,982.0,189.16,11.03,7.74 -2016-12-26 13:00:00,959.0,216.46,10.99,6.89 -2016-12-26 14:00:00,1024.0,294.84,10.96,6.05 -2016-12-26 15:00:00,1051.0,244.24,10.92,5.2 -2016-12-26 16:00:00,975.0,0.0,10.86,4.96 -2016-12-26 17:00:00,975.0,0.0,10.79,4.72 -2016-12-26 18:00:00,1087.0,0.0,10.73,4.48 -2016-12-26 19:00:00,1165.0,0.0,10.61,4.44 -2016-12-26 20:00:00,1125.0,0.0,10.5,4.4 -2016-12-26 21:00:00,1085.0,0.0,10.38,4.36 -2016-12-26 22:00:00,1159.0,0.0,10.26,4.89 -2016-12-26 23:00:00,1088.0,0.0,10.14,5.43 -2016-12-27 00:00:00,1074.0,0.0,10.02,5.96 -2016-12-27 01:00:00,1024.0,0.0,9.73,6.4 -2016-12-27 02:00:00,1010.0,0.0,9.45,6.84 -2016-12-27 03:00:00,1049.0,0.0,9.17,7.28 -2016-12-27 04:00:00,1076.0,0.0,9.1,7.31 -2016-12-27 05:00:00,1022.0,0.0,9.03,7.34 -2016-12-27 06:00:00,1009.0,0.0,8.97,7.37 -2016-12-27 07:00:00,1072.0,0.0,8.94,7.51 -2016-12-27 08:00:00,1144.0,0.0,8.91,7.65 -2016-12-27 09:00:00,1245.0,53.09,8.89,7.79 -2016-12-27 10:00:00,1281.0,54.65,8.87,7.94 -2016-12-27 11:00:00,1235.0,179.74,8.86,8.1 -2016-12-27 12:00:00,1279.0,232.33,8.85,8.25 -2016-12-27 13:00:00,1287.0,179.6,8.9,8.13 -2016-12-27 14:00:00,1334.0,89.61,8.95,8.01 -2016-12-27 15:00:00,1332.0,103.82,9.0,7.89 -2016-12-27 16:00:00,1238.0,0.0,9.14,7.95 -2016-12-27 17:00:00,1198.0,0.0,9.29,8.02 -2016-12-27 18:00:00,1297.0,0.0,9.43,8.08 -2016-12-27 19:00:00,1421.0,0.0,9.44,8.29 -2016-12-27 20:00:00,1410.0,0.0,9.45,8.51 -2016-12-27 21:00:00,1352.0,0.0,9.45,8.72 -2016-12-27 22:00:00,1431.0,0.0,9.4,8.77 -2016-12-27 23:00:00,1338.0,0.0,9.34,8.83 -2016-12-28 00:00:00,1282.0,0.0,9.29,8.88 -2016-12-28 01:00:00,1273.0,0.0,9.28,8.49 -2016-12-28 02:00:00,1256.0,0.0,9.27,8.09 -2016-12-28 03:00:00,1296.0,0.0,9.27,7.7 -2016-12-28 04:00:00,1308.0,0.0,9.22,7.56 -2016-12-28 05:00:00,1233.0,0.0,9.17,7.42 -2016-12-28 06:00:00,1219.0,0.0,9.12,7.28 -2016-12-28 07:00:00,1254.0,0.0,9.14,7.29 -2016-12-28 08:00:00,1339.0,0.0,9.15,7.3 -2016-12-28 09:00:00,1467.0,103.79,9.17,7.31 -2016-12-28 10:00:00,1422.0,320.97,9.33,7.08 -2016-12-28 11:00:00,1319.0,497.33,9.5,6.85 -2016-12-28 12:00:00,1326.0,542.54,9.67,6.62 -2016-12-28 13:00:00,1295.0,258.75,10.1,5.9 -2016-12-28 14:00:00,1321.0,137.96,10.52,5.18 -2016-12-28 15:00:00,1339.0,68.99,10.95,4.46 -2016-12-28 16:00:00,1294.0,0.0,10.95,4.21 -2016-12-28 17:00:00,1277.0,0.0,10.94,3.96 -2016-12-28 18:00:00,1451.0,0.0,10.94,3.71 -2016-12-28 19:00:00,1566.0,0.0,10.88,3.69 -2016-12-28 20:00:00,1554.0,0.0,10.82,3.67 -2016-12-28 21:00:00,1456.0,0.0,10.75,3.66 -2016-12-28 22:00:00,1529.0,0.0,10.73,3.37 -2016-12-28 23:00:00,1442.0,0.0,10.72,3.09 -2016-12-29 00:00:00,1394.0,0.0,10.7,2.8 -2016-12-29 01:00:00,1358.0,0.0,10.22,2.23 -2016-12-29 02:00:00,1314.0,0.0,9.74,1.67 -2016-12-29 03:00:00,1326.0,0.0,9.26,1.1 -2016-12-29 04:00:00,1333.0,0.0,9.28,1.43 -2016-12-29 05:00:00,1259.0,0.0,9.3,1.75 -2016-12-29 06:00:00,1231.0,0.0,9.31,2.07 -2016-12-29 07:00:00,1261.0,0.0,9.37,2.08 -2016-12-29 08:00:00,1376.0,0.0,9.43,2.1 -2016-12-29 09:00:00,1506.0,25.66,9.49,2.11 -2016-12-29 10:00:00,1492.0,65.0,9.53,2.24 -2016-12-29 11:00:00,1423.0,469.57,9.57,2.38 -2016-12-29 12:00:00,1499.0,535.56,9.61,2.51 -2016-12-29 13:00:00,1492.0,519.5,9.51,2.98 -2016-12-29 14:00:00,1490.0,450.26,9.4,3.45 -2016-12-29 15:00:00,1480.0,300.83,9.29,3.92 -2016-12-29 16:00:00,1429.0,0.0,9.33,3.81 -2016-12-29 17:00:00,1370.0,0.0,9.38,3.7 -2016-12-29 18:00:00,1469.0,0.0,9.42,3.59 -2016-12-29 19:00:00,1540.0,0.0,9.42,3.45 -2016-12-29 20:00:00,1529.0,0.0,9.42,3.32 -2016-12-29 21:00:00,1464.0,0.0,9.41,3.19 -2016-12-29 22:00:00,1515.0,0.0,9.39,3.15 -2016-12-29 23:00:00,1453.0,0.0,9.37,3.12 -2016-12-30 00:00:00,1379.0,0.0,9.35,3.09 -2016-12-30 01:00:00,1304.0,0.0,9.49,2.9 -2016-12-30 02:00:00,1274.0,0.0,9.63,2.7 -2016-12-30 03:00:00,1309.0,0.0,9.78,2.51 -2016-12-30 04:00:00,1308.0,0.0,9.8,2.44 -2016-12-30 05:00:00,1246.0,0.0,9.82,2.37 -2016-12-30 06:00:00,1218.0,0.0,9.85,2.3 -2016-12-30 07:00:00,1315.0,0.0,9.78,2.24 -2016-12-30 08:00:00,1398.0,0.0,9.72,2.17 -2016-12-30 09:00:00,1479.0,149.17,9.66,2.11 -2016-12-30 10:00:00,1472.0,340.16,9.63,2.26 -2016-12-30 11:00:00,1448.0,259.51,9.6,2.41 -2016-12-30 12:00:00,1348.0,492.09,9.57,2.57 -2016-12-30 13:00:00,1450.0,537.16,9.55,2.83 -2016-12-30 14:00:00,1479.0,451.31,9.53,3.1 -2016-12-30 15:00:00,1508.0,306.78,9.51,3.37 -2016-12-30 16:00:00,1410.0,0.0,9.41,3.54 -2016-12-30 17:00:00,1370.0,0.0,9.3,3.72 -2016-12-30 18:00:00,1467.0,0.0,9.2,3.9 -2016-12-30 19:00:00,1587.0,0.0,9.11,3.92 -2016-12-30 20:00:00,1561.0,0.0,9.02,3.93 -2016-12-30 21:00:00,1472.0,0.0,8.92,3.94 -2016-12-30 22:00:00,1533.0,0.0,8.85,3.93 -2016-12-30 23:00:00,1483.0,0.0,8.78,3.91 diff --git a/examples/new_examples/data/SOURCES.md b/examples/new_examples/data/SOURCES.md deleted file mode 100644 index a5c85af..0000000 --- a/examples/new_examples/data/SOURCES.md +++ /dev/null @@ -1,26 +0,0 @@ -# Sources for Ouessant load, PV, wind, temperature data - -Ouessant data 2016 (trimmed at 365 days), by Pierre Haessig, April 2021 - -## Load data - -Load data from EDF https://opendata-iles-ponant.edf.fr/, -collected and cleaned by Benjamin Bonvalet & Mohamed Chtiba, -in 2nd year Supélec student project 2018-2019. - -Unit: kW - -File `Ouessant_load_2016_filled_365j.csv` created by P. Haessig for HOMER ETS demo, 2019 -in `~/Travail/46 Séminaires/Efficient Tools Seminar/ETS HOMER/Conso Ouessant` - -## PV, wind, temperature data - -PV (W/kWp, 40° slope, 0° azimuth), Wind speed (m/s at 10 m) -and temperature (°C at 2 m) from PVGIS (c) European Communities, 2001-2021. - -File PVGIS_Ouessant_40deg_2013_2016.csv.xz created by P. Haessig, 2021, -in `~/Travail/11 Solar data/PVGIS/Ouessant hourly` - -## Merge - -Done by Python script `merge_PVGIS_load_data.py`. diff --git a/examples/new_examples/df2_dataset.csv b/examples/new_examples/df2_dataset.csv deleted file mode 100644 index 73655b7..0000000 --- a/examples/new_examples/df2_dataset.csv +++ /dev/null @@ -1,351 +0,0 @@ -fc_elyz_capex,shed_max,dispatch,algo,srand,ret,lcoe_opt,shed_rate_opt,capex,opex,xopt_sto,xopt_pv,xopt_wind,xopt_elyz,xopt_fc,xopt_hyTank -1000.0,0.0,1,GN_DIRECT,1,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 -1000.0,0.0,1,GN_DIRECT,2,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 -1000.0,0.0,1,GN_DIRECT,3,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 -1000.0,0.0,1,GN_DIRECT,4,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 -1000.0,0.0,1,GN_DIRECT,5,MAXEVAL_REACHED,0.5924741964061354,0.0,4.3144954107243314e7,1.2408565316591173e7,12433.7,20904.0,3213.75,140.59,1517.33,2904.4 -1000.0,0.0,1,GN_CRS2_LM,1,XTOL_REACHED,0.5445597792148019,0.0,4.0077712808008686e7,1.0522524513640165e7,17070.0,22953.91,911.04,1168.24,1398.41,4022.51 -1000.0,0.0,1,GN_CRS2_LM,2,XTOL_REACHED,0.5754477805343237,0.0,4.147192456350136e7,1.1559294538528604e7,16686.32,21164.32,2089.43,695.77,1398.43,34140.0 -1000.0,0.0,1,GN_CRS2_LM,3,XTOL_REACHED,0.533946388193207,0.0,3.92471883235602e7,1.0800943073667623e7,11452.36,21138.87,2127.88,223.39,1398.1,3947.51 -1000.0,0.0,1,GN_CRS2_LM,4,XTOL_REACHED,0.6322395200649391,0.0,4.522031769045234e7,1.3194851502402632e7,17070.0,20659.91,3321.81,598.48,1401.81,34140.0 -1000.0,0.0,1,GN_CRS2_LM,5,XTOL_REACHED,0.5253314584722704,0.0,3.8823994702491105e7,1.05968910654404e7,9785.8,21212.32,2127.92,264.61,1428.16,4623.44 -1000.0,0.0,1,GN_AGS,1,MAXEVAL_REACHED,0.5577623335425667,0.0,4.101589419628906e7,1.1196570827092486e7,12933.78,21207.44,2132.71,1329.43,1427.36,23600.44 -1000.0,0.0,1,GN_AGS,2,MAXEVAL_REACHED,0.5577623335425667,0.0,4.101589419628906e7,1.1196570827092486e7,12933.78,21207.44,2132.71,1329.43,1427.36,23600.44 -1000.0,0.0,1,GN_AGS,3,MAXEVAL_REACHED,0.5577623335425667,0.0,4.101589419628906e7,1.1196570827092486e7,12933.78,21207.44,2132.71,1329.43,1427.36,23600.44 -1000.0,0.0,1,GN_AGS,4,MAXEVAL_REACHED,0.5577623335425667,0.0,4.101589419628906e7,1.1196570827092486e7,12933.78,21207.44,2132.71,1329.43,1427.36,23600.44 -1000.0,0.0,1,GN_AGS,5,MAXEVAL_REACHED,0.5577623335425667,0.0,4.101589419628906e7,1.1196570827092486e7,12933.78,21207.44,2132.71,1329.43,1427.36,23600.44 -1000.0,0.0,1,GN_ISRES,1,MAXEVAL_REACHED,0.609425301084544,0.0,4.498560610691364e7,1.1912378249064865e7,16019.41,23839.37,1671.38,2341.25,1756.55,29908.21 -1000.0,0.0,1,GN_ISRES,2,MAXEVAL_REACHED,0.6089675483399123,0.0,4.38448055315354e7,1.2714175511137165e7,17065.13,20452.69,3032.51,320.89,1587.63,8064.96 -1000.0,0.0,1,GN_ISRES,3,MAXEVAL_REACHED,0.6126134897852237,0.0,4.487454793172934e7,1.2821682615194732e7,12195.38,20961.14,3416.33,1249.14,1435.47,13802.25 -1000.0,0.0,1,GN_ISRES,4,MAXEVAL_REACHED,0.5913237639757448,0.0,4.324863690818209e7,1.176071861863087e7,16229.83,23319.16,1814.77,994.85,1426.8,14834.86 -1000.0,0.0,1,GN_ISRES,5,MAXEVAL_REACHED,0.6004685724327401,0.0,4.419194249527442e7,1.205259593794547e7,12845.96,24751.36,2149.51,235.55,1431.6,4724.46 -1000.0,0.0,1,GN_ESCH,1,MAXEVAL_REACHED,0.5183307778469887,0.0,3.7421648559194274e7,1.148491972261785e7,8838.05,14031.14,4291.59,264.16,1402.92,4155.7 -1000.0,0.0,1,GN_ESCH,2,MAXEVAL_REACHED,0.5196360580228612,0.0,3.756235659981291e7,1.151507891672186e7,8833.33,13984.85,4309.0,401.41,1402.91,3803.44 -1000.0,0.0,1,GN_ESCH,3,MAXEVAL_REACHED,0.5761095081998341,0.0,4.1092361932890914e7,1.322949286762858e7,9554.82,12817.74,5708.83,184.43,1398.56,3749.83 -1000.0,0.0,1,GN_ESCH,4,MAXEVAL_REACHED,0.5517916094421772,0.0,3.963909751143246e7,1.242817604551772e7,8906.86,13880.89,4991.68,189.69,1400.82,4050.07 -1000.0,0.0,1,GN_ESCH,5,MAXEVAL_REACHED,0.5269170876271478,0.0,3.7940432397856064e7,1.1731187409686906e7,9013.08,13928.1,4475.78,186.63,1416.89,4226.53 -1000.0,0.0,1,LN_SBPLX,1,XTOL_REACHED,0.6634342637211951,0.0,4.840461762919363e7,1.3647654262013199e7,14703.15,25309.76,2987.84,229.95,1398.01,1762.23 -1000.0,0.0,1,LN_SBPLX,2,XTOL_REACHED,0.5823332706931273,0.0,4.2234392480206e7,1.2122881340971572e7,13855.38,20690.57,2926.07,107.91,1405.01,2692.9 -1000.0,0.0,1,LN_SBPLX,3,XTOL_REACHED,0.7951575013477811,0.0,5.710470878449561e7,1.716370611978646e7,17070.0,25533.23,5223.16,10.44,1398.0,1066.44 -1000.0,0.0,1,LN_SBPLX,4,XTOL_REACHED,0.821108142445707,0.0,5.909224889043894e7,1.846752212726055e7,10794.21,22953.85,7285.74,69.53,1398.16,2328.9 -1000.0,0.0,1,LN_SBPLX,5,XTOL_REACHED,0.7241356019740983,0.0,5.202142227597665e7,1.5458333520296155e7,17070.0,23830.05,4353.31,15.13,1398.0,1436.59 -1000.0,0.0,1,LN_NELDERMEAD,1,XTOL_REACHED,0.7683013808479842,0.0,5.595241444606317e7,1.6682008673845964e7,12394.8,23780.01,5558.7,418.08,2396.45,10290.57 -1000.0,0.0,1,LN_NELDERMEAD,2,XTOL_REACHED,0.9346405120021258,0.0,7.713001589461705e7,1.7882363345678266e7,13861.06,23576.15,3490.28,29020.74,1933.59,21203.48 -1000.0,0.0,1,LN_NELDERMEAD,3,XTOL_REACHED,0.8696550308943546,0.0,6.273781508091164e7,1.911515687162635e7,16673.92,25248.97,6561.27,12.01,2825.91,1015.48 -1000.0,0.0,1,LN_NELDERMEAD,4,XTOL_REACHED,0.8525945485538747,0.0,6.1741743445339605e7,1.9105748690627947e7,15290.14,19847.14,7510.26,4061.98,1398.41,34140.0 -1000.0,0.0,1,LN_NELDERMEAD,5,XTOL_REACHED,1.037593282062117,1.0804948435349009e-5,8.46394969999596e7,2.0403587045586023e7,15978.77,25457.39,4643.86,28381.24,3045.46,22296.28 -1000.0,0.0,2,GN_DIRECT,1,MAXEVAL_REACHED,0.24698357355570866,0.0,1.5972626597918453e7,5.156133219438419e6,3161.11,801.65,2880.7,1693.72,1308.85,24024.44 -1000.0,0.0,2,GN_DIRECT,2,MAXEVAL_REACHED,0.24698357355570866,0.0,1.5972626597918453e7,5.156133219438419e6,3161.11,801.65,2880.7,1693.72,1308.85,24024.44 -1000.0,0.0,2,GN_DIRECT,3,MAXEVAL_REACHED,0.24698357355570866,0.0,1.5972626597918453e7,5.156133219438419e6,3161.11,801.65,2880.7,1693.72,1308.85,24024.44 -1000.0,0.0,2,GN_DIRECT,4,MAXEVAL_REACHED,0.24698357355570866,0.0,1.5972626597918453e7,5.156133219438419e6,3161.11,801.65,2880.7,1693.72,1308.85,24024.44 -1000.0,0.0,2,GN_DIRECT,5,MAXEVAL_REACHED,0.24698357355570866,0.0,1.5972626597918453e7,5.156133219438419e6,3161.11,801.65,2880.7,1693.72,1308.85,24024.44 -1000.0,0.0,2,GN_CRS2_LM,1,XTOL_REACHED,0.2398832997686731,0.0,1.5793285579723205e7,4.935288072385639e6,3197.87,1951.78,2521.26,1384.4,1310.82,15339.12 -1000.0,0.0,2,GN_CRS2_LM,2,XTOL_REACHED,0.23989122469653698,0.0,1.5792465757293193e7,4.934115743575194e6,3198.5,1951.89,2520.03,1387.23,1311.1,15349.66 -1000.0,0.0,2,GN_CRS2_LM,3,XTOL_REACHED,0.2398736519181953,0.0,1.5792501880160773e7,4.934913276294289e6,3197.5,1951.95,2520.99,1384.73,1310.58,15339.99 -1000.0,0.0,2,GN_CRS2_LM,4,XTOL_REACHED,0.23988709756685786,0.0,1.5794705342698853e7,4.93554828179022e6,3197.75,1954.4,2521.02,1383.71,1310.68,15343.13 -1000.0,0.0,2,GN_CRS2_LM,5,XTOL_REACHED,0.23992288412017768,0.0,1.5794059908572292e7,4.93403239057211e6,3203.1,1956.4,2518.69,1386.9,1310.56,15508.01 -1000.0,0.0,2,GN_AGS,1,MAXEVAL_REACHED,0.27190167810392757,0.0,1.7487628926757812e7,5.515841274678944e6,6415.84,1804.41,2591.13,1671.16,1519.88,20999.93 -1000.0,0.0,2,GN_AGS,2,MAXEVAL_REACHED,0.27190167810392757,0.0,1.7487628926757812e7,5.515841274678944e6,6415.84,1804.41,2591.13,1671.16,1519.88,20999.93 -1000.0,0.0,2,GN_AGS,3,MAXEVAL_REACHED,0.27190167810392757,0.0,1.7487628926757812e7,5.515841274678944e6,6415.84,1804.41,2591.13,1671.16,1519.88,20999.93 -1000.0,0.0,2,GN_AGS,4,MAXEVAL_REACHED,0.27190167810392757,0.0,1.7487628926757812e7,5.515841274678944e6,6415.84,1804.41,2591.13,1671.16,1519.88,20999.93 -1000.0,0.0,2,GN_AGS,5,MAXEVAL_REACHED,0.27190167810392757,0.0,1.7487628926757812e7,5.515841274678944e6,6415.84,1804.41,2591.13,1671.16,1519.88,20999.93 -1000.0,0.0,2,GN_ISRES,1,XTOL_REACHED,0.2398769695442701,0.0,1.5793080449094411e7,4.934969310782498e6,3197.68,1953.17,2520.79,1384.4,1310.64,15343.28 -1000.0,0.0,2,GN_ISRES,2,XTOL_REACHED,0.24002423985919386,0.0,1.5788370618053747e7,4.960674057406775e6,3154.59,1821.13,2570.12,1379.4,1311.93,15191.76 -1000.0,0.0,2,GN_ISRES,3,XTOL_REACHED,0.2398760590648595,0.0,1.5793356480099026e7,4.93526667938508e6,3197.38,1952.41,2521.2,1384.28,1310.64,15338.72 -1000.0,0.0,2,GN_ISRES,4,XTOL_REACHED,0.23988902524785546,0.0,1.5795167990541026e7,4.93670721990056e6,3196.37,1952.8,2522.55,1381.32,1310.58,15348.52 -1000.0,0.0,2,GN_ISRES,5,XTOL_REACHED,0.23988534632414113,0.0,1.5785875920391364e7,4.937662623142891e6,3189.71,1918.38,2529.92,1389.81,1310.69,15281.37 -1000.0,0.0,2,GN_ESCH,1,MAXEVAL_REACHED,0.2559666604388703,0.0,1.6982191026915144e7,5.649642630191215e6,2878.2,1162.42,3247.13,1093.09,1310.67,13996.77 -1000.0,0.0,2,GN_ESCH,2,MAXEVAL_REACHED,0.248536683851493,0.0,1.6370854730140783e7,5.364937772133596e6,2967.4,1143.38,3024.01,1245.02,1318.24,16145.21 -1000.0,0.0,2,GN_ESCH,3,MAXEVAL_REACHED,0.2925977619091738,0.0,1.951424121512955e7,6.8432774934339505e6,2464.78,536.19,4278.94,907.88,1312.81,13947.91 -1000.0,0.0,2,GN_ESCH,4,MAXEVAL_REACHED,0.2442322477172539,0.0,1.5841604073200954e7,5.052253332976117e6,3105.5,1071.58,2759.39,1686.82,1308.79,19126.14 -1000.0,0.0,2,GN_ESCH,5,MAXEVAL_REACHED,0.29169525191397105,0.0,1.925115194050892e7,6.762405685251045e6,2449.69,78.5,4289.37,1121.11,1350.77,18603.79 -1000.0,0.0,2,LN_SBPLX,1,XTOL_REACHED,0.6180545482999165,-1.1158964047692939e-18,4.29734767819842e7,1.3594268593846215e7,17070.0,15968.57,4678.26,259.45,400.63,3371.81 -1000.0,0.0,2,LN_SBPLX,2,XTOL_REACHED,0.2499811916433519,0.0,1.6272444207583345e7,5.331175302426264e6,3531.72,890.3,2977.26,1431.08,1301.19,19115.07 -1000.0,0.0,2,LN_SBPLX,3,XTOL_REACHED,0.9958203158249397,9.39702235595195e-19,7.82576072400913e7,1.996710898112274e7,17059.75,22516.84,5401.3,25099.51,461.16,1549.78 -1000.0,0.0,2,LN_SBPLX,4,XTOL_REACHED,0.33379298655000245,0.0,2.293357096104157e7,8.0118022389706755e6,2213.69,2052.19,4866.58,556.89,1298.57,9586.32 -1000.0,0.0,2,LN_SBPLX,5,XTOL_REACHED,0.24502659165976168,0.0,1.6166936191200558e7,5.057811197425113e6,3127.94,1789.82,2624.39,1316.62,1610.64,14684.69 -1000.0,0.0,2,LN_NELDERMEAD,1,XTOL_REACHED,0.2641076279991136,0.0,1.7823953267859336e7,5.835086195529334e6,2898.33,2228.13,3187.57,857.26,1304.33,22147.0 -1000.0,0.0,2,LN_NELDERMEAD,2,XTOL_REACHED,0.31046980124305684,0.0,2.109527694294113e7,6.85310081788915e6,3281.26,994.26,3908.78,847.37,3410.92,18401.8 -1000.0,0.0,2,LN_NELDERMEAD,3,XTOL_REACHED,0.9000051846476124,0.0,7.1774026694368e7,1.6419516197375234e7,14059.3,25604.4,2446.43,26174.22,589.32,2465.82 -1000.0,0.0,2,LN_NELDERMEAD,4,XTOL_REACHED,0.3025085794653493,0.0,1.9612346641083706e7,6.132818173118102e6,3215.4,1.0,3479.74,2100.67,3389.06,21154.34 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z_|TL@NRJa3hNW}(5%q*eXsizV_qe7B!^G}MjDZ!5>3t;`_Z!4WCygn;88h^OpkLok z!*)(3F^-b6uf~P0YErS}6HvkcA4{nqpf`(rK6xFrG*K37HaoIbFaMRnz2Ke z4Nz1VXV9dWObY*g*s2~>CuB_E=sLIDfrU8tnlQ_P=iB$6kH+34wHzGq%-Se|vIu7t z3Zk8*gG_vb3d7CLUM@jY=4yFYSd!3%kl}f;iURBrvR;yKYa}$FjGRid#dlCF29U7jq1p z3+Ib3gv{wlaXkISY^W)bO&(Pv-OYwyt+0<&kL4X" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "using CSV, DataFrames\n", - "using Microgrids\n", - "using NLopt # optimization solvers\n", - "using Printf # pretty print results\n", - "using PyPlot\n", - "using PyCall\n", - "using BenchmarkTools # for timing performance, optional\n", - "np=pyimport(\"numpy\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "04b025c7-5c46-4efe-8497-83dc2637438e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "true" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with_plots = isdefined(Main, :PyPlot)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5f54d21-8371-49d8-a81a-f170548a9bdf", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a5a038f2-99e7-4727-8e3e-193569b45618", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `nsteps` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `nsteps` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[3]:1" - ] - } - ], - "source": [ - "td = collect((0:nsteps-1)/24) " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "17f47887-a56f-4cac-940e-fb4ed0f2b7eb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1800.0, 0.0, 0.0625, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 56.0, 0.0, 1000.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0), Tank{Float64}(10000.0, 0.8, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 1.0, 1.0)), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1800.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg= default_microgrid()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "824c7932-f17b-40e4-8ea6-f220ec80263c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "simulate_microgrid" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", - "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", - "\n", - "Returns mg, traj, stats, costs\n", - "\"\"\"\n", - " function simulate_microgrid(x,dispatch)\n", - " # Split decision variables (converted MW → kW):\n", - " \n", - " mg = new_microgrid(x,dispatch)\n", - " # Launch simulation:\n", - " traj, stats, costs = simulate(mg,dispatch)\n", - "\n", - " return traj ,stats, costs\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a34cb909-6b9b-47c6-aaea-a45c6d0ebff1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([5.0, 3.0, 1.8, 2.0, 1.8, 10.0], 0.26345419104330475, 23.59720165769015)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " # Baseline sizing: same as in Microgrid_Wind-Solar.ipynb notebook\n", - "x_base = [energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank]/1000.\n", - "# run simulation:\n", - "traj, stats, costs = simulate_microgrid(x_base,dispatch_2)\n", - "x_base, costs.lcoe, costs.npc/1e6" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "f8c047df-66cc-47a2-9468-7c9082b82c1f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "plot_oper_traj (generic function with 1 method)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "function plot_oper_traj(mg, oper_traj)\n", - " \n", - " z=zeros(Float64,12)\n", - "tm=zeros(Float64,13)\n", - "tm[13]=8760/24\n", - " for i=1:12 \n", - " z[i]= oper_traj.LoH[i*730+1]\n", - " tm[i]=(i-1)*730/24 \n", - "end\n", - " fig,( ax1, ax2) = plt.subplots(2,1)\n", - " y1 = min.(oper_traj.Prenew_pot,mg.load)\n", - " y2 = max.( oper_traj.Pbatt,0.0)\n", - " y3 = oper_traj.Pfc\n", - " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", - " y5 = oper_traj.Pelyz\n", - " y6 = oper_traj.power_shedding\n", - " y7 = oper_traj.power_curtailment\n", - " \n", - " \n", - " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", - " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", - " \n", - " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", - " \n", - " \n", - " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, mg.load,lw=2, label=\"load\")\n", - " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", - " ax2.plot(td, oper_traj.LoH[1:end-1]/mg.tanks.h2Tank.capacity, \"C2\")\n", - " stacks[2].set_hatch(\"-\")\n", - " stacks[4].set_hatch(\"+\")\n", - " stacks[6].set_hatch(\"\\\\\") \n", - " stacks[7].set_hatch(\"\\\\\") \n", - " \n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim(0, 2)\n", - " ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", - " ax2.plot(td, oper_traj.LoH[1:end-1], \"C2\",label=\"hourly H2 level\")\n", - " \n", - " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", - " \n", - " \n", - " ax1.set_title(\"hourly power repartition\")\n", - " ax2.set_title(\"H2 level\")\n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(ylabel=\"kW\")\n", - " ax2.legend()\n", - " ax3.legend()\n", - " ax2.grid(true)\n", - " ax2.set(ylabel=\"kg\")\n", - " \n", - " fig.tight_layout()\n", - "\n", - "\n", - " \n", - " return fig, ax1, ax2\n", - " \n", - "end\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4d8e8035-6db7-49e1-87ff-1d5884aa2789", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6-element Vector{Float64}:\n", - " 17.07\n", - " 25.605\n", - " 8.535\n", - " 34.14\n", - " 2.0484\n", - " 17.07" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Pload_max = maximum(Pload)\n", - "\n", - "xmin = [0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", - "x0 = [3.0, 3.0, 2.0, 1.0, 1.0, 5.0] * (Pload_max/1000)\n", - "xmax = [10.0, 15.0, 5.0, 20., 1.2 , 20. ] * (Pload_max/1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dd051772-f6d5-416a-a858-e3cac878b834", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "optim_mg" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", - "\n", - "Parameters:\n", - "- `x0`: initial sizing (for the algorithms which need them)\n", - "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", - "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", - "- `maxeval`: maximum allowed number of calls to the objective function,\n", - " that is to the microgrid simulation\n", - "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", - "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", - "\n", - "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", - "but could be added to the parameters as well.\n", - "\"\"\"\n", - "function optim_mg(x0, shed_max,dispatch, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-8, srand=1)\n", - " nx = length(x0) # number of optim variables\n", - " opt = Opt(algo, nx)\n", - " NLopt.srand(srand)\n", - " \n", - " opt.lower_bounds = xmin\n", - " opt.upper_bounds = xmax\n", - " opt.min_objective = (x, grad) -> obj(x, grad,dispatch, shed_max)\n", - " opt.xtol_rel = xtol_rel\n", - " opt.maxeval = maxeval\n", - " \n", - " (fopt, xopt, ret) = optimize(opt, x0)\n", - " return xopt, ret, opt.numevals\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "221d69a8-6262-4e95-9a3a-940a254914ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "obj_multi" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", - "function obj_multi(x,dispatch)\n", - " traj,stats, costs = simulate_microgrid(x,dispatch)\n", - " # Extract KPIs of interest\n", - " lcoe = costs.lcoe # $/kWh\n", - " shed_rate = stats.shed_rate; # in [0,1]\n", - " return lcoe, shed_rate\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0482bd16-e7b2-40b2-82ac-e4cc1505e7b4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "obj (generic function with 2 methods)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "function obj(x, grad,dispatch, shed_max, w_shed_max=1e5)\n", - " lcoe, shed_rate = obj_multi(x,dispatch)\n", - " over_shed = shed_rate - shed_max\n", - " if over_shed > 0.0\n", - " penalty = w_shed_max*over_shed\n", - " else\n", - " penalty = 0.0\n", - " end\n", - " J = lcoe + penalty\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee419820-5562-49ae-9625-8e2384703a57", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "97220b80-1eb3-431e-826c-12fcf5f04c7f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79006d3e-ab65-497d-89bd-a0c62fbfc2b9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1561109-812c-43c0-96be-773a032d4952", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "6305c0ad-6e41-49e0-9b3a-bc59009581ad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GN_CRS2_LM algo: XTOL_REACHED after 69177 iterations. \n", - "x*=[6750.3, 4362.3, 2143.2, 887.7, 1495.3, 12606.8]\n", - "LCOE*: 0.25620326210747557\n", - "shed*: 9.999998227674223e-5\n" - ] - } - ], - "source": [ - "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0.0001 # in [0,1]\n", - "maxeval=100000\n", - "xopt, ret, numevals = optim_mg(x0, shed_max,dispatch_1, algo, maxeval)\n", - "\n", - "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt*1000; digits=1)) # kW\n", - "lcoe_opt, shed_rate_opt = obj_multi(xopt,dispatch_1)\n", - "println(\"LCOE*: \", lcoe_opt)\n", - "println(\"shed*: \", shed_rate_opt)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "1959b112-8609-49d0-a0e3-721b1dd5d7d1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OperationStats with fields:\n", - "- served_energy: 6.7682e6 kWh\n", - "- shed_energy: 6775.0 kWh\n", - "- shed_max: 256.54 kW\n", - "- shed_hours: 141.0 h\n", - "- shed_duration_max: 8.0 h\n", - "- shed_rate: 0.001 in [0,1]\n", - "- gen_energy: 0.0 kWh\n", - "- gen_hours: 0.0 h\n", - "- gen_fuel: 0.0 L\n", - "- fc_energy: 983780.0 kWh\n", - "- fc_hours: 1829.0 h\n", - "- h2_consumed: 61486.0 kg\n", - "- h2_produced: 64898.0 kg\n", - "- elyz_hours: 4051.0 h\n", - "- elyz_consumed_energy: 3.6343e6 kWh\n", - "- h2_chain_loss: 2.6503e6 kWh\n", - "- storage_cycles: 154.64 \n", - "- storage_char_energy: 483310.0 kWh\n", - "- storage_dis_energy: 440120.0 kWh\n", - "- storage_loss_energy: 46172.0 kWh\n", - "- spilled_energy: 785770.0 kWh\n", - "- spilled_max: 3136.2 kW\n", - "- spilled_rate: 0.076678 in [0,1]\n", - "- renew_potential_energy: 1.0248e7 kWh\n", - "- renew_energy: 9.4619e6 kWh\n", - "- renew_rate: 1.0 in [0,1]\n" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg, traj, stats, costs = simulate_microgrid(xopt,dispatch_1)\n", - "stats" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "08163c89-f8e5-48ff-b29c-942421942fb9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(Figure(PyObject

), PyObject , PyObject )" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot_oper_traj(mg,traj)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4da1dd01-e71f-49c9-96ea-09a8ad0e5726", - "metadata": {}, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `optim_mg` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `optim_mg` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[6]:4" - ] - } - ], - "source": [ - "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0.001 # in [0,1]\n", - "maxeval=100000\n", - "xopt2, ret, numevals = optim_mg(x0, shed_max,dispatch_2, algo, maxeval)\n", - "\n", - "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt2*1000; digits=1)) # kW\n", - "lcoe_opt, shed_rate_opt = obj_multi(xopt2,dispatch_2)\n", - "println(\"LCOE*: \", lcoe_opt)\n", - "println(\"shed*: \", shed_rate_opt)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "46dbc313-3f17-47f1-8589-dddbb5d76f79", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OperationStats with fields:\n", - "- served_energy: 6.7682e6 kWh\n", - "- shed_energy: 6775.0 kWh\n", - "- shed_max: 1203.8 kW\n", - "- shed_hours: 65.0 h\n", - "- shed_duration_max: 11.0 h\n", - "- shed_rate: 0.001 in [0,1]\n", - "- gen_energy: 0.0 kWh\n", - "- gen_hours: 0.0 h\n", - "- gen_fuel: 0.0 L\n", - "- fc_energy: 1.3541e6 kWh\n", - "- fc_hours: 2843.0 h\n", - "- h2_consumed: 84630.0 kg\n", - "- h2_produced: 87968.0 kg\n", - "- elyz_hours: 5421.0 h\n", - "- elyz_consumed_energy: 4.9262e6 kWh\n", - "- h2_chain_loss: 3.5719e6 kWh\n", - "- storage_cycles: 46.307 \n", - "- storage_char_energy: 8484.3 kWh\n", - "- storage_dis_energy: 7844.2 kWh\n", - "- storage_loss_energy: 816.42 kWh\n", - "- spilled_energy: 534940.0 kWh\n", - "- spilled_max: 1974.7 kW\n", - "- spilled_rate: 0.049186 in [0,1]\n", - "- renew_potential_energy: 1.0876e7 kWh\n", - "- renew_energy: 1.0341e7 kWh\n", - "- renew_rate: 1.0 in [0,1]\n" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg2, traj2, stats2, costs2 = simulate_microgrid(xopt2,dispatch_2)\n", - "stats2" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "88c56bff-3c06-4532-b16f-40f58a0433c8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `plot_oper_traj` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `plot_oper_traj` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[5]:1" - ] - } - ], - "source": [ - "plot_oper_traj(mg2,traj2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "49cb7f56-213d-4e3a-8c53-80a355889344", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.9.3", - "language": "julia", - "name": "julia-1.9" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.9.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/new_examples/prices_scenario_comparison.ipynb b/examples/new_examples/prices_scenario_comparison.ipynb deleted file mode 100644 index ce26e5c..0000000 --- a/examples/new_examples/prices_scenario_comparison.ipynb +++ /dev/null @@ -1,2157 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "a057597f-0993-4915-813c-915eff752105", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using CSV, DataFrames\n", - "using Microgrids\n", - "using NLopt # optimization solvers\n", - "using Printf # pretty print results\n", - "using PyCall\n", - "using PyPlot\n", - "using BenchmarkTools # for timing performance, optional" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e483c6f8-940c-4072-ac8a-4c86bd17fb82", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using Printf # pretty print results\n", - "using Random, Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3a87a676-9923-4d66-bac1-576ad1720dd8", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "\n", - "np=pyimport(\"numpy\");\n", - "td = collect((0:nsteps-1)/24) ;\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "7a3a52ff-9714-48c7-aad4-908c5f746401", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "true" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with_plots = isdefined(Main, :PyPlot)" - ] - }, - { - "cell_type": "markdown", - "id": "11e43d38-ea1c-4256-9546-dba78952c001", - "metadata": {}, - "source": [ - "#### default Hydrogen prices scenario\n", - "#electrolyzer and fuel cell investment price : 1600\\$/kW" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "40e20332-bb1f-4ad4-a054-4de92fefe834", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1.8e6, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(2.0e6, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(1.0e7, 500.0, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(5.0e6, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3.0e6, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1.8e6, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_base = new_microgrid()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "a14a3ea4-a3c4-40d1-9f66-f092261b1985", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "500.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_base.tanks.h2Tank.investment_price" - ] - }, - { - "cell_type": "markdown", - "id": "8f7753e1-cfe3-4d1f-b4e6-1f8665b442e4", - "metadata": { - "tags": [] - }, - "source": [ - "#### Hydrogen High prices scenario\n", - "#electrolyzer and fuel cell investment price : 2200\\$/kW" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "ef803eae-d36c-45e2-a124-4b3c236dbd10", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8-element Vector{Float64}:\n", - " 400.0\n", - " 0.0\n", - " 2200.0\n", - " 500.0\n", - " 2200.0\n", - " 350.0\n", - " 1200.0\n", - " 3500.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "capex_hi= [400., 0.0, 2200., 500., 2200., 350., 1200., 3500.]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9c0f76fc-8d18-463b-bbc4-a27a0f881d16", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(2.2e6, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(500000.0, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(350000.0, 500.0, 0.0025, 25.0, 0.0, 0.0, 0.1, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(400000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(0.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(2.2e6, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_high= new_microgrid(capex_hi)" - ] - }, - { - "cell_type": "markdown", - "id": "819de33d-f525-4523-ab33-fedcde3675f3", - "metadata": {}, - "source": [ - "#### function to compare 2 operations trajectories" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c4b766e0-570d-4562-983b-7986cb97c364", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "comp_oper_traj (generic function with 1 method)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "function comp_oper_traj(mg1,mg2, oper_traj1, oper_traj2, costs1,costs2)\n", - " \n", - " ns=Int64(nsteps/730)\n", - " z=zeros(Float64,ns)\n", - "tm=zeros(Float64,ns +1)\n", - " tm[end]=nsteps/24\n", - " for i=1:ns\n", - " z[i]= oper_traj1.LoH[i*730+1]\n", - " tm[i]=(i-1)*730/24 \n", - "end\n", - " fig,( ax1, ax2) = plt.subplots(2,1,sharex=true)\n", - " y1 = min.(oper_traj1.Prenew_pot,mg1.load)\n", - " y2 = max.( oper_traj1.Pbatt,0.0)\n", - " y3 = oper_traj1.Pfc\n", - " y4 = abs.(min.( oper_traj1.Pbatt,0.0))\n", - " y5 = oper_traj1.Pelyz\n", - " y6 = oper_traj1.power_shedding\n", - " y7 = oper_traj1.power_curtailment\n", - " \n", - " Y1=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", - " \n", - " \n", - " y1 = min.(oper_traj2.Prenew_pot,mg2.load)\n", - " y2 = max.( oper_traj2.Pbatt,0.0)\n", - " y3 = oper_traj2.Pfc\n", - " y4 = abs.(min.( oper_traj2.Pbatt,0.0))\n", - " y5 = oper_traj2.Pelyz\n", - " y6 = oper_traj2.power_shedding\n", - " y7 = oper_traj2.power_curtailment\n", - " \n", - " Y2=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", - " \n", - " \n", - " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", - " \n", - " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", - " \n", - " \n", - " ax1.plot(td, oper_traj1.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, mg1.load,lw=2, label=\"load\")\n", - " stacks1 = ax1.stackplot(td,Y1,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", - " ax2.plot(td, oper_traj2.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", - " ax2.plot(td, mg2.load,lw=2, label=\"load\")\n", - " stacks2 = ax2.stackplot(td,Y2,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", - " \n", - " \n", - " stacks1[2].set_hatch(\"-\")\n", - " stacks1[4].set_hatch(\"+\")\n", - " stacks1[6].set_hatch(\"\\\\\") \n", - " stacks1[7].set_hatch(\"\\\\\") \n", - " \n", - " stacks2[2].set_hatch(\"-\")\n", - " stacks2[4].set_hatch(\"+\")\n", - " stacks2[6].set_hatch(\"\\\\\") \n", - " stacks2[7].set_hatch(\"\\\\\") \n", - " \n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim(0, 2)\n", - " ax3.plot(td, oper_traj1.Ebatt[1:end-1]/mg1.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", - "\n", - " \n", - " \n", - " ax4=ax2.twinx()\n", - " ax4.set_ylim(0, 2)\n", - " ax4.plot(td, oper_traj2.Ebatt[1:end-1]/mg2.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", - "\n", - " \n", - " \n", - " \n", - " ax1.set_title(\"hourly power repartition\")\n", - " ax2.set_title(\"hourly power repartition\")\n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(ylabel=\"kW\")\n", - " ax2.legend()\n", - " ax3.legend()\n", - " ax4.legend()\n", - " ax2.grid(true)\n", - " ax2.set(ylabel=\"kw\")\n", - " fig.tight_layout()\n", - " \n", - " return fig,(ax1, ax2,ax3,ax4)\n", - " \n", - "end\n" - ] - }, - { - "cell_type": "markdown", - "id": "d7b9e5a5-3de5-4371-afdb-7582fa493404", - "metadata": {}, - "source": [ - "#### optimization setup" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "3a789b7d-eee8-4047-8bc2-34ad25ab09f0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "simulate_microgrid" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Simulate the performance of a Microgrid project of size `x`\n", - "with x=[power_rated_gen, energy_rated_sto, power_rated_pv, power_rated_wind]\n", - "\n", - "Returns mg, traj, stats, costs\n", - "\"\"\"\n", - " function simulate_microgrid(x,capex,dispatch)\n", - " mg=new_microgrid(x)\n", - " # Split decision variables (converted MW → kW):\n", - " oper_traj = operation(mg, dispatch)\n", - " ini=[0.0,oper_traj.LoH[end]/mg.tanks.h2Tank.capacity, oper_traj.Ebatt[end]/mg.storage.energy_rated]\n", - " mg=new_microgrid(x,capex,ini)\n", - " # Launch simulation:\n", - " traj, stats, costs = simulate(mg,dispatch)\n", - "\n", - " return traj ,stats, costs\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e695c1ab-4853-461a-8b3f-b6cd12dc3ecd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "obj_multi" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"Multi-objective criterion for microgrid performance: lcoe, shedding rate\"\n", - "function obj_multi(x,capex,dispatch)\n", - " traj,stats, costs = simulate_microgrid(x,capex,dispatch)\n", - " # Extract KPIs of interest\n", - " lcoe = costs.lcoe # $/kWh\n", - " shed_rate = stats.shed_rate; # in [0,1]\n", - " return lcoe, shed_rate, costs,traj\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "31a72ca9-cec2-4325-8758-9b2374847d0b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "obj (generic function with 2 methods)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "function obj(x, grad,capex,dispatch, shed_max, w_shed_max=1e5)\n", - " lcoe, shed_rate,costs,traj = obj_multi(x,capex,dispatch)\n", - " over_shed = shed_rate - shed_max\n", - " if over_shed > 0.0\n", - " penalty = w_shed_max*over_shed\n", - " else\n", - " penalty = 0.0\n", - " end\n", - " J = lcoe + penalty\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "60c0f843-3bb6-4944-ab52-1a25ef54767d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6-element Vector{Float64}:\n", - " 34.14\n", - " 17.07\n", - " 1.8777000000000001\n", - " 8.535\n", - " 3.414\n", - " 25.605" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Pload_max = maximum(Pload)\n", - "\n", - "xmin = [0., 1e-3, 0., 0., 0., 0.] # 1e-3 instead of 0.0, because LCOE is NaN if ther is exactly zero generation\n", - "x0 = X * (1e-3)\n", - "xmax = [20.0, 10.0, 1.1, 5., 2. , 15. ] * (Pload_max/1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "8c71df41-6029-4e2d-ada6-bec22e395d7f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6-element Vector{Float64}:\n", - " 5.0\n", - " 3.0\n", - " 1.8\n", - " 2.0\n", - " 1.8\n", - " 10.0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x0" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e6507580-4fd7-470f-a392-8477e9d598ef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "optim_mg" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"Optimize sizing of microgrid based on the `obj` function\n", - "\n", - "Parameters:\n", - "- `x0`: initial sizing (for the algorithms which need them)\n", - "- `shed_max`: load shedding penalty threshold (same as in `obj`)\n", - "- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH...\n", - "- `maxeval`: maximum allowed number of calls to the objective function,\n", - " that is to the microgrid simulation\n", - "- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc.\n", - "- `srand`: random number generation seed (for algorithms which use some stochastic search)\n", - "\n", - "Problem bounds are taken as the global variables `xmin`, `xmax`,\n", - "but could be added to the parameters as well.\n", - "\"\"\"\n", - "function optim_mg(x0,capex, shed_max,dispatch, algo=:LN_SBPLX, maxeval=1000, xtol_rel=1e-8, srand=1)\n", - " nx = length(x0) # number of optim variables\n", - " opt = Opt(algo, nx)\n", - " NLopt.srand(srand)\n", - " \n", - " opt.lower_bounds = xmin\n", - " opt.upper_bounds = xmax\n", - " opt.min_objective = (x, grad) -> obj(x, grad,capex,dispatch, shed_max)\n", - " opt.xtol_rel = xtol_rel\n", - " opt.maxeval = maxeval\n", - " \n", - " (fopt, xopt, ret) = optimize(opt, x0)\n", - " return xopt, ret, opt.numevals\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "8344f0ba-9fdf-47bd-b84a-ef24bb942cf5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Vector{Function}:\n", - " dispatch_1 (generic function with 2 methods)\n", - " dispatch_2 (generic function with 1 method)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "capexlist=[capex_def,capex_hi]\n", - "dispatchlist=[dispatch_1,dispatch_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "626688e2-98e9-438d-9494-d44aecc5621f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GN_CRS2_LM algo: XTOL_REACHED after 44902 iterations. \n", - "x*=[11380.11584, 16425.59372, 1499.74022, 354.72914, 1405.23487, 6377.35589]\n", - "LCOE*: 0.472324294850319\n", - "shed*: 0.0\n" - ] - } - ], - "source": [ - "algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "shed_max = 0.00# in [0,1]\n", - "maxeval=100000\n", - "xtol_rel=1e-8\n", - "xopt_base, ret, numevals = optim_mg(x0,capex_def, shed_max,dispatch_1, algo, maxeval,xtol_rel,6)\n", - "\n", - "@printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt_base*1000; digits=5)) # kW\n", - "lcoe_opt, shed_rate_opt,costs,traj_opt = obj_multi(xopt_base,capex_def,dispatch_1)\n", - "println(\"LCOE*: \", lcoe_opt)\n", - "println(\"shed*: \", shed_rate_opt)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "b1afbd7f-1e6c-4743-ac94-05749b3a947e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.13910787752736056" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traj_opt.LoH[1]/(xopt_base[end]*1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0c5ad5a7-b088-46cc-981c-32a36013d445", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traj_opt.Ebatt[end]/(xopt_base[1]*1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "22df5e8a-3293-48b6-8ed7-e00a0b22d866", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.13910787752736056" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traj_opt.LoH[end]/(xopt_base[end]*1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d4fbf0c6-8179-4027-ba00-8d171523ba0d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traj_opt.Ebatt[1]/(xopt_base[1]*1000)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5bea2759-0a3f-42d4-a283-fe8da461c192", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "3-element Vector{Float64}:\n", - " 0.0\n", - " 0.13910787752736056\n", - " 0.0" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ini_opt=[0.0,traj_opt.LoH[1]/(xopt_base[end]*1000),traj_opt.Ebatt[end]/(xopt_base[1]*1000)]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ef5f3774-8b30-4152-a449-1520c3d574b0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableCompound{Float64}(ProductionUnit{Float64}[ProductionUnit{Float64}(2000.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\", \"KWh\")], ProductionUnit{Float64}[ProductionUnit{Float64}(1405.2348706758517, 0.0, 0.0625, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"Kg\", \"KWh\")]), ProductionUnit{Float64}[ProductionUnit{Float64}(354.7291392005546, 0.0, 56.0, 0.0, 1600.0, 10.0, 45000.0, 0.05, 1.0, 1.0, \"KWh\", \"Kg\")], TankCompound{Float64}(Tank{Float64}(0.0, 0.0, 0.0, 25.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0), Tank{Float64}(6377.355888656224, 500.0, 0.0025, 25.0, 0.0, 0.13910787752736056, 0.1, 1.0, 14.0, 1.0, 1.0)), Battery{Float64}(11380.115841362738, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 1.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(16425.593716119478, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(1499.7402169294517, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " mg=new_microgrid(xopt_base,capex_def,ini_opt)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "45dae8ba-c879-4fc8-8a0b-a0543efeba13", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(OperationTraj{Float64}([1405.234703485372, 1165.2888071173184, 845.1166282068943, 479.78521739130485, 95.05103381608512, -235.4169256696523, -404.6630978467483, -388.0389352980492, -333.3648992931039, -278.442595827634 … -354.7291392005545, -354.7291392005545, 1374.5398909533117, 1325.4614992280208, 1412.4708809444858, 1531.3027020820045, 1504.7141803771758, 1415.12269446997, 1476.7141803771758, 1427.8882665978665], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [47.76529651462817, 165.71119288268167, 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CostFactors(645019.6739856536, 567566.6227208873, 182491.42007790215, 49995.32823853387, 0.0, -155033.69705166973), CostFactors(1.875430437754179e6, 2.2483757930813627e6, 0.0, 198053.02369578512, 0.0, -570998.3790229688), CostFactors(0.0, 0.0, 0.0, 0.0, 0.0, -0.0), CostFactors(3.2013226157657495e6, 3.201097910514818e6, 0.0, 224.70525093164966, 0.0, -0.0)))" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " traj, stats, costs = simulate(mg,dispatch_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "c6f0de38-f578-4ac7-8397-53c5ae2beb3e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if with_plots\n", - " fig1, ax1, ax2= plot_oper_traj(mg,traj)\n", - " \n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8e34ffff-e843-4d1d-9cd8-df925a9870e7", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "ArgumentError: ref of NULL PyObject", - "output_type": "error", - "traceback": [ - "ArgumentError: ref of NULL PyObject", - "", - "Stacktrace:", - " [1] _getproperty(o::PyObject, s::Symbol)", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:299", - " [2] __getproperty", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:312 [inlined]", - " [3] getproperty(o::PyObject, s::Symbol)", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\PyCall.jl:318", - " [4] top-level scope", - " @ In[27]:9" - ] - } - ], - "source": [ - "loh_ini=zeros(Float64,100)\n", - "loh_end=zeros(Float64,100)\n", - "for i in 1:100\n", - " mg=new_microgrid(xopt_base,capex_def,[0.0,i/(100.0),0.0])\n", - " traj, stats, costs = simulate(mg,dispatch_2)\n", - " loh_ini[i]=traj.LoH[1]/(xopt_base[end]*1000)\n", - " loh_end[i]=traj.LoH[end]/(xopt_base[end]*1000)\n", - "end\n", - "fig,ax = plt.subplots()\n", - "ax.plot(loh_ini,loh_end)\n", - "ax.plot(loh_ini,loh_ini,label=\"x=x\")\n", - "ax.set(title=\"niveau d'hydrogène final en fonction du niveau initial\",xlabel=\"initial level\",ylabel=\"final level\")\n", - "ax.legend()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "2f10eb38-b4d2-436f-904b-aa540d4f30c3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "100-element Vector{Float64}:\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " ⋮\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002\n", - " 0.10000000000000002" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loh_end" - ] - }, - { - "cell_type": "markdown", - "id": "16ee0cc1-edb4-4e6b-b77d-7e7e1b541ef6", - "metadata": {}, - "source": [ - "#### Pareto front of simple dispatch strategies" - ] - }, - { - "cell_type": "markdown", - "id": "6cddca75-5e07-4936-8050-5f6188f4ae56", - "metadata": {}, - "source": [ - "## Dispatch 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c70b32f-46ef-4761-9126-2b7b8def03ea", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "90cab27f-ebc4-4ef5-b0e9-91458d34eb6b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "17-element Vector{Float64}:\n", - " 0.0\n", - " 2.5e-6\n", - " 5.0e-6\n", - " 7.5e-6\n", - " 1.0e-5\n", - " 1.25e-5\n", - " 1.5e-5\n", - " 1.75e-5\n", - " 2.0e-5\n", - " 2.25e-5\n", - " 2.5e-5\n", - " 2.75e-5\n", - " 3.0e-5\n", - " 3.25e-5\n", - " 3.5e-5\n", - " 3.75e-5\n", - " 4.0e-5" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_shed_1=collect(0.0:0.0000025:0.00004)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "47331a38-7b65-4925-bf07-62da78e201ef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20-element Vector{Float64}:\n", - " 5.0e-5\n", - " 0.0001\n", - " 0.00015\n", - " 0.0002\n", - " 0.00025\n", - " 0.0003\n", - " 0.00035\n", - " 0.0004\n", - " 0.00045\n", - " 0.0005\n", - " 0.00055\n", - " 0.0006\n", - " 0.00065\n", - " 0.0007\n", - " 0.00075\n", - " 0.0008\n", - " 0.00085\n", - " 0.0009\n", - " 0.00095\n", - " 0.001" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_shed_2=collect(0.00005:0.00005:0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a783a8a1-44ad-4d35-aafa-fc423eadf04d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "37-element Vector{Float64}:\n", - " 0.0\n", - " 2.5e-6\n", - " 5.0e-6\n", - " 7.5e-6\n", - " 1.0e-5\n", - " 1.25e-5\n", - " 1.5e-5\n", - " 1.75e-5\n", - " 2.0e-5\n", - " 2.25e-5\n", - " 2.5e-5\n", - " 2.75e-5\n", - " 3.0e-5\n", - " ⋮\n", - " 0.00045\n", - " 0.0005\n", - " 0.00055\n", - " 0.0006\n", - " 0.00065\n", - " 0.0007\n", - " 0.00075\n", - " 0.0008\n", - " 0.00085\n", - " 0.0009\n", - " 0.00095\n", - " 0.001" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_shed_new=[pareto_shed_1;pareto_shed_2]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2815e28c-df86-4721-9cd3-57f1b397d2b9", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GN_CRS2_LM algo: XTOL_REACHED after 27579 iterations. \n", - "x*=[23347.453054, 16995.434773, 1171.625098, 145.151887, 1069.897236, 3671.421094]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23474 iterations. \n", - "x*=[14479.12082, 12330.740779, 1454.01445, 357.983297, 1398.000414, 6604.40332]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 43408 iterations. \n", - "x*=[11292.04383, 12381.74684, 1513.152367, 403.535709, 1404.807522, 7483.875178]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 36424 iterations. \n", - "x*=[14368.285183, 12086.645801, 1466.594398, 377.82366, 1397.999828, 6644.018807]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 27129 iterations. \n", - "x*=[23288.523592, 13932.486344, 1503.644564, 153.792031, 1148.491, 3589.505247]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 35784 iterations. \n", - "x*=[9411.805542, 7854.990826, 1838.971553, 691.517119, 1467.810727, 8330.188014]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20457 iterations. \n", - "x*=[14087.670875, 11110.284058, 1784.509317, 329.459563, 1385.015408, 5878.375851]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 22029 iterations. \n", - "x*=[14405.253374, 11813.932567, 1496.122689, 383.340527, 1397.999977, 6584.038891]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 47439 iterations. \n", - "x*=[10611.466631, 11232.66635, 1512.622291, 564.376584, 1407.365635, 7994.852876]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 38529 iterations. \n", - "x*=[7131.232799, 10966.715354, 1812.920408, 638.289819, 1492.371105, 8242.184069]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 27793 iterations. \n", - "x*=[11264.102655, 6639.264557, 1877.7, 760.310972, 1487.403058, 8711.156151]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 53255 iterations. \n", - "x*=[8094.484481, 7860.159018, 1821.057204, 805.562698, 1464.654945, 8844.206887]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 59720 iterations. \n", - "x*=[9851.84816, 6717.642763, 1877.699976, 788.660908, 1469.781366, 8911.878499]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 28291 iterations. \n", - "x*=[9691.559945, 6717.642801, 1877.699995, 795.756824, 1476.270073, 8946.069339]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 34600 iterations. \n", - "x*=[10134.563139, 6639.847971, 1871.211462, 798.094289, 1475.43316, 8959.900061]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 45924 iterations. \n", - "x*=[9419.558271, 6405.206432, 1877.7, 868.966092, 1487.384934, 9289.30934]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 91208 iterations. \n", - "x*=[8600.945503, 6687.8133, 1877.425377, 873.069392, 1477.280538, 9208.912174]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 64488 iterations. \n", - "x*=[8044.561869, 6483.919668, 1877.699989, 961.552616, 1487.988272, 9527.776927]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 37047 iterations. \n", - "x*=[7756.01255, 5543.964486, 1877.7, 1022.518717, 1489.759176, 11280.19173]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20018 iterations. \n", - "x*=[7433.16685, 5424.047781, 1877.699992, 1024.595648, 1462.097288, 11686.878832]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23944 iterations. \n", - "x*=[6699.602337, 4410.279562, 1876.715621, 967.93892, 1454.402581, 14622.247988]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 29661 iterations. \n", - "x*=[6698.183894, 4419.573597, 1877.69999, 998.395432, 1425.187712, 14473.152826]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 34555 iterations. \n", - "x*=[5831.249527, 3962.219673, 1877.7, 939.264247, 1428.117955, 16339.083631]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 26570 iterations. \n", - "x*=[5832.553034, 3963.289726, 1877.699997, 937.80574, 1403.29558, 16318.477008]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 19486 iterations. \n", - "x*=[5440.601761, 3966.690102, 1877.7, 955.149671, 1398.0976, 16504.400391]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20077 iterations. \n", - "x*=[5425.779695, 3968.692118, 1877.7, 955.544439, 1384.652854, 16487.336099]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 21239 iterations. \n", - "x*=[5426.769876, 3971.500979, 1877.7, 953.302231, 1365.790773, 16463.057204]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16320 iterations. \n", - "x*=[5427.491355, 3995.177625, 1877.7, 944.750723, 1352.045948, 16413.865385]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 19664 iterations. \n", - "x*=[5435.27913, 3973.924764, 1877.7, 950.240831, 1341.432578, 16421.884026]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 14452 iterations. \n", - "x*=[5428.648025, 3974.997594, 1877.699994, 949.124293, 1330.01409, 16407.002081]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16699 iterations. \n", - "x*=[5429.31133, 3999.484389, 1877.7, 940.418351, 1317.381037, 16356.813923]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23355 iterations. \n", - "x*=[5213.88283, 3994.23801, 1877.7, 942.980111, 1311.276586, 16502.505703]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23601 iterations. \n", - "x*=[5430.217543, 3975.536951, 1877.7, 945.985404, 1300.122465, 16355.646134]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20378 iterations. \n", - "x*=[5182.995462, 4002.3016, 1877.699994, 937.683782, 1294.698428, 16485.381508]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16317 iterations. \n", - "x*=[5431.090714, 3975.836686, 1877.7, 943.946247, 1283.485224, 16323.922167]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23448 iterations. \n", - "x*=[5431.624254, 4004.956494, 1877.7, 934.622483, 1273.327917, 16267.159026]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 19040 iterations. \n", - "x*=[5432.052693, 4005.970452, 1877.7, 932.743124, 1265.16652, 16252.641495]\n" - ] - } - ], - "source": [ - " algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "pareto_lcoe1 = zeros(Float64,37)\n", - "maxeval=100000\n", - "for i in eachindex(pareto_shed_new)\n", - " xopt, ret, numevals = optim_mg(x0,capex_def, pareto_shed_new[i],dispatch_1, algo, maxeval)\n", - " lcoe_opt, shed_rate_opt = obj_multi(xopt,capex_def,dispatch_1)\n", - " mg = new_microgrid(xopt) ;\n", - " pareto_lcoe1[i]=lcoe_opt\n", - " @printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt*1000; digits=6)) # kW\n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "c5028b91-d889-4aa9-a362-c73641e02914", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "37-element Vector{Float64}:\n", - " 0.5191261655500403\n", - " 0.4274416512728466\n", - " 0.4168988491304812\n", - " 0.42413328106116827\n", - " 0.4891206964493164\n", - " 0.36157491950261683\n", - " 0.4184650491601368\n", - " 0.42143634282172665\n", - " 0.39983759213324405\n", - " 0.3945343534500265\n", - " 0.359868392102726\n", - " 0.3575728953788394\n", - " 0.3535145026153155\n", - " ⋮\n", - " 0.33358420913900316\n", - " 0.33317240505760065\n", - " 0.33288403545080547\n", - " 0.33259178476622053\n", - " 0.3323038865016544\n", - " 0.3320324002658368\n", - " 0.33182090244724916\n", - " 0.3315309115556263\n", - " 0.3313519234645792\n", - " 0.3310820186183037\n", - " 0.33086752538870745\n", - " 0.3306481155866563" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_lcoe1" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "46f3150f-27fb-45dc-9489-353b1202b7c5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1-element Vector{PyObject}:\n", - " PyObject " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "plot(pareto_shed_new, pareto_lcoe1,\"or\")" - ] - }, - { - "cell_type": "markdown", - "id": "4eda341b-6228-4bff-8be9-943592999959", - "metadata": {}, - "source": [ - "#### Dispatch 2" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4f921ae7-214f-42be-861b-24a0d7a81fca", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "21-element Vector{Float64}:\n", - " 0.0\n", - " 5.0e-5\n", - " 0.0001\n", - " 0.00015\n", - " 0.0002\n", - " 0.00025\n", - " 0.0003\n", - " 0.00035\n", - " 0.0004\n", - " 0.00045\n", - " 0.0005\n", - " 0.00055\n", - " 0.0006\n", - " 0.00065\n", - " 0.0007\n", - " 0.00075\n", - " 0.0008\n", - " 0.00085\n", - " 0.0009\n", - " 0.00095\n", - " 0.001" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_shed=collect(0.0:0.00005:0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4b136793-6969-463b-94a9-5dc3706e82e8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GN_CRS2_LM algo: XTOL_REACHED after 15658 iterations. \n", - "x*=[385.10361, 5736.596414, 1877.7, 1824.243216, 1451.582529, 18784.003256]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16156 iterations. \n", - "x*=[283.262884, 5851.085497, 1877.7, 1787.004647, 1433.122784, 18681.967208]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 30896 iterations. \n", - "x*=[234.617202, 5718.847261, 1877.7, 1831.120711, 1418.332267, 18770.023747]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 19427 iterations. \n", - "x*=[213.034068, 5729.508642, 1877.7, 1827.352604, 1398.602756, 18744.084675]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 17800 iterations. \n", - "x*=[207.048184, 5733.970119, 1877.699999, 1825.041847, 1382.46422, 18725.700322]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 24532 iterations. \n", - "x*=[207.047483, 5743.432015, 1877.7, 1822.431568, 1365.557753, 18697.878631]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 19744 iterations. \n", - "x*=[207.047212, 5745.756866, 1877.7, 1820.428026, 1354.513028, 18677.917241]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 18188 iterations. \n", - "x*=[207.047855, 5755.624997, 1877.699999, 1816.554274, 1344.165622, 18651.691569]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20687 iterations. \n", - "x*=[207.048372, 5763.406932, 1877.7, 1813.356553, 1333.835591, 18628.641788]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16274 iterations. \n", - "x*=[207.069742, 5758.648372, 1877.69993, 1814.406736, 1323.943596, 18613.424205]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 21208 iterations. \n", - "x*=[204.332353, 5763.237138, 1877.7, 1812.45612, 1313.059678, 18593.573633]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 18598 iterations. \n", - "x*=[195.520723, 5766.096522, 1877.7, 1811.087593, 1306.283173, 18576.786285]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 23352 iterations. \n", - "x*=[201.723754, 5589.973737, 1877.699999, 1872.408294, 1298.259449, 18709.566988]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20915 iterations. \n", - "x*=[201.867367, 5587.171543, 1877.699997, 1872.408457, 1288.720283, 18699.002137]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 21158 iterations. \n", - "x*=[207.047022, 5576.870538, 1877.7, 1875.09313, 1280.250877, 18686.808198]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 20557 iterations. \n", - "x*=[207.04718, 5586.65935, 1877.7, 1870.391565, 1272.805158, 18663.79938]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 26457 iterations. \n", - "x*=[201.773157, 5589.011838, 1877.7, 1868.661675, 1265.513547, 18649.823463]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 16320 iterations. \n", - "x*=[201.078392, 5674.467114, 1877.699998, 1837.465756, 1258.128843, 18560.602928]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 24210 iterations. \n", - "x*=[181.771774, 5551.334946, 1877.7, 1881.373862, 1255.118735, 18658.113108]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 30038 iterations. \n", - "x*=[201.08262, 5558.494099, 1877.7, 1877.709364, 1245.537604, 18631.73682]\n", - "GN_CRS2_LM algo: XTOL_REACHED after 18864 iterations. \n", - "x*=[199.970551, 5623.957623, 1877.7, 1853.322059, 1238.732818, 18557.772978]\n" - ] - } - ], - "source": [ - " algo = :GN_CRS2_LM # could be one of GN_CRS2_LM, GN_DIRECT, GN_ESCH, LN_SBPLX...\n", - "pareto_lcoe2 = zeros(Float64,21)\n", - "maxeval=100000\n", - "xtol_rel=1e-8\n", - "srand=6\n", - "for i in eachindex(pareto_shed)\n", - " xopt, ret, numevals = optim_mg(x0,capex_def, pareto_shed[i],dispatch_2, algo, maxeval,xtol_rel,srand)\n", - " lcoe_opt, shed_rate_opt,costs = obj_multi(xopt,capex_def,dispatch_2)\n", - " pareto_lcoe2[i]=lcoe_opt\n", - " @printf(\"%s algo: %s after %d iterations. \\nx*=\", algo, ret, numevals)\n", - "println(round.(xopt*1000; digits=6)) # kW\n", - " \n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "4c5597f2-67ab-4251-85b7-fc0f69a98051", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "21-element Vector{Float64}:\n", - " 0.39078145064078484\n", - " 0.3897167595872884\n", - " 0.3889187421559924\n", - " 0.3882208773709961\n", - " 0.3877457126222478\n", - " 0.3872830140871662\n", - " 0.3869314617040828\n", - " 0.3866093997742454\n", - " 0.3862748492377523\n", - " 0.3859420172864854\n", - " 0.3855989821868398\n", - " 0.3853056534794736\n", - " 0.3850450309623384\n", - " 0.38475375716614985\n", - " 0.38445168894265797\n", - " 0.3841779167937007\n", - " 0.38391067889055736\n", - " 0.3836582968516015\n", - " 0.38340062329469277\n", - " 0.38318131724331905\n", - " 0.3829285732063151" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pareto_lcoe2" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6b59a6da-3163-44c3-9d53-b658fbc27e53", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "1-element Vector{PyObject}:\n", - " PyObject " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot(pareto_shed, pareto_lcoe2,\"ob\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "545e2ceb-d8c9-41e1-875f-ec52e88eb5e5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "UndefVarError: `mg_hi` not defined", - "output_type": "error", - "traceback": [ - "UndefVarError: `mg_hi` not defined", - "", - "Stacktrace:", - " [1] top-level scope", - " @ In[24]:2" - ] - } - ], - "source": [ - "if with_plots\n", - " fig,(ax1,ax2,ax3,ax4) = comp_oper_traj(mg_base,mg_hi, traj1,traj2,costs1,costs2)\n", - " \n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "67910661-b3aa-49e0-83be-ff3fdf8fa7d6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
0×17 DataFrame
Rowfc_elyz_capexshed_maxdispatchalgosrandretnumevalslcoe_optshed_rate_optcapexopexxopt_stoxopt_pvxopt_windxopt_elyzxopt_fcxopt_hyTank
Float64Float64Int64StringInt64StringInt64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
" - ], - "text/latex": [ - "\\begin{tabular}{r|cccccccccc}\n", - "\t& fc\\_elyz\\_capex & shed\\_max & dispatch & algo & srand & ret & numevals & lcoe\\_opt & shed\\_rate\\_opt & \\\\\n", - "\t\\hline\n", - "\t& Float64 & Float64 & Int64 & String & Int64 & String & Int64 & Float64 & Float64 & \\\\\n", - "\t\\hline\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m0×17 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m fc_elyz_capex \u001b[0m\u001b[1m shed_max \u001b[0m\u001b[1m dispatch \u001b[0m\u001b[1m algo \u001b[0m\u001b[1m srand \u001b[0m\u001b[1m ret \u001b[0m\u001b[1m numevals \u001b[0m\u001b[1m lco\u001b[0m ⋯\n", - " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Flo\u001b[0m ⋯\n", - "─────┴──────────────────────────────────────────────────────────────────────────\n", - "\u001b[36m 10 columns omitted\u001b[0m" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2 = DataFrame( fc_elyz_capex=Float64[],shed_max=Float64[],dispatch=Int64[],algo=String[],\n", - " srand=Int[], ret=String[],numevals=Int64[],\n", - " lcoe_opt=Float64[], shed_rate_opt=Float64[], capex=Float64[],opex=Float64[],\n", - " xopt_sto=Float64[], xopt_pv=Float64[], xopt_wind=Float64[] ,xopt_elyz=Float64[], \n", - " xopt_fc=Float64[], xopt_hyTank=Float64[]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9f5dbe3-4795-4411-8ecc-06443e479ff9", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "afb25a12-2269-4c22-9f7a-3ae5680bb956", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6×4 Matrix{Float64}:\n", - " 28.1193 28.1193 8.05818 12.5224\n", - " 15.5399 15.5399 5.9157 8.94308\n", - " 0.309005 0.309005 0.58717 0.394798\n", - " 1.5135 1.5135 0.0675057 6.99305\n", - " 0.952097 0.952097 1.66812 1.71168\n", - " 5.21002 5.21002 5.40184 14.3223" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"\"\"uniformly random vector between xmin and xmax, for a given PRNG seed\"\"\"\n", - "function x0_rng(seed, xmin, xmax)\n", - " n = length(xmin)\n", - " rng = MersenneTwister(seed)\n", - " xmin + rand(rng, n).*(xmax-xmin)\n", - "end\n", - "# Test with 3 different seeds:\n", - "[x0_rng(0, xmin, xmax) x0_rng(0, xmin, xmax) x0_rng(1, xmin, xmax) x0_rng(2, xmin, xmax)]\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "55334e07-879f-4379-9867-eab2499d35b2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5-element Vector{Float64}:\n", - " 0.0\n", - " 1.0e-5\n", - " 0.0001\n", - " 0.001\n", - " 0.01" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nrand = 10\n", - "srand_list = collect(1:nrand)\n", - "algo_list = [:GN_CRS2_LM, :GN_ISRES, :LN_SBPLX]\n", - "shed_max_list = [0.,0.00001,0.0001, 0.001, 0.01]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "b7019192-4f8c-453e-8773-84baeb0ef5ad", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6-element Vector{Float64}:\n", - " 8.058178420902282\n", - " 5.915698915242573\n", - " 0.587169874444634\n", - " 0.06750573373843591\n", - " 1.668124201891417\n", - " 5.4018408162693134" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " x0 = x0_rng(1, xmin, xmax)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "5dc551bc-c188-46f3-9b17-999e1eefdba8", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Vector{Vector{Float64}}:\n", - " [400.0, 0.0, 1600.0, 500.0, 1600.0, 350.0, 1200.0, 3500.0]\n", - " [400.0, 0.0, 2200.0, 500.0, 2200.0, 350.0, 1200.0, 3500.0]" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "capexlist" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "3cfcf179-78f9-46be-8198-70b9b4200fba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1, GN_CRS2_LM algo: XTOL_REACHED after 21888 iterations. 2, GN_CRS2_LM algo: XTOL_REACHED after 23035 iterations. 3, GN_CRS2_LM algo: XTOL_REACHED after 20841 iterations. 4, GN_CRS2_LM algo: XTOL_REACHED after 33104 iterations. 5, GN_CRS2_LM algo: XTOL_REACHED after 19458 iterations. 6, GN_CRS2_LM algo: XTOL_REACHED after 34149 iterations. 7, GN_CRS2_LM algo: XTOL_REACHED after 13242 iterations. 8, GN_CRS2_LM algo: XTOL_REACHED after 13759 iterations. 9, GN_CRS2_LM algo: XTOL_REACHED after 15504 iterations. 10, GN_CRS2_LM algo: XTOL_REACHED after 17949 iterations. 11, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 12, GN_ISRES algo: XTOL_REACHED after 66964 iterations. 13, GN_ISRES algo: XTOL_REACHED after 72958 iterations. 14, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 15, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 16, GN_ISRES algo: XTOL_REACHED after 67785 iterations. 17, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 18, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 19, GN_ISRES algo: XTOL_REACHED after 68181 iterations. 20, GN_ISRES algo: XTOL_REACHED after 73784 iterations. 21, LN_SBPLX algo: XTOL_REACHED after 863 iterations. 22, LN_SBPLX algo: XTOL_REACHED after 1757 iterations. 23, LN_SBPLX algo: XTOL_REACHED after 1019 iterations. 24, LN_SBPLX algo: XTOL_REACHED after 1007 iterations. 25, LN_SBPLX algo: XTOL_REACHED after 1199 iterations. 26, LN_SBPLX algo: XTOL_REACHED after 1941 iterations. 27, LN_SBPLX algo: XTOL_REACHED after 1247 iterations. 28, LN_SBPLX algo: XTOL_REACHED after 1383 iterations. 29, LN_SBPLX algo: XTOL_REACHED after 1706 iterations. 30, LN_SBPLX algo: XTOL_REACHED after 1086 iterations. 31, GN_CRS2_LM algo: XTOL_REACHED after 15356 iterations. 32, GN_CRS2_LM algo: XTOL_REACHED after 16166 iterations. 33, GN_CRS2_LM algo: XTOL_REACHED after 15653 iterations. 34, GN_CRS2_LM algo: XTOL_REACHED after 14559 iterations. 35, GN_CRS2_LM algo: XTOL_REACHED after 15257 iterations. 36, GN_CRS2_LM algo: XTOL_REACHED after 18828 iterations. 37, GN_CRS2_LM algo: XTOL_REACHED after 22965 iterations. 38, GN_CRS2_LM algo: XTOL_REACHED after 14908 iterations. 39, GN_CRS2_LM algo: XTOL_REACHED after 16116 iterations. 40, GN_CRS2_LM algo: XTOL_REACHED after 14225 iterations. 41, GN_ISRES algo: XTOL_REACHED after 69022 iterations. 42, GN_ISRES algo: XTOL_REACHED after 69871 iterations. 43, GN_ISRES algo: XTOL_REACHED after 65848 iterations. 44, GN_ISRES algo: XTOL_REACHED after 65225 iterations. 45, GN_ISRES algo: XTOL_REACHED after 64431 iterations. 46, GN_ISRES algo: XTOL_REACHED after 68925 iterations. 47, GN_ISRES algo: XTOL_REACHED after 63912 iterations. 48, GN_ISRES algo: XTOL_REACHED after 62425 iterations. 49, GN_ISRES algo: XTOL_REACHED after 66683 iterations. 50, GN_ISRES algo: XTOL_REACHED after 67153 iterations. 51, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 52, LN_SBPLX algo: XTOL_REACHED after 2583 iterations. 53, LN_SBPLX algo: XTOL_REACHED after 790 iterations. 54, LN_SBPLX algo: XTOL_REACHED after 1682 iterations. 55, LN_SBPLX algo: XTOL_REACHED after 1009 iterations. 56, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 57, LN_SBPLX algo: XTOL_REACHED after 402 iterations. 58, LN_SBPLX algo: XTOL_REACHED after 2070 iterations. 59, LN_SBPLX algo: XTOL_REACHED after 1617 iterations. 60, LN_SBPLX algo: XTOL_REACHED after 694 iterations. 61, GN_CRS2_LM algo: XTOL_REACHED after 27534 iterations. 62, GN_CRS2_LM algo: XTOL_REACHED after 33577 iterations. 63, GN_CRS2_LM algo: XTOL_REACHED after 52262 iterations. 64, GN_CRS2_LM algo: XTOL_REACHED after 27103 iterations. 65, GN_CRS2_LM algo: XTOL_REACHED after 26649 iterations. 66, GN_CRS2_LM algo: XTOL_REACHED after 31949 iterations. 67, GN_CRS2_LM algo: XTOL_REACHED after 22780 iterations. 68, GN_CRS2_LM algo: XTOL_REACHED after 55790 iterations. 69, GN_CRS2_LM algo: XTOL_REACHED after 35955 iterations. 70, GN_CRS2_LM algo: XTOL_REACHED after 20555 iterations. 71, GN_ISRES algo: XTOL_REACHED after 73502 iterations. 72, GN_ISRES algo: XTOL_REACHED after 70926 iterations. 73, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 74, GN_ISRES algo: XTOL_REACHED after 69016 iterations. 75, GN_ISRES algo: XTOL_REACHED after 74690 iterations. 76, GN_ISRES algo: XTOL_REACHED after 71783 iterations. 77, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 78, GN_ISRES algo: XTOL_REACHED after 71600 iterations. 79, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 80, GN_ISRES algo: XTOL_REACHED after 70274 iterations. 81, LN_SBPLX algo: XTOL_REACHED after 705 iterations. 82, LN_SBPLX algo: XTOL_REACHED after 766 iterations. 83, LN_SBPLX algo: XTOL_REACHED after 859 iterations. 84, LN_SBPLX algo: XTOL_REACHED after 836 iterations. 85, LN_SBPLX algo: XTOL_REACHED after 898 iterations. 86, LN_SBPLX algo: XTOL_REACHED after 496 iterations. 87, LN_SBPLX algo: XTOL_REACHED after 1241 iterations. 88, LN_SBPLX algo: XTOL_REACHED after 1173 iterations. 89, LN_SBPLX algo: XTOL_REACHED after 2103 iterations. 90, LN_SBPLX algo: XTOL_REACHED after 1223 iterations. 91, GN_CRS2_LM algo: XTOL_REACHED after 16955 iterations. 92, GN_CRS2_LM algo: XTOL_REACHED after 17990 iterations. 93, GN_CRS2_LM algo: XTOL_REACHED after 22652 iterations. 94, GN_CRS2_LM algo: XTOL_REACHED after 16653 iterations. 95, GN_CRS2_LM algo: XTOL_REACHED after 18456 iterations. 96, GN_CRS2_LM algo: XTOL_REACHED after 17265 iterations. 97, GN_CRS2_LM algo: XTOL_REACHED after 15905 iterations. 98, GN_CRS2_LM algo: XTOL_REACHED after 16731 iterations. 99, GN_CRS2_LM algo: XTOL_REACHED after 19484 iterations. 100, GN_CRS2_LM algo: XTOL_REACHED after 14744 iterations. 101, GN_ISRES algo: XTOL_REACHED after 68648 iterations. 102, GN_ISRES algo: XTOL_REACHED after 65245 iterations. 103, GN_ISRES algo: XTOL_REACHED after 75041 iterations. 104, GN_ISRES algo: XTOL_REACHED after 69922 iterations. 105, GN_ISRES algo: XTOL_REACHED after 72519 iterations. 106, GN_ISRES algo: XTOL_REACHED after 88361 iterations. 107, GN_ISRES algo: XTOL_REACHED after 70582 iterations. 108, GN_ISRES algo: XTOL_REACHED after 70321 iterations. 109, GN_ISRES algo: XTOL_REACHED after 67986 iterations. 110, GN_ISRES algo: XTOL_REACHED after 72993 iterations. 111, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 112, LN_SBPLX algo: XTOL_REACHED after 1085 iterations. 113, LN_SBPLX algo: XTOL_REACHED after 791 iterations. 114, LN_SBPLX algo: XTOL_REACHED after 1449 iterations. 115, LN_SBPLX algo: XTOL_REACHED after 671 iterations. 116, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 117, LN_SBPLX algo: XTOL_REACHED after 1214 iterations. 118, LN_SBPLX algo: XTOL_REACHED after 1540 iterations. 119, LN_SBPLX algo: XTOL_REACHED after 2885 iterations. 120, LN_SBPLX algo: XTOL_REACHED after 1515 iterations. 121, GN_CRS2_LM algo: XTOL_REACHED after 27045 iterations. 122, GN_CRS2_LM algo: XTOL_REACHED after 18078 iterations. 123, GN_CRS2_LM algo: XTOL_REACHED after 53169 iterations. 124, GN_CRS2_LM algo: XTOL_REACHED after 17783 iterations. 125, GN_CRS2_LM algo: XTOL_REACHED after 25340 iterations. 126, GN_CRS2_LM algo: XTOL_REACHED after 50245 iterations. 127, GN_CRS2_LM algo: XTOL_REACHED after 24327 iterations. 128, GN_CRS2_LM algo: XTOL_REACHED after 40327 iterations. 129, GN_CRS2_LM algo: XTOL_REACHED after 25819 iterations. 130, GN_CRS2_LM algo: XTOL_REACHED after 22809 iterations. 131, GN_ISRES algo: XTOL_REACHED after 68260 iterations. 132, GN_ISRES algo: XTOL_REACHED after 70554 iterations. 133, GN_ISRES algo: XTOL_REACHED after 69795 iterations. 134, GN_ISRES algo: XTOL_REACHED after 78437 iterations. 135, GN_ISRES algo: XTOL_REACHED after 69303 iterations. 136, GN_ISRES algo: XTOL_REACHED after 70090 iterations. 137, GN_ISRES algo: XTOL_REACHED after 71151 iterations. 138, GN_ISRES algo: XTOL_REACHED after 73061 iterations. 139, GN_ISRES algo: XTOL_REACHED after 65846 iterations. 140, GN_ISRES algo: XTOL_REACHED after 69739 iterations. 141, LN_SBPLX algo: XTOL_REACHED after 1196 iterations. 142, LN_SBPLX algo: XTOL_REACHED after 1254 iterations. 143, LN_SBPLX algo: XTOL_REACHED after 981 iterations. 144, LN_SBPLX algo: XTOL_REACHED after 1088 iterations. 145, LN_SBPLX algo: XTOL_REACHED after 1541 iterations. 146, LN_SBPLX algo: XTOL_REACHED after 2116 iterations. 147, LN_SBPLX algo: XTOL_REACHED after 1274 iterations. 148, LN_SBPLX algo: XTOL_REACHED after 967 iterations. 149, LN_SBPLX algo: XTOL_REACHED after 1285 iterations. 150, LN_SBPLX algo: XTOL_REACHED after 721 iterations. 151, GN_CRS2_LM algo: XTOL_REACHED after 23526 iterations. 152, GN_CRS2_LM algo: XTOL_REACHED after 23743 iterations. 153, GN_CRS2_LM algo: XTOL_REACHED after 26097 iterations. 154, GN_CRS2_LM algo: XTOL_REACHED after 28478 iterations. 155, GN_CRS2_LM algo: XTOL_REACHED after 27480 iterations. 156, GN_CRS2_LM algo: XTOL_REACHED after 42015 iterations. 157, GN_CRS2_LM algo: XTOL_REACHED after 28229 iterations. 158, GN_CRS2_LM algo: XTOL_REACHED after 26720 iterations. 159, GN_CRS2_LM algo: XTOL_REACHED after 22279 iterations. 160, GN_CRS2_LM algo: XTOL_REACHED after 41152 iterations. 161, GN_ISRES algo: XTOL_REACHED after 75301 iterations. 162, GN_ISRES algo: XTOL_REACHED after 64747 iterations. 163, GN_ISRES algo: XTOL_REACHED after 65022 iterations. 164, GN_ISRES algo: XTOL_REACHED after 63800 iterations. 165, GN_ISRES algo: XTOL_REACHED after 71515 iterations. 166, GN_ISRES algo: XTOL_REACHED after 70722 iterations. 167, GN_ISRES algo: XTOL_REACHED after 67666 iterations. 168, GN_ISRES algo: XTOL_REACHED after 70520 iterations. 169, GN_ISRES algo: XTOL_REACHED after 74930 iterations. 170, GN_ISRES algo: XTOL_REACHED after 63631 iterations. 171, LN_SBPLX algo: XTOL_REACHED after 495 iterations. 172, LN_SBPLX algo: XTOL_REACHED after 2043 iterations. 173, LN_SBPLX algo: XTOL_REACHED after 907 iterations. 174, LN_SBPLX algo: XTOL_REACHED after 1656 iterations. 175, LN_SBPLX algo: XTOL_REACHED after 1460 iterations. 176, LN_SBPLX algo: XTOL_REACHED after 518 iterations. 177, LN_SBPLX algo: XTOL_REACHED after 408 iterations. 178, LN_SBPLX algo: XTOL_REACHED after 1114 iterations. 179, LN_SBPLX algo: XTOL_REACHED after 1941 iterations. 180, LN_SBPLX algo: XTOL_REACHED after 734 iterations. 181, GN_CRS2_LM algo: XTOL_REACHED after 16538 iterations. 182, GN_CRS2_LM algo: XTOL_REACHED after 23842 iterations. 183, GN_CRS2_LM algo: XTOL_REACHED after 14490 iterations. 184, GN_CRS2_LM algo: XTOL_REACHED after 20208 iterations. 185, GN_CRS2_LM algo: XTOL_REACHED after 28194 iterations. 186, GN_CRS2_LM algo: XTOL_REACHED after 22852 iterations. 187, GN_CRS2_LM algo: XTOL_REACHED after 19966 iterations. 188, GN_CRS2_LM algo: XTOL_REACHED after 20662 iterations. 189, GN_CRS2_LM algo: XTOL_REACHED after 20560 iterations. 190, GN_CRS2_LM algo: XTOL_REACHED after 19650 iterations. 191, GN_ISRES algo: XTOL_REACHED after 64851 iterations. 192, GN_ISRES algo: XTOL_REACHED after 66709 iterations. 193, GN_ISRES algo: XTOL_REACHED after 73943 iterations. 194, GN_ISRES algo: XTOL_REACHED after 68454 iterations. 195, GN_ISRES algo: XTOL_REACHED after 75000 iterations. 196, GN_ISRES algo: XTOL_REACHED after 60686 iterations. 197, GN_ISRES algo: XTOL_REACHED after 72941 iterations. 198, GN_ISRES algo: XTOL_REACHED after 75061 iterations. 199, GN_ISRES algo: XTOL_REACHED after 73249 iterations. 200, GN_ISRES algo: XTOL_REACHED after 70289 iterations. 201, LN_SBPLX algo: XTOL_REACHED after 1781 iterations. 202, LN_SBPLX algo: XTOL_REACHED after 2292 iterations. 203, LN_SBPLX algo: XTOL_REACHED after 921 iterations. 204, LN_SBPLX algo: XTOL_REACHED after 858 iterations. 205, LN_SBPLX algo: XTOL_REACHED after 1450 iterations. 206, LN_SBPLX algo: XTOL_REACHED after 837 iterations. 207, LN_SBPLX algo: XTOL_REACHED after 1961 iterations. 208, LN_SBPLX algo: XTOL_REACHED after 2656 iterations. 209, LN_SBPLX algo: XTOL_REACHED after 2038 iterations. 210, LN_SBPLX algo: XTOL_REACHED after 773 iterations. 211, GN_CRS2_LM algo: XTOL_REACHED after 23358 iterations. 212, GN_CRS2_LM algo: XTOL_REACHED after 20003 iterations. 213, GN_CRS2_LM algo: XTOL_REACHED after 18591 iterations. 214, GN_CRS2_LM algo: XTOL_REACHED after 24654 iterations. 215, GN_CRS2_LM algo: XTOL_REACHED after 21259 iterations. 216, GN_CRS2_LM algo: XTOL_REACHED after 24244 iterations. 217, GN_CRS2_LM algo: XTOL_REACHED after 19315 iterations. 218, GN_CRS2_LM algo: XTOL_REACHED after 24602 iterations. 219, GN_CRS2_LM algo: XTOL_REACHED after 19726 iterations. 220, GN_CRS2_LM algo: XTOL_REACHED after 22409 iterations. 221, GN_ISRES algo: XTOL_REACHED after 65438 iterations. 222, GN_ISRES algo: XTOL_REACHED after 69035 iterations. 223, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 224, GN_ISRES algo: XTOL_REACHED after 61265 iterations. 225, GN_ISRES algo: XTOL_REACHED after 64950 iterations. 226, GN_ISRES algo: XTOL_REACHED after 68113 iterations. 227, GN_ISRES algo: XTOL_REACHED after 70704 iterations. 228, GN_ISRES algo: XTOL_REACHED after 67190 iterations. 229, GN_ISRES algo: XTOL_REACHED after 61612 iterations. 230, GN_ISRES algo: XTOL_REACHED after 73379 iterations. 231, LN_SBPLX algo: XTOL_REACHED after 510 iterations. 232, LN_SBPLX algo: XTOL_REACHED after 1710 iterations. 233, LN_SBPLX algo: XTOL_REACHED after 727 iterations. 234, LN_SBPLX algo: XTOL_REACHED after 1886 iterations. 235, LN_SBPLX algo: XTOL_REACHED after 1323 iterations. 236, LN_SBPLX algo: XTOL_REACHED after 515 iterations. 237, LN_SBPLX algo: XTOL_REACHED after 6116 iterations. 238, LN_SBPLX algo: XTOL_REACHED after 1166 iterations. 239, LN_SBPLX algo: XTOL_REACHED after 1617 iterations. 240, LN_SBPLX algo: XTOL_REACHED after 607 iterations. 241, GN_CRS2_LM algo: XTOL_REACHED after 24710 iterations. 242, GN_CRS2_LM algo: XTOL_REACHED after 31916 iterations. 243, GN_CRS2_LM algo: XTOL_REACHED after 26202 iterations. 244, GN_CRS2_LM algo: XTOL_REACHED after 24525 iterations. 245, GN_CRS2_LM algo: XTOL_REACHED after 29903 iterations. 246, GN_CRS2_LM algo: XTOL_REACHED after 20729 iterations. 247, GN_CRS2_LM algo: XTOL_REACHED after 24564 iterations. 248, GN_CRS2_LM algo: XTOL_REACHED after 23860 iterations. 249, GN_CRS2_LM algo: XTOL_REACHED after 21809 iterations. 250, GN_CRS2_LM algo: XTOL_REACHED after 22572 iterations. 251, GN_ISRES algo: XTOL_REACHED after 96515 iterations. 252, GN_ISRES algo: XTOL_REACHED after 78342 iterations. 253, GN_ISRES algo: XTOL_REACHED after 80328 iterations. 254, GN_ISRES algo: XTOL_REACHED after 87769 iterations. 255, GN_ISRES algo: XTOL_REACHED after 79259 iterations. 256, GN_ISRES algo: XTOL_REACHED after 77745 iterations. 257, GN_ISRES algo: XTOL_REACHED after 80064 iterations. 258, GN_ISRES algo: XTOL_REACHED after 76440 iterations. 259, GN_ISRES algo: XTOL_REACHED after 81831 iterations. 260, GN_ISRES algo: XTOL_REACHED after 78199 iterations. 261, LN_SBPLX algo: XTOL_REACHED after 3840 iterations. 262, LN_SBPLX algo: XTOL_REACHED after 1602 iterations. 263, LN_SBPLX algo: XTOL_REACHED after 1093 iterations. 264, LN_SBPLX algo: XTOL_REACHED after 2075 iterations. 265, LN_SBPLX algo: XTOL_REACHED after 2850 iterations. 266, LN_SBPLX algo: XTOL_REACHED after 1153 iterations. 267, LN_SBPLX algo: XTOL_REACHED after 3279 iterations. 268, LN_SBPLX algo: XTOL_REACHED after 668 iterations. 269, LN_SBPLX algo: XTOL_REACHED after 4280 iterations. 270, LN_SBPLX algo: XTOL_REACHED after 2148 iterations. 271, GN_CRS2_LM algo: XTOL_REACHED after 40941 iterations. 272, GN_CRS2_LM algo: XTOL_REACHED after 24908 iterations. 273, GN_CRS2_LM algo: XTOL_REACHED after 26502 iterations. 274, GN_CRS2_LM algo: XTOL_REACHED after 31417 iterations. 275, GN_CRS2_LM algo: XTOL_REACHED after 23586 iterations. 276, GN_CRS2_LM algo: XTOL_REACHED after 24466 iterations. 277, GN_CRS2_LM algo: XTOL_REACHED after 21057 iterations. 278, GN_CRS2_LM algo: XTOL_REACHED after 33134 iterations. 279, GN_CRS2_LM algo: XTOL_REACHED after 23282 iterations. 280, GN_CRS2_LM algo: XTOL_REACHED after 21436 iterations. 281, GN_ISRES algo: XTOL_REACHED after 72081 iterations. 282, GN_ISRES algo: XTOL_REACHED after 68744 iterations. 283, GN_ISRES algo: XTOL_REACHED after 70963 iterations. 284, GN_ISRES algo: XTOL_REACHED after 72601 iterations. 285, GN_ISRES algo: XTOL_REACHED after 69032 iterations. 286, GN_ISRES algo: XTOL_REACHED after 84002 iterations. 287, GN_ISRES algo: XTOL_REACHED after 72183 iterations. 288, GN_ISRES algo: XTOL_REACHED after 66216 iterations. 289, GN_ISRES algo: XTOL_REACHED after 78353 iterations. 290, GN_ISRES algo: XTOL_REACHED after 70123 iterations. 291, LN_SBPLX algo: XTOL_REACHED after 1719 iterations. 292, LN_SBPLX algo: XTOL_REACHED after 1319 iterations. 293, LN_SBPLX algo: XTOL_REACHED after 1548 iterations. 294, LN_SBPLX algo: XTOL_REACHED after 1991 iterations. 295, LN_SBPLX algo: XTOL_REACHED after 1492 iterations. 296, LN_SBPLX algo: XTOL_REACHED after 1098 iterations. 297, LN_SBPLX algo: XTOL_REACHED after 2402 iterations. 298, LN_SBPLX algo: XTOL_REACHED after 949 iterations. 299, LN_SBPLX algo: XTOL_REACHED after 1252 iterations. 300, LN_SBPLX algo: XTOL_REACHED after 1400 iterations. 301, GN_CRS2_LM algo: XTOL_REACHED after 22606 iterations. 302, GN_CRS2_LM algo: XTOL_REACHED after 26310 iterations. 303, GN_CRS2_LM algo: XTOL_REACHED after 38425 iterations. 304, GN_CRS2_LM algo: XTOL_REACHED after 21306 iterations. 305, GN_CRS2_LM algo: XTOL_REACHED after 24840 iterations. 306, GN_CRS2_LM algo: XTOL_REACHED after 41424 iterations. 307, GN_CRS2_LM algo: XTOL_REACHED after 27892 iterations. 308, GN_CRS2_LM algo: XTOL_REACHED after 18117 iterations. 309, GN_CRS2_LM algo: XTOL_REACHED after 45052 iterations. 310, GN_CRS2_LM algo: XTOL_REACHED after 20077 iterations. 311, GN_ISRES algo: XTOL_REACHED after 74931 iterations. 312, GN_ISRES algo: XTOL_REACHED after 65689 iterations. 313, GN_ISRES algo: XTOL_REACHED after 74093 iterations. 314, GN_ISRES algo: XTOL_REACHED after 67892 iterations. 315, GN_ISRES algo: XTOL_REACHED after 71502 iterations. 316, GN_ISRES algo: XTOL_REACHED after 70363 iterations. 317, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 318, GN_ISRES algo: XTOL_REACHED after 68820 iterations. 319, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 320, GN_ISRES algo: XTOL_REACHED after 70031 iterations. 321, LN_SBPLX algo: XTOL_REACHED after 892 iterations. 322, LN_SBPLX algo: XTOL_REACHED after 1189 iterations. 323, LN_SBPLX algo: XTOL_REACHED after 928 iterations. 324, LN_SBPLX algo: XTOL_REACHED after 1113 iterations. 325, LN_SBPLX algo: XTOL_REACHED after 1157 iterations. 326, LN_SBPLX algo: XTOL_REACHED after 1591 iterations. 327, LN_SBPLX algo: XTOL_REACHED after 1005 iterations. 328, LN_SBPLX algo: XTOL_REACHED after 1885 iterations. 329, LN_SBPLX algo: XTOL_REACHED after 936 iterations. 330, LN_SBPLX algo: XTOL_REACHED after 1441 iterations. 331, GN_CRS2_LM algo: XTOL_REACHED after 14473 iterations. 332, GN_CRS2_LM algo: XTOL_REACHED after 16479 iterations. 333, GN_CRS2_LM algo: XTOL_REACHED after 14267 iterations. 334, GN_CRS2_LM algo: XTOL_REACHED after 14175 iterations. 335, GN_CRS2_LM algo: XTOL_REACHED after 13845 iterations. 336, GN_CRS2_LM algo: XTOL_REACHED after 12397 iterations. 337, GN_CRS2_LM algo: XTOL_REACHED after 15777 iterations. 338, GN_CRS2_LM algo: XTOL_REACHED after 13851 iterations. 339, GN_CRS2_LM algo: XTOL_REACHED after 14228 iterations. 340, GN_CRS2_LM algo: XTOL_REACHED after 14237 iterations. 341, GN_ISRES algo: XTOL_REACHED after 70658 iterations. 342, GN_ISRES algo: XTOL_REACHED after 74822 iterations. 343, GN_ISRES algo: XTOL_REACHED after 68917 iterations. 344, GN_ISRES algo: XTOL_REACHED after 71369 iterations. 345, GN_ISRES algo: XTOL_REACHED after 65860 iterations. 346, GN_ISRES algo: XTOL_REACHED after 74551 iterations. 347, GN_ISRES algo: XTOL_REACHED after 63153 iterations. 348, GN_ISRES algo: XTOL_REACHED after 67124 iterations. 349, GN_ISRES algo: XTOL_REACHED after 66322 iterations. 350, GN_ISRES algo: XTOL_REACHED after 73525 iterations. 351, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 352, LN_SBPLX algo: XTOL_REACHED after 2055 iterations. 353, LN_SBPLX algo: XTOL_REACHED after 1083 iterations. 354, LN_SBPLX algo: XTOL_REACHED after 2022 iterations. 355, LN_SBPLX algo: XTOL_REACHED after 896 iterations. 356, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 357, LN_SBPLX algo: XTOL_REACHED after 1404 iterations. 358, LN_SBPLX algo: XTOL_REACHED after 1088 iterations. 359, LN_SBPLX algo: XTOL_REACHED after 1592 iterations. 360, LN_SBPLX algo: XTOL_REACHED after 838 iterations. 361, GN_CRS2_LM algo: XTOL_REACHED after 19633 iterations. 362, GN_CRS2_LM algo: XTOL_REACHED after 27915 iterations. 363, GN_CRS2_LM algo: XTOL_REACHED after 21443 iterations. 364, GN_CRS2_LM algo: XTOL_REACHED after 31081 iterations. 365, GN_CRS2_LM algo: XTOL_REACHED after 34474 iterations. 366, GN_CRS2_LM algo: XTOL_REACHED after 20944 iterations. 367, GN_CRS2_LM algo: XTOL_REACHED after 68829 iterations. 368, GN_CRS2_LM algo: XTOL_REACHED after 35717 iterations. 369, GN_CRS2_LM algo: XTOL_REACHED after 41410 iterations. 370, GN_CRS2_LM algo: MAXEVAL_REACHED after 100000 iterations. 371, GN_ISRES algo: XTOL_REACHED after 77464 iterations. 372, GN_ISRES algo: XTOL_REACHED after 70600 iterations. 373, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 374, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 375, GN_ISRES algo: XTOL_REACHED after 74468 iterations. 376, GN_ISRES algo: XTOL_REACHED after 69560 iterations. 377, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 378, GN_ISRES algo: XTOL_REACHED after 66558 iterations. 379, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 380, GN_ISRES algo: XTOL_REACHED after 75767 iterations. 381, LN_SBPLX algo: XTOL_REACHED after 852 iterations. 382, LN_SBPLX algo: XTOL_REACHED after 1325 iterations. 383, LN_SBPLX algo: XTOL_REACHED after 953 iterations. 384, LN_SBPLX algo: XTOL_REACHED after 758 iterations. 385, LN_SBPLX algo: XTOL_REACHED after 1489 iterations. 386, LN_SBPLX algo: XTOL_REACHED after 688 iterations. 387, LN_SBPLX algo: XTOL_REACHED after 1359 iterations. 388, LN_SBPLX algo: XTOL_REACHED after 1288 iterations. 389, LN_SBPLX algo: XTOL_REACHED after 1745 iterations. 390, LN_SBPLX algo: XTOL_REACHED after 1908 iterations. 391, GN_CRS2_LM algo: XTOL_REACHED after 15341 iterations. 392, GN_CRS2_LM algo: XTOL_REACHED after 14264 iterations. 393, GN_CRS2_LM algo: XTOL_REACHED after 14601 iterations. 394, GN_CRS2_LM algo: XTOL_REACHED after 15980 iterations. 395, GN_CRS2_LM algo: XTOL_REACHED after 15569 iterations. 396, GN_CRS2_LM algo: XTOL_REACHED after 13733 iterations. 397, GN_CRS2_LM algo: XTOL_REACHED after 15537 iterations. 398, GN_CRS2_LM algo: XTOL_REACHED after 14136 iterations. 399, GN_CRS2_LM algo: XTOL_REACHED after 15655 iterations. 400, GN_CRS2_LM algo: XTOL_REACHED after 13799 iterations. 401, GN_ISRES algo: MAXEVAL_REACHED after 100000 iterations. 402, GN_ISRES algo: XTOL_REACHED after 66165 iterations. 403, GN_ISRES algo: XTOL_REACHED after 72708 iterations. 404, GN_ISRES algo: XTOL_REACHED after 68931 iterations. 405, GN_ISRES algo: XTOL_REACHED after 70705 iterations. 406, GN_ISRES algo: XTOL_REACHED after 70794 iterations. 407, GN_ISRES algo: XTOL_REACHED after 62430 iterations. 408, GN_ISRES algo: XTOL_REACHED after 68191 iterations. 409, GN_ISRES algo: XTOL_REACHED after 63479 iterations. 410, GN_ISRES algo: XTOL_REACHED after 77859 iterations. 411, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 412, LN_SBPLX algo: XTOL_REACHED after 1026 iterations. 413, LN_SBPLX algo: XTOL_REACHED after 850 iterations. 414, LN_SBPLX algo: XTOL_REACHED after 1349 iterations. 415, LN_SBPLX algo: XTOL_REACHED after 2010 iterations. 416, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 417, LN_SBPLX algo: XTOL_REACHED after 1754 iterations. 418, LN_SBPLX algo: XTOL_REACHED after 2081 iterations. 419, LN_SBPLX algo: XTOL_REACHED after 1309 iterations. 420, LN_SBPLX algo: XTOL_REACHED after 842 iterations. 421, GN_CRS2_LM algo: XTOL_REACHED after 46935 iterations. 422, GN_CRS2_LM algo: XTOL_REACHED after 28426 iterations. 423, GN_CRS2_LM algo: XTOL_REACHED after 40841 iterations. 424, GN_CRS2_LM algo: XTOL_REACHED after 29448 iterations. 425, GN_CRS2_LM algo: XTOL_REACHED after 27575 iterations. 426, GN_CRS2_LM algo: XTOL_REACHED after 25576 iterations. 427, GN_CRS2_LM algo: XTOL_REACHED after 25562 iterations. 428, GN_CRS2_LM algo: XTOL_REACHED after 40021 iterations. 429, GN_CRS2_LM algo: XTOL_REACHED after 31471 iterations. 430, GN_CRS2_LM algo: XTOL_REACHED after 56004 iterations. 431, GN_ISRES algo: XTOL_REACHED after 70770 iterations. 432, GN_ISRES algo: XTOL_REACHED after 70792 iterations. 433, GN_ISRES algo: XTOL_REACHED after 70044 iterations. 434, GN_ISRES algo: XTOL_REACHED after 72570 iterations. 435, GN_ISRES algo: XTOL_REACHED after 69314 iterations. 436, GN_ISRES algo: XTOL_REACHED after 65103 iterations. 437, GN_ISRES algo: XTOL_REACHED after 68601 iterations. 438, GN_ISRES algo: XTOL_REACHED after 67645 iterations. 439, GN_ISRES algo: XTOL_REACHED after 73987 iterations. 440, GN_ISRES algo: XTOL_REACHED after 75779 iterations. 441, LN_SBPLX algo: XTOL_REACHED after 1018 iterations. 442, LN_SBPLX algo: XTOL_REACHED after 1030 iterations. 443, LN_SBPLX algo: XTOL_REACHED after 856 iterations. 444, LN_SBPLX algo: XTOL_REACHED after 775 iterations. 445, LN_SBPLX algo: XTOL_REACHED after 1914 iterations. 446, LN_SBPLX algo: XTOL_REACHED after 1062 iterations. 447, LN_SBPLX algo: XTOL_REACHED after 1697 iterations. 448, LN_SBPLX algo: XTOL_REACHED after 1425 iterations. 449, LN_SBPLX algo: XTOL_REACHED after 1378 iterations. 450, LN_SBPLX algo: XTOL_REACHED after 1181 iterations. 451, GN_CRS2_LM algo: XTOL_REACHED after 19160 iterations. 452, GN_CRS2_LM algo: XTOL_REACHED after 22517 iterations. 453, GN_CRS2_LM algo: XTOL_REACHED after 17847 iterations. 454, GN_CRS2_LM algo: XTOL_REACHED after 21926 iterations. 455, GN_CRS2_LM algo: XTOL_REACHED after 18903 iterations. 456, GN_CRS2_LM algo: XTOL_REACHED after 17194 iterations. 457, GN_CRS2_LM algo: XTOL_REACHED after 15868 iterations. 458, GN_CRS2_LM algo: XTOL_REACHED after 19586 iterations. 459, GN_CRS2_LM algo: XTOL_REACHED after 23611 iterations. 460, GN_CRS2_LM algo: XTOL_REACHED after 17792 iterations. 461, GN_ISRES algo: XTOL_REACHED after 68614 iterations. 462, GN_ISRES algo: XTOL_REACHED after 67463 iterations. 463, GN_ISRES algo: XTOL_REACHED after 72160 iterations. 464, GN_ISRES algo: XTOL_REACHED after 73615 iterations. 465, GN_ISRES algo: XTOL_REACHED after 66520 iterations. 466, GN_ISRES algo: XTOL_REACHED after 69112 iterations. 467, GN_ISRES algo: XTOL_REACHED after 59609 iterations. 468, GN_ISRES algo: XTOL_REACHED after 64054 iterations. 469, GN_ISRES algo: XTOL_REACHED after 66795 iterations. 470, GN_ISRES algo: XTOL_REACHED after 70159 iterations. 471, LN_SBPLX algo: XTOL_REACHED after 536 iterations. 472, LN_SBPLX algo: XTOL_REACHED after 1383 iterations. 473, LN_SBPLX algo: XTOL_REACHED after 874 iterations. 474, LN_SBPLX algo: XTOL_REACHED after 1701 iterations. 475, LN_SBPLX algo: XTOL_REACHED after 2095 iterations. 476, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 477, LN_SBPLX algo: XTOL_REACHED after 1648 iterations. 478, LN_SBPLX algo: XTOL_REACHED after 865 iterations. 479, LN_SBPLX algo: XTOL_REACHED after 1246 iterations. 480, LN_SBPLX algo: XTOL_REACHED after 810 iterations. 481, GN_CRS2_LM algo: XTOL_REACHED after 22240 iterations. 482, GN_CRS2_LM algo: XTOL_REACHED after 22494 iterations. 483, GN_CRS2_LM algo: XTOL_REACHED after 19766 iterations. 484, GN_CRS2_LM algo: XTOL_REACHED after 22813 iterations. 485, GN_CRS2_LM algo: XTOL_REACHED after 23353 iterations. 486, GN_CRS2_LM algo: XTOL_REACHED after 24616 iterations. 487, GN_CRS2_LM algo: XTOL_REACHED after 23935 iterations. 488, GN_CRS2_LM algo: XTOL_REACHED after 21075 iterations. 489, GN_CRS2_LM algo: XTOL_REACHED after 23847 iterations. 490, GN_CRS2_LM algo: XTOL_REACHED after 18241 iterations. 491, GN_ISRES algo: XTOL_REACHED after 68228 iterations. 492, GN_ISRES algo: XTOL_REACHED after 73350 iterations. 493, GN_ISRES algo: XTOL_REACHED after 82609 iterations. 494, GN_ISRES algo: XTOL_REACHED after 70788 iterations. 495, GN_ISRES algo: XTOL_REACHED after 71202 iterations. 496, GN_ISRES algo: XTOL_REACHED after 76069 iterations. 497, GN_ISRES algo: XTOL_REACHED after 75524 iterations. 498, GN_ISRES algo: XTOL_REACHED after 73479 iterations. 499, GN_ISRES algo: XTOL_REACHED after 69196 iterations. 500, GN_ISRES algo: XTOL_REACHED after 62904 iterations. 501, LN_SBPLX algo: XTOL_REACHED after 1034 iterations. 502, LN_SBPLX algo: XTOL_REACHED after 1075 iterations. 503, LN_SBPLX algo: XTOL_REACHED after 2276 iterations. 504, LN_SBPLX algo: XTOL_REACHED after 1477 iterations. 505, LN_SBPLX algo: XTOL_REACHED after 2399 iterations. 506, LN_SBPLX algo: XTOL_REACHED after 876 iterations. 507, LN_SBPLX algo: XTOL_REACHED after 3353 iterations. 508, LN_SBPLX algo: XTOL_REACHED after 1292 iterations. 509, LN_SBPLX algo: XTOL_REACHED after 1953 iterations. 510, LN_SBPLX algo: XTOL_REACHED after 955 iterations. 511, GN_CRS2_LM algo: XTOL_REACHED after 15408 iterations. 512, GN_CRS2_LM algo: XTOL_REACHED after 15288 iterations. 513, GN_CRS2_LM algo: XTOL_REACHED after 24561 iterations. 514, GN_CRS2_LM algo: XTOL_REACHED after 19171 iterations. 515, GN_CRS2_LM algo: XTOL_REACHED after 17644 iterations. 516, GN_CRS2_LM algo: XTOL_REACHED after 22012 iterations. 517, GN_CRS2_LM algo: XTOL_REACHED after 24766 iterations. 518, GN_CRS2_LM algo: XTOL_REACHED after 17903 iterations. 519, GN_CRS2_LM algo: XTOL_REACHED after 19550 iterations. 520, GN_CRS2_LM algo: XTOL_REACHED after 14556 iterations. 521, GN_ISRES algo: XTOL_REACHED after 70479 iterations. 522, GN_ISRES algo: XTOL_REACHED after 69016 iterations. 523, GN_ISRES algo: XTOL_REACHED after 66497 iterations. 524, GN_ISRES algo: XTOL_REACHED after 67782 iterations. 525, GN_ISRES algo: XTOL_REACHED after 64292 iterations. 526, GN_ISRES algo: XTOL_REACHED after 72103 iterations. 527, GN_ISRES algo: XTOL_REACHED after 61510 iterations. 528, GN_ISRES algo: XTOL_REACHED after 66862 iterations. 529, GN_ISRES algo: XTOL_REACHED after 75645 iterations. 530, GN_ISRES algo: XTOL_REACHED after 63460 iterations. 531, LN_SBPLX algo: XTOL_REACHED after 534 iterations. 532, LN_SBPLX algo: XTOL_REACHED after 1310 iterations. 533, LN_SBPLX algo: XTOL_REACHED after 768 iterations. 534, LN_SBPLX algo: XTOL_REACHED after 2290 iterations. 535, LN_SBPLX algo: XTOL_REACHED after 1159 iterations. 536, LN_SBPLX algo: XTOL_REACHED after 513 iterations. 537, LN_SBPLX algo: XTOL_REACHED after 1399 iterations. 538, LN_SBPLX algo: XTOL_REACHED after 1398 iterations. 539, LN_SBPLX algo: XTOL_REACHED after 3156 iterations. 540, LN_SBPLX algo: XTOL_REACHED after 541 iterations. 541, GN_CRS2_LM algo: XTOL_REACHED after 27692 iterations. 542, GN_CRS2_LM algo: XTOL_REACHED after 61417 iterations. 543, GN_CRS2_LM algo: XTOL_REACHED after 24997 iterations. 544, GN_CRS2_LM algo: XTOL_REACHED after 32714 iterations. 545, GN_CRS2_LM algo: XTOL_REACHED after 27973 iterations. 546, GN_CRS2_LM algo: XTOL_REACHED after 24857 iterations. 547, GN_CRS2_LM algo: XTOL_REACHED after 25005 iterations. 548, GN_CRS2_LM algo: XTOL_REACHED after 46022 iterations. 549, GN_CRS2_LM algo: XTOL_REACHED after 21227 iterations. 550, GN_CRS2_LM algo: XTOL_REACHED after 28268 iterations. 551, GN_ISRES algo: XTOL_REACHED after 73545 iterations. 552, GN_ISRES algo: XTOL_REACHED after 74310 iterations. 553, GN_ISRES algo: XTOL_REACHED after 79551 iterations. 554, GN_ISRES algo: XTOL_REACHED after 94623 iterations. 555, GN_ISRES algo: XTOL_REACHED after 72923 iterations. 556, GN_ISRES algo: XTOL_REACHED after 86162 iterations. 557, GN_ISRES algo: XTOL_REACHED after 78704 iterations. 558, GN_ISRES algo: XTOL_REACHED after 88194 iterations. 559, GN_ISRES algo: XTOL_REACHED after 91274 iterations. 560, GN_ISRES algo: XTOL_REACHED after 74998 iterations. 561, LN_SBPLX algo: XTOL_REACHED after 1287 iterations. 562, LN_SBPLX algo: XTOL_REACHED after 1800 iterations. 563, LN_SBPLX algo: XTOL_REACHED after 1776 iterations. 564, LN_SBPLX algo: XTOL_REACHED after 1712 iterations. 565, LN_SBPLX algo: XTOL_REACHED after 2861 iterations. 566, LN_SBPLX algo: XTOL_REACHED after 1343 iterations. 567, LN_SBPLX algo: XTOL_REACHED after 3026 iterations. 568, LN_SBPLX algo: XTOL_REACHED after 616 iterations. 569, LN_SBPLX algo: XTOL_REACHED after 2644 iterations. 570, LN_SBPLX algo: XTOL_REACHED after 4020 iterations. 571, GN_CRS2_LM algo: XTOL_REACHED after 44998 iterations. 572, GN_CRS2_LM algo: XTOL_REACHED after 23092 iterations. 573, GN_CRS2_LM algo: XTOL_REACHED after 40531 iterations. 574, GN_CRS2_LM algo: XTOL_REACHED after 25857 iterations. 575, GN_CRS2_LM algo: XTOL_REACHED after 23137 iterations. 576, GN_CRS2_LM algo: XTOL_REACHED after 20374 iterations. 577, GN_CRS2_LM algo: XTOL_REACHED after 24680 iterations. 578, GN_CRS2_LM algo: XTOL_REACHED after 18143 iterations. 579, GN_CRS2_LM algo: XTOL_REACHED after 24696 iterations. 580, GN_CRS2_LM algo: XTOL_REACHED after 17921 iterations. 581, GN_ISRES algo: XTOL_REACHED after 69590 iterations. 582, GN_ISRES algo: XTOL_REACHED after 63389 iterations. 583, GN_ISRES algo: XTOL_REACHED after 65479 iterations. 584, GN_ISRES algo: XTOL_REACHED after 68875 iterations. 585, GN_ISRES algo: XTOL_REACHED after 77661 iterations. 586, GN_ISRES algo: XTOL_REACHED after 77780 iterations. 587, GN_ISRES algo: XTOL_REACHED after 72368 iterations. 588, GN_ISRES algo: XTOL_REACHED after 70138 iterations. 589, GN_ISRES algo: XTOL_REACHED after 68975 iterations. 590, GN_ISRES algo: XTOL_REACHED after 64701 iterations. 591, LN_SBPLX algo: XTOL_REACHED after 1365 iterations. 592, LN_SBPLX algo: XTOL_REACHED after 749 iterations. 593, LN_SBPLX algo: XTOL_REACHED after 819 iterations. 594, LN_SBPLX algo: XTOL_REACHED after 1666 iterations. 595, LN_SBPLX algo: XTOL_REACHED after 1693 iterations. 596, LN_SBPLX algo: XTOL_REACHED after 2365 iterations. 597, LN_SBPLX algo: XTOL_REACHED after 1816 iterations. 598, LN_SBPLX algo: XTOL_REACHED after 800 iterations. 599, LN_SBPLX algo: XTOL_REACHED after 1541 iterations. 600, LN_SBPLX algo: XTOL_REACHED after 1280 iterations." - ] - } - ], - "source": [ - "xtol_rel=1e-8\n", - "maxeval=100000\n", - "i=0\n", - "for capex in capexlist, shed in shed_max_list, dispatch in dispatchlist,algo in algo_list, srand in srand_list\n", - " x0 = x0_rng(srand, xmin, xmax)\n", - " xopt, ret, numevals = optim_mg(x0,capex, shed,dispatch, algo, maxeval,xtol_rel,srand)\n", - " lcoe_opt, shed_rate_opt,costs = obj_multi(xopt,capex,dispatch)\n", - " xopt = round.(xopt*1000; digits=2)\n", - " push!(df2,(capex[3],shed,findfirst(==(dispatch),dispatchlist),String(algo),srand,\n", - " String(ret),numevals,lcoe_opt,shed_rate_opt, costs.system.investment,costs.system.om, \n", - " xopt[1], xopt[2], xopt[3], xopt[4], xopt[5], xopt[6]\n", - " ))\n", - " i= i+1\n", - " @printf(\" %d, %s algo: %s after %d iterations.\",i, algo, ret, numevals)\n", - " \n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f315ecbf-790c-4a2c-a99e-ec3e0f85ca47", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "023269ee-048b-4d93-b02b-7950fba5148b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
600×17 DataFrame
575 rows omitted
Rowfc_elyz_capexshed_maxdispatchalgosrandretnumevalslcoe_optshed_rate_optcapexopexxopt_stoxopt_pvxopt_windxopt_elyzxopt_fcxopt_hyTank
Float64Float64Int64StringInt64StringInt64Float64Float64Float64Float64Float64Float64Float64Float64Float64Float64
11600.00.01GN_CRS2_LM1XTOL_REACHED218880.5165050.03.54974e71.04221e730304.512227.81811.5976.75990.082732.21
21600.00.01GN_CRS2_LM2XTOL_REACHED230350.4946570.03.66674e79.1398e616979.516846.31251.93254.451398.05348.99
31600.00.01GN_CRS2_LM3XTOL_REACHED208410.5191260.03.72518e79.90446e623347.516995.41171.63145.151069.93671.42
41600.00.01GN_CRS2_LM4XTOL_REACHED331040.4287140.03.19499e77.85836e614406.912391.21482.9335.071398.06528.59
51600.00.01GN_CRS2_LM5XTOL_REACHED194580.5336250.03.77074e71.06257e724400.915913.21811.5961.05983.02513.25
61600.00.01GN_CRS2_LM6XTOL_REACHED341490.5191260.03.72522e79.90425e623347.516996.31171.27145.411069.873671.71
71600.00.01GN_CRS2_LM7XTOL_REACHED132420.5195430.03.72996e79.90014e623353.217070.01152.01157.621067.983687.44
81600.00.01GN_CRS2_LM8XTOL_REACHED137590.5195430.03.72996e79.90014e623353.217070.01152.01157.621067.983687.43
91600.00.01GN_CRS2_LM9XTOL_REACHED155040.5355480.03.78175e71.06989e724386.315853.31877.753.61983.02448.82
101600.00.01GN_CRS2_LM10XTOL_REACHED179490.5473050.03.83693e71.0981e726551.315782.51877.731.73983.02276.36
111600.00.01GN_ISRES1MAXEVAL_REACHED1000000.4878280.03.62623e79.20606e614760.116168.51652.6262.291428.74792.9
121600.00.01GN_ISRES2XTOL_REACHED669640.5212820.03.73204e71.00513e723316.316699.61338.22136.021077.493379.94
131600.00.01GN_ISRES3XTOL_REACHED729580.4857510.03.61593e79.18354e614328.616104.71688.17336.661398.04653.52
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
5892200.00.012GN_ISRES9XTOL_REACHED689750.3809590.012.74359e74.97881e62941.195880.711877.71173.11665.1715819.1
5902200.00.012GN_ISRES10XTOL_REACHED647010.3809470.012.74415e74.9743e62904.285882.441877.71174.09665.6615844.9
5912200.00.012LN_SBPLX1XTOL_REACHED13650.4855720.013.29395e79.69609e626156.316124.41038.70.00.00.0
5922200.00.012LN_SBPLX2XTOL_REACHED7490.493060.013.36263e79.78229e625669.217070.0959.450.00.00.0
5932200.00.012LN_SBPLX3XTOL_REACHED8190.6879230.015.17438e71.04343e719569.015522.91519.177744.03478.234102.88
5942200.00.012LN_SBPLX4XTOL_REACHED16660.3893010.012.8066e74.79278e6347.276181.091877.71608.57903.1215216.3
5952200.00.012LN_SBPLX5XTOL_REACHED16930.4651070.013.12498e79.68801e624387.312785.21877.70.00.00.0
5962200.00.012LN_SBPLX6XTOL_REACHED23650.4645340.013.10583e79.69861e625333.712349.71877.70.00.00.0
5972200.00.012LN_SBPLX7XTOL_REACHED18160.4746720.013.19262e79.61458e626636.814502.81257.160.00.00.0
5982200.00.012LN_SBPLX8XTOL_REACHED8000.4724080.013.44413e76.05422e6494.779221.421877.72337.442897.978601.11
5992200.00.012LN_SBPLX9XTOL_REACHED15410.4650050.013.10483e79.68347e625854.712404.71803.840.00.00.0
6002200.00.012LN_SBPLX10XTOL_REACHED12800.4858780.013.30676e79.69817e625400.116370.31066.620.00.00.0
" - ], - "text/latex": [ - "\\begin{tabular}{r|cccccccc}\n", - "\t& fc\\_elyz\\_capex & shed\\_max & dispatch & algo & srand & ret & numevals & \\\\\n", - "\t\\hline\n", - "\t& Float64 & Float64 & Int64 & String & Int64 & String & Int64 & \\\\\n", - "\t\\hline\n", - "\t1 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 1 & XTOL\\_REACHED & 21888 & $\\dots$ \\\\\n", - "\t2 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 2 & XTOL\\_REACHED & 23035 & $\\dots$ \\\\\n", - "\t3 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 3 & XTOL\\_REACHED & 20841 & $\\dots$ \\\\\n", - "\t4 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 4 & XTOL\\_REACHED & 33104 & $\\dots$ \\\\\n", - "\t5 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 5 & XTOL\\_REACHED & 19458 & $\\dots$ \\\\\n", - "\t6 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 6 & XTOL\\_REACHED & 34149 & $\\dots$ \\\\\n", - "\t7 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 7 & XTOL\\_REACHED & 13242 & $\\dots$ \\\\\n", - "\t8 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 8 & XTOL\\_REACHED & 13759 & $\\dots$ \\\\\n", - "\t9 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 9 & XTOL\\_REACHED & 15504 & $\\dots$ \\\\\n", - "\t10 & 1600.0 & 0.0 & 1 & GN\\_CRS2\\_LM & 10 & XTOL\\_REACHED & 17949 & $\\dots$ \\\\\n", - "\t11 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 1 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", - "\t12 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 2 & XTOL\\_REACHED & 66964 & $\\dots$ \\\\\n", - "\t13 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 3 & XTOL\\_REACHED & 72958 & $\\dots$ \\\\\n", - "\t14 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 4 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", - "\t15 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 5 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", - "\t16 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 6 & XTOL\\_REACHED & 67785 & $\\dots$ \\\\\n", - "\t17 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 7 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", - "\t18 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 8 & MAXEVAL\\_REACHED & 100000 & $\\dots$ \\\\\n", - "\t19 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 9 & XTOL\\_REACHED & 68181 & $\\dots$ \\\\\n", - "\t20 & 1600.0 & 0.0 & 1 & GN\\_ISRES & 10 & XTOL\\_REACHED & 73784 & $\\dots$ \\\\\n", - "\t21 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 1 & XTOL\\_REACHED & 863 & $\\dots$ \\\\\n", - "\t22 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 2 & XTOL\\_REACHED & 1757 & $\\dots$ \\\\\n", - "\t23 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 3 & XTOL\\_REACHED & 1019 & $\\dots$ \\\\\n", - "\t24 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 4 & XTOL\\_REACHED & 1007 & $\\dots$ \\\\\n", - "\t25 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 5 & XTOL\\_REACHED & 1199 & $\\dots$ \\\\\n", - "\t26 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 6 & XTOL\\_REACHED & 1941 & $\\dots$ \\\\\n", - "\t27 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 7 & XTOL\\_REACHED & 1247 & $\\dots$ \\\\\n", - "\t28 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 8 & XTOL\\_REACHED & 1383 & $\\dots$ \\\\\n", - "\t29 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 9 & XTOL\\_REACHED & 1706 & $\\dots$ \\\\\n", - "\t30 & 1600.0 & 0.0 & 1 & LN\\_SBPLX & 10 & XTOL\\_REACHED & 1086 & $\\dots$ \\\\\n", - "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\u001b[1m600×17 DataFrame\u001b[0m\n", - "\u001b[1m Row \u001b[0m│\u001b[1m fc_elyz_capex \u001b[0m\u001b[1m shed_max \u001b[0m\u001b[1m dispatch \u001b[0m\u001b[1m algo \u001b[0m\u001b[1m srand \u001b[0m\u001b[1m ret \u001b[0m\u001b[1m \u001b[0m ⋯\n", - " │\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\u001b[90m \u001b[0m ⋯\n", - "─────┼──────────────────────────────────────────────────────────────────────────\n", - " 1 │ 1600.0 0.0 1 GN_CRS2_LM 1 XTOL_REACHED ⋯\n", - " 2 │ 1600.0 0.0 1 GN_CRS2_LM 2 XTOL_REACHED\n", - " 3 │ 1600.0 0.0 1 GN_CRS2_LM 3 XTOL_REACHED\n", - " 4 │ 1600.0 0.0 1 GN_CRS2_LM 4 XTOL_REACHED\n", - " 5 │ 1600.0 0.0 1 GN_CRS2_LM 5 XTOL_REACHED ⋯\n", - " 6 │ 1600.0 0.0 1 GN_CRS2_LM 6 XTOL_REACHED\n", - " 7 │ 1600.0 0.0 1 GN_CRS2_LM 7 XTOL_REACHED\n", - " 8 │ 1600.0 0.0 1 GN_CRS2_LM 8 XTOL_REACHED\n", - " 9 │ 1600.0 0.0 1 GN_CRS2_LM 9 XTOL_REACHED ⋯\n", - " 10 │ 1600.0 0.0 1 GN_CRS2_LM 10 XTOL_REACHED\n", - " 11 │ 1600.0 0.0 1 GN_ISRES 1 MAXEVAL_REACHED\n", - " ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱\n", - " 591 │ 2200.0 0.01 2 LN_SBPLX 1 XTOL_REACHED\n", - " 592 │ 2200.0 0.01 2 LN_SBPLX 2 XTOL_REACHED ⋯\n", - " 593 │ 2200.0 0.01 2 LN_SBPLX 3 XTOL_REACHED\n", - " 594 │ 2200.0 0.01 2 LN_SBPLX 4 XTOL_REACHED\n", - " 595 │ 2200.0 0.01 2 LN_SBPLX 5 XTOL_REACHED\n", - " 596 │ 2200.0 0.01 2 LN_SBPLX 6 XTOL_REACHED ⋯\n", - " 597 │ 2200.0 0.01 2 LN_SBPLX 7 XTOL_REACHED\n", - " 598 │ 2200.0 0.01 2 LN_SBPLX 8 XTOL_REACHED\n", - " 599 │ 2200.0 0.01 2 LN_SBPLX 9 XTOL_REACHED\n", - " 600 │ 2200.0 0.01 2 LN_SBPLX 10 XTOL_REACHED ⋯\n", - "\u001b[36m 11 columns and 579 rows omitted\u001b[0m" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df2" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "9ea7de07-7fe7-45ff-acfa-681509ed243a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\"df4_dataset.csv\"" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CSV.write(\"df4_dataset.csv\", df2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "05b41e59-dd80-4d5b-b7b8-e3b00d570091", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df_gsrand = groupby(df2, [ :dispatch, :shed_max, :algo])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30e0262e-a76e-47e0-99ad-50c5543876a2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "df_stats = combine(df_gsrand,\n", - " :lcoe_opt => minimum, :lcoe_opt => mean,:lcoe_opt=> maximum\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "59c0f2bc-269c-44c3-8bb9-7d5322fe0aac", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "6-element Vector{Float64}:\n", - " 16553.83\n", - " 12465.94\n", - " 1520.34\n", - " 1090.55\n", - " 1594.19\n", - 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] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " traj_opt_1, stats_opt_1, costs_opt_1 = simulate_microgrid(x/1000,capex_def,dispatch_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "854c68c2-aa59-4f85-b67f-298aa5497456", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(OperationTraj{Float64}([1393.19768160701, 1123.5290065975537, 752.1567275920686, 311.89676072710176, -152.65554553374636, -555.5664368161733, -766.7072261674093, -755.9738564611696, -705.1620170168376, -651.9508880879123 … -857.3078513294276, -100.44125782780884, 1365.6038193793947, 1314.2376411691146, 1398.7293521155375, 1517.2667880916197, 1490.5299568576174, 1400.789414431866, 1462.5299568576174, 1413.9999169146868], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [59.802318392990045, 207.47099340244637, 461.84327240793135, 834.1032392728982, 1230.6555455337464, 1590.5664368161733, 1798.7072261674093, 1827.9738564611696, 1847.1620170168376, 1859.9508880879123 … 2336.3078513294276, 1608.4412578278088, 44.3961806206053, 55.76235883088544, 68.27064788446253, 69.7332119083804, 70.47004314238262, 71.21058556813414, 70.47004314238262, 69.00008308531335], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1393.19768160701, 1123.5290065975537, 752.1567275920686, 311.89676072710176, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 1365.6038193793947, 1314.2376411691146, 1398.7293521155375, 1449.69, 1449.69, 1400.789414431866, 1449.69, 1413.9999169146868], [0.0, 0.0, 0.0, 0.0, 152.65554553374636, 555.5664368161733, 766.7072261674093, 755.9738564611696, 705.1620170168376, 651.9508880879123 … 857.3078513294276, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 33.123678341608574, 128.54287327802697, 128.54287327802697, 128.54287327802697, 128.54287327802697, 57.58724578182637, 14.70529108132812, 14.70529108132812, 1.2233363808298705, 1.2233363808298705], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, -100.44125782780884, 0.0, 0.0, 0.0, 67.57678809161962, 40.83995685761738, 0.0, 12.83995685761738, 0.0], [2290.9032, 2203.828344899562, 2133.607781987215, 2086.5979865127106, 2067.1044389672666, 2069.8304308517977, 2079.751260080658, 2093.4424605479335, 2106.9419936990257, 2119.5341725743265 … 2612.4810039875256, 2612.4810039875256, 2527.1307652763135, 2444.990912703244, 2357.5703281960227, 2266.9647031960226, 2176.3590781960224, 2088.809739794031, 1998.204114794031, 1909.8291199868631], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), OperationStats(6.774977520715849e6, 1.4792841514041015, 1.4792841514041015, 1.0, 1.0, 2.1834520098204018e-7, 0.0, 0.0, 0.0, 1.4383827560788672e6, 2912.0, 89898.9222549292, 89517.84817491642, 4844.0, 5.012999497795319e6, 3.5746405588464527e6, 26.909797550016137, 10883.983279521392, 9846.24836110904, 1036.5115820315225, 1.493622134973457e6, 5218.6929994914135, 0.1261052083387177, 1.1844254132324168e7, 1.0350631997350711e7, 1.0), MicrogridCosts(0.2904182658512201, 2.7730924306812663e7, CostFactors(2.7730924306812663e7, 1.93130685588e7, 4.587230517425491e6, 4.6430206886376105e6, 0.0, -812395.4580504415), CostFactors(564321.5404244324, 800000.0, 0.0, 563.7577826417898, 0.0, -236242.21735820948), CostFactors(240677.19620413234, 134813.0, 64847.35804493794, 54287.05567949116, 0.0, -13270.21752029679), CostFactors[CostFactors(7.602023893687278e6, 6.155988e6, 0.0, 1.4460358936872778e6, 0.0, -0.0), CostFactors(9.21827178327709e6, 6.57188e6, 0.0, 2.64639178327709e6, 0.0, -0.0)], CostFactors(6.721460359329116e6, 3.3007199999999995e6, 3.4310669638575735e6, 290751.0294252201, 0.0, -301077.6339536779), CostFactors(3.3533333112842166e6, 2.319504e6, 1.0913161955229798e6, 204318.50497949412, 0.0, -261805.3892182573), CostFactors(0.0, 0.0, 0.0, 0.0, 0.0, -0.0), CostFactors(30836.222606395306, 30163.558800000003, 0.0, 672.6638063953026, 0.0, -0.0)))" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - " traj_opt_2, stats_opt_2, costs_opt_2 = simulate_microgrid(x_2/1000,capex_def,dispatch_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "fe7e451c-e293-4eb4-8674-58d988276adb", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "plot_oper_traj (generic function with 1 method)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "function plot_oper_traj(mg, oper_traj)\n", - " ns=Int64(nsteps/730)\n", - " z=zeros(Float64,ns)\n", - "tm=zeros(Float64,ns +1)\n", - " tm[end]=nsteps/24\n", - " for i=1:ns\n", - " z[i]= oper_traj.LoH[i*730+1]\n", - " tm[i]=(i-1)*730/24 \n", - "end\n", - " fig,( ax1, ax2) = plt.subplots(2,1)\n", - " y1 = min.(oper_traj.Prenew_pot,mg.load)\n", - " y2 = max.( oper_traj.Pbatt,0.0)\n", - " y3 = oper_traj.Pfc\n", - " y4 = abs.(min.( oper_traj.Pbatt,0.0))\n", - " y5 = oper_traj.Pelyz\n", - " y6 = oper_traj.power_shedding\n", - " y7 = oper_traj.power_curtailment\n", - " \n", - " \n", - " y=np.vstack([y1,y2,y3,y4,y5,y6,y7])\n", - " fieldNames =([\"Pren used by the load\",\"battery_discharge\",\"fuel cell\",\"battery_charge\", \"elyz\",\"shedding\",\"spillage\"])\n", - " \n", - " fieldColors = ([\"orange\",\"green\",\"pink\",\"green\",\"magenta\",\"black\",\"red\"])\n", - " \n", - " \n", - " ax1.plot(td, oper_traj.Prenew_pot,lw=2, \"tab:orange\", label=\"ren\")\n", - " ax1.plot(td, mg.load,lw=2, label=\"load\")\n", - " stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8)\n", - " \n", - " stacks[2].set_hatch(\"-\")\n", - " stacks[4].set_hatch(\"+\")\n", - " stacks[6].set_hatch(\"\\\\\") \n", - " stacks[7].set_hatch(\"\\\\\") \n", - " \n", - " ax3=ax1.twinx()\n", - " ax3.set_ylim(0, 2)\n", - " ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls=\"--\",\"C2\",label=\"hourly battery level\")\n", - " \n", - " ax4=ax2.twinx()\n", - " \n", - " ax4.plot(td, oper_traj.LoH[1:end-1], \"C2\")\n", - " \n", - " ax2.stairs(z,tm,fill=\"True\",label=\"level of H2 at the end of each month\")\n", - " \n", - " \n", - " ax1.set_title(\"hourly power repartition\")\n", - " ax2.set_title(\"H2 level\")\n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(ylabel=\"kW\")\n", - " ax2.legend()\n", - " ax3.legend()\n", - " ax2.grid(true)\n", - " ax2.set(ylabel=\"kg\")\n", - " \n", - " fig.tight_layout()\n", - "\n", - "\n", - " \n", - " return fig, ax1, ax2\n", - " \n", - "end" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "bea92522-3fb7-4444-9ce1-889d3d0a4a8b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if with_plots\n", - " fig1, ax1, ax2= plot_oper_traj(mg_best_1,traj_opt_1)\n", - " \n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "93e473b9-e8a7-4575-a285-65b9b987ecef", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "if with_plots\n", - " fig2, ax1, ax2= plot_oper_traj(mg_best_2,traj_opt_2)\n", - " \n", - " pygui(true)\n", - " plt.show()\n", - " \n", - "end;" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "b1ecc08c-1241-4e48-84bb-a57b291b3475", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(Figure(PyObject
), (PyObject , PyObject , PyObject , PyObject ))" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comp_oper_traj(mg_best_1,mg_best_2,traj_opt_1,traj_opt_2,costs_opt_1,costs_opt_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0a6f9dbf-1a76-48d4-8060-5e0c11791493", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_best_1.tanks.h2Tank.investment_price" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86f26f9c-d2e8-4d66-8873-22ebb765259f", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.10.0", - "language": "julia", - "name": "julia-1.10" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/perf/Microgrids.jl_performance_analysis.ipynb b/perf/Microgrids.jl_performance_analysis.ipynb index 781bdab..1774904 100644 --- a/perf/Microgrids.jl_performance_analysis.ipynb +++ b/perf/Microgrids.jl_performance_analysis.ipynb @@ -746,15 +746,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.7.3", - "language": "julia", - "name": "julia-1.7" + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.7.3" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" } }, "nbformat": 4, diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 4859d5a..5d5ced0 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -18,7 +18,7 @@ include("dispatch.jl") include("production.jl") include("operation.jl") include("economics.jl") -include("Microgrid_Wind-Solar_data.jl") + import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions @@ -27,12 +27,9 @@ export simulate, Project, DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, capacity_from_wind, OperationTraj, OperationStats, increment, - operation, aggregation, dispatch_1,dispatch_2, production, nsteps, - CostFactors, MicrogridCosts, component_costs, economics, new_microgrid, - energy_rated_sto, power_rated_pv,power_rated_wind, power_rated_elyz,power_rated_fc ,capacity_rated_hytank,Pload,X,capex_def - - - + operation, aggregation, dispatch_1,dispatch_2, production, + CostFactors, MicrogridCosts, component_costs, economics, MicrogridCashflow + """ simulate(mg::Microgrid, ε::Real=0.0) @@ -65,48 +62,5 @@ end - """ - new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}) - -TBW -""" -function new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state) - project = Project(lifetime, discount_rate, timestep, "€") - gen = ProductionUnit(power_rated_gen, - fuel_intercept, fuel_slope, fuel_price, - capex[1], om_price_gen, lifetime_gen, - load_ratio_min_gen, replacement_price_ratio, - salvage_price_ratio, input_unit_gen,output_unit_gen) - ftank = Tank(capacity_rated_ftank,capex[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,initial_fill_rate[1], - fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio) - fuel_cell = ProductionUnit(x[5]*1000,cons_intercept_fc, cons_rate_fc,cons_price_fc,capex[3], om_price_fc,lifetime_fc, - load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc) - hytank = Tank(x[6]*1000,capex[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,initial_fill_rate[2], - LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio) - dispatchables = DispatchableCompound{Float64}([gen], [fuel_cell]) - tanks = TankCompound{Float64}(ftank,hytank) - - elyz = ProductionUnit(x[4]*1000,cons_intercept_elyz,cons_slope_elyz,cons_price_elyz, capex[5], om_price_elyz, lifetime_elyz, - load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz) - - batt = Battery(x[1]*1000, - capex[6], om_price_sto, lifetime_sto, lifetime_cycles, - charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,initial_fill_rate[3], - replacement_price_ratio, salvage_price_ratio) - pv = Photovoltaic(x[2]*1000, irradiance, - capex[7], om_price_pv, - lifetime_pv, derating_factor_pv, - replacement_price_ratio, salvage_price_ratio) - windgen = WindPower(x[3]*1000, cf_wind, - capex[8], om_price_wind, - lifetime_wind, - replacement_price_ratio, salvage_price_ratio) - mg = Microgrid(project, Pload,dispatchables, - [elyz,],tanks,batt, [ - pv, - windgen - ]) - return mg - end end \ No newline at end of file diff --git a/src/components.jl b/src/components.jl index 9f1cefe..b1b4d8e 100644 --- a/src/components.jl +++ b/src/components.jl @@ -23,6 +23,7 @@ All component parameters should be `Float64` except for the *sizing parameter(s)* (here `power_rated`) which type is parametrized as `Topt` and may be also `Float64` or or any another `Real` type (e.g. ForwardDiff's dual number type). +Please only one O&M price should be use . Choose before a size based price (om_price) or an hourly based one om_price_hours) """ struct ProductionUnit{Topt<:Real} # Main technical parameters @@ -39,9 +40,15 @@ struct ProductionUnit{Topt<:Real} "initial investiment price (\$/kW)" investment_price::Float64 "operation & maintenance price (\$/kW/h of operation)" - om_price_hours::Float64 + om_price_hourly::Float64 + "operation & maintenance price (\$/kW of operation)" + om_price::Float64 + "generator calendar lifetime " + lifetime_calendar::Float64 "generator lifetime (h of operation)" lifetime_hours::Float64 + "prod unit maximum starts" + lifetime_on_off :: Float64 # Secondary technical parameters (which should have a default value) "minimum load ratio ∈ [0,1]" @@ -120,7 +127,7 @@ We assumed that a `Microgrid` can only have one `Tank` of each type. struct TankCompound{Topt<:Real} "fuel tank, can be used by all diesel generators of the `Microgrid`" fuelTank :: Tank{Topt} - "hydrogen tank, can be used by all diesel generators of the `Microgrid`" + "hydrogen tank, can be used by all Electrolyzers generators of the `Microgrid`" h2Tank :: Tank{Topt} end diff --git a/src/dispatch.jl b/src/dispatch.jl index 34685e9..5a96af1 100644 --- a/src/dispatch.jl +++ b/src/dispatch.jl @@ -78,30 +78,47 @@ function dispatch_1(Pnl_req, Pshed = 0.0 Pelyz = 0.0 Pfc = 0.0 + Pdump = 0.0 if Pnl_req >= 0.0 ## Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) # battery discharging --> Pbatt positive - Pbatt = min(Pbatt_dmax,Pnl_req) - if Pnl_req - Pbatt > Pfc_min - # fuel_cell starts on if the battery can't supply the power net load request - Pfc = min(Pfc_max,Pnl_req-Pbatt) + if Pnl_req > Pbatt_dmax && Pfc_max >= Pfc_min + if Pnl_req - Pbatt_dmax >= Pfc_min + Pbatt = Pbatt_dmax + Pfc = min(Pfc_max,Pnl_req-Pbatt) + Pshed = Pnl_req - Pbatt - Pfc - Pgen + else + Pfc=Pfc_min + if Pnl_req>=Pfc + Pbatt= Pnl_req-Pfc + Pshed = 0.0 + else + + Pbatt = max(Pnl_req-Pfc,Pbatt_cmax) + Pdump = Pnl_req-Pfc-Pbatt + end + end + else + Pbatt=min(Pnl_req,Pbatt_dmax) + Pshed = Pnl_req - Pbatt - Pfc - Pgen end - - if Pnl_req - Pbatt - Pfc > Pgen_min + + """ if Pnl_req - Pbatt - Pfc > Pgen_min # Diesel generator starts on if the battery & the fuel_cell can't supply the power net load request Pgen = min(Pgen_max,Pnl_req-Pbatt-Pfc) end - - Pshed = Pnl_req - Pbatt - Pfc - Pgen + """ + + ## Pnl_req < 0 - VRE excess elseif Pnl_req < 0.0 # battery charging --> Pbatt negative Pbatt = max(Pbatt_cmax,Pnl_req) - if Pbatt - Pnl_req > Pelyz_min + if Pbatt - Pnl_req > Pelyz_min && Pelyz_max > Pelyz_min #Electrolyzer starts on if its remain enough power Pelyz = min(Pelyz_max,Pbatt-Pnl_req) end @@ -111,7 +128,7 @@ function dispatch_1(Pnl_req, Pnl = Pnl_req - Pshed + Pspill - return Pnl, Pgen , Pbatt, Pspill, Pshed, Pelyz, Pfc + return Pnl, Pgen , Pbatt, Pspill, Pshed,Pdump, Pelyz, Pfc end """ @@ -142,29 +159,41 @@ function dispatch_2(Pnl_req, Pshed = 0.0 Pelyz = 0.0 Pfc = 0.0 + Pdump = 0.0 + # Pnl_req >= 0 - load excess - after evaluating the production (Pnl = Pload - VRE generation) if Pnl_req >= 0.0 - # fuel_cell starts on if possible . the battery can't supply the power net load request - if Pnl_req > Pfc_min - Pfc = min(Pfc_max, Pnl_req) - end + if Pfc_max >= Pfc_min + if Pnl_req >= Pfc_min + Pfc=min(Pfc_max,Pnl_req) + Pbatt=min(Pnl_req-Pfc,Pbatt_dmax) + else + if Pbatt_dmax < Pnl_req + Pfc=Pfc_min + Pbatt=max(Pnl_req-Pfc,Pbatt_cmax) + Pdump = Pnl_req-Pfc-Pbatt + else + Pbatt = Pnl_req + end - # Battery starts on if the fuel cell can't supply the power net load request - Pbatt = min(Pbatt_dmax, Pnl_req - Pfc) - - if Pnl_req - Pbatt - Pfc > Pgen_min + end + else + Pbatt=min(Pnl_req,Pbatt_dmax) + end + +""" + if Pnl_req - Pbatt - Pfc > Pgen_min # Diesel generator starts on if the battery & the fuel_cell can't supply the power net load request - Pgen = min(Pgen_max, Pnl_req - Pbatt - Pfc) + Pgen = min(Pgen_max,Pnl_req-Pbatt-Pfc) end - - Pshed = Pnl_req - Pbatt - Pfc - Pgen - +""" + Pshed = Pnl_req - Pbatt - Pfc - Pgen - Pdump # Pnl_req < 0 - VRE excess elseif Pnl_req < 0.0 - if -1 * Pnl_req > Pelyz_min + if -1 * Pnl_req > Pelyz_min && Pelyz_max >= Pelyz_min #Electrolyzer starts on - Pelyz=min(Pelyz_max, - Pnl_req) + Pelyz=min(Pelyz_max, -1* Pnl_req) end @@ -176,5 +205,5 @@ function dispatch_2(Pnl_req, Pnl = Pnl_req - Pshed + Pspill - return Pnl, Pgen , Pbatt, Pspill, Pshed, Pelyz, Pfc + return Pnl, Pgen , Pbatt, Pspill, Pshed, Pdump,Pelyz, Pfc end \ No newline at end of file diff --git a/src/economics.jl b/src/economics.jl index 1c08e41..8caf1d9 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -23,6 +23,7 @@ Cost factors are expressed as Net Present Values, meaning they represent fuel "salvage cost (negative)" salvage + end # Arithmetic for CostFactors: +,*,/ and round @@ -101,6 +102,19 @@ function Base.round(c::CostFactors, r=RoundNearest; return cr end +@kwdef struct MicrogridCashflow + + gen :: Vector{Float64} + fuel_tank :: Vector{Float64} + batt :: Vector{Float64} + fc :: Vector{Float64} + elyz :: Vector{Float64} + h2_tank :: Vector{Float64} + pv :: Vector{Float64} + wind :: Vector{Float64} + + +end """Cost factors of each component of a Microgrid @@ -129,8 +143,10 @@ also includes `system` cost (all components) and two key economic data: fuel_cell:: CostFactors fuel_tank:: CostFactors h2_tank:: CostFactors + cashflow :: MicrogridCashflow end + ### component_costs methods """ @@ -162,6 +178,13 @@ function component_costs(mg_project::Project, lifetime::Real, discount_factors = [ 1/((1 + mg_project.discount_rate)^i) for i=1:mg_lifetime ] sum_discounts = sum(discount_factors) + cashflow=zeros(Real,mg_lifetime+1) + + cashflow[1]= investment + for i=2:mg_lifetime + cashflow[i]= om_annual + end + ### Operation & maintenance and fuel costs om_cost = om_annual * sum_discounts fuel_cost = fuel_annual * sum_discounts @@ -180,12 +203,26 @@ function component_costs(mg_project::Project, lifetime::Real, # discount factors for the replacements years replacement_factors = [1/(1 + mg_project.discount_rate)^i for i in replacement_years] replacement_cost = replacement * sum(replacement_factors) + for i=1:replacements_number + cashflow[1+i*(ceil(Integer,lifetime)-1)]+= replacement + end end - # component remaining life at the project end - remaining_life = lifetime*(1+replacements_number) - mg_lifetime - # nominal *effective* salvage value (that is given remaining life) - salvage_effective = salvage * remaining_life / lifetime + salvage_formula = :LinearSalvage + if salvage_formula == :LinearSalvage + # remaining lifetime of last component at the project end + remaining_life = lifetime*(1+replacements_number) - mg_lifetime + # salvage exactly proportional to remaining lifetime + salvage_effective = salvage * remaining_life / lifetime + elseif salvage_formula == :ConsistentSalvage + dp1 = 1. + mg_project.discount_rate + # usage duration of the last component at the end of the project + usage_duration = mg_lifetime - lifetime*replacements_number + salvage_effective = salvage * + (dp1^lifetime - dp1^usage_duration) / (dp1^lifetime - 1) + elseif salvage_formula == :ZeroSalvage + salvage_effective = 0.0 + end else # Infinite lifetime (happens for components with zero usage) replacement_cost = 0.0 @@ -195,34 +232,41 @@ function component_costs(mg_project::Project, lifetime::Real, # net present salvage cost (<0) salvage_cost = -salvage_effective * discount_factors[mg_lifetime] + cashflow[mg_lifetime+1] = -1*salvage_effective + ### Total total_cost = investment + replacement_cost + om_cost + fuel_cost + salvage_cost + cashflow*=-1 + - return CostFactors(total_cost, investment, replacement_cost, om_cost, fuel_cost, salvage_cost) + return CostFactors(total_cost, investment, replacement_cost, om_cost, fuel_cost, salvage_cost),cashflow end - """ component_costs(gen::ProductionUnit, mg_project::Project, oper_stats::OperationStats) Compute net present cost factors for a `ProductionUnit`. """ -function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_unit_hours, prod_unit_cons) +function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_unit_hours, prod_unit_starts,prod_unit_cons) rating = prod_unit.power_rated investment = prod_unit.investment_price * rating replacement = investment * prod_unit.replacement_price_ratio salvage = investment * prod_unit.salvage_price_ratio - om_annual = prod_unit.om_price_hours * rating + om_annual = prod_unit.om_price_hourly * rating * prod_unit_hours + prod_unit.om_price*rating fuel_annual = prod_unit.combustible_price * prod_unit_cons - # effective generator lifetime (in years) - lifetime = prod_unit.lifetime_hours / prod_unit_hours + # effective production unit lifetime (in years) + if prod_unit_hours > 0.0 || prod_unit_hours >0.0 + lifetime = min(prod_unit.lifetime_hours / prod_unit_hours,prod_unit.lifetime_on_off / prod_unit_starts, prod_unit.lifetime_calendar) + else + lifetime = prod_unit.lifetime_calendar + end - c = component_costs( + c,cashflow = component_costs( mg_project, lifetime, investment, replacement, salvage, om_annual, fuel_annual ) - return c + return c,cashflow end """ @@ -238,12 +282,12 @@ function component_costs(tank::Tank, mg_project::Project) om_annual = tank.om_price * rating fuel_annual = 0.0 - c = component_costs( + c, cashflow = component_costs( mg_project, tank.lifetime, investment, replacement, salvage, om_annual, fuel_annual ) - return c + return c,cashflow end """ @@ -269,13 +313,13 @@ function component_costs(bt::Battery, mg_project::Project, oper_stats::Operation lifetime = bt.lifetime_calendar end - c = component_costs( + c,cashflow = component_costs( mg_project, lifetime, investment, replacement, salvage, om_annual, fuel_annual ) - return c + return c,cashflow end """ @@ -293,12 +337,12 @@ function component_costs(nd::NonDispatchableSource, mg_project::Project) om_annual = nd.om_price * rating fuel_annual = 0.0 - c = component_costs( + c,cashflow = component_costs( mg_project, nd.lifetime, investment, replacement, salvage, om_annual, fuel_annual ) - return c + return c,cashflow end """ @@ -315,7 +359,7 @@ function component_costs(pv::PVInverter, mg_project::Project) om_annual = pv.om_price_ac * rating fuel_annual = 0.0 - c_ac = component_costs( + c_ac,cashflow_ac = component_costs( mg_project, pv.lifetime_ac, investment, replacement, salvage, om_annual, fuel_annual @@ -328,14 +372,15 @@ function component_costs(pv::PVInverter, mg_project::Project) salvage = investment * pv.salvage_price_ratio om_annual = pv.om_price_dc * rating - c_dc = component_costs( + c_dc,cashflow_dc = component_costs( mg_project, pv.lifetime_dc, investment, replacement, salvage, om_annual, fuel_annual ) c = c_ac + c_dc - return c + cashflow=cashflow_ac.+cashflow_dc + return c, cashflow end ### Economic evaluation of an entire microgrid project @@ -350,35 +395,40 @@ See also: [`aggregation`](@ref) """ function economics(mg::Microgrid, oper_stats::OperationStats) # Dispatchable generator - gen_costs = component_costs(mg.dispatchables.generator[1], mg.project, oper_stats.gen_hours,oper_stats.gen_fuel) - elyz_costs = component_costs(mg.electrolyzer[1], mg.project, oper_stats.elyz_hours,oper_stats.elyz_consumed_energy) - fc_costs = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.h2_consumed) - fuel_tantk_cost = component_costs(mg.tanks.fuelTank, mg.project) - h2_tantk_cost = component_costs(mg.tanks.h2Tank, mg.project) + gen_costs, gen_cashflow = component_costs(mg.dispatchables.generator[1], mg.project, oper_stats.gen_hours,oper_stats.gen_starts,oper_stats.gen_fuel) + elyz_costs, elyz_cashflow = component_costs(mg.electrolyzer[1], mg.project, oper_stats.elyz_hours,oper_stats.elyz_starts,oper_stats.elyz_consumed_energy) + fc_costs,fc_cashflow = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.fc_starts,oper_stats.h2_consumed) + fuel_tank_cost,fuel_tank_cashflow = component_costs(mg.tanks.fuelTank, mg.project) + h2_tank_cost ,h2_tank_cashflow = component_costs(mg.tanks.h2Tank, mg.project) # Energy storage - sto_costs = component_costs(mg.storage, mg.project, oper_stats) + sto_costs, sto_cashflow = component_costs(mg.storage, mg.project, oper_stats) # Non-dispatchable sources (e.g. renewables like wind and solar) # NonDispatchables costs - nd_costs = collect( + nd_tots= collect( component_costs(mg.nondispatchables[i], mg.project) for i in 1:length(mg.nondispatchables) ) + + pv_cashflow= nd_tots[1][2] + wind_cashflow=nd_tots[2][2] + nd_costs=[nd_tots[1][1],nd_tots[2][1]] # Capital recovery factor (CRF) discount_factors = [1/((1 + mg.project.discount_rate)^i) for i = 1:mg.project.lifetime] crf = 1/sum(discount_factors) # Cost of all components and NPC of the project - system_costs = gen_costs + sto_costs + sum(nd_costs) + elyz_costs + fc_costs + fuel_tantk_cost + h2_tantk_cost + system_costs = gen_costs + sto_costs + sum(nd_costs) + elyz_costs + fc_costs + fuel_tank_cost + h2_tank_cost npc = system_costs.total + # levelized cost of energy annualized_cost = npc*crf # $/y lcoe = annualized_cost / oper_stats.served_energy # ($/y) / (kWh/y) → $/kWh costs = MicrogridCosts(lcoe, npc, - system_costs, gen_costs, sto_costs, nd_costs, elyz_costs,fc_costs,fuel_tantk_cost,h2_tantk_cost + system_costs, gen_costs, sto_costs, nd_costs, elyz_costs,fc_costs,fuel_tank_cost,h2_tank_cost,MicrogridCashflow(gen_cashflow,fuel_tank_cashflow,sto_cashflow,fc_cashflow,elyz_cashflow,h2_tank_cashflow,pv_cashflow,wind_cashflow) ) return costs diff --git a/src/operation.jl b/src/operation.jl index d044cc9..2ddc281 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -35,6 +35,7 @@ struct OperationTraj{T<:Real} # renewables sources "Renewables curtailment power at each time instant (kW)" power_curtailment::Vector{T} + Pdump::Vector{T} end """Aggregated statistics over the simulated Microgrid operation @@ -62,25 +63,80 @@ struct OperationStats gen_energy "cumulated operating hours of the dispatchable generator (h/y)" gen_hours + "cumulated starts on of the dispatchable generator " + gen_starts "fuel consumption (L/y)" gen_fuel + "running time at nominal power" + gen_rt_at_nom + "minimum running power" + gen_min_rp + "average running power" + gen_avg_rp + "average running time by per start" + gen_avg_rt + "maximum running time per start" + gen_max_rt + "minimum running time per start" + gen_min_rt + "number of runs that last 2h or less" + gen_ru2 + "number of runs that last more than 2h" + gen_ro2 # Hydrogen chain statistics "energy supplied by the fuel cell (kWh/y)" fc_energy "cumulated operating hours of the fuel_cell (h/y)" fc_hours + "number of starts" + fc_starts "total amount of consumed hydrogen" h2_consumed + "running time at nominal power" + fc_rt_at_nom + "minimum running power" + fc_min_rp + "average running power" + fc_avg_rp + "average running time per start" + fc_avg_rt + "maximum running time per start" + fc_max_rt + "minimum running time per start" + fc_min_rt + "number of runs that last 2h or less" + fc_ru2 + "number of runs that last more than 2h" + fc_ro2 "total amount of produced hydrogen" h2_produced "cumulated operating hours of the electrolyzer (h/y)" elyz_hours + "number of starts" + elyz_starts "energy consumed by the the electrolyzer to produce hydrogen (kWh/y)" elyz_consumed_energy + "running time at nominal power" + elyz_rt_at_nom + "minimum running power" + elyz_min_rp + "average running power" + elyz_avg_rp + "average running time per start" + elyz_avg_rt + "maximum running time per start" + elyz_max_rt + "minimum running time per start" + elyz_min_rt + "number of runs that last 2h or less" + elyz_ru2 + "number of runs that last more than 2h" + elyz_ro2 "energy lossed in the Power-Gaz-Power chain (kWh/y)" h2_chain_loss + # Energy storage (e.g. battery) statistics "cycling of the energy storage (cycles/y)" storage_cycles @@ -99,6 +155,8 @@ struct OperationStats "ratio of spilled energy to the energy potentially supplied by renewables (∈ [0,1])" spilled_rate "energy potentially supplied by renewables in the absence spillage (kWh/y)" + dumped_energy + dump_max renew_potential_energy "energy actually supplied by renewables when substracting spillage (kWh/y)" renew_energy @@ -115,13 +173,40 @@ const oper_stats_units = Dict( :shed_rate => "in [0,1]", :gen_energy => "kWh", :gen_hours => "h", + :gen_starts => "", :gen_fuel => "L",# TODO: use instead the generator's fuel unit string + :gen_rt_at_nom =>"h", + :gen_min_rp =>"KW", + :gen_avg_rp => "kW", + :gen_avg_rt => "h", + :gen_max_rt =>"h", + :gen_min_rt =>"h", + :gen_ru2 =>"", + :gen_ro2 =>"", :fc_energy => "kWh", :fc_hours => "h", + :fc_starts => "", :h2_consumed => "kg", + :fc_rt_at_nom =>"h", + :fc_min_rp =>"KW", + :fc_avg_rp => "kW", + :fc_avg_rt => "h", + :fc_max_rt =>"h", + :fc_min_rt =>"h", + :fc_ru2 =>"", + :fc_ro2 =>"", :h2_produced => "kg", :elyz_hours => "h", + :elyz_starts => "", :elyz_consumed_energy => "kWh", + :elyz_rt_at_nom =>"h", + :elyz_min_rp =>"KW", + :elyz_avg_rp => "kW", + :elyz_avg_rt => "h", + :elyz_max_rt =>"h", + :elyz_min_rt =>"h", + :elyz_ru2 =>"", + :elyz_ro2 =>"", :h2_chain_loss => "kWh", :storage_cycles => "", :storage_char_energy => "kWh", @@ -130,6 +215,8 @@ const oper_stats_units = Dict( :spilled_energy => "kWh", :spilled_max => "kW", :spilled_rate => "in [0,1]", + :dump_energy => "kWh", + :dump_max => "kW", :renew_potential_energy => "kWh", :renew_energy => "kWh", :renew_rate => "in [0,1]" @@ -153,37 +240,58 @@ function Base.show(io::IO, ::MIME"text/plain", stats::OperationStats) end end + """ - increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Real=0.0) + usage(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Int64,n_starts::Int64) -Allow to compute operating hours and fuel consumption of 'ProductionUnit'. +Allow to operate running statistics of production units """ -function increment(prod_unit_power::Float64, prod_unit::ProductionUnit, ε::Real=0.0) +function usage(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Float64,n_starts::Int64,ε::Real=0.0) Pprod_unit_max = prod_unit.power_rated Pprod_unit_eps = Pprod_unit_max*1e-6 prod_unit_intercept = prod_unit.consumption_intercept prod_unit_slope = prod_unit.consumption_slope time_inc = 0 cons_rate = 0 - if prod_unit_power > Pprod_unit_eps # prod_unit ON - Pprod_unit_norm = prod_unit_power / (ε * Pprod_unit_max) # can be Inf e.g. for ε=0.0 - - if Pprod_unit_norm <= 1.0 - time_inc = Pprod_unit_norm # relaxation of discontinuous generator statistics for small Pgen - cons_rate = Pprod_unit_norm * prod_unit_intercept * Pprod_unit_max + - prod_unit_slope * prod_unit_power # (L/h) - else - time_inc=1 - cons_rate = prod_unit_intercept * Pprod_unit_max + - prod_unit_slope * prod_unit_power # (L/h) - end - + if prod_unit_power > Pprod_unit_eps # prod_unit ON + Pprod_unit_norm = prod_unit_power / (ε * Pprod_unit_max) # can be Inf e.g. for ε=0.0 + if Pprod_unit_norm <= 1.0 + time_inc = 1 # relaxation of discontinuous prod_unit statistics for small Prod_unit_power + cons_rate = Pprod_unit_norm * prod_unit_intercept * Pprod_unit_max + + prod_unit_slope * prod_unit_power # (L/h) + else + time_inc=1 + cons_rate = prod_unit_intercept * Pprod_unit_max + + prod_unit_slope * prod_unit_power # (L/h) end - return time_inc , cons_rate - - + if cr == 0.0 + n_starts+=1 + end + if prod_unit_power == Pprod_unit_max + c1+=1*dt + end + if prod_unit_power < minP + minP=prod_unit_power + end + cr+=dt + else + if cr>max_rt + max_rt=cr + end + if cr0.0 + min_rt=cr + end + if cr<2*dt && cr>0.0 + r_u_2+=1 + elseif cr>=2*dt && cr>0.0 + r_o_2+=1 + end + cr=0 + end + hours+=time_inc*dt + cons+=cons_rate*dt + return n_starts,minP,c1,max_rt,min_rt,r_u_2,r_o_2,cr,hours,cons end - """ operation(mg::Microgrid, dispatch::Function) @@ -246,6 +354,7 @@ function operation(mg::Microgrid, dispatch::Function) Pspill = zeros(Topt,K) Pshed = zeros(Topt,K) + Pdump = zeros(Topt,K) Pelyz = zeros(Topt,K, n_elyz) Pelyz_emax = zeros(Topt, n_elyz) @@ -292,7 +401,7 @@ function operation(mg::Microgrid, dispatch::Function) Pgen_dmax .= min.(Pgen_emax,Pgen_max) # dispatch - Pnl[k], Pgen[k,1], Psto[k], Pspill[k], Pshed[k], Pelyz[k,1], Pfc[k,1] = dispatch(Pnl_request[k], Psto_cmax, Psto_dmax, Pgen_min[1], Pgen_dmax[1], Pelyz_min[1],Pelyz_cmax[1], Pfc_min[1],Pfc_pmax[1]) + Pnl[k], Pgen[k,1], Psto[k], Pspill[k], Pshed[k], Pdump[k], Pelyz[k,1], Pfc[k,1] = dispatch(Pnl_request[k], Psto_cmax, Psto_dmax, Pgen_min[1], Pgen_dmax[1], Pelyz_min[1],Pelyz_cmax[1], Pfc_min[1],Pfc_pmax[1]) # Battery storage dynamics Esto[k+1] = Esto[k] - (Psto[k] + sto_loss * abs(Psto[k])) * dt @@ -311,7 +420,7 @@ function operation(mg::Microgrid, dispatch::Function) end - oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1], Esto, Psto, LoH, LoF, Pspill) + oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1], Esto, Psto, LoH, LoF, Pspill,Pdump) return oper_traj end @@ -371,6 +480,8 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) # Non-dispatchable (typ. renewables) sources statistics spilled_energy = sum(oper_traj.power_curtailment) * dt # kWh/y spilled_max = maximum(oper_traj.power_curtailment) # kW + dumped_energy = sum(oper_traj.Pdump)*dt # kWh/y + dumped_max = maximum(oper_traj.Pdump)# kW renew_potential_energy = sum(oper_traj.Prenew_pot) * dt # kWh/y spilled_rate = spilled_energy / renew_potential_energy renew_energy = renew_potential_energy - spilled_energy @@ -382,31 +493,53 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) shed_hours = 0.0 # h/y shed_duration_max = 0.0 # h gen_hours = 0.0 # h/y + gen_starts = 0 # gen_fuel = 0.0 # L/y + gen_rt_at_nom = 0.0; + gen_min_rp= mg.dispatchables.generator[1].power_rated; + gen_avg_rp= 0.0; + gen_avg_rt=0.0; + gen_max_rt=0.0; + gen_min_rt=K*dt + gen_ru2=0; + gen_ro2=0; elyz_hours = 0.0 + elyz_starts=0 + elyz_cons=0.0; + elyz_rt_at_nom=0.0; + elyz_min_rp=mg.electrolyzer[1].power_rated; + elyz_avg_rp=0.0; + elyz_avg_rt=0.0; + elyz_max_rt=0.0; + elyz_min_rt=K*dt + elyz_ru2=0; + elyz_ro2=0; fc_hours = 0.0 - + fc_starts=0 + fc_cons=0.0; + fc_rt_at_nom=0.0; + fc_min_rp=mg.dispatchables.fuel_cell[1].power_rated; + fc_avg_rp=0.0; + fc_avg_rt=0.0; + fc_max_rt=0.0; + fc_min_rt=K*dt; + fc_ru2=0; + fc_ro2=0; # auxilliary counter: shed_duration = 0.0; # duration of current load shedding event (h) Pload_avg = load_energy/(K*dt) # kW - - for K = 1:K - # Dispatchable generator : operating hours and fuel consumption - time_inc, cons_rate = increment(oper_traj.Pgen[K],mg.dispatchables.generator[1],ε) - gen_hours += time_inc * dt - gen_fuel += cons_rate * dt - - # Electrolyzer : operating hours - time_inc, cons_rate = increment(oper_traj.Pelyz[K],mg.electrolyzer[1],ε) - elyz_hours += time_inc * dt - - # Fuel cell : operating hours - time_inc, cons_rate = increment(oper_traj.Pfc[K],mg.dispatchables.fuel_cell[1],ε) - fc_hours += time_inc * dt - + cr=zeros(Float64,3) + for k = 1:K + gen_starts,gen_min_rp,gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_hours,gen_fuel=usage(oper_traj.Pgen[k],mg.dispatchables.generator[1],gen_hours,gen_fuel,gen_min_rp,dt, + gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_starts) + + fc_starts,fc_min_rp,fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_hours,fc_cons=usage(oper_traj.Pfc[k],mg.dispatchables.fuel_cell[1],fc_hours,fc_cons,fc_min_rp,dt, + fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_starts) + elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, + elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_starts) # Load shedding: shedding duration and maximum shedding duration - Pshed = oper_traj.power_shedding[K] + Pshed = oper_traj.power_shedding[k] if Pshed > 0.0 Pshed_norm = Pshed / (ε * Pload_avg) # can be Inf e.g. for ε=0.0 if Pshed_norm <= 1.0 @@ -425,20 +558,29 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) end end + gen_avg_rp= sum(oper_traj.Pgen)/gen_hours; + gen_avg_rt=gen_hours/gen_starts; + fc_avg_rp= sum(oper_traj.Pfc)/fc_hours; + fc_avg_rt=fc_hours/fc_starts; + elyz_avg_rp= sum(oper_traj.Pelyz)/elyz_hours; + elyz_avg_rt=elyz_hours/elyz_starts; + # Outputs oper_stats = OperationStats( # Load statistics served_energy, shed_energy, shed_max, shed_hours, shed_duration_max, shed_rate, # Dispatchable generator statistics - gen_energy, gen_hours, gen_fuel, + gen_energy, gen_hours, gen_starts,gen_fuel,gen_rt_at_nom,gen_min_rp,gen_avg_rp,gen_avg_rt,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2, #H2 chain statistics - fc_produced_energy, fc_hours, fc_consumed_h2, elyz_produced_h2, elyz_hours, elyz_consumed_energy, chain_loss, + fc_produced_energy, fc_hours,fc_starts, fc_consumed_h2,fc_rt_at_nom,fc_min_rp,fc_avg_rp,fc_avg_rt,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2, + elyz_produced_h2, elyz_hours,elyz_starts, elyz_consumed_energy,elyz_rt_at_nom,elyz_min_rp,elyz_avg_rp,elyz_avg_rt,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2, + chain_loss, # Energy storage (e.g. battery) statistics storage_cycles, storage_char_energy, storage_dis_energy, storage_loss_energy, # Non-dispatchable (typ. renewables) sources statistics - spilled_energy, spilled_max, spilled_rate, + spilled_energy, spilled_max, spilled_rate,dumped_energy,dumped_max, renew_potential_energy, renew_energy, renew_rate ) return oper_stats -end \ No newline at end of file +end diff --git a/test2/Typetest.jl b/test2/Typetest.jl deleted file mode 100644 index e69de29..0000000 From 08e6758cc4a4fa6b57c579e6b5e8e764335531d3 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 14 Jun 2024 16:01:05 +0200 Subject: [PATCH 08/19] Read me updated --- README.md | 8 +- examples/Microgrid_Wind-Solar.ipynb | 1365 --------------------------- 2 files changed, 5 insertions(+), 1368 deletions(-) delete mode 100644 examples/Microgrid_Wind-Solar.ipynb diff --git a/README.md b/README.md index 8444762..68d2a6d 100644 --- a/README.md +++ b/README.md @@ -18,11 +18,13 @@ The unit tests which ship with the package can be run with `] test Microgrids`. ## Description of Microgrids.jl -Microgrid sizing illustration +Microgrid sizing illustration `Microgrids.jl` can model a microgrid project consisting of: - One load (described by a time series) -- One dispatchable generator (e.g. Diesel or hydrogen-powered) +- One dispatchable diesel generator +- One fuel cell +- One Electrolyzer - One energy storage (battery) - Any number of non-dispatchable sources, typically renewable like wind or solar power, also modeled from on time series @@ -37,7 +39,7 @@ which provides sibling packages in other languages (e.g. in Python) to better serve the need of different users (e.g. students). The work on the Julia package specifically focuses on: -- **simulation speed** (about 0.2 ms to evaluate one microgrid project, +- **simulation speed** (about 0.5 ms to evaluate one microgrid project, using 1 year of load/solar/wind data at an hourly timestep), way better than pure Python (11 ms for the same task). See the [perf](perf) folder for simulation performance benchark. - **differentiable model**: the exact gradient on the objective function diff --git a/examples/Microgrid_Wind-Solar.ipynb b/examples/Microgrid_Wind-Solar.ipynb deleted file mode 100644 index a018b2b..0000000 --- a/examples/Microgrid_Wind-Solar.ipynb +++ /dev/null @@ -1,1365 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6edd7377-ce3a-468e-82f0-d3a1f5292a1d", - "metadata": {}, - "source": [ - "# Microgrid performance simulation\n", - "\n", - "Case of Microgrid project with *wind* and *solar* sources,\n", - "plus a *battery* and a *dispatchable generator*:\n", - "\n", - "schematic of a microgrid with wind\n", - "\n", - "Demo of main data structures and functions of [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl).\n", - "Main steps are:\n", - "\n", - "1. Describe the Microgrid project and components (including loading time series data)\n", - "2. Simulation the Microgrid\n", - "3. Analyze simulation results (technical and economic)\n", - "\n", - "After this demonstration, you can go to the next step: [Microgrid sizing optimization](Microgrid_sizing_optimization.ipynb)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "855af8dd-d21d-4587-84cb-ae2b932deeaa", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using Microgrids\n", - "using Printf, CSV, DataFrames, Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f7cc6435-9bd2-487f-943f-225c452db904", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "using PyPlot # For plotting only. Can be skipped or commented out" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c910f9e4-1c92-44ca-8ce3-8c520ec9d231", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "true" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with_plots = isdefined(Main, :PyPlot)" - ] - }, - { - "cell_type": "markdown", - "id": "e60c3e6e-75fc-42b5-bfc3-6e47d1092747", - "metadata": { - "tags": [] - }, - "source": [ - "## Load time series data\n", - "\n", - "Read load and solar data:\n", - "- Load: real consumption data at an hourly timestep from the [Ushant island](https://en.wikipedia.org/wiki/Ushant) in 2016\n", - "- Solar and wind data comes from [PVGIS](https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html). See [data/SOURCES.md](data/SOURCES.md)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "27688e3f-f2d5-4ee1-b4f5-ed1d451c44d6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time series statistics:\n", - "- Load: min 294, mean 773, max 1707 kW\n", - "- PV capacity factor: 11.83 %\n", - "- Wind speed: mean 8.03 m/s\n" - ] - } - ], - "source": [ - "data = DataFrame(CSV.File(\"$(@__DIR__)/data/Ouessant_data_2016.csv\"))\n", - "\n", - "# Simulation steps\n", - "nsteps = length(data.Load)\n", - "@assert(nsteps == 8760) # 1 year at an hourly timestep\n", - "\n", - "# Split load, solar and wind data:\n", - "Pload = data.Load # kW\n", - "Ppv1k = data.Ppv1k ./ 1000; # convert to kW/kWp\n", - "wind_speed = data.Wind; # m/s\n", - "\n", - "# Calibrate wind speed data against a mast measurement \n", - "ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean\n", - "wind_speed = ws_gain*wind_speed\n", - "\n", - "println(\"Time series statistics:\")\n", - "@printf(\"- Load: min %.0f, mean %.0f, max %.0f kW\\n\",\n", - " minimum(Pload), mean(Pload), maximum(Pload))\n", - "@printf(\"- PV capacity factor: %.2f %%\\n\", mean(Ppv1k)*100) # yearly capacity factor of 12% (quite low in 2016 in Ouessant)\n", - "@printf(\"- Wind speed: mean %.2f m/s\\n\", mean(wind_speed))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c24c3df6-4062-450d-ba2d-32d71af7a823", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1453.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Pload[1]" - ] - }, - { - "cell_type": "markdown", - "id": "9b775b85-27eb-4815-8963-15d24a0e1840", - "metadata": {}, - "source": [ - "Display load time series" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "efbc2b71-9a61-45e4-8458-e7e86735077b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "td = collect((0:nsteps-1)/24) # time in days\n", - "\n", - "\"\"\"helper: plot time series with a temporal zoom\"\"\"\n", - "function plot_ts_zoom(t, ts; zoom, color::String, title::String, xlabel::String, ylabel::String)\n", - " fig, (ax1, ax2) = plt.subplots(1,2, figsize=(7.5,2.7), sharey=true,\n", - " gridspec_kw=Dict(\"width_ratios\" => [2,1]))\n", - " ax1.plot(t, ts, color=color)\n", - " ax1.axvspan(zoom[1], zoom[2], color=\"k\", alpha=0.3)\n", - " ax2.plot(t, ts, color=color)\n", - " ax2.set_xlim(zoom[1], zoom[2])\n", - "\n", - " ax1.grid(true)\n", - " ax2.grid(true)\n", - " fig.suptitle(title)\n", - " ax1.set_xlim(t[1], t[end])\n", - " ax1.set(\n", - " title=\"full data length\",\n", - " xlabel=xlabel,\n", - " ylabel=ylabel\n", - " )\n", - " ax2.set(\n", - " title=\"temporal zoom $(zoom[1])–$(zoom[2])\",\n", - " xlabel=xlabel,\n", - " )\n", - " fig.tight_layout()\n", - " return fig, (ax1, ax2)\n", - "end\n", - "\n", - "if with_plots\n", - " fig, (ax1, ax2) = plot_ts_zoom(\n", - " td, Pload;\n", - " zoom=(100,103), color=\"tab:blue\",\n", - " title=\"Electricity consumption in Ushant island in 2016\",\n", - " xlabel=\"day of the year\", ylabel=\"kW\"\n", - " )\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "40e49a64-5483-4d89-8228-9accaf80a5e0", - "metadata": {}, - "source": [ - "### Generate wind power capacity factor time series from wind speed" - ] - }, - { - "cell_type": "markdown", - "id": "5a4f59f2-2d4d-4ded-bd62-a8f433c372ed", - "metadata": {}, - "source": [ - "We use the generic wind power curve model `capacity_from_wind` to transform wind speed time series into a capacity factor time series (normalized power).\n", - "The main parameters of this power curve are:\n", - "- Rotor swept area (m²), i.e. derived from the rotor diameter with $S=\\pi(D/2)^2$\n", - "- Turbine Specific Power (W/m²), i.e. rated power compared to area" - ] - }, - { - "cell_type": "markdown", - "id": "55a6fc2d-8450-40f1-bc14-900b1a7e5c9b", - "metadata": {}, - "source": [ - "Wind turbine parameters used here are fitted to an [EWT 900 kW DW52](https://ewtdirectwind.com/products/dw52/):" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "92076b55-4e2f-4180-abdd-59cb1d99d865", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "S_D52 = pi * (52/2)^2 # rotor swept area m²\n", - "TSP_D52 = 900e3/S_D52 # W/m²\n", - "v_out = 10.0 # m/s\n", - "Cp_D52, α_D52 = 0.521, 3.1; # fitted from actual power curve" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "4e007fcd-f967-4f68-b228-cfdd8cc1f123", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wind power capacity factor: 15.21 %\n" - ] - } - ], - "source": [ - "cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52)\n", - "@printf(\"Wind power capacity factor: %.2f %%\\n\", mean(cf_wind)*100) # (pretty high in Ouessant)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "3ca02a25-7af8-4752-b2cf-22d2038aaae4", - "metadata": {}, - "outputs": [], - "source": [ - "ctest=0.5*Cp_D52*1.225/TSP_D52*(wind_speed.^3);" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "3c65badd-b724-4726-a784-5b3f99cee532", - "metadata": {}, - "outputs": [ - { - "ename": "LoadError", - "evalue": "PyError ($(Expr(:escape, :(ccall(#= C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) \nValueError('x and y must have same first dimension, but have shapes (8760,) and (1,)')\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py\", line 1721, in plot\n lines = [*self._get_lines(self, *args, data=data, **kwargs)]\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 303, in __call__\n yield from self._plot_args(\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 499, in _plot_args\n raise ValueError(f\"x and y must have same first dimension, but \"\n", - "output_type": "error", - "traceback": [ - "PyError ($(Expr(:escape, :(ccall(#= C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 =# @pysym(:PyObject_Call), PyPtr, (PyPtr, PyPtr, PyPtr), o, pyargsptr, kw))))) \nValueError('x and y must have same first dimension, but have shapes (8760,) and (1,)')\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_axes.py\", line 1721, in plot\n lines = [*self._get_lines(self, *args, data=data, **kwargs)]\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 303, in __call__\n yield from self._plot_args(\n File \"C:\\Users\\nikiemaje\\.julia\\conda\\3\\x86_64\\lib\\site-packages\\matplotlib\\axes\\_base.py\", line 499, in _plot_args\n raise ValueError(f\"x and y must have same first dimension, but \"\n", - "", - "Stacktrace:", - " [1] pyerr_check", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:75 [inlined]", - " [2] pyerr_check", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:79 [inlined]", - " [3] _handle_error(msg::String)", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:96", - " [4] macro expansion", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\exception.jl:110 [inlined]", - " [5] #107", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:43 [inlined]", - " [6] disable_sigint", - " @ .\\c.jl:473 [inlined]", - " [7] __pycall!", - " @ C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:42 [inlined]", - " [8] _pycall!(ret::PyCall.PyObject, o::PyCall.PyObject, args::Tuple{Vector{Float64}, Float64, String}, nargs::Int64, kw::PyCall.PyObject)", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:29", - " [9] _pycall!(ret::PyCall.PyObject, o::PyCall.PyObject, args::Tuple{Vector{Float64}, Float64, String}, kwargs::@Kwargs{label::String})", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:11", - " [10] (::PyCall.PyObject)(::Vector{Float64}, ::Vararg{Any}; kwargs::@Kwargs{label::String})", - " @ PyCall C:\\Users\\nikiemaje\\.julia\\packages\\PyCall\\1gn3u\\src\\pyfncall.jl:86", - " [11] top-level scope", - " @ In[37]:3" - ] - } - ], - "source": [ - "fig, (ax1, ax2,ax3) = plt.subplots(3,1, sharex=true)\n", - " ax1.plot(td, ctest, label=\"cf\")\n", - " ax2.plot(td, -log.(exp(-3.1) .+ exp.(-3.1*td)) / 3.1, \"tab:red\", label=\"cf-corrected\")\n", - " ax3.plot(td, cf_wind, \"tab:red\", label=\"cf-corrected\")\n", - " \n", - "pygui(true)\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "ed9b6a54-b62e-4eac-b128-ddc2ad0473f5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9999999444469194" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d= -log(exp(-3.1) + exp(-3.1*te)) / 3.1" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "2bac3dc2-13dc-4ffa-829f-ff3ba38181ae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9886446785035883" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "te= 0.5*Cp_D52*(1.225/TSP_D52)* 10.95^3" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "98c25cd8-07ed-4865-8760-380262eb0fa2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1799.999900004455" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cv=d*1800" - ] - }, - { - "cell_type": "markdown", - "id": "d6734baf-c018-4130-9c6d-c5d37b237abb", - "metadata": {}, - "source": [ - "## Microgrid description\n", - "\n", - "Describe the Microgrid project and its components using dedicated data structures" - ] - }, - { - "cell_type": "markdown", - "id": "cdfff296-430d-4581-9171-40464d82db2d", - "metadata": {}, - "source": [ - "### Project parameters\n", - "\n", - "Financial parameters like discount rate, as well as technical details like the timestep of input data." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dfa81d70-79a5-4e45-88c4-aab803d5d708", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Project(25, 0.05, 1.0, \"€\")" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lifetime = 25 # yr\n", - "discount_rate = 0.05\n", - "timestep = 1. # h\n", - "\n", - "project = Project(lifetime, discount_rate, timestep, \"€\")" - ] - }, - { - "cell_type": "markdown", - "id": "e3103c27-e833-4aa9-8ea2-93b2a1ab93a9", - "metadata": {}, - "source": [ - "Parameters chosen to be common to all Components (but they could differ)\n", - "\n", - "- replacement price relative to initial investment price\n", - "- salvage price relative to initial investment price" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6a9a9819-2f3a-4305-9710-b62d6d21f8f6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "replacement_price_ratio = 1.0\n", - "salvage_price_ratio = 1.0;" - ] - }, - { - "cell_type": "markdown", - "id": "08d88c9c-5eaf-4044-bced-3f914481b061", - "metadata": {}, - "source": [ - "### Dispatchable generator (Diesel)\n", - "\n", - "Used as last recourse, when there is not enough production from renewables and the battery is empty" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "324dd7e0-50e7-4cb6-9017-5b5f8e897773", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "DispatchableGenerator{Float64}(1800.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\")" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "power_rated_gen = 1800. # rated power (kW) → /2 to see some load shedding\n", - "# fuel parameters:\n", - "fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max)\n", - "fuel_slope = 0.240 # fuel curve slope (L/h/kW)\n", - "# prices:\n", - "fuel_price = 1. # fuel price ($/L)\n", - "investment_price_gen = 400. # initial investiment price ($/kW)\n", - "om_price_gen = 0.02 # operation & maintenance price ($/kW/h of operation)\n", - "# other technical parameters\n", - "lifetime_gen = 15000. # generator lifetime (h)\n", - "load_ratio_min = 0.0 # minimum load ratio ∈ [0,1]\n", - "fuel_unit = \"L\" # fuel quantity unit (used in fuel price and consumption curve parameters)\n", - "\n", - "generator = DispatchableGenerator(power_rated_gen,\n", - " fuel_intercept, fuel_slope, fuel_price,\n", - " investment_price_gen, om_price_gen, lifetime_gen,\n", - " load_ratio_min,\n", - " replacement_price_ratio, salvage_price_ratio, fuel_unit)" - ] - }, - { - "cell_type": "markdown", - "id": "f97c5fc3-81f0-4848-a358-45e404e42031", - "metadata": {}, - "source": [ - "### Battery energy storage\n", - "\n", - "Used as a buffer between the renewables production and the consumption" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f744269a-b6bc-4fce-804a-66311843ed76", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "energy_rated_sto = 5000. # rated energy capacity (kWh)\n", - "investment_price_sto = 350. # initial investiment price ($/kWh)\n", - "om_price_sto = 10. # operation and maintenance price ($/kWh/y)\n", - "lifetime_sto = 15. # calendar lifetime (y)\n", - "lifetime_cycles = 3000. # maximum number of cycles over life (1)\n", - "# Parameters with default values\n", - "charge_rate = 1.0 # max charge power for 1 kWh (kW/kWh = h^-1)\n", - "discharge_rate = 1.0 # max discharge power for 1 kWh (kW/kWh = h^-1)\n", - "loss_factor_sto = 0.05 # linear loss factor α (round-trip efficiency is about 1 − 2α) ∈ [0,1]\n", - "SoC_min = 0. # minimum State of Charge ∈ [0,1]\n", - "SoC_ini = 0. # initial State of Charge ∈ [0,1]\n", - "\n", - "battery = Battery(energy_rated_sto,\n", - " investment_price_sto, om_price_sto, lifetime_sto, lifetime_cycles,\n", - " charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_ini,\n", - " replacement_price_ratio, salvage_price_ratio)" - ] - }, - { - "cell_type": "markdown", - "id": "8165bd94-05a7-49d0-9d51-7416cf180beb", - "metadata": {}, - "source": [ - "### Photovoltaic (PV) generation\n", - "\n", - "Used in priority to feed the load (along wind power). PV generation is assumed proportional to the irradiance data load in previous section." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "10ce4795-54f9-4e77-bf7f-886851756d84", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "power_rated_pv = 3000. # rated power (kW)\n", - "irradiance = Ppv1k # global solar irradiance incident on the PV array (kW/m²)\n", - "investment_price_pv = 1200. # initial investiment price ($/kW)\n", - "om_price_pv = 20.# operation and maintenance price ($/kW/y)\n", - "lifetime_pv = 25. # lifetime (y)\n", - "# Parameters with default values\n", - "derating_factor_pv = 1.0 # derating factor (or performance ratio) ∈ [0,1]\n", - "\n", - "photovoltaic = Photovoltaic(power_rated_pv, irradiance,\n", - " investment_price_pv, om_price_pv,\n", - " lifetime_pv, derating_factor_pv,\n", - " replacement_price_ratio, salvage_price_ratio)" - ] - }, - { - "cell_type": "markdown", - "id": "69bf0b76-a3b9-4355-9e9e-c86692193b2c", - "metadata": {}, - "source": [ - "Display PV production time series (which is proportional to the rated power of the plant `power_rated_pv`)\n", - "- PV production is computed by calling the `production` method on the `Photovoltaic` instance" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1a047087-37e1-4f3f-a936-c562ebfb6f8d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if with_plots\n", - " fig, (ax1, ax2) = plot_ts_zoom(\n", - " td, production(photovoltaic);\n", - " zoom=(100,103), color=\"tab:orange\",\n", - " title=\"Production of a $power_rated_pv kW PV plant in Ushant\",\n", - " xlabel=\"day of the year\", ylabel=\"kW\"\n", - " )\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "1c5696fe-e207-4f78-819b-6043d850c62b", - "metadata": {}, - "source": [ - "### Wind power generation\n", - "\n", - "Used in priority to feed the load along solar power.\n", - "\n", - "The simple wind power model use the fixed capacity factor data derived from wind speed in the previous section." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "fefdc955-8473-4f10-b19d-6674bedf773e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "power_rated_wind = 900. # rated power (kW)\n", - "investment_price_wind = 3500. # initial investiment price ($/kW)\n", - "om_price_wind = 100.# operation and maintenance price ($/kW/y)\n", - "lifetime_wind = 25. # lifetime (y)\n", - "\n", - "windgen = WindPower(power_rated_wind, cf_wind,\n", - " investment_price_wind, om_price_wind,\n", - " lifetime_wind,\n", - " replacement_price_ratio, salvage_price_ratio)" - ] - }, - { - "cell_type": "markdown", - "id": "5e2a34a9-2ea6-49ca-8a6d-1bc6bc86b07a", - "metadata": {}, - "source": [ - "Display wind power time series:\n", - "- Wind production is computed by calling the `production` method on the `WindPower` instance" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ea472eff-4cdd-42aa-8515-a1b72dc66c62", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if with_plots\n", - " fig, (ax1, ax2) = plot_ts_zoom(\n", - " td, production(windgen);\n", - " zoom=(100,103), color=\"tab:purple\",\n", - " title=\"Production of $power_rated_pv kW of wind power in Ushant\",\n", - " xlabel=\"day of the year\", ylabel=\"kW\"\n", - " )\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "a63e4cc3-96cd-4e55-bdc1-33c72f8efacc", - "metadata": {}, - "source": [ - "### Microgrid data structure\n", - "\n", - "the `Microgrid` data structure groups:\n", - "- project parameters\n", - "- load time series\n", - "- all components: generator, battery, renewable power generators" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "b549561f-3e09-4552-a695-d17723cd80e4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Microgrid{Float64}(Project(25, 0.05, 1.0, \"€\"), [1453.0, 1331.0, 1214.0, 1146.0, 1078.0, 1035.0, 1032.0, 1072.0, 1142.0, 1208.0 … 1479.0, 1508.0, 1410.0, 1370.0, 1467.0, 1587.0, 1561.0, 1472.0, 1533.0, 1483.0], DispatchableGenerator{Float64}(1800.0, 0.0, 0.24, 1.0, 400.0, 0.02, 15000.0, 0.0, 1.0, 1.0, \"L\"), Battery{Float64}(5000.0, 350.0, 10.0, 15.0, 3000.0, 1.0, 1.0, 0.05, 0.0, 0.0, 1.0, 1.0), NonDispatchableSource[Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0), WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "microgrid = Microgrid(project, Pload,\n", - " generator, battery, [\n", - " photovoltaic,\n", - " windgen\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d0742994-6dd3-43d4-9ba3-b38f52d38d62", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "search: \u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22m \u001b[0m\u001b[1mm\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22m \u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22ms \u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22mhy \u001b[0m\u001b[1mM\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1mc\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mo\u001b[22m\u001b[0m\u001b[1mg\u001b[22m\u001b[0m\u001b[1mr\u001b[22m\u001b[0m\u001b[1mi\u001b[22m\u001b[0m\u001b[1md\u001b[22mCosts\n", - "\n" - ] - }, - { - "data": { - "text/latex": [ - "Microgrid system description\n", - "\n" - ], - "text/markdown": [ - "Microgrid system description\n" - ], - "text/plain": [ - " Microgrid system description" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Microgrid(" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "073acd6d-bf20-4f0f-8c6c-a08dfcbb4397", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Vector{NonDispatchableSource}:\n", - " Photovoltaic{Float64}(3000.0, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 7.000000000000001e-5 … 0.45131, 0.30678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 1200.0, 20.0, 25.0, 1.0, 1.0, 1.0)\n", - " WindPower{Float64}(900.0, [0.03184904690521816, 0.1104932647748532, 0.24596484619739856, 0.44422012231738006, 0.6554128208926687, 0.8470913237698507, 0.9579413031865969, 0.9735278942424532, 0.9837469734016646, 0.9903667231838823 … 0.011233045262998936, 0.018460507449516883, 0.023644167600765466, 0.02969747711584798, 0.036359043012900244, 0.03713796382151399, 0.037530379586714784, 0.03792477182913709, 0.037530379586714784, 0.03674751985711801], 3500.0, 100.0, 25.0, 1.0, 1.0)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[photovoltaic, windgen]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "3832020f-6142-4503-8bb3-41bd967a99c2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "true" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[Photovoltaic{Float64} <: NonDispatchableSource" - ] - }, - { - "cell_type": "markdown", - "id": "0e43de8d-82fe-4d33-9010-cbd6b620cca9", - "metadata": {}, - "source": [ - "## Simulate the microgrid\n", - "\n", - "launched with the `simulate` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "9038c442-f3bc-4fe7-8e3f-83e77a42d2e7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "oper_traj, oper_stats, mg_costs = simulate(microgrid);" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c127ba4e-0a97-4ca0-a450-11083e0bc81a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "LoadError", - "evalue": "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", - "output_type": "error", - "traceback": [ - "LoadError: UndefVarError: `@btime` not defined\nin expression starting at In[1]:1", - "" - ] - } - ], - "source": [ - "@btime oper_traj, oper_stats = operation(microgrid)" - ] - }, - { - "cell_type": "markdown", - "id": "8ab2808c-d25a-4bac-858b-abd436664730", - "metadata": {}, - "source": [ - "In details, the simulation is done in two stages:\n", - "\n", - "1. simulate the **operation**, in particular the energy flow at an hourly timestep between components: \n", - " - generates operation statistics as `OperationStats` data structure\n", - " - records trajectories of operation variables\n", - "2. evaluate the **economic cost** of the project, based on its description and on the operation statistics: \n", - " - generates cost data as `MicrogridCosts` data structure\n", - "\n", - "These successive stages can be run manually with their dedicated functions (see `simulate` code)." - ] - }, - { - "cell_type": "markdown", - "id": "ddc7b937-ec29-4fa7-be9e-5afd4d167d43", - "metadata": {}, - "source": [ - "Microgrid simulation code is optimized to run in less than 1 ms:\n", - "\n", - "(you can use [Benchmarktools](https://juliaci.github.io/BenchmarkTools.jl/)'s `@btime` for more accurate timing results)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "c9bf9e90-a9dc-43ca-95f8-4a3d76eb0cf1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0.001151 seconds (152 allocations: 829.234 KiB)\n" - ] - } - ], - "source": [ - "@time oper_traj, oper_stats, mg_costs = simulate(microgrid);" - ] - }, - { - "cell_type": "markdown", - "id": "c35a1953-57c4-4312-a593-d9d80f874f87", - "metadata": {}, - "source": [ - "## Analyze Microgrid simulation results" - ] - }, - { - "cell_type": "markdown", - "id": "57bf3e9f-04f9-4697-9dcb-5e88c4091336", - "metadata": {}, - "source": [ - "### Technical performance\n", - "\n", - "Operation statistics are in `oper_stats` (`OperationStats` data structure) with fields such as `renew_rate` (fraction of renewables in the energy mix), `shed_rate` (fraction of shed, i.e. unserved load)..." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "3695a442-4e04-4436-9374-70c85036beda", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Load shedding rate: 0.0%\n", - "Renewable rate: 75.3%\n" - ] - } - ], - "source": [ - "@printf(\"Load shedding rate: %.1f%%\\n\", oper_stats.shed_rate*100)\n", - "@printf(\"Renewable rate: %.1f%%\\n\", oper_stats.renew_rate*100)" - ] - }, - { - "cell_type": "markdown", - "id": "142b0cdd-a853-49c3-be2b-8a2b53a3990d", - "metadata": {}, - "source": [ - "All available statistics:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "0090bf4d-f684-492b-9583-bd56854329b0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "OperationStats with fields:\n", - "- served_energy: 6.775e6 kWh\n", - "- shed_energy: 0.0 kWh\n", - "- shed_max: 0.0 kW\n", - "- shed_hours: 0.0 h\n", - "- shed_duration_max: 0.0 h\n", - "- shed_rate: 0.0 in [0,1]\n", - "- gen_energy: 1.6729e6 kWh\n", - "- gen_hours: 3310.0 h\n", - "- gen_fuel: 401490.0 L\n", - "- storage_cycles: 188.65 \n", - "- storage_char_energy: 990430.0 kWh\n", - "- storage_dis_energy: 896110.0 kWh\n", - "- storage_loss_energy: 94327.0 kWh\n", - "- spilled_energy: 1.0413e6 kWh\n", - "- spilled_max: 2644.9 kW\n", - "- spilled_rate: 0.16693 in [0,1]\n", - "- renew_potential_energy: 6.2377e6 kWh\n", - "- renew_energy: 5.1964e6 kWh\n", - "- renew_rate: 0.75308 in [0,1]\n" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oper_stats" - ] - }, - { - "cell_type": "markdown", - "id": "5120f110-3a6b-4073-8be7-d69804dfb1b3", - "metadata": {}, - "source": [ - "### Economic performance\n", - "\n", - "Economic performance indicators are stored in `mg_costs` (as a `MicrogridCosts` data structure). This structure contains the following fields:\n", - "- two highlevel cost indicators: Net present Cost `npc` and Levelized Costs of Electricity `lcoe`\n", - "- a break down of cost factors for the entire project in `system`: initial investment, fuel... (as a `CostFactors` structure)\n", - "- a detailed break down of cost factors for each component in `generator`, `storage`, `nondispatchables[n]`... (also as `CostFactors` structures)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8b904163-9ef8-4b87-a11c-d06ed53af5ac", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Levelized Cost of Electricity: 0.229 $/kWh\n", - "Net Present Cost: 21.89 M$ (over 25 years at 5% discount rate)\n" - ] - } - ], - "source": [ - "@printf(\"Levelized Cost of Electricity: %.3f \\$/kWh\\n\", mg_costs.lcoe)\n", - "@printf(\"Net Present Cost: %.2f M\\$ (over %d years at %.0f%% discount rate)\\n\",\n", - " mg_costs.npc/1e6, project.lifetime, project.discount_rate*100)" - ] - }, - { - "cell_type": "markdown", - "id": "e3133a9b-c89c-453a-9b8f-9885b713193c", - "metadata": {}, - "source": [ - "Break down of cost factors for the entire project:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "7e5fde52-1cd8-43a1-9f9b-c04d659d4f1a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CostFactors(2.189002772908652e7, 9.22e6, 2.7882202252348172e6, 4.498223347698841e6, 5.65860947086071e6, -275025.3147078488)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_costs.system" - ] - }, - { - "cell_type": "markdown", - "id": "ca89063d-0123-43ef-8283-d54713a5b346", - "metadata": {}, - "source": [ - "Break down of cost factors for each nondispatchable power sources (i.e. renewables), in the same order as in `microgrid.nondispatchables`:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "82c503dc-02a8-4fcf-86c0-b0e3bc3784dd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "2-element Vector{CostFactors}:\n", - " CostFactors(4.445636673962684e6, 3.6e6, 0.0, 845636.6739626848, 0.0, -0.0)\n", - " CostFactors(4.418455010944027e6, 3.15e6, 0.0, 1.2684550109440272e6, 0.0, -0.0)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mg_costs.nondispatchables" - ] - }, - { - "cell_type": "markdown", - "id": "01744584-246a-4a53-ab67-5eb043547d32", - "metadata": {}, - "source": [ - "Definition of the fields of cost factors (`total` is the sum of all the other ones):" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "82273c01-fa92-414a-af84-46bb793c7988", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(:total, :investment, :replacement, :om, :fuel, :salvage)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fieldnames(CostFactors)" - ] - }, - { - "cell_type": "markdown", - "id": "cb3a7eae-8696-4780-a63e-8c2811908061", - "metadata": {}, - "source": [ - "Remark 1: `npc` is equal to `system.total`\n", - "\n", - "Remark 2: `CostFactors` structures like `mg_costs.system` support arithmetic operations (+,*,/) and rounding:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "d0dc9505-34d3-4657-a40b-2d5aa684a19b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "CostFactors(21.89, 9.22, 2.79, 4.5, 5.66, -0.28)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "round(mg_costs.system/1e6; digits=2) # rounded costs in M$" - ] - }, - { - "cell_type": "markdown", - "id": "ea65446a-8e6b-4559-a98f-d71fe2a5df4f", - "metadata": {}, - "source": [ - "Individual cost factors can be accessed by field names. For examples fuel costs for the dispatchable generator:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "5eb2896b-4e36-4eb8-adcc-fb8cbd67809d", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5.66" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "round(mg_costs.generator.fuel/1e6; digits=2) # M$" - ] - }, - { - "cell_type": "markdown", - "id": "cdb834c3-7616-453f-a1f5-89ebfe5fa434", - "metadata": {}, - "source": [ - "### Operation trajectories" - ] - }, - { - "cell_type": "markdown", - "id": "3f2b70c2-08ef-458e-9090-919ff4e3efa6", - "metadata": {}, - "source": [ - "`oper_traj` is of type `OperationTraj` with fields such as `Pgen` (generator power), `Pbatt` (battery power)...\n", - "\n", - "**Graph 1** Zoom to first week of January: high load, wind at maximum, few solar → battery often empty" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "5594c21a-c653-48f9-a134-9c28e68cdc79", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "function plot_oper_traj(microgrid, oper_traj)\n", - " fig, (ax1, ax2) = plt.subplots(2,1, sharex=true)\n", - " ax1.plot(td, Pload, label=\"load\")\n", - " ax1.plot(td, oper_traj.Pgen, \"tab:red\", label=\"gen\")\n", - " ax2.plot(td, oper_traj.Ebatt[1:end-1], \"C2\")\n", - " \n", - " ax1.legend()\n", - " ax1.grid(true)\n", - " ax1.set(\n", - " ylabel=\"kW\"\n", - " )\n", - " ax2.grid(true)\n", - " ax2.set(\n", - " title=\"Energy storage state\",\n", - " xlabel=\"time (d)\",\n", - " ylabel=\"kWh\"\n", - " )\n", - " \n", - " fig.tight_layout()\n", - " return fig, (ax1, ax2)\n", - "end\n", - "\n", - "if with_plots\n", - " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", - " ax1.set_xlim(0, 7)\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "fac01e62-1ae5-425a-95dc-8827bf9fa50e", - "metadata": {}, - "source": [ - "**Graph 2** Zoom to one week in summer: much solar → battery often full → spillage" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "256a0b88-4972-46f3-a29e-fb7016aee700", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "Figure(PyObject
)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if with_plots\n", - " fig, (ax1, ax2) = plot_oper_traj(microgrid, oper_traj)\n", - " ax1.set_xlim(150, 157)\n", - "end;" - ] - }, - { - "cell_type": "markdown", - "id": "4570d462-880a-461f-b847-8c23cebe3a08", - "metadata": {}, - "source": [ - "---\n", - "**Next steps**: \n", - "after this demonstration which focused on Microgrid *simulation*, you can go to the [Microgrid sizing optimization](Microgrid_sizing_optimization.ipynb)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Julia 1.10.0_threads", - "language": "julia", - "name": "julia-1.10" - }, - "language_info": { - "file_extension": ".jl", - "mimetype": "application/julia", - "name": "julia", - "version": "1.10.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From eff8bd18916fbb51814e59d0393563069f50976e Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 14 Jun 2024 16:04:48 +0200 Subject: [PATCH 09/19] Readme Updated --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 68d2a6d..d28bbe3 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ The unit tests which ship with the package can be run with `] test Microgrids`. ## Description of Microgrids.jl -Microgrid sizing illustration +Microgrid sizing illustration `Microgrids.jl` can model a microgrid project consisting of: - One load (described by a time series) From adc369ab8a30c5bfdf05d06c239c5fc6f8098ac1 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 14 Jun 2024 16:08:14 +0200 Subject: [PATCH 10/19] Readme Updated --- README.md | 2 +- examples/Microgrid_Wind-Solar-H2.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index d28bbe3..0bd07ce 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ The unit tests which ship with the package can be run with `] test Microgrids`. ## Description of Microgrids.jl -Microgrid sizing illustration +Microgrid sizing illustration `Microgrids.jl` can model a microgrid project consisting of: - One load (described by a time series) diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index ee5107c..924cb2a 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -10,7 +10,7 @@ "Case of Microgrid project with *wind* and *solar* sources,\n", "plus a *battery* and a *dispatchable generator*:\n", "\n", - "schematic of a microgrid with wind and H2\n", + "schematic of a microgrid with wind and H2\n", "\n", "Demo of main data structures and functions of [Microgrids.jl](https://github.com/Microgrids-X/Microgrids.jl).\n", "Main steps are:\n", From 7edfe968abad7fdd18f9ae64ff8fc38b9851c528 Mon Sep 17 00:00:00 2001 From: Jean NIKIEMA <51806294+nikiema-fruc@users.noreply.github.com> Date: Fri, 14 Jun 2024 16:11:09 +0200 Subject: [PATCH 11/19] Update LICENSE.md --- LICENSE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.md b/LICENSE.md index 33dd90a..71c6feb 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,6 +1,6 @@ MIT License -Copyright © 2021-2023 by Evelise de G. Antunes, Nabil Sadou and Pierre Haessig +Copyright © 2021-2023 by Evelise de G. Antunes, Nabil Sadou Pierre Haessig and Jean Fructhieu NIKIEMA Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal From a4b02a022d71a15a694be31159d3554e5e062781 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 24 Jan 2025 14:24:10 +0100 Subject: [PATCH 12/19] usefull functions added --- Project.toml | 3 +- examples/Useful_functions.jl | 63 +----- examples/data/Microgrid_Wind-Solar-H2_data.jl | 12 +- src/Microgrids.jl | 203 +++++++++++++----- src/components.jl | 12 ++ src/economics.jl | 2 +- src/operation.jl | 41 ++-- 7 files changed, 202 insertions(+), 134 deletions(-) diff --git a/Project.toml b/Project.toml index f991e4a..56a59b7 100644 --- a/Project.toml +++ b/Project.toml @@ -1,12 +1,13 @@ name = "Microgrids" uuid = "bd581358-d3fa-499e-a26e-e70307242c03" -authors = ["Pierre Haessig ", "evelisea <59697806+evelisea@users.noreply.github.com>","Jean NIKIEMA "] +authors = ["Pierre Haessig ", "evelisea <59697806+evelisea@users.noreply.github.com>", "Jean NIKIEMA "] version = "0.10.5" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" +Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b" IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" [compat] diff --git a/examples/Useful_functions.jl b/examples/Useful_functions.jl index 7bd979c..5c9eb26 100644 --- a/examples/Useful_functions.jl +++ b/examples/Useful_functions.jl @@ -6,55 +6,6 @@ This file must be included after the instructions: - Include ("../examples/data/Microgrid_Wind-Solar-H2_data.jl") """ -""" - Create a Microgrid of size `x` - with x=[energy_rated_sto, power_rated_pv, power_rated_wind, power_rated_elyz, power_rated_fc, capacity_rated_hy_tank] - You can also specify capex prices and initial filling rate of either Hydrogen tank or batteries. - new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}) - -""" - - -function new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state) - project = Project(lifetime, discount_rate, timestep, "€") - gen = ProductionUnit(0.0, - fuel_intercept, fuel_slope, fuel_price, - capex[1], om_price_hour_gen, om_price_gen, lifetime_gen_y,lifetime_gen_h,lifetime_gen_starts, - load_ratio_min_gen, replacement_price_ratio, - salvage_price_ratio, input_unit_gen,output_unit_gen) - ftank = Tank(capacity_rated_ftank,capex[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,initial_fill_rate[1], - fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio) - fuel_cell = ProductionUnit(x[5],cons_intercept_fc, cons_rate_fc,cons_price_fc,capex[3], om_price_hour_fc, om_price_fc,lifetime_fc_y,lifetime_fc_h,lifetime_fc_starts, - load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc) - hytank = Tank(x[6],capex[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,initial_fill_rate[2], - LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio) - dispatchables = DispatchableCompound{Float64}([gen], [fuel_cell]) - tanks = TankCompound{Float64}(ftank,hytank) - - elyz = ProductionUnit(x[4],cons_intercept_elyz,cons_rate_elyz,cons_price_elyz, capex[5], om_price_hour_elyz,om_price_elyz, lifetime_elyz_y,lifetime_elyz_h,lifetime_elyz_starts, - load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz) - - batt = Battery(x[1], - capex[6], om_price_sto, lifetime_sto, lifetime_cycles, - charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,initial_fill_rate[3], - replacement_price_ratio, salvage_price_ratio) - pv = Photovoltaic(x[2], irradiance, - capex[7], om_price_pv, - lifetime_pv, derating_factor_pv, - replacement_price_ratio, salvage_price_ratio) - windgen = WindPower(x[3], cf_wind, - capex[8], om_price_wind, - lifetime_wind, - replacement_price_ratio, salvage_price_ratio) - - mg = Microgrid(project, Pload,dispatchables, - [elyz,],tanks,batt, [ - pv, - windgen - ]) - return mg - end - """ Simulate the performance of a Microgrid project Returns mg, traj, stats, costs @@ -88,14 +39,16 @@ Plot the instantaneous power sharing of a microgrid furing one year plot_oper_traj(mg, oper_traj) """ function plot_oper_traj(mg, oper_traj) + """ ns=Int64(nsteps/730) z=zeros(Float64,ns) tm=zeros(Float64,ns +1) - tm[end]=nsteps/24 + tm[end]=nsteps/24 for i=1:ns z[i]= oper_traj.LoH[i*730+1] tm[i]=(i-1)*730/24 end + """ fig,( ax1, ax2) = plt.subplots(2,1) y1 = min.(oper_traj.Prenew_pot,mg.load) y2 = max.( oper_traj.Pbatt,0.0) @@ -109,25 +62,25 @@ tm=zeros(Float64,ns +1) y=np.vstack([y1,y2,y3,y4,y5,y6,y7]) fieldNames =(["Pren used by the load","battery_discharge","fuel cell","battery_charge", "elyz","shedding","spillage"]) - fieldColors = (["orange","green","pink","green","magenta","black","red"]) + fieldColors = (["orange","mediumseagreen","hotpink","seagreen","orchid","black","red"]) ax1.plot(td, oper_traj.Prenew_pot,lw=2, "tab:orange", label="ren") ax1.plot(td, mg.load,lw=2, label="load") stacks = ax1.stackplot(td,y,labels=fieldNames, colors = fieldColors,alpha=0.8) - stacks[2].set_hatch("-") - stacks[4].set_hatch("+") + stacks[2].set_hatch("--") + stacks[4].set_hatch("++") stacks[6].set_hatch("\\") stacks[7].set_hatch("\\") ax3=ax1.twinx() ax3.set_ylim(0, 2) - ax3.plot(td, oper_traj.Ebatt[1:end-1]/mg.storage.energy_rated, ls="--","C2",label="hourly battery level") + ax3.plot(td, oper_traj.Ebatt[1:8760]/mg.storage.energy_rated, ls="--","C2",label="hourly battery level") # ax4=ax2.twinx() - ax2.plot(td, oper_traj.LoH[1:end-1], "C2",label="level of H2") + ax2.plot(td, oper_traj.LoH[1:8760], "C2",label="level of H2") # ax2.stairs(z,tm,fill="True",label="level of H2 at the end of each month") diff --git a/examples/data/Microgrid_Wind-Solar-H2_data.jl b/examples/data/Microgrid_Wind-Solar-H2_data.jl index 6bff26a..e15c851 100644 --- a/examples/data/Microgrid_Wind-Solar-H2_data.jl +++ b/examples/data/Microgrid_Wind-Solar-H2_data.jl @@ -49,15 +49,15 @@ power_rated_gen = 2000. # rated power (kW) → /2 to see some load shedding fuel_intercept = 0.0 # fuel curve intercept (L/h/kW_max) fuel_slope = 0.240 # fuel curve slope (L/h/kW) # prices: -fuel_price = 1. # fuel price ($/L) +fuel_price = 1.0 # fuel price ($/L) investment_price_gen = 400. # initial investiment price ($/kW) om_price_hour_gen = 0.02 # operation & maintenance price ($/kW/h of operation) -om_price_gen = 0.0 # +om_price_gen = 0. # # other technical parameters lifetime_gen_y = 25. -lifetime_gen_h = 15000. # generator lifetime (h) -lifetime_gen_starts = 15000. # generator lmax starts on -load_ratio_min_gen = 0.0 # minimum load ratio ∈ [0,1] +lifetime_gen_h = 20000. # generator lifetime (h) +lifetime_gen_starts = 5000. # generator lmax starts on +load_ratio_min_gen = 0.1 # minimum load ratio ∈ [0,1] input_unit_gen = "L" # fuel quantity unit (used in fuel price and consumption curve parameters) output_unit_gen = "KWh" @@ -145,6 +145,6 @@ investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) lifetime_wind = 25. # lifetime (y) -X=[5000., 5000., 1800., 2000., 1800., 12000.]./1000 +default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0) capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] ini_filling_state=[0.0,0.0,0.0]; \ No newline at end of file diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 5d5ced0..f24b75b 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -1,66 +1,165 @@ module Microgrids -using CSV, DataFrames -using DocStringExtensions + using CSV, DataFrames + using DocStringExtensions -@template (TYPES) = -""" -$(TYPEDEF) -$(DOCSTRING) - -### Parameters: -$(FIELDS) -""" - -include("components.jl") -include("dispatch.jl") -include("production.jl") -include("operation.jl") -include("economics.jl") + @template (TYPES) = + """ + $(TYPEDEF) + $(DOCSTRING) + ### Parameters: + $(FIELDS) + """ -import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions - -export simulate, - NonDispatchableSource, ProductionUnit, Tank, TankCompound, - Project, DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, - capacity_from_wind, - OperationTraj, OperationStats, increment, - operation, aggregation, dispatch_1,dispatch_2, production, - CostFactors, MicrogridCosts, component_costs, economics, MicrogridCashflow - -""" - simulate(mg::Microgrid, ε::Real=0.0) + include("components.jl") + include("dispatch.jl") + include("production.jl") + include("operation.jl") + include("economics.jl") + include("../examples/data/Microgrid_Wind-Solar-H2_data.jl") -Simulate the technical and economic performance of a Microgrid `mg`. -Discontinuous computations can optionally be relaxed (smoothed) -using the relaxation parameter `ε`: -- 0.0 means no relaxation (default value) -- 1.0 yields the strongest relaxation + import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions -Using relaxation (`ε` > 0) is recommended when using gradient-based optimization -and then a “small enough” value between 0.05 and 0.30 is suggested. + export simulate,save_mg,load_mg,new_microgrid, + NonDispatchableSource, ProductionUnit, Tank, TankCompound, + Project, Sizing,DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, + capacity_from_wind, + OperationTraj, OperationStats, increment, + operation, aggregation, dispatch_1,dispatch_2, production, + CostFactors, MicrogridCosts, component_costs, economics, MicrogridCashflow + -Returns: -- Operational trajectories from `sim_operation` (should be optional in future version) -- Operational statistics from `sim_operation` -- Microgrid project costs from `sim_economics` """ -function simulate(mg::Microgrid, dispatch::Function,ε::Real=0.0) - # Run the microgrid operation - oper_traj = operation(mg, dispatch) - # Aggregate the operation variables - oper_stats = aggregation(mg, oper_traj, ε) - - # Eval the microgrid costs - mg_costs = economics(mg, oper_stats) - - return oper_traj, oper_stats, mg_costs -end - - + Create a Microgrid of size `x` + with x=[energy_rated_sto, power_rated_pv, power_rated_wind, power_rated_elyz, power_rated_fc, capacity_rated_hy_tank] + You can also specify capex prices and initial filling rate of either Hydrogen tank or batteries. + new_microgrid(x::Vector{Float64}= X,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}) +""" + +function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state,load::Vector{Float64}=Pload) + project = Project(lifetime, discount_rate, timestep, "€") + gen = ProductionUnit(sizing.Cgen, + fuel_intercept, fuel_slope, fuel_price, + capex[1], om_price_hour_gen, om_price_gen, lifetime_gen_y,lifetime_gen_h,lifetime_gen_starts, + load_ratio_min_gen, replacement_price_ratio, + salvage_price_ratio, input_unit_gen,output_unit_gen) + ftank = Tank(sizing.Ftank,capex[2], om_price_ftank,lifetime_ftank,loss_factor_ftank,initial_fill_rate[1], + fuel_min_ratio, fuel_max_ratio,fuel_price, replacement_price_ratio, salvage_price_ratio) + fuel_cell = ProductionUnit(sizing.Cfc,cons_intercept_fc, cons_rate_fc,cons_price_fc,capex[3], om_price_hour_fc, om_price_fc,lifetime_fc_y,lifetime_fc_h,lifetime_fc_starts, + load_min_ratio_fc,replacement_price_ratio, salvage_price_ratio,input_unit_fc,output_unit_fc) + hytank = Tank(sizing.Htank,capex[4], om_price_hytank,lifetime_hytank,loss_factor_hytank,initial_fill_rate[2], + LoH_min_ratio, LoH_max_ratio,hy_price,replacement_price_ratio, salvage_price_ratio) + dispatchables = DispatchableCompound{Float64}([gen], [fuel_cell]) + tanks = TankCompound{Float64}(ftank,hytank) + + elyz = ProductionUnit(sizing.Cel,cons_intercept_elyz,cons_rate_elyz,cons_price_elyz, capex[5], om_price_hour_elyz,om_price_elyz, lifetime_elyz_y,lifetime_elyz_h,lifetime_elyz_starts, + load_min_ratio_elyz,replacement_price_ratio, salvage_price_ratio,input_unit_elyz,output_unit_elyz) + + batt = Battery(sizing.Cbatt, + capex[6], om_price_sto, lifetime_sto, lifetime_cycles, + charge_rate, discharge_rate, loss_factor_sto, SoC_min, SoC_max,initial_fill_rate[3], + replacement_price_ratio, salvage_price_ratio) + pv = Photovoltaic(sizing.Cpv, irradiance, + capex[7], om_price_pv, + lifetime_pv, derating_factor_pv, + replacement_price_ratio, salvage_price_ratio) + windgen = WindPower(sizing.Cwind, cf_wind, + capex[8], om_price_wind, + lifetime_wind, + replacement_price_ratio, salvage_price_ratio) + mg = Microgrid(project, load,dispatchables, + [elyz,],tanks,batt, [ + pv, + windgen + ]) + return mg + end + + + + """ + simulate(mg::Microgrid, ε::Real=0.0) + + Simulate the technical and economic performance of a Microgrid `mg`. + + Discontinuous computations can optionally be relaxed (smoothed) + using the relaxation parameter `ε`: + - 0.0 means no relaxation (default value) + - 1.0 yields the strongest relaxation + + Using relaxation (`ε` > 0) is recommended when using gradient-based optimization + and then a “small enough” value between 0.05 and 0.30 is suggested. + + Returns: + - Operational trajectories from `sim_operation` (should be optional in future version) + - Operational statistics from `sim_operation` + - Microgrid project costs from `sim_economics` + """ + function simulate(mg::Microgrid, dispatch::Function,ε::Real=0.0) + # Run the microgrid operation + oper_traj = operation(mg, dispatch) + # Aggregate the operation variables + oper_stats = aggregation(mg, oper_traj, ε) + + # Eval the microgrid costs + mg_costs = economics(mg, oper_stats) + + return oper_traj, oper_stats, mg_costs + end + + + + function save_mg(mg::Microgrid , stats::OperationStats, traj::OperationTraj, path::String,mg_name::String) + mkdir(mg_name) + proj_path=path*mg_name*"/" + nsteps=Int(length(mg.load)/mg.project.timestep) + size_frame=DataFrame(Cgen=Float64[], + Cbatt=Float64[],Cpv=Float64[],Cwind=Float64[],Cfc=Float64[],Cel=Float64[],Htank=Float64[],Ftank=Float64[]) + push!(size_frame,(mg.dispatchables.generator[1].power_rated,mg.storage.energy_rated,mg.nondispatchables[1].power_rated,mg.nondispatchables[2].power_rated, + mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity)) + CSV.write(proj_path*"sizing.csv",size_frame) + + op_frame=DataFrame([name => [] for name in propertynames(stats)]) + push!(op_frame,collect(getproperty(stats, Symbol(name)) for name in propertynames(stats) )) + CSV.write(proj_path*"stats.csv",op_frame) + + traj_frame=DataFrame([name => getproperty(traj,name)[1:nsteps] for name in propertynames(traj)]) + CSV.write(proj_path*"traj.csv",traj_frame) + + end + + function load_mg(project_path :: String) + sizing_df = DataFrame(CSV.File(project_path*"/sizing.csv")) + sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8]) + + traj_df = DataFrame(CSV.File(project_path*"/traj.csv")) + traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],) + + if sizing.Ftank>0.0 + a = traj.LoF[1]/sizing.Ftank + else + a=0.0 + end + + if sizing.Htank>0.0 + b = traj.LoH[1]/sizing.Htank + else + b=0.0 + end + if sizing.Cbatt>0.0 + c = traj.Ebatt[1]/sizing.Cbatt + else + c=0.0 + end + mg=new_microgrid(sizing,capex_def,[a,b,c]) + stats = aggregation(mg, traj, 0.0) + + return mg,traj,stats + + end end \ No newline at end of file diff --git a/src/components.jl b/src/components.jl index b1b4d8e..ef9d41b 100644 --- a/src/components.jl +++ b/src/components.jl @@ -14,6 +14,18 @@ struct Project currency::String end +struct Sizing + + Cgen::Float64 + Cbatt::Float64 + Cpv::Float64 + Cwind::Float64 + Cfc::Float64 + Cel::Float64 + Htank::Float64 + Ftank::Float64 +end + """ ProductionUnit Unit used to product gaz or energy diff --git a/src/economics.jl b/src/economics.jl index 8caf1d9..3a86f67 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -208,7 +208,7 @@ function component_costs(mg_project::Project, lifetime::Real, end end - salvage_formula = :LinearSalvage + salvage_formula = :ConsistentSalvage if salvage_formula == :LinearSalvage # remaining lifetime of last component at the project end remaining_life = lifetime*(1+replacements_number) - mg_lifetime diff --git a/src/operation.jl b/src/operation.jl index 2ddc281..3807d23 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -38,6 +38,7 @@ struct OperationTraj{T<:Real} Pdump::Vector{T} end + """Aggregated statistics over the simulated Microgrid operation (simulation duration is assumed to be 1 year) @@ -222,6 +223,7 @@ const oper_stats_units = Dict( :renew_rate => "in [0,1]" ) + """ Base.show(io::IO, ::MIME"text/plain", stats::OperationStats) @@ -242,33 +244,33 @@ end """ - usage(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Int64,n_starts::Int64) + usage(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Float64,n_starts::Int64,ε::Real=0.0) Allow to operate running statistics of production units """ -function usage(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Float64,n_starts::Int64,ε::Real=0.0) +function usage!(prod_unit_power::Float64,prod_unit::ProductionUnit,hours::Float64,cons::Float64,minP::Float64,dt::Float64,c1::Float64,max_rt::Float64,min_rt::Float64,r_u_2::Int64,r_o_2::Int64,cr::Float64,n_starts::Int64,ε::Real=0.0) Pprod_unit_max = prod_unit.power_rated - Pprod_unit_eps = Pprod_unit_max*1e-6 + Pprod_unit_eps = Pprod_unit_max*1e-5 prod_unit_intercept = prod_unit.consumption_intercept prod_unit_slope = prod_unit.consumption_slope - time_inc = 0 + time_inc = 0.0 cons_rate = 0 if prod_unit_power > Pprod_unit_eps # prod_unit ON Pprod_unit_norm = prod_unit_power / (ε * Pprod_unit_max) # can be Inf e.g. for ε=0.0 if Pprod_unit_norm <= 1.0 - time_inc = 1 # relaxation of discontinuous prod_unit statistics for small Prod_unit_power + time_inc = Pprod_unit_norm*dt # relaxation of discontinuous prod_unit statistics for small Prod_unit_power cons_rate = Pprod_unit_norm * prod_unit_intercept * Pprod_unit_max + prod_unit_slope * prod_unit_power # (L/h) else - time_inc=1 + time_inc=dt cons_rate = prod_unit_intercept * Pprod_unit_max + prod_unit_slope * prod_unit_power # (L/h) end if cr == 0.0 n_starts+=1 end - if prod_unit_power == Pprod_unit_max - c1+=1*dt + if prod_unit_power >= Pprod_unit_max - Pprod_unit_eps + c1+=dt end if prod_unit_power < minP minP=prod_unit_power @@ -443,6 +445,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) ### Retrieve parameters dt = mg.project.timestep K = length(mg.load) + co= 8760/K ### Compute simple yearly statistics (sum, max...): # Load statistics @@ -531,12 +534,12 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) Pload_avg = load_energy/(K*dt) # kW cr=zeros(Float64,3) for k = 1:K - gen_starts,gen_min_rp,gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_hours,gen_fuel=usage(oper_traj.Pgen[k],mg.dispatchables.generator[1],gen_hours,gen_fuel,gen_min_rp,dt, + gen_starts,gen_min_rp,gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_hours,gen_fuel=usage!(oper_traj.Pgen[k],mg.dispatchables.generator[1],gen_hours,gen_fuel,gen_min_rp,dt, gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_starts) - fc_starts,fc_min_rp,fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_hours,fc_cons=usage(oper_traj.Pfc[k],mg.dispatchables.fuel_cell[1],fc_hours,fc_cons,fc_min_rp,dt, + fc_starts,fc_min_rp,fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_hours,fc_cons=usage!(oper_traj.Pfc[k],mg.dispatchables.fuel_cell[1],fc_hours,fc_cons,fc_min_rp,dt, fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_starts) - elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, + elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage!(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_starts) # Load shedding: shedding duration and maximum shedding duration Pshed = oper_traj.power_shedding[k] @@ -568,19 +571,19 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) # Outputs oper_stats = OperationStats( # Load statistics - served_energy, shed_energy, shed_max, shed_hours, shed_duration_max, shed_rate, + served_energy*co, shed_energy*co, shed_max, shed_hours*co, shed_duration_max, shed_rate, # Dispatchable generator statistics - gen_energy, gen_hours, gen_starts,gen_fuel,gen_rt_at_nom,gen_min_rp,gen_avg_rp,gen_avg_rt,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2, + gen_energy*co, gen_hours*co, gen_starts*co,gen_fuel*co,gen_rt_at_nom*co,gen_min_rp,gen_avg_rp,gen_avg_rt,gen_max_rt,gen_min_rt,gen_ru2*co,gen_ro2*co, #H2 chain statistics - fc_produced_energy, fc_hours,fc_starts, fc_consumed_h2,fc_rt_at_nom,fc_min_rp,fc_avg_rp,fc_avg_rt,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2, - elyz_produced_h2, elyz_hours,elyz_starts, elyz_consumed_energy,elyz_rt_at_nom,elyz_min_rp,elyz_avg_rp,elyz_avg_rt,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2, - chain_loss, + fc_produced_energy*co, fc_hours*co,fc_starts*co, fc_consumed_h2*co,fc_rt_at_nom*co,fc_min_rp,fc_avg_rp,fc_avg_rt,fc_max_rt,fc_min_rt,fc_ru2*co,fc_ro2*co, + elyz_produced_h2*co, elyz_hours*co,elyz_starts*co, elyz_consumed_energy*co,elyz_rt_at_nom*co,elyz_min_rp,elyz_avg_rp,elyz_avg_rt,elyz_max_rt,elyz_min_rt,elyz_ru2*co,elyz_ro2*co, + chain_loss*co, # Energy storage (e.g. battery) statistics - storage_cycles, storage_char_energy, storage_dis_energy, storage_loss_energy, + storage_cycles*co, storage_char_energy*co, storage_dis_energy*co, storage_loss_energy*co, # Non-dispatchable (typ. renewables) sources statistics - spilled_energy, spilled_max, spilled_rate,dumped_energy,dumped_max, - renew_potential_energy, renew_energy, renew_rate + spilled_energy*co, spilled_max, spilled_rate,dumped_energy*co,dumped_max, + renew_potential_energy*co, renew_energy*co, renew_rate ) return oper_stats end From 58cc29ab3fb9a01bf8f19fc2f715417a3c17c77d Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Thu, 27 Feb 2025 11:20:31 +0100 Subject: [PATCH 13/19] ajout microgrid avec haber_bosch struct sizing updated --- examples/Microgrid_Wind-Solar-H2.ipynb | 2 +- examples/data/Microgrid_Wind-Solar-H2_data.jl | 19 +- examples/data/Ouessant_data_2016.csv | 17522 ++++++++-------- src/Microgrids.jl | 27 +- src/components.jl | 21 +- 5 files changed, 8818 insertions(+), 8773 deletions(-) diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index 924cb2a..ddd7769 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -1371,7 +1371,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.10.0_threads", + "display_name": "Julia 1.10.0", "language": "julia", "name": "julia-1.10" }, diff --git a/examples/data/Microgrid_Wind-Solar-H2_data.jl b/examples/data/Microgrid_Wind-Solar-H2_data.jl index e15c851..babb4e8 100644 --- a/examples/data/Microgrid_Wind-Solar-H2_data.jl +++ b/examples/data/Microgrid_Wind-Solar-H2_data.jl @@ -17,7 +17,7 @@ nsteps = length(data.Load) @assert(nsteps == 8760) # 1 year at an hourly timestep # Split load, solar and wind data: -Pload = data.Load# kW +Pload = float.(data.Load)# kW Ppv1k = data.Ppv1k./ 1000; # convert to kW/kWp wind_speed = data.Wind # m/s @@ -99,6 +99,21 @@ load_min_ratio_fc = 0.05 # minimum load ratio ∈ [0,1] input_unit_fc= "Kg" output_unit_fc="KWh" +#Haber Bosch numbers to update!!! they have been taken arbitrary +power_rated_hb = 4000. # rated power capacity (KW) +cons_intercept_hb=0. # +cons_rate_hb = 0.0625 # consumption rate (KWhé/KgH2) +cons_price_hb = 0. # +investment_price_hb = 1600. # initial investment price ($/KW) +om_price_hb = 45. # operation and maintenance price ($/kW/y) +om_price_hour_hb = 0. # operation and maintenance price ($/kW/h) +lifetime_hb_y = 15. +lifetime_hb_h = 45000. # Fuel Cell lifetime (h) +lifetime_hb_starts = 5000. # fuel cell maximum starts on +load_min_ratio_hb= 0.05 # minimum load ratio ∈ [0,1] +input_unit_hb= "Kwh" +output_unit_hb="Kg" + # Battery energy storage energy_rated_sto = 5000. # rated energy capacity (kWh) @@ -145,6 +160,6 @@ investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) lifetime_wind = 25. # lifetime (y) -default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0) +default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0,4000.0) capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] ini_filling_state=[0.0,0.0,0.0]; \ No newline at end of file diff --git a/examples/data/Ouessant_data_2016.csv b/examples/data/Ouessant_data_2016.csv index 3b3f415..f56d705 100644 --- a/examples/data/Ouessant_data_2016.csv +++ b/examples/data/Ouessant_data_2016.csv @@ -1,8761 +1,8761 @@ -time,Load,Ppv1k,Temp,Wind -2016-01-01 00:00:00,1453.0,0.0,10.23,3.78 -2016-01-01 01:00:00,1331.0,0.0,10.69,5.28 -2016-01-01 02:00:00,1214.0,0.0,11.14,6.78 -2016-01-01 03:00:00,1146.0,0.0,11.6,8.28 -2016-01-01 04:00:00,1078.0,0.0,11.39,9.6 -2016-01-01 05:00:00,1035.0,0.0,11.18,10.91 -2016-01-01 06:00:00,1032.0,0.0,10.97,12.23 -2016-01-01 07:00:00,1072.0,0.0,11.14,12.61 -2016-01-01 08:00:00,1142.0,0.0,11.31,12.98 -2016-01-01 09:00:00,1208.0,0.07,11.49,13.35 -2016-01-01 10:00:00,1299.0,0.0,11.65,14.18 -2016-01-01 11:00:00,1352.0,0.0,11.82,15.02 -2016-01-01 12:00:00,1363.0,0.0,11.98,15.85 -2016-01-01 13:00:00,1366.0,0.0,12.09,15.83 -2016-01-01 14:00:00,1336.0,0.0,12.19,15.8 -2016-01-01 15:00:00,1336.0,0.0,12.3,15.78 -2016-01-01 16:00:00,1307.0,0.0,12.31,15.99 -2016-01-01 17:00:00,1315.0,0.0,12.32,16.19 -2016-01-01 18:00:00,1396.0,0.0,12.34,16.4 -2016-01-01 19:00:00,1432.0,0.0,12.26,16.53 -2016-01-01 20:00:00,1452.0,0.0,12.19,16.66 -2016-01-01 21:00:00,1580.0,0.0,12.11,16.79 -2016-01-01 22:00:00,1692.0,0.0,12.08,16.32 -2016-01-01 23:00:00,1521.0,0.0,12.06,15.85 -2016-01-02 00:00:00,1344.0,0.0,12.04,15.38 -2016-01-02 01:00:00,1200.0,0.0,11.82,15.69 -2016-01-02 02:00:00,1071.0,0.0,11.61,16.0 -2016-01-02 03:00:00,1036.0,0.0,11.4,16.3 -2016-01-02 04:00:00,1059.0,0.0,11.09,16.9 -2016-01-02 05:00:00,1043.0,0.0,10.77,17.5 -2016-01-02 06:00:00,1029.0,0.0,10.46,18.1 -2016-01-02 07:00:00,1070.0,0.0,10.36,18.76 -2016-01-02 08:00:00,1197.0,0.0,10.25,19.42 -2016-01-02 09:00:00,1339.0,25.91,10.15,20.08 -2016-01-02 10:00:00,1352.0,11.78,10.06,19.68 -2016-01-02 11:00:00,1314.0,9.98,9.98,19.28 -2016-01-02 12:00:00,1273.0,0.0,9.89,18.88 -2016-01-02 13:00:00,1160.0,49.17,9.92,17.97 -2016-01-02 14:00:00,1098.0,188.44,9.94,17.06 -2016-01-02 15:00:00,1075.0,17.93,9.97,16.15 -2016-01-02 16:00:00,1106.0,2.34,10.17,15.06 -2016-01-02 17:00:00,1142.0,0.0,10.37,13.97 -2016-01-02 18:00:00,1262.0,0.0,10.57,12.88 -2016-01-02 19:00:00,1257.0,0.0,10.9,12.08 -2016-01-02 20:00:00,1251.0,0.0,11.22,11.27 -2016-01-02 21:00:00,1365.0,0.0,11.55,10.47 -2016-01-02 22:00:00,1499.0,0.0,11.37,11.36 -2016-01-02 23:00:00,1324.0,0.0,11.2,12.25 -2016-01-03 00:00:00,1176.0,0.0,11.03,13.14 -2016-01-03 01:00:00,1073.0,0.0,11.0,14.22 -2016-01-03 02:00:00,946.0,0.0,10.97,15.29 -2016-01-03 03:00:00,916.0,0.0,10.94,16.36 -2016-01-03 04:00:00,932.0,0.0,11.34,16.38 -2016-01-03 05:00:00,943.0,0.0,11.73,16.4 -2016-01-03 06:00:00,926.0,0.0,12.13,16.41 -2016-01-03 07:00:00,924.0,0.0,12.11,15.58 -2016-01-03 08:00:00,960.0,0.0,12.09,14.74 -2016-01-03 09:00:00,1075.0,27.98,12.07,13.9 -2016-01-03 10:00:00,1145.0,432.06,11.85,14.1 -2016-01-03 11:00:00,1146.0,106.64,11.64,14.29 -2016-01-03 12:00:00,1129.0,0.0,11.42,14.48 -2016-01-03 13:00:00,1023.0,492.74,11.28,14.53 -2016-01-03 14:00:00,948.0,153.18,11.14,14.58 -2016-01-03 15:00:00,891.0,86.08,11.01,14.63 -2016-01-03 16:00:00,907.0,0.0,10.89,15.11 -2016-01-03 17:00:00,934.0,0.0,10.78,15.58 -2016-01-03 18:00:00,1045.0,0.0,10.67,16.06 -2016-01-03 19:00:00,1086.0,0.0,10.74,16.41 -2016-01-03 20:00:00,1073.0,0.0,10.81,16.77 -2016-01-03 21:00:00,1210.0,0.0,10.88,17.13 -2016-01-03 22:00:00,1343.0,0.0,10.88,17.24 -2016-01-03 23:00:00,1213.0,0.0,10.88,17.35 -2016-01-04 00:00:00,1088.0,0.0,10.88,17.46 -2016-01-04 01:00:00,972.0,0.0,10.9,17.55 -2016-01-04 02:00:00,880.0,0.0,10.93,17.65 -2016-01-04 03:00:00,863.0,0.0,10.96,17.74 -2016-01-04 04:00:00,857.0,0.0,10.92,17.7 -2016-01-04 05:00:00,866.0,0.0,10.89,17.66 -2016-01-04 06:00:00,884.0,0.0,10.85,17.63 -2016-01-04 07:00:00,932.0,0.0,10.71,17.49 -2016-01-04 08:00:00,978.0,0.0,10.56,17.36 -2016-01-04 09:00:00,1037.0,0.0,10.42,17.23 -2016-01-04 10:00:00,1100.0,24.59,10.36,16.91 -2016-01-04 11:00:00,1091.0,4.89,10.31,16.58 -2016-01-04 12:00:00,1101.0,113.0,10.25,16.26 -2016-01-04 13:00:00,1046.0,0.0,10.37,16.05 -2016-01-04 14:00:00,939.0,520.39,10.48,15.83 -2016-01-04 15:00:00,928.0,173.77,10.6,15.61 -2016-01-04 16:00:00,941.0,22.56,10.56,15.91 -2016-01-04 17:00:00,972.0,0.0,10.52,16.21 -2016-01-04 18:00:00,1069.0,0.0,10.48,16.51 -2016-01-04 19:00:00,1119.0,0.0,10.47,16.45 -2016-01-04 20:00:00,1115.0,0.0,10.47,16.38 -2016-01-04 21:00:00,1199.0,0.0,10.46,16.32 -2016-01-04 22:00:00,1289.0,0.0,10.41,16.24 -2016-01-04 23:00:00,1170.0,0.0,10.37,16.16 -2016-01-05 00:00:00,1065.0,0.0,10.33,16.08 -2016-01-05 01:00:00,991.0,0.0,10.33,16.0 -2016-01-05 02:00:00,922.0,0.0,10.34,15.93 -2016-01-05 03:00:00,859.0,0.0,10.35,15.85 -2016-01-05 04:00:00,871.0,0.0,10.27,15.72 -2016-01-05 05:00:00,861.0,0.0,10.2,15.6 -2016-01-05 06:00:00,878.0,0.0,10.12,15.48 -2016-01-05 07:00:00,932.0,0.0,10.16,15.08 -2016-01-05 08:00:00,1015.0,0.0,10.2,14.68 -2016-01-05 09:00:00,1063.0,2.68,10.25,14.28 -2016-01-05 10:00:00,1068.0,1.16,10.26,13.85 -2016-01-05 11:00:00,1079.0,0.0,10.27,13.42 -2016-01-05 12:00:00,1063.0,0.0,10.28,12.99 -2016-01-05 13:00:00,995.0,818.76,10.16,12.83 -2016-01-05 14:00:00,970.0,221.93,10.04,12.66 -2016-01-05 15:00:00,962.0,10.33,9.93,12.5 -2016-01-05 16:00:00,957.0,1.7,10.12,12.18 -2016-01-05 17:00:00,976.0,0.0,10.32,11.87 -2016-01-05 18:00:00,1069.0,0.0,10.52,11.56 -2016-01-05 19:00:00,1123.0,0.0,10.5,11.74 -2016-01-05 20:00:00,1121.0,0.0,10.47,11.92 -2016-01-05 21:00:00,1232.0,0.0,10.45,12.1 -2016-01-05 22:00:00,1315.0,0.0,10.57,12.29 -2016-01-05 23:00:00,1173.0,0.0,10.69,12.48 -2016-01-06 00:00:00,1045.0,0.0,10.82,12.68 -2016-01-06 01:00:00,1001.0,0.0,10.83,12.63 -2016-01-06 02:00:00,921.0,0.0,10.84,12.57 -2016-01-06 03:00:00,875.0,0.0,10.85,12.52 -2016-01-06 04:00:00,864.0,0.0,10.72,12.42 -2016-01-06 05:00:00,854.0,0.0,10.6,12.32 -2016-01-06 06:00:00,858.0,0.0,10.47,12.22 -2016-01-06 07:00:00,940.0,0.0,10.39,11.49 -2016-01-06 08:00:00,1025.0,0.0,10.31,10.77 -2016-01-06 09:00:00,1069.0,6.0,10.24,10.04 -2016-01-06 10:00:00,1050.0,8.56,10.42,9.5 -2016-01-06 11:00:00,984.0,0.0,10.61,8.97 -2016-01-06 12:00:00,991.0,0.0,10.79,8.43 -2016-01-06 13:00:00,977.0,0.0,10.69,9.12 -2016-01-06 14:00:00,979.0,0.0,10.59,9.82 -2016-01-06 15:00:00,973.0,0.0,10.49,10.51 -2016-01-06 16:00:00,1014.0,1.51,10.73,12.69 -2016-01-06 17:00:00,1019.0,0.0,10.97,14.87 -2016-01-06 18:00:00,1092.0,0.0,11.2,17.05 -2016-01-06 19:00:00,1127.0,0.0,11.83,15.55 -2016-01-06 20:00:00,1141.0,0.0,12.47,14.06 -2016-01-06 21:00:00,1253.0,0.0,13.1,12.57 -2016-01-06 22:00:00,1292.0,0.0,13.05,12.73 -2016-01-06 23:00:00,1045.0,0.0,13.01,12.9 -2016-01-07 00:00:00,947.0,0.0,12.97,13.06 -2016-01-07 01:00:00,904.0,0.0,12.82,13.75 -2016-01-07 02:00:00,839.0,0.0,12.67,14.43 -2016-01-07 03:00:00,799.0,0.0,12.52,15.12 -2016-01-07 04:00:00,792.0,0.0,12.15,15.66 -2016-01-07 05:00:00,746.0,0.0,11.79,16.21 -2016-01-07 06:00:00,758.0,0.0,11.42,16.76 -2016-01-07 07:00:00,841.0,0.0,10.98,16.39 -2016-01-07 08:00:00,964.0,0.0,10.53,16.02 -2016-01-07 09:00:00,1071.0,0.0,10.09,15.66 -2016-01-07 10:00:00,1081.0,190.33,10.0,14.96 -2016-01-07 11:00:00,993.0,232.17,9.9,14.26 -2016-01-07 12:00:00,975.0,0.0,9.81,13.56 -2016-01-07 13:00:00,931.0,631.4,9.81,12.68 -2016-01-07 14:00:00,924.0,507.84,9.8,11.81 -2016-01-07 15:00:00,914.0,52.04,9.8,10.94 -2016-01-07 16:00:00,909.0,82.32,9.82,9.76 -2016-01-07 17:00:00,931.0,0.0,9.84,8.57 -2016-01-07 18:00:00,1012.0,0.0,9.86,7.39 -2016-01-07 19:00:00,1060.0,0.0,9.9,7.5 -2016-01-07 20:00:00,1069.0,0.0,9.93,7.6 -2016-01-07 21:00:00,1198.0,0.0,9.97,7.71 -2016-01-07 22:00:00,1317.0,0.0,10.09,8.17 -2016-01-07 23:00:00,1130.0,0.0,10.22,8.63 -2016-01-08 00:00:00,1002.0,0.0,10.35,9.09 -2016-01-08 01:00:00,949.0,0.0,10.24,9.27 -2016-01-08 02:00:00,884.0,0.0,10.14,9.45 -2016-01-08 03:00:00,870.0,0.0,10.03,9.63 -2016-01-08 04:00:00,898.0,0.0,9.93,9.39 -2016-01-08 05:00:00,857.0,0.0,9.82,9.15 -2016-01-08 06:00:00,822.0,0.0,9.72,8.91 -2016-01-08 07:00:00,898.0,0.0,9.9,8.67 -2016-01-08 08:00:00,977.0,0.0,10.08,8.43 -2016-01-08 09:00:00,1023.0,83.95,10.26,8.19 -2016-01-08 10:00:00,991.0,0.0,10.44,8.18 -2016-01-08 11:00:00,923.0,205.58,10.63,8.17 -2016-01-08 12:00:00,830.0,28.39,10.81,8.15 -2016-01-08 13:00:00,749.0,28.76,10.89,8.09 -2016-01-08 14:00:00,736.0,693.81,10.96,8.02 -2016-01-08 15:00:00,762.0,370.82,11.03,7.96 -2016-01-08 16:00:00,811.0,0.0,11.2,8.28 -2016-01-08 17:00:00,829.0,0.0,11.37,8.59 -2016-01-08 18:00:00,904.0,0.0,11.54,8.91 -2016-01-08 19:00:00,975.0,0.0,11.62,9.35 -2016-01-08 20:00:00,986.0,0.0,11.7,9.79 -2016-01-08 21:00:00,1094.0,0.0,11.78,10.23 -2016-01-08 22:00:00,1232.0,0.0,11.7,10.53 -2016-01-08 23:00:00,1084.0,0.0,11.63,10.82 -2016-01-09 00:00:00,939.0,0.0,11.56,11.12 -2016-01-09 01:00:00,894.0,0.0,11.53,11.67 -2016-01-09 02:00:00,824.0,0.0,11.51,12.22 -2016-01-09 03:00:00,815.0,0.0,11.48,12.77 -2016-01-09 04:00:00,863.0,0.0,11.45,12.89 -2016-01-09 05:00:00,852.0,0.0,11.42,13.0 -2016-01-09 06:00:00,803.0,0.0,11.39,13.12 -2016-01-09 07:00:00,824.0,0.0,11.49,12.54 -2016-01-09 08:00:00,874.0,0.0,11.58,11.96 -2016-01-09 09:00:00,946.0,24.38,11.68,11.38 -2016-01-09 10:00:00,991.0,5.43,11.62,12.4 -2016-01-09 11:00:00,1029.0,56.75,11.56,13.41 -2016-01-09 12:00:00,1044.0,64.37,11.5,14.43 -2016-01-09 13:00:00,1022.0,84.9,11.08,15.15 -2016-01-09 14:00:00,965.0,450.69,10.66,15.87 -2016-01-09 15:00:00,931.0,20.08,10.24,16.59 -2016-01-09 16:00:00,979.0,96.73,10.34,16.73 -2016-01-09 17:00:00,1042.0,0.0,10.43,16.86 -2016-01-09 18:00:00,1114.0,0.0,10.52,16.99 -2016-01-09 19:00:00,1084.0,0.0,10.4,16.8 -2016-01-09 20:00:00,1072.0,0.0,10.28,16.61 -2016-01-09 21:00:00,1190.0,0.0,10.16,16.41 -2016-01-09 22:00:00,1357.0,0.0,9.94,15.92 -2016-01-09 23:00:00,1247.0,0.0,9.73,15.42 -2016-01-10 00:00:00,1099.0,0.0,9.52,14.92 -2016-01-10 01:00:00,1003.0,0.0,9.62,14.51 -2016-01-10 02:00:00,913.0,0.0,9.73,14.09 -2016-01-10 03:00:00,871.0,0.0,9.83,13.67 -2016-01-10 04:00:00,906.0,0.0,9.51,12.93 -2016-01-10 05:00:00,956.0,0.0,9.18,12.2 -2016-01-10 06:00:00,911.0,0.0,8.86,11.46 -2016-01-10 07:00:00,881.0,0.0,8.92,11.24 -2016-01-10 08:00:00,952.0,0.0,8.98,11.02 -2016-01-10 09:00:00,997.0,122.56,9.05,10.8 -2016-01-10 10:00:00,1048.0,183.57,9.04,10.9 -2016-01-10 11:00:00,1114.0,185.38,9.02,10.99 -2016-01-10 12:00:00,1111.0,577.75,9.01,11.09 -2016-01-10 13:00:00,937.0,518.42,9.56,11.2 -2016-01-10 14:00:00,822.0,204.14,10.11,11.31 -2016-01-10 15:00:00,803.0,29.48,10.66,11.42 -2016-01-10 16:00:00,743.0,3.33,10.79,11.25 -2016-01-10 17:00:00,823.0,0.0,10.91,11.08 -2016-01-10 18:00:00,987.0,0.0,11.03,10.91 -2016-01-10 19:00:00,1038.0,0.0,10.55,11.28 -2016-01-10 20:00:00,1066.0,0.0,10.07,11.65 -2016-01-10 21:00:00,1163.0,0.0,9.59,12.01 -2016-01-10 22:00:00,1297.0,0.0,9.65,12.0 -2016-01-10 23:00:00,1170.0,0.0,9.71,11.98 -2016-01-11 00:00:00,1068.0,0.0,9.78,11.96 -2016-01-11 01:00:00,971.0,0.0,9.99,11.97 -2016-01-11 02:00:00,907.0,0.0,10.2,11.99 -2016-01-11 03:00:00,874.0,0.0,10.41,12.0 -2016-01-11 04:00:00,875.0,0.0,10.09,12.42 -2016-01-11 05:00:00,871.0,0.0,9.76,12.84 -2016-01-11 06:00:00,887.0,0.0,9.44,13.26 -2016-01-11 07:00:00,941.0,0.0,9.44,13.38 -2016-01-11 08:00:00,999.0,0.0,9.43,13.5 -2016-01-11 09:00:00,1040.0,20.71,9.43,13.63 -2016-01-11 10:00:00,1045.0,5.5,9.7,13.73 -2016-01-11 11:00:00,1066.0,24.99,9.96,13.84 -2016-01-11 12:00:00,1101.0,163.31,10.23,13.94 -2016-01-11 13:00:00,1046.0,334.53,9.95,14.25 -2016-01-11 14:00:00,904.0,219.11,9.67,14.55 -2016-01-11 15:00:00,933.0,30.1,9.39,14.86 -2016-01-11 16:00:00,997.0,9.02,9.52,15.36 -2016-01-11 17:00:00,1038.0,0.0,9.66,15.86 -2016-01-11 18:00:00,1145.0,0.0,9.8,16.36 -2016-01-11 19:00:00,1187.0,0.0,9.81,16.43 -2016-01-11 20:00:00,1175.0,0.0,9.83,16.5 -2016-01-11 21:00:00,1264.0,0.0,9.84,16.57 -2016-01-11 22:00:00,1379.0,0.0,9.78,16.07 -2016-01-11 23:00:00,1245.0,0.0,9.72,15.58 -2016-01-12 00:00:00,1167.0,0.0,9.66,15.09 -2016-01-12 01:00:00,1072.0,0.0,9.46,14.55 -2016-01-12 02:00:00,953.0,0.0,9.27,14.01 -2016-01-12 03:00:00,901.0,0.0,9.07,13.48 -2016-01-12 04:00:00,908.0,0.0,8.84,13.03 -2016-01-12 05:00:00,917.0,0.0,8.62,12.59 -2016-01-12 06:00:00,959.0,0.0,8.39,12.15 -2016-01-12 07:00:00,991.0,0.0,8.29,12.46 -2016-01-12 08:00:00,1059.0,0.0,8.18,12.78 -2016-01-12 09:00:00,1175.0,47.73,8.08,13.09 -2016-01-12 10:00:00,1114.0,157.34,8.35,13.39 -2016-01-12 11:00:00,1070.0,280.26,8.61,13.7 -2016-01-12 12:00:00,1032.0,325.59,8.88,14.0 -2016-01-12 13:00:00,988.0,550.77,8.95,14.28 -2016-01-12 14:00:00,889.0,224.68,9.02,14.55 -2016-01-12 15:00:00,907.0,148.32,9.09,14.83 -2016-01-12 16:00:00,937.0,6.65,9.22,14.62 -2016-01-12 17:00:00,980.0,0.0,9.35,14.41 -2016-01-12 18:00:00,1162.0,0.0,9.48,14.21 -2016-01-12 19:00:00,1224.0,0.0,9.47,13.57 -2016-01-12 20:00:00,1177.0,0.0,9.47,12.93 -2016-01-12 21:00:00,1247.0,0.0,9.46,12.29 -2016-01-12 22:00:00,1419.0,0.0,9.44,11.55 -2016-01-12 23:00:00,1266.0,0.0,9.42,10.81 -2016-01-13 00:00:00,1143.0,0.0,9.4,10.07 -2016-01-13 01:00:00,1123.0,0.0,9.33,9.42 -2016-01-13 02:00:00,991.0,0.0,9.27,8.77 -2016-01-13 03:00:00,926.0,0.0,9.21,8.12 -2016-01-13 04:00:00,912.0,0.0,9.17,7.6 -2016-01-13 05:00:00,923.0,0.0,9.14,7.09 -2016-01-13 06:00:00,951.0,0.0,9.1,6.57 -2016-01-13 07:00:00,1035.0,0.0,9.02,6.74 -2016-01-13 08:00:00,1092.0,0.0,8.93,6.92 -2016-01-13 09:00:00,1074.0,46.17,8.85,7.1 -2016-01-13 10:00:00,1099.0,400.13,8.87,7.7 -2016-01-13 11:00:00,1071.0,554.92,8.89,8.3 -2016-01-13 12:00:00,1053.0,103.57,8.91,8.9 -2016-01-13 13:00:00,974.0,455.87,8.79,8.86 -2016-01-13 14:00:00,960.0,355.28,8.66,8.82 -2016-01-13 15:00:00,912.0,347.44,8.53,8.79 -2016-01-13 16:00:00,920.0,11.11,8.72,8.93 -2016-01-13 17:00:00,986.0,0.0,8.9,9.07 -2016-01-13 18:00:00,1104.0,0.0,9.09,9.21 -2016-01-13 19:00:00,1153.0,0.0,9.17,9.4 -2016-01-13 20:00:00,1159.0,0.0,9.24,9.59 -2016-01-13 21:00:00,1209.0,0.0,9.32,9.78 -2016-01-13 22:00:00,1345.0,0.0,9.27,10.0 -2016-01-13 23:00:00,1192.0,0.0,9.22,10.22 -2016-01-14 00:00:00,1062.0,0.0,9.18,10.44 -2016-01-14 01:00:00,1037.0,0.0,9.12,10.51 -2016-01-14 02:00:00,965.0,0.0,9.06,10.57 -2016-01-14 03:00:00,887.0,0.0,9.0,10.63 -2016-01-14 04:00:00,912.0,0.0,9.13,11.14 -2016-01-14 05:00:00,887.0,0.0,9.25,11.64 -2016-01-14 06:00:00,934.0,0.0,9.38,12.14 -2016-01-14 07:00:00,1027.0,0.0,9.41,12.26 -2016-01-14 08:00:00,1119.0,0.0,9.44,12.38 -2016-01-14 09:00:00,1117.0,58.38,9.48,12.5 -2016-01-14 10:00:00,1092.0,341.42,9.44,12.41 -2016-01-14 11:00:00,1094.0,517.61,9.41,12.33 -2016-01-14 12:00:00,1066.0,283.89,9.37,12.25 -2016-01-14 13:00:00,1013.0,460.03,9.25,11.62 -2016-01-14 14:00:00,992.0,273.01,9.13,10.99 -2016-01-14 15:00:00,957.0,155.57,9.01,10.36 -2016-01-14 16:00:00,937.0,64.1,8.94,10.19 -2016-01-14 17:00:00,970.0,0.0,8.88,10.02 -2016-01-14 18:00:00,1094.0,0.0,8.81,9.85 -2016-01-14 19:00:00,1194.0,0.0,8.78,9.52 -2016-01-14 20:00:00,1209.0,0.0,8.74,9.2 -2016-01-14 21:00:00,1326.0,0.0,8.71,8.87 -2016-01-14 22:00:00,1405.0,0.0,8.65,9.0 -2016-01-14 23:00:00,1262.0,0.0,8.6,9.13 -2016-01-15 00:00:00,1171.0,0.0,8.55,9.26 -2016-01-15 01:00:00,1127.0,0.0,8.62,10.74 -2016-01-15 02:00:00,1075.0,0.0,8.7,12.23 -2016-01-15 03:00:00,1052.0,0.0,8.78,13.71 -2016-01-15 04:00:00,1041.0,0.0,8.73,13.93 -2016-01-15 05:00:00,1021.0,0.0,8.68,14.14 -2016-01-15 06:00:00,1038.0,0.0,8.63,14.36 -2016-01-15 07:00:00,1143.0,0.0,8.57,14.18 -2016-01-15 08:00:00,1225.0,0.0,8.51,14.01 -2016-01-15 09:00:00,1215.0,107.36,8.45,13.83 -2016-01-15 10:00:00,1158.0,407.35,8.45,13.6 -2016-01-15 11:00:00,1136.0,293.12,8.45,13.36 -2016-01-15 12:00:00,1088.0,375.64,8.45,13.12 -2016-01-15 13:00:00,1040.0,331.34,8.31,13.08 -2016-01-15 14:00:00,1044.0,229.93,8.16,13.03 -2016-01-15 15:00:00,1053.0,191.53,8.02,12.99 -2016-01-15 16:00:00,1029.0,112.73,8.24,12.69 -2016-01-15 17:00:00,1022.0,0.0,8.46,12.39 -2016-01-15 18:00:00,1145.0,0.0,8.68,12.08 -2016-01-15 19:00:00,1250.0,0.0,8.75,11.58 -2016-01-15 20:00:00,1282.0,0.0,8.82,11.07 -2016-01-15 21:00:00,1397.0,0.0,8.89,10.57 -2016-01-15 22:00:00,1498.0,0.0,9.04,10.38 -2016-01-15 23:00:00,1309.0,0.0,9.19,10.2 -2016-01-16 00:00:00,1199.0,0.0,9.35,10.01 -2016-01-16 01:00:00,1109.0,0.0,9.24,9.39 -2016-01-16 02:00:00,1040.0,0.0,9.13,8.77 -2016-01-16 03:00:00,1024.0,0.0,9.02,8.15 -2016-01-16 04:00:00,1016.0,0.0,9.09,7.59 -2016-01-16 05:00:00,953.0,0.0,9.15,7.03 -2016-01-16 06:00:00,948.0,0.0,9.22,6.47 -2016-01-16 07:00:00,989.0,0.0,9.19,5.69 -2016-01-16 08:00:00,1110.0,0.0,9.16,4.91 -2016-01-16 09:00:00,1164.0,27.15,9.14,4.14 -2016-01-16 10:00:00,1136.0,217.9,9.15,2.82 -2016-01-16 11:00:00,1063.0,497.56,9.15,1.5 -2016-01-16 12:00:00,1015.0,566.02,9.16,0.18 -2016-01-16 13:00:00,972.0,445.11,9.17,1.25 -2016-01-16 14:00:00,941.0,214.47,9.18,2.32 -2016-01-16 15:00:00,964.0,30.67,9.19,3.39 -2016-01-16 16:00:00,998.0,93.34,9.11,3.51 -2016-01-16 17:00:00,995.0,0.0,9.03,3.62 -2016-01-16 18:00:00,1084.0,0.0,8.95,3.74 -2016-01-16 19:00:00,1110.0,0.0,9.02,3.0 -2016-01-16 20:00:00,1113.0,0.0,9.1,2.26 -2016-01-16 21:00:00,1240.0,0.0,9.17,1.52 -2016-01-16 22:00:00,1374.0,0.0,9.27,2.01 -2016-01-16 23:00:00,1206.0,0.0,9.38,2.5 -2016-01-17 00:00:00,1071.0,0.0,9.49,2.99 -2016-01-17 01:00:00,964.0,0.0,9.71,3.68 -2016-01-17 02:00:00,888.0,0.0,9.94,4.37 -2016-01-17 03:00:00,894.0,0.0,10.17,5.06 -2016-01-17 04:00:00,934.0,0.0,10.29,5.4 -2016-01-17 05:00:00,916.0,0.0,10.4,5.74 -2016-01-17 06:00:00,870.0,0.0,10.52,6.08 -2016-01-17 07:00:00,891.0,0.0,10.59,6.47 -2016-01-17 08:00:00,966.0,0.0,10.65,6.86 -2016-01-17 09:00:00,1067.0,6.59,10.72,7.26 -2016-01-17 10:00:00,1139.0,23.46,10.78,7.8 -2016-01-17 11:00:00,1181.0,50.36,10.84,8.34 -2016-01-17 12:00:00,1107.0,64.63,10.9,8.88 -2016-01-17 13:00:00,1024.0,56.83,10.59,9.39 -2016-01-17 14:00:00,982.0,18.01,10.28,9.9 -2016-01-17 15:00:00,995.0,29.99,9.98,10.41 -2016-01-17 16:00:00,1026.0,7.83,9.82,10.97 -2016-01-17 17:00:00,1079.0,0.0,9.66,11.54 -2016-01-17 18:00:00,1110.0,0.0,9.5,12.1 -2016-01-17 19:00:00,1127.0,0.0,9.52,13.08 -2016-01-17 20:00:00,1150.0,0.0,9.55,14.06 -2016-01-17 21:00:00,1271.0,0.0,9.57,15.03 -2016-01-17 22:00:00,1434.0,0.0,9.99,15.1 -2016-01-17 23:00:00,1310.0,0.0,10.41,15.17 -2016-01-18 00:00:00,1193.0,0.0,10.83,15.24 -2016-01-18 01:00:00,1057.0,0.0,10.85,13.27 -2016-01-18 02:00:00,964.0,0.0,10.88,11.31 -2016-01-18 03:00:00,947.0,0.0,10.9,9.34 -2016-01-18 04:00:00,921.0,0.0,10.83,8.83 -2016-01-18 05:00:00,932.0,0.0,10.75,8.32 -2016-01-18 06:00:00,919.0,0.0,10.68,7.81 -2016-01-18 07:00:00,932.0,0.0,10.58,7.59 -2016-01-18 08:00:00,977.0,0.0,10.48,7.37 -2016-01-18 09:00:00,1030.0,96.66,10.38,7.16 -2016-01-18 10:00:00,1045.0,178.86,10.39,6.83 -2016-01-18 11:00:00,1027.0,203.76,10.4,6.51 -2016-01-18 12:00:00,988.0,193.76,10.41,6.18 -2016-01-18 13:00:00,836.0,571.42,10.47,4.72 -2016-01-18 14:00:00,785.0,508.21,10.53,3.26 -2016-01-18 15:00:00,764.0,230.6,10.59,1.81 -2016-01-18 16:00:00,783.0,78.39,10.68,1.49 -2016-01-18 17:00:00,859.0,0.0,10.77,1.18 -2016-01-18 18:00:00,962.0,0.0,10.86,0.87 -2016-01-18 19:00:00,1026.0,0.0,10.77,1.76 -2016-01-18 20:00:00,1007.0,0.0,10.67,2.64 -2016-01-18 21:00:00,1095.0,0.0,10.58,3.53 -2016-01-18 22:00:00,1273.0,0.0,10.47,3.6 -2016-01-18 23:00:00,1140.0,0.0,10.36,3.67 -2016-01-19 00:00:00,1029.0,0.0,10.26,3.74 -2016-01-19 01:00:00,967.0,0.0,10.29,3.19 -2016-01-19 02:00:00,861.0,0.0,10.32,2.63 -2016-01-19 03:00:00,801.0,0.0,10.35,2.08 -2016-01-19 04:00:00,816.0,0.0,10.21,2.48 -2016-01-19 05:00:00,840.0,0.0,10.08,2.87 -2016-01-19 06:00:00,863.0,0.0,9.94,3.27 -2016-01-19 07:00:00,943.0,0.0,9.84,3.55 -2016-01-19 08:00:00,981.0,0.0,9.74,3.84 -2016-01-19 09:00:00,1005.0,7.21,9.64,4.12 -2016-01-19 10:00:00,1020.0,103.41,9.61,4.09 -2016-01-19 11:00:00,1042.0,70.17,9.57,4.05 -2016-01-19 12:00:00,1054.0,78.03,9.54,4.01 -2016-01-19 13:00:00,1043.0,88.02,9.77,4.26 -2016-01-19 14:00:00,1006.0,60.92,9.99,4.51 -2016-01-19 15:00:00,954.0,43.71,10.22,4.76 -2016-01-19 16:00:00,913.0,39.51,10.1,5.12 -2016-01-19 17:00:00,980.0,0.0,9.99,5.49 -2016-01-19 18:00:00,1069.0,0.0,9.88,5.85 -2016-01-19 19:00:00,1102.0,0.0,9.78,6.13 -2016-01-19 20:00:00,1120.0,0.0,9.67,6.42 -2016-01-19 21:00:00,1165.0,0.0,9.57,6.7 -2016-01-19 22:00:00,1298.0,0.0,9.56,6.62 -2016-01-19 23:00:00,1146.0,0.0,9.55,6.53 -2016-01-20 00:00:00,1066.0,0.0,9.55,6.44 -2016-01-20 01:00:00,1001.0,0.0,9.56,6.07 -2016-01-20 02:00:00,941.0,0.0,9.57,5.71 -2016-01-20 03:00:00,879.0,0.0,9.58,5.34 -2016-01-20 04:00:00,864.0,0.0,9.57,5.43 -2016-01-20 05:00:00,860.0,0.0,9.56,5.53 -2016-01-20 06:00:00,915.0,0.0,9.55,5.63 -2016-01-20 07:00:00,984.0,0.0,9.6,5.8 -2016-01-20 08:00:00,1123.0,0.0,9.65,5.98 -2016-01-20 09:00:00,1130.0,30.56,9.71,6.15 -2016-01-20 10:00:00,1114.0,93.37,9.83,6.46 -2016-01-20 11:00:00,1160.0,129.35,9.96,6.76 -2016-01-20 12:00:00,1152.0,132.95,10.08,7.06 -2016-01-20 13:00:00,1096.0,284.56,10.37,7.23 -2016-01-20 14:00:00,1066.0,132.86,10.66,7.4 -2016-01-20 15:00:00,1061.0,46.85,10.94,7.57 -2016-01-20 16:00:00,1070.0,33.67,11.05,7.79 -2016-01-20 17:00:00,1064.0,0.0,11.15,8.0 -2016-01-20 18:00:00,1111.0,0.0,11.26,8.22 -2016-01-20 19:00:00,1161.0,0.0,11.28,8.54 -2016-01-20 20:00:00,1154.0,0.0,11.29,8.86 -2016-01-20 21:00:00,1260.0,0.0,11.31,9.19 -2016-01-20 22:00:00,1416.0,0.0,11.35,9.51 -2016-01-20 23:00:00,1250.0,0.0,11.4,9.83 -2016-01-21 00:00:00,1124.0,0.0,11.45,10.15 -2016-01-21 01:00:00,1053.0,0.0,11.48,10.91 -2016-01-21 02:00:00,979.0,0.0,11.52,11.66 -2016-01-21 03:00:00,936.0,0.0,11.55,12.41 -2016-01-21 04:00:00,936.0,0.0,11.62,12.2 -2016-01-21 05:00:00,917.0,0.0,11.7,11.98 -2016-01-21 06:00:00,926.0,0.0,11.77,11.77 -2016-01-21 07:00:00,981.0,0.0,11.81,11.84 -2016-01-21 08:00:00,1080.0,0.0,11.85,11.91 -2016-01-21 09:00:00,1104.0,13.7,11.89,11.99 -2016-01-21 10:00:00,1114.0,43.53,11.85,12.08 -2016-01-21 11:00:00,1152.0,19.22,11.82,12.17 -2016-01-21 12:00:00,1171.0,24.75,11.78,12.26 -2016-01-21 13:00:00,1130.0,25.46,11.73,12.37 -2016-01-21 14:00:00,1104.0,13.27,11.68,12.47 -2016-01-21 15:00:00,1096.0,10.18,11.64,12.58 -2016-01-21 16:00:00,1105.0,4.61,11.75,12.29 -2016-01-21 17:00:00,1113.0,0.0,11.86,12.01 -2016-01-21 18:00:00,1147.0,0.0,11.98,11.72 -2016-01-21 19:00:00,1184.0,0.0,12.11,11.64 -2016-01-21 20:00:00,1166.0,0.0,12.25,11.56 -2016-01-21 21:00:00,1230.0,0.0,12.38,11.48 -2016-01-21 22:00:00,1344.0,0.0,12.51,11.53 -2016-01-21 23:00:00,1207.0,0.0,12.64,11.58 -2016-01-22 00:00:00,1079.0,0.0,12.77,11.63 -2016-01-22 01:00:00,997.0,0.0,12.76,11.37 -2016-01-22 02:00:00,888.0,0.0,12.76,11.12 -2016-01-22 03:00:00,861.0,0.0,12.76,10.87 -2016-01-22 04:00:00,858.0,0.0,12.85,11.2 -2016-01-22 05:00:00,828.0,0.0,12.95,11.53 -2016-01-22 06:00:00,823.0,0.0,13.04,11.86 -2016-01-22 07:00:00,886.0,0.0,13.04,11.71 -2016-01-22 08:00:00,914.0,0.0,13.03,11.55 -2016-01-22 09:00:00,931.0,216.93,13.03,11.39 -2016-01-22 10:00:00,957.0,369.68,12.89,10.92 -2016-01-22 11:00:00,963.0,529.58,12.74,10.46 -2016-01-22 12:00:00,898.0,472.13,12.6,9.99 -2016-01-22 13:00:00,806.0,387.18,12.55,9.8 -2016-01-22 14:00:00,745.0,532.68,12.5,9.62 -2016-01-22 15:00:00,704.0,376.68,12.44,9.43 -2016-01-22 16:00:00,715.0,175.47,12.35,9.14 -2016-01-22 17:00:00,742.0,0.0,12.26,8.85 -2016-01-22 18:00:00,868.0,0.0,12.17,8.55 -2016-01-22 19:00:00,935.0,0.0,12.13,8.1 -2016-01-22 20:00:00,974.0,0.0,12.08,7.65 -2016-01-22 21:00:00,1036.0,0.0,12.04,7.2 -2016-01-22 22:00:00,1175.0,0.0,12.0,6.86 -2016-01-22 23:00:00,1057.0,0.0,11.96,6.52 -2016-01-23 00:00:00,872.0,0.0,11.93,6.18 -2016-01-23 01:00:00,836.0,0.0,11.79,5.88 -2016-01-23 02:00:00,791.0,0.0,11.66,5.58 -2016-01-23 03:00:00,771.0,0.0,11.52,5.28 -2016-01-23 04:00:00,789.0,0.0,11.44,5.94 -2016-01-23 05:00:00,790.0,0.0,11.37,6.6 -2016-01-23 06:00:00,788.0,0.0,11.29,7.26 -2016-01-23 07:00:00,831.0,0.0,11.43,8.06 -2016-01-23 08:00:00,905.0,0.0,11.56,8.86 -2016-01-23 09:00:00,973.0,120.34,11.7,9.67 -2016-01-23 10:00:00,1008.0,162.51,11.91,10.21 -2016-01-23 11:00:00,1030.0,54.28,12.13,10.75 -2016-01-23 12:00:00,1041.0,21.9,12.34,11.3 -2016-01-23 13:00:00,1014.0,45.02,12.44,11.4 -2016-01-23 14:00:00,1001.0,27.47,12.54,11.51 -2016-01-23 15:00:00,976.0,6.51,12.65,11.61 -2016-01-23 16:00:00,988.0,2.32,12.79,11.25 -2016-01-23 17:00:00,984.0,0.0,12.94,10.89 -2016-01-23 18:00:00,1007.0,0.0,13.08,10.52 -2016-01-23 19:00:00,1026.0,0.0,13.06,10.31 -2016-01-23 20:00:00,1014.0,0.0,13.04,10.1 -2016-01-23 21:00:00,1096.0,0.0,13.02,9.89 -2016-01-23 22:00:00,1213.0,0.0,12.97,9.71 -2016-01-23 23:00:00,1055.0,0.0,12.93,9.53 -2016-01-24 00:00:00,920.0,0.0,12.89,9.35 -2016-01-24 01:00:00,863.0,0.0,12.92,9.6 -2016-01-24 02:00:00,784.0,0.0,12.95,9.85 -2016-01-24 03:00:00,744.0,0.0,12.98,10.1 -2016-01-24 04:00:00,753.0,0.0,12.96,9.8 -2016-01-24 05:00:00,744.0,0.0,12.94,9.51 -2016-01-24 06:00:00,731.0,0.0,12.92,9.21 -2016-01-24 07:00:00,760.0,0.0,12.99,9.09 -2016-01-24 08:00:00,820.0,0.0,13.06,8.97 -2016-01-24 09:00:00,861.0,99.1,13.14,8.84 -2016-01-24 10:00:00,900.0,39.58,13.26,8.94 -2016-01-24 11:00:00,841.0,134.94,13.38,9.04 -2016-01-24 12:00:00,761.0,571.26,13.5,9.14 -2016-01-24 13:00:00,677.0,661.25,13.53,9.59 -2016-01-24 14:00:00,624.0,472.77,13.56,10.03 -2016-01-24 15:00:00,608.0,316.35,13.59,10.47 -2016-01-24 16:00:00,614.0,148.23,13.55,10.83 -2016-01-24 17:00:00,649.0,0.0,13.52,11.2 -2016-01-24 18:00:00,752.0,0.0,13.48,11.56 -2016-01-24 19:00:00,818.0,0.0,13.42,11.49 -2016-01-24 20:00:00,848.0,0.0,13.35,11.43 -2016-01-24 21:00:00,976.0,0.0,13.29,11.37 -2016-01-24 22:00:00,1152.0,0.0,13.34,11.13 -2016-01-24 23:00:00,991.0,0.0,13.4,10.9 -2016-01-25 00:00:00,881.0,0.0,13.46,10.66 -2016-01-25 01:00:00,809.0,0.0,13.6,10.75 -2016-01-25 02:00:00,717.0,0.0,13.74,10.85 -2016-01-25 03:00:00,689.0,0.0,13.88,10.94 -2016-01-25 04:00:00,680.0,0.0,13.91,11.45 -2016-01-25 05:00:00,676.0,0.0,13.93,11.97 -2016-01-25 06:00:00,683.0,0.0,13.96,12.48 -2016-01-25 07:00:00,735.0,0.0,13.75,12.42 -2016-01-25 08:00:00,809.0,0.0,13.54,12.35 -2016-01-25 09:00:00,822.0,103.02,13.33,12.29 -2016-01-25 10:00:00,824.0,162.42,13.05,11.93 -2016-01-25 11:00:00,820.0,259.59,12.78,11.57 -2016-01-25 12:00:00,784.0,522.37,12.5,11.21 -2016-01-25 13:00:00,715.0,530.98,12.1,11.03 -2016-01-25 14:00:00,667.0,581.9,11.7,10.86 -2016-01-25 15:00:00,679.0,433.2,11.3,10.68 -2016-01-25 16:00:00,690.0,187.76,11.15,9.88 -2016-01-25 17:00:00,715.0,0.0,11.01,9.09 -2016-01-25 18:00:00,803.0,0.0,10.86,8.3 -2016-01-25 19:00:00,889.0,0.0,10.78,7.57 -2016-01-25 20:00:00,894.0,0.0,10.69,6.84 -2016-01-25 21:00:00,970.0,0.0,10.61,6.11 -2016-01-25 22:00:00,1131.0,0.0,10.54,7.08 -2016-01-25 23:00:00,989.0,0.0,10.47,8.04 -2016-01-26 00:00:00,886.0,0.0,10.4,9.01 -2016-01-26 01:00:00,828.0,0.0,10.43,10.27 -2016-01-26 02:00:00,769.0,0.0,10.47,11.54 -2016-01-26 03:00:00,757.0,0.0,10.5,12.8 -2016-01-26 04:00:00,780.0,0.0,10.68,13.72 -2016-01-26 05:00:00,773.0,0.0,10.85,14.65 -2016-01-26 06:00:00,789.0,0.0,11.03,15.57 -2016-01-26 07:00:00,856.0,0.0,11.35,15.8 -2016-01-26 08:00:00,963.0,0.0,11.66,16.03 -2016-01-26 09:00:00,991.0,1.87,11.98,16.26 -2016-01-26 10:00:00,1004.0,11.97,12.06,16.44 -2016-01-26 11:00:00,1013.0,68.07,12.14,16.62 -2016-01-26 12:00:00,1035.0,15.18,12.22,16.8 -2016-01-26 13:00:00,1033.0,24.02,12.16,16.79 -2016-01-26 14:00:00,949.0,11.34,12.1,16.78 -2016-01-26 15:00:00,924.0,24.73,12.03,16.77 -2016-01-26 16:00:00,938.0,15.85,12.12,16.65 -2016-01-26 17:00:00,934.0,0.0,12.2,16.52 -2016-01-26 18:00:00,984.0,0.0,12.28,16.4 -2016-01-26 19:00:00,1004.0,0.0,12.42,16.27 -2016-01-26 20:00:00,1005.0,0.0,12.55,16.13 -2016-01-26 21:00:00,1084.0,0.0,12.69,16.0 -2016-01-26 22:00:00,1222.0,0.0,12.71,16.16 -2016-01-26 23:00:00,1075.0,0.0,12.74,16.32 -2016-01-27 00:00:00,950.0,0.0,12.77,16.48 -2016-01-27 01:00:00,870.0,0.0,12.66,16.27 -2016-01-27 02:00:00,808.0,0.0,12.56,16.05 -2016-01-27 03:00:00,771.0,0.0,12.45,15.83 -2016-01-27 04:00:00,777.0,0.0,12.52,15.71 -2016-01-27 05:00:00,765.0,0.0,12.58,15.59 -2016-01-27 06:00:00,778.0,0.0,12.65,15.46 -2016-01-27 07:00:00,837.0,0.0,12.66,15.31 -2016-01-27 08:00:00,922.0,0.0,12.67,15.16 -2016-01-27 09:00:00,934.0,2.8,12.68,15.01 -2016-01-27 10:00:00,940.0,5.95,12.59,14.64 -2016-01-27 11:00:00,961.0,64.19,12.5,14.28 -2016-01-27 12:00:00,956.0,30.35,12.41,13.92 -2016-01-27 13:00:00,912.0,23.3,12.24,12.28 -2016-01-27 14:00:00,894.0,24.71,12.07,10.63 -2016-01-27 15:00:00,871.0,19.88,11.9,8.99 -2016-01-27 16:00:00,880.0,39.99,11.45,9.48 -2016-01-27 17:00:00,870.0,0.0,11.01,9.97 -2016-01-27 18:00:00,947.0,0.0,10.57,10.46 -2016-01-27 19:00:00,977.0,0.0,10.21,10.27 -2016-01-27 20:00:00,971.0,0.0,9.84,10.09 -2016-01-27 21:00:00,1052.0,0.0,9.48,9.9 -2016-01-27 22:00:00,1205.0,0.0,9.52,9.57 -2016-01-27 23:00:00,1076.0,0.0,9.56,9.23 -2016-01-28 00:00:00,959.0,0.0,9.61,8.9 -2016-01-28 01:00:00,902.0,0.0,9.93,8.23 -2016-01-28 02:00:00,833.0,0.0,10.26,7.57 -2016-01-28 03:00:00,810.0,0.0,10.58,6.91 -2016-01-28 04:00:00,818.0,0.0,10.39,6.53 -2016-01-28 05:00:00,814.0,0.0,10.19,6.15 -2016-01-28 06:00:00,834.0,0.0,10.0,5.77 -2016-01-28 07:00:00,903.0,0.0,9.89,5.35 -2016-01-28 08:00:00,1003.0,0.0,9.77,4.93 -2016-01-28 09:00:00,1034.0,196.99,9.66,4.51 -2016-01-28 10:00:00,987.0,325.28,9.69,5.38 -2016-01-28 11:00:00,944.0,583.87,9.72,6.25 -2016-01-28 12:00:00,900.0,338.15,9.75,7.12 -2016-01-28 13:00:00,869.0,611.9,9.84,8.06 -2016-01-28 14:00:00,801.0,283.28,9.93,9.01 -2016-01-28 15:00:00,765.0,70.49,10.02,9.96 -2016-01-28 16:00:00,764.0,144.74,10.31,10.17 -2016-01-28 17:00:00,800.0,0.0,10.61,10.37 -2016-01-28 18:00:00,900.0,0.0,10.91,10.58 -2016-01-28 19:00:00,1011.0,0.0,11.2,10.84 -2016-01-28 20:00:00,1015.0,0.0,11.48,11.09 -2016-01-28 21:00:00,1087.0,0.0,11.77,11.35 -2016-01-28 22:00:00,1226.0,0.0,11.86,11.66 -2016-01-28 23:00:00,1091.0,0.0,11.95,11.96 -2016-01-29 00:00:00,968.0,0.0,12.04,12.26 -2016-01-29 01:00:00,915.0,0.0,11.86,12.83 -2016-01-29 02:00:00,831.0,0.0,11.69,13.39 -2016-01-29 03:00:00,784.0,0.0,11.52,13.96 -2016-01-29 04:00:00,803.0,0.0,11.52,14.44 -2016-01-29 05:00:00,786.0,0.0,11.51,14.92 -2016-01-29 06:00:00,801.0,0.0,11.51,15.41 -2016-01-29 07:00:00,873.0,0.0,11.61,14.61 -2016-01-29 08:00:00,917.0,0.0,11.71,13.81 -2016-01-29 09:00:00,916.0,37.72,11.81,13.01 -2016-01-29 10:00:00,910.0,124.38,11.84,13.03 -2016-01-29 11:00:00,899.0,17.87,11.86,13.06 -2016-01-29 12:00:00,906.0,64.23,11.89,13.09 -2016-01-29 13:00:00,874.0,37.02,11.71,13.55 -2016-01-29 14:00:00,846.0,30.49,11.52,14.02 -2016-01-29 15:00:00,828.0,15.25,11.34,14.48 -2016-01-29 16:00:00,822.0,35.85,11.52,15.47 -2016-01-29 17:00:00,832.0,0.0,11.7,16.46 -2016-01-29 18:00:00,918.0,0.0,11.88,17.45 -2016-01-29 19:00:00,979.0,0.0,11.89,17.12 -2016-01-29 20:00:00,953.0,0.0,11.9,16.8 -2016-01-29 21:00:00,1073.0,0.0,11.91,16.47 -2016-01-29 22:00:00,1212.0,0.0,11.87,15.88 -2016-01-29 23:00:00,1062.0,0.0,11.83,15.29 -2016-01-30 00:00:00,948.0,0.0,11.79,14.7 -2016-01-30 01:00:00,909.0,0.0,11.74,14.45 -2016-01-30 02:00:00,816.0,0.0,11.69,14.19 -2016-01-30 03:00:00,774.0,0.0,11.64,13.93 -2016-01-30 04:00:00,777.0,0.0,11.54,13.42 -2016-01-30 05:00:00,788.0,0.0,11.45,12.91 -2016-01-30 06:00:00,762.0,0.0,11.35,12.4 -2016-01-30 07:00:00,797.0,0.0,11.01,12.34 -2016-01-30 08:00:00,846.0,0.0,10.67,12.27 -2016-01-30 09:00:00,895.0,15.96,10.34,12.21 -2016-01-30 10:00:00,935.0,34.37,10.02,11.96 -2016-01-30 11:00:00,970.0,89.6,9.71,11.71 -2016-01-30 12:00:00,996.0,105.39,9.39,11.46 -2016-01-30 13:00:00,958.0,68.77,9.55,11.28 -2016-01-30 14:00:00,944.0,35.16,9.71,11.1 -2016-01-30 15:00:00,960.0,32.19,9.88,10.92 -2016-01-30 16:00:00,958.0,25.89,9.77,10.53 -2016-01-30 17:00:00,977.0,0.0,9.66,10.14 -2016-01-30 18:00:00,1040.0,0.0,9.55,9.75 -2016-01-30 19:00:00,1098.0,0.0,9.64,9.14 -2016-01-30 20:00:00,1053.0,0.0,9.74,8.54 -2016-01-30 21:00:00,1129.0,0.0,9.83,7.93 -2016-01-30 22:00:00,1287.0,0.0,10.09,7.89 -2016-01-30 23:00:00,1125.0,0.0,10.35,7.85 -2016-01-31 00:00:00,984.0,0.0,10.61,7.81 -2016-01-31 01:00:00,932.0,0.0,10.91,9.2 -2016-01-31 02:00:00,846.0,0.0,11.21,10.59 -2016-01-31 03:00:00,802.0,0.0,11.51,11.99 -2016-01-31 04:00:00,813.0,0.0,11.8,12.7 -2016-01-31 05:00:00,800.0,0.0,12.08,13.41 -2016-01-31 06:00:00,794.0,0.0,12.37,14.12 -2016-01-31 07:00:00,816.0,0.0,12.64,14.56 -2016-01-31 08:00:00,865.0,0.0,12.91,15.0 -2016-01-31 09:00:00,896.0,35.47,13.19,15.43 -2016-01-31 10:00:00,959.0,43.71,13.09,14.9 -2016-01-31 11:00:00,989.0,47.92,12.98,14.37 -2016-01-31 12:00:00,948.0,35.34,12.88,13.83 -2016-01-31 13:00:00,894.0,51.76,12.7,13.97 -2016-01-31 14:00:00,836.0,73.46,12.51,14.1 -2016-01-31 15:00:00,833.0,50.17,12.32,14.23 -2016-01-31 16:00:00,835.0,50.85,12.29,14.1 -2016-01-31 17:00:00,859.0,0.0,12.26,13.96 -2016-01-31 18:00:00,944.0,0.0,12.22,13.82 -2016-01-31 19:00:00,982.0,0.0,12.13,13.34 -2016-01-31 20:00:00,963.0,0.0,12.03,12.86 -2016-01-31 21:00:00,1050.0,0.0,11.94,12.39 -2016-01-31 22:00:00,1188.0,0.0,11.85,11.96 -2016-01-31 23:00:00,1062.0,0.0,11.76,11.54 -2016-02-01 00:00:00,923.0,0.0,11.68,11.12 -2016-02-01 01:00:00,835.0,0.0,11.76,11.78 -2016-02-01 02:00:00,769.0,0.0,11.84,12.44 -2016-02-01 03:00:00,755.0,0.0,11.92,13.1 -2016-02-01 04:00:00,745.0,0.0,11.97,13.17 -2016-02-01 05:00:00,741.0,0.0,12.03,13.24 -2016-02-01 06:00:00,752.0,0.0,12.09,13.31 -2016-02-01 07:00:00,811.0,0.0,12.11,13.01 -2016-02-01 08:00:00,900.0,0.0,12.13,12.7 -2016-02-01 09:00:00,910.0,121.45,12.15,12.4 -2016-02-01 10:00:00,953.0,111.47,12.11,12.36 -2016-02-01 11:00:00,951.0,68.8,12.08,12.33 -2016-02-01 12:00:00,961.0,86.72,12.04,12.29 -2016-02-01 13:00:00,911.0,154.3,11.94,12.57 -2016-02-01 14:00:00,853.0,55.02,11.84,12.86 -2016-02-01 15:00:00,819.0,45.2,11.73,13.14 -2016-02-01 16:00:00,834.0,28.88,11.85,13.0 -2016-02-01 17:00:00,873.0,0.0,11.97,12.85 -2016-02-01 18:00:00,960.0,0.0,12.09,12.7 -2016-02-01 19:00:00,1000.0,0.0,12.09,12.19 -2016-02-01 20:00:00,983.0,0.0,12.09,11.68 -2016-02-01 21:00:00,1081.0,0.0,12.09,11.17 -2016-02-01 22:00:00,1224.0,0.0,12.01,10.28 -2016-02-01 23:00:00,1063.0,0.0,11.94,9.38 -2016-02-02 00:00:00,951.0,0.0,11.87,8.48 -2016-02-02 01:00:00,864.0,0.0,11.78,9.0 -2016-02-02 02:00:00,779.0,0.0,11.69,9.52 -2016-02-02 03:00:00,775.0,0.0,11.61,10.04 -2016-02-02 04:00:00,768.0,0.0,11.5,9.72 -2016-02-02 05:00:00,769.0,0.0,11.39,9.4 -2016-02-02 06:00:00,766.0,0.0,11.29,9.08 -2016-02-02 07:00:00,823.0,0.0,11.13,8.92 -2016-02-02 08:00:00,903.0,0.0,10.98,8.77 -2016-02-02 09:00:00,955.0,280.13,10.83,8.62 -2016-02-02 10:00:00,994.0,463.18,10.58,8.86 -2016-02-02 11:00:00,956.0,458.62,10.33,9.1 -2016-02-02 12:00:00,855.0,104.82,10.08,9.34 -2016-02-02 13:00:00,769.0,380.71,10.07,9.55 -2016-02-02 14:00:00,733.0,328.59,10.06,9.77 -2016-02-02 15:00:00,734.0,314.29,10.05,9.99 -2016-02-02 16:00:00,732.0,244.53,9.89,10.37 -2016-02-02 17:00:00,787.0,0.0,9.74,10.76 -2016-02-02 18:00:00,886.0,0.0,9.59,11.14 -2016-02-02 19:00:00,994.0,0.0,9.59,11.39 -2016-02-02 20:00:00,1005.0,0.0,9.58,11.64 -2016-02-02 21:00:00,1105.0,0.0,9.57,11.89 -2016-02-02 22:00:00,1283.0,0.0,9.5,12.14 -2016-02-02 23:00:00,1147.0,0.0,9.43,12.39 -2016-02-03 00:00:00,1034.0,0.0,9.36,12.63 -2016-02-03 01:00:00,959.0,0.0,9.31,12.45 -2016-02-03 02:00:00,880.0,0.0,9.26,12.26 -2016-02-03 03:00:00,855.0,0.0,9.21,12.07 -2016-02-03 04:00:00,900.0,0.0,9.12,12.5 -2016-02-03 05:00:00,908.0,0.0,9.04,12.93 -2016-02-03 06:00:00,920.0,0.0,8.96,13.37 -2016-02-03 07:00:00,1016.0,0.0,8.81,13.46 -2016-02-03 08:00:00,1117.0,0.0,8.67,13.55 -2016-02-03 09:00:00,1132.0,125.94,8.52,13.64 -2016-02-03 10:00:00,1101.0,464.69,8.84,13.2 -2016-02-03 11:00:00,995.0,577.3,9.16,12.76 -2016-02-03 12:00:00,946.0,375.84,9.48,12.32 -2016-02-03 13:00:00,879.0,112.45,9.45,11.97 -2016-02-03 14:00:00,856.0,615.56,9.43,11.62 -2016-02-03 15:00:00,829.0,74.69,9.4,11.27 -2016-02-03 16:00:00,841.0,237.56,9.53,10.92 -2016-02-03 17:00:00,889.0,0.0,9.65,10.57 -2016-02-03 18:00:00,982.0,0.0,9.77,10.22 -2016-02-03 19:00:00,1095.0,0.0,9.92,9.69 -2016-02-03 20:00:00,1079.0,0.0,10.07,9.15 -2016-02-03 21:00:00,1181.0,0.0,10.23,8.62 -2016-02-03 22:00:00,1317.0,0.0,10.45,8.89 -2016-02-03 23:00:00,1184.0,0.0,10.67,9.16 -2016-02-04 00:00:00,1032.0,0.0,10.89,9.43 -2016-02-04 01:00:00,949.0,0.0,11.06,10.48 -2016-02-04 02:00:00,876.0,0.0,11.23,11.53 -2016-02-04 03:00:00,842.0,0.0,11.4,12.58 -2016-02-04 04:00:00,853.0,0.0,11.5,12.32 -2016-02-04 05:00:00,842.0,0.0,11.61,12.06 -2016-02-04 06:00:00,832.0,0.0,11.72,11.81 -2016-02-04 07:00:00,893.0,0.0,11.69,10.6 -2016-02-04 08:00:00,981.0,0.0,11.66,9.4 -2016-02-04 09:00:00,998.0,132.06,11.63,8.19 -2016-02-04 10:00:00,996.0,42.17,11.61,7.69 -2016-02-04 11:00:00,1020.0,65.72,11.58,7.18 -2016-02-04 12:00:00,1022.0,81.26,11.56,6.68 -2016-02-04 13:00:00,936.0,72.76,11.33,6.61 -2016-02-04 14:00:00,901.0,55.86,11.1,6.54 -2016-02-04 15:00:00,888.0,47.56,10.86,6.47 -2016-02-04 16:00:00,875.0,32.14,10.87,6.38 -2016-02-04 17:00:00,890.0,0.0,10.88,6.29 -2016-02-04 18:00:00,971.0,0.0,10.89,6.19 -2016-02-04 19:00:00,1031.0,0.0,10.89,6.1 -2016-02-04 20:00:00,1029.0,0.0,10.89,6.01 -2016-02-04 21:00:00,1093.0,0.0,10.89,5.92 -2016-02-04 22:00:00,1244.0,0.0,10.91,6.01 -2016-02-04 23:00:00,1111.0,0.0,10.93,6.1 -2016-02-05 00:00:00,960.0,0.0,10.96,6.19 -2016-02-05 01:00:00,883.0,0.0,10.89,7.26 -2016-02-05 02:00:00,806.0,0.0,10.82,8.32 -2016-02-05 03:00:00,791.0,0.0,10.76,9.38 -2016-02-05 04:00:00,787.0,0.0,10.75,9.61 -2016-02-05 05:00:00,779.0,0.0,10.75,9.84 -2016-02-05 06:00:00,792.0,0.0,10.75,10.07 -2016-02-05 07:00:00,876.0,0.0,10.8,10.31 -2016-02-05 08:00:00,909.0,0.0,10.86,10.56 -2016-02-05 09:00:00,943.0,54.99,10.91,10.8 -2016-02-05 10:00:00,919.0,38.61,11.05,11.16 -2016-02-05 11:00:00,927.0,33.44,11.18,11.53 -2016-02-05 12:00:00,902.0,48.29,11.32,11.89 -2016-02-05 13:00:00,891.0,52.82,11.46,12.43 -2016-02-05 14:00:00,882.0,75.92,11.6,12.97 -2016-02-05 15:00:00,871.0,85.1,11.74,13.5 -2016-02-05 16:00:00,904.0,56.51,11.6,13.25 -2016-02-05 17:00:00,918.0,0.0,11.46,12.99 -2016-02-05 18:00:00,975.0,0.0,11.32,12.73 -2016-02-05 19:00:00,1047.0,0.0,11.28,12.71 -2016-02-05 20:00:00,1032.0,0.0,11.24,12.69 -2016-02-05 21:00:00,1116.0,0.0,11.2,12.68 -2016-02-05 22:00:00,1266.0,0.0,11.17,11.95 -2016-02-05 23:00:00,1138.0,0.0,11.13,11.22 -2016-02-06 00:00:00,1015.0,0.0,11.09,10.5 -2016-02-06 01:00:00,937.0,0.0,11.27,11.75 -2016-02-06 02:00:00,864.0,0.0,11.45,13.01 -2016-02-06 03:00:00,822.0,0.0,11.64,14.26 -2016-02-06 04:00:00,831.0,0.0,11.66,15.08 -2016-02-06 05:00:00,815.0,0.0,11.69,15.9 -2016-02-06 06:00:00,825.0,0.0,11.72,16.72 -2016-02-06 07:00:00,870.0,0.0,11.75,17.63 -2016-02-06 08:00:00,955.0,0.0,11.79,18.54 -2016-02-06 09:00:00,994.0,11.36,11.82,19.45 -2016-02-06 10:00:00,1049.0,43.0,11.66,19.65 -2016-02-06 11:00:00,1078.0,32.69,11.51,19.85 -2016-02-06 12:00:00,1112.0,40.08,11.35,20.06 -2016-02-06 13:00:00,1111.0,29.1,11.3,18.91 -2016-02-06 14:00:00,1075.0,30.55,11.25,17.76 -2016-02-06 15:00:00,1080.0,50.64,11.2,16.61 -2016-02-06 16:00:00,1074.0,31.27,10.84,15.51 -2016-02-06 17:00:00,1050.0,0.0,10.47,14.42 -2016-02-06 18:00:00,1106.0,0.0,10.1,13.32 -2016-02-06 19:00:00,1159.0,0.0,9.78,13.55 -2016-02-06 20:00:00,1137.0,0.0,9.47,13.78 -2016-02-06 21:00:00,1241.0,0.0,9.15,14.01 -2016-02-06 22:00:00,1387.0,0.0,8.79,14.24 -2016-02-06 23:00:00,1265.0,0.0,8.42,14.46 -2016-02-07 00:00:00,1139.0,0.0,8.06,14.69 -2016-02-07 01:00:00,1073.0,0.0,8.27,14.82 -2016-02-07 02:00:00,1006.0,0.0,8.48,14.96 -2016-02-07 03:00:00,973.0,0.0,8.69,15.09 -2016-02-07 04:00:00,985.0,0.0,8.63,15.25 -2016-02-07 05:00:00,988.0,0.0,8.58,15.4 -2016-02-07 06:00:00,982.0,0.0,8.53,15.56 -2016-02-07 07:00:00,1030.0,0.0,8.69,15.49 -2016-02-07 08:00:00,1070.0,1.56,8.86,15.42 -2016-02-07 09:00:00,1125.0,57.22,9.03,15.35 -2016-02-07 10:00:00,1155.0,81.1,9.25,15.25 -2016-02-07 11:00:00,1134.0,54.92,9.46,15.15 -2016-02-07 12:00:00,1152.0,80.71,9.68,15.05 -2016-02-07 13:00:00,1086.0,64.92,9.84,15.87 -2016-02-07 14:00:00,1039.0,30.01,10.0,16.69 -2016-02-07 15:00:00,1043.0,26.4,10.16,17.52 -2016-02-07 16:00:00,1059.0,55.85,10.73,17.85 -2016-02-07 17:00:00,1063.0,0.0,11.3,18.19 -2016-02-07 18:00:00,1094.0,0.0,11.86,18.52 -2016-02-07 19:00:00,1164.0,0.0,11.98,18.68 -2016-02-07 20:00:00,1144.0,0.0,12.1,18.83 -2016-02-07 21:00:00,1229.0,0.0,12.22,18.98 -2016-02-07 22:00:00,1386.0,0.0,12.15,19.05 -2016-02-07 23:00:00,1255.0,0.0,12.08,19.12 -2016-02-08 00:00:00,1123.0,0.0,12.01,19.19 -2016-02-08 01:00:00,1026.0,0.0,11.58,19.16 -2016-02-08 02:00:00,951.0,0.0,11.15,19.14 -2016-02-08 03:00:00,941.0,0.0,10.72,19.12 -2016-02-08 04:00:00,941.0,0.0,10.63,19.72 -2016-02-08 05:00:00,944.0,0.0,10.55,20.33 -2016-02-08 06:00:00,953.0,0.0,10.47,20.94 -2016-02-08 07:00:00,1016.0,0.0,10.34,20.91 -2016-02-08 08:00:00,1082.0,2.4,10.22,20.89 -2016-02-08 09:00:00,1130.0,290.31,10.1,20.87 -2016-02-08 10:00:00,1140.0,106.9,10.01,20.73 -2016-02-08 11:00:00,1118.0,529.59,9.92,20.58 -2016-02-08 12:00:00,1068.0,57.96,9.83,20.44 -2016-02-08 13:00:00,1024.0,79.9,9.95,20.28 -2016-02-08 14:00:00,1001.0,146.86,10.07,20.12 -2016-02-08 15:00:00,1000.0,497.15,10.19,19.96 -2016-02-08 16:00:00,981.0,71.73,10.13,18.92 -2016-02-08 17:00:00,964.0,0.0,10.07,17.88 -2016-02-08 18:00:00,1047.0,0.0,10.01,16.84 -2016-02-08 19:00:00,1140.0,0.0,9.91,16.01 -2016-02-08 20:00:00,1164.0,0.0,9.81,15.18 -2016-02-08 21:00:00,1254.0,0.0,9.7,14.34 -2016-02-08 22:00:00,1400.0,0.0,9.78,13.83 -2016-02-08 23:00:00,1250.0,0.0,9.85,13.31 -2016-02-09 00:00:00,1138.0,0.0,9.93,12.79 -2016-02-09 01:00:00,1052.0,0.0,10.03,11.87 -2016-02-09 02:00:00,971.0,0.0,10.14,10.95 -2016-02-09 03:00:00,920.0,0.0,10.25,10.03 -2016-02-09 04:00:00,917.0,0.0,10.08,10.43 -2016-02-09 05:00:00,898.0,0.0,9.91,10.83 -2016-02-09 06:00:00,898.0,0.0,9.75,11.23 -2016-02-09 07:00:00,982.0,0.0,9.08,12.16 -2016-02-09 08:00:00,1085.0,3.35,8.42,13.09 -2016-02-09 09:00:00,1145.0,16.9,7.76,14.03 -2016-02-09 10:00:00,1167.0,79.96,7.4,14.8 -2016-02-09 11:00:00,1193.0,183.7,7.04,15.57 -2016-02-09 12:00:00,1227.0,52.56,6.68,16.34 -2016-02-09 13:00:00,1194.0,149.49,7.02,16.67 -2016-02-09 14:00:00,1202.0,14.25,7.37,17.0 -2016-02-09 15:00:00,1209.0,199.83,7.72,17.32 -2016-02-09 16:00:00,1207.0,124.42,7.66,17.42 -2016-02-09 17:00:00,1226.0,28.43,7.61,17.51 -2016-02-09 18:00:00,1291.0,0.0,7.55,17.6 -2016-02-09 19:00:00,1406.0,0.0,7.39,17.19 -2016-02-09 20:00:00,1425.0,0.0,7.22,16.78 -2016-02-09 21:00:00,1508.0,0.0,7.05,16.37 -2016-02-09 22:00:00,1651.0,0.0,7.06,16.04 -2016-02-09 23:00:00,1489.0,0.0,7.07,15.7 -2016-02-10 00:00:00,1360.0,0.0,7.08,15.37 -2016-02-10 01:00:00,1292.0,0.0,7.15,14.56 -2016-02-10 02:00:00,1209.0,0.0,7.22,13.75 -2016-02-10 03:00:00,1161.0,0.0,7.29,12.94 -2016-02-10 04:00:00,1171.0,0.0,7.18,12.6 -2016-02-10 05:00:00,1149.0,0.0,7.07,12.27 -2016-02-10 06:00:00,1149.0,0.0,6.97,11.93 -2016-02-10 07:00:00,1215.0,0.0,7.15,11.68 -2016-02-10 08:00:00,1266.0,41.24,7.34,11.43 -2016-02-10 09:00:00,1304.0,28.18,7.53,11.17 -2016-02-10 10:00:00,1301.0,131.07,7.67,10.87 -2016-02-10 11:00:00,1231.0,181.19,7.8,10.57 -2016-02-10 12:00:00,1208.0,752.71,7.94,10.28 -2016-02-10 13:00:00,1110.0,621.37,8.16,9.92 -2016-02-10 14:00:00,1020.0,646.19,8.39,9.56 -2016-02-10 15:00:00,999.0,469.46,8.61,9.2 -2016-02-10 16:00:00,996.0,295.97,8.6,8.57 -2016-02-10 17:00:00,1025.0,26.29,8.59,7.94 -2016-02-10 18:00:00,1116.0,0.0,8.58,7.31 -2016-02-10 19:00:00,1279.0,0.0,8.64,7.17 -2016-02-10 20:00:00,1272.0,0.0,8.7,7.03 -2016-02-10 21:00:00,1375.0,0.0,8.76,6.88 -2016-02-10 22:00:00,1551.0,0.0,8.7,6.88 -2016-02-10 23:00:00,1386.0,0.0,8.64,6.88 -2016-02-11 00:00:00,1269.0,0.0,8.58,6.88 -2016-02-11 01:00:00,1169.0,0.0,8.44,7.01 -2016-02-11 02:00:00,1085.0,0.0,8.31,7.14 -2016-02-11 03:00:00,1058.0,0.0,8.18,7.27 -2016-02-11 04:00:00,1074.0,0.0,8.0,7.15 -2016-02-11 05:00:00,1062.0,0.0,7.82,7.04 -2016-02-11 06:00:00,1084.0,0.0,7.64,6.92 -2016-02-11 07:00:00,1125.0,0.0,7.66,6.77 -2016-02-11 08:00:00,1199.0,26.08,7.69,6.61 -2016-02-11 09:00:00,1223.0,162.9,7.71,6.46 -2016-02-11 10:00:00,1269.0,404.14,7.77,6.77 -2016-02-11 11:00:00,1172.0,546.23,7.84,7.09 -2016-02-11 12:00:00,1086.0,640.34,7.9,7.41 -2016-02-11 13:00:00,986.0,269.68,8.07,7.31 -2016-02-11 14:00:00,973.0,621.72,8.24,7.22 -2016-02-11 15:00:00,961.0,151.76,8.41,7.13 -2016-02-11 16:00:00,986.0,231.92,8.45,7.22 -2016-02-11 17:00:00,1051.0,0.0,8.48,7.31 -2016-02-11 18:00:00,1135.0,0.0,8.52,7.39 -2016-02-11 19:00:00,1243.0,0.0,8.86,7.46 -2016-02-11 20:00:00,1227.0,0.0,9.2,7.53 -2016-02-11 21:00:00,1326.0,0.0,9.53,7.6 -2016-02-11 22:00:00,1491.0,0.0,9.61,8.16 -2016-02-11 23:00:00,1348.0,0.0,9.69,8.71 -2016-02-12 00:00:00,1219.0,0.0,9.77,9.27 -2016-02-12 01:00:00,1155.0,0.0,10.17,9.16 -2016-02-12 02:00:00,1067.0,0.0,10.57,9.05 -2016-02-12 03:00:00,1001.0,0.0,10.97,8.94 -2016-02-12 04:00:00,1002.0,0.0,10.74,10.38 -2016-02-12 05:00:00,1004.0,0.0,10.52,11.83 -2016-02-12 06:00:00,1015.0,0.0,10.3,13.27 -2016-02-12 07:00:00,1106.0,0.0,10.35,13.7 -2016-02-12 08:00:00,1148.0,0.0,10.41,14.13 -2016-02-12 09:00:00,1189.0,250.69,10.46,14.57 -2016-02-12 10:00:00,1191.0,307.09,10.35,14.56 -2016-02-12 11:00:00,1183.0,407.5,10.23,14.56 -2016-02-12 12:00:00,1161.0,273.45,10.12,14.55 -2016-02-12 13:00:00,1056.0,406.65,10.27,13.77 -2016-02-12 14:00:00,1020.0,629.63,10.43,12.98 -2016-02-12 15:00:00,979.0,427.86,10.59,12.19 -2016-02-12 16:00:00,970.0,95.63,10.5,11.72 -2016-02-12 17:00:00,1001.0,61.52,10.41,11.26 -2016-02-12 18:00:00,1097.0,0.0,10.33,10.79 -2016-02-12 19:00:00,1250.0,0.0,10.19,9.87 -2016-02-12 20:00:00,1294.0,0.0,10.06,8.96 -2016-02-12 21:00:00,1362.0,0.0,9.93,8.04 -2016-02-12 22:00:00,1525.0,0.0,9.83,7.73 -2016-02-12 23:00:00,1359.0,0.0,9.74,7.42 -2016-02-13 00:00:00,1206.0,0.0,9.65,7.1 -2016-02-13 01:00:00,1129.0,0.0,10.2,8.81 -2016-02-13 02:00:00,1056.0,0.0,10.75,10.52 -2016-02-13 03:00:00,1021.0,0.0,11.3,12.23 -2016-02-13 04:00:00,999.0,0.0,11.1,14.99 -2016-02-13 05:00:00,997.0,0.0,10.9,17.75 -2016-02-13 06:00:00,1027.0,0.0,10.7,20.51 -2016-02-13 07:00:00,1083.0,0.0,10.52,20.39 -2016-02-13 08:00:00,1146.0,22.87,10.34,20.26 -2016-02-13 09:00:00,1199.0,40.45,10.16,20.14 -2016-02-13 10:00:00,1269.0,277.04,10.07,19.86 -2016-02-13 11:00:00,1304.0,206.02,9.98,19.58 -2016-02-13 12:00:00,1250.0,349.51,9.89,19.3 -2016-02-13 13:00:00,1163.0,244.63,9.98,17.37 -2016-02-13 14:00:00,1072.0,471.12,10.08,15.43 -2016-02-13 15:00:00,1046.0,532.06,10.18,13.5 -2016-02-13 16:00:00,1037.0,325.93,9.84,12.77 -2016-02-13 17:00:00,1052.0,21.1,9.51,12.03 -2016-02-13 18:00:00,1136.0,0.0,9.17,11.3 -2016-02-13 19:00:00,1265.0,0.0,9.09,11.31 -2016-02-13 20:00:00,1260.0,0.0,9.01,11.31 -2016-02-13 21:00:00,1393.0,0.0,8.93,11.32 -2016-02-13 22:00:00,1530.0,0.0,8.98,11.77 -2016-02-13 23:00:00,1393.0,0.0,9.03,12.21 -2016-02-14 00:00:00,1274.0,0.0,9.09,12.65 -2016-02-14 01:00:00,1199.0,0.0,9.1,13.01 -2016-02-14 02:00:00,1102.0,0.0,9.11,13.37 -2016-02-14 03:00:00,1058.0,0.0,9.13,13.72 -2016-02-14 04:00:00,1072.0,0.0,8.94,12.96 -2016-02-14 05:00:00,1059.0,0.0,8.75,12.2 -2016-02-14 06:00:00,1055.0,0.0,8.57,11.43 -2016-02-14 07:00:00,1097.0,0.0,8.66,10.54 -2016-02-14 08:00:00,1159.0,62.8,8.76,9.65 -2016-02-14 09:00:00,1209.0,305.53,8.85,8.76 -2016-02-14 10:00:00,1265.0,55.71,8.93,7.68 -2016-02-14 11:00:00,1259.0,470.44,9.01,6.6 -2016-02-14 12:00:00,1245.0,730.19,9.09,5.52 -2016-02-14 13:00:00,1205.0,719.55,8.84,5.7 -2016-02-14 14:00:00,1186.0,599.07,8.6,5.89 -2016-02-14 15:00:00,1122.0,535.26,8.35,6.07 -2016-02-14 16:00:00,1029.0,63.82,8.54,7.75 -2016-02-14 17:00:00,1048.0,1.47,8.73,9.43 -2016-02-14 18:00:00,1148.0,0.0,8.91,11.1 -2016-02-14 19:00:00,1295.0,0.0,8.64,11.3 -2016-02-14 20:00:00,1356.0,0.0,8.37,11.5 -2016-02-14 21:00:00,1469.0,0.0,8.1,11.7 -2016-02-14 22:00:00,1637.0,0.0,7.84,11.79 -2016-02-14 23:00:00,1484.0,0.0,7.58,11.89 -2016-02-15 00:00:00,1363.0,0.0,7.33,11.99 -2016-02-15 01:00:00,1290.0,0.0,7.3,11.77 -2016-02-15 02:00:00,1193.0,0.0,7.28,11.56 -2016-02-15 03:00:00,1163.0,0.0,7.26,11.35 -2016-02-15 04:00:00,1174.0,0.0,7.29,11.52 -2016-02-15 05:00:00,1152.0,0.0,7.32,11.68 -2016-02-15 06:00:00,1168.0,0.0,7.35,11.85 -2016-02-15 07:00:00,1251.0,0.0,7.34,11.95 -2016-02-15 08:00:00,1326.0,78.46,7.34,12.05 -2016-02-15 09:00:00,1437.0,298.84,7.33,12.15 -2016-02-15 10:00:00,1419.0,72.52,7.44,11.52 -2016-02-15 11:00:00,1383.0,657.23,7.55,10.88 -2016-02-15 12:00:00,1332.0,682.98,7.66,10.25 -2016-02-15 13:00:00,1265.0,714.16,7.8,9.68 -2016-02-15 14:00:00,1175.0,300.08,7.94,9.12 -2016-02-15 15:00:00,1132.0,251.42,8.07,8.55 -2016-02-15 16:00:00,1126.0,333.87,8.0,8.05 -2016-02-15 17:00:00,1146.0,21.43,7.93,7.55 -2016-02-15 18:00:00,1225.0,0.0,7.86,7.05 -2016-02-15 19:00:00,1384.0,0.0,7.74,6.65 -2016-02-15 20:00:00,1422.0,0.0,7.62,6.25 -2016-02-15 21:00:00,1529.0,0.0,7.49,5.85 -2016-02-15 22:00:00,1670.0,0.0,7.37,5.11 -2016-02-15 23:00:00,1517.0,0.0,7.24,4.37 -2016-02-16 00:00:00,1363.0,0.0,7.12,3.63 -2016-02-16 01:00:00,1251.0,0.0,7.14,2.8 -2016-02-16 02:00:00,1170.0,0.0,7.16,1.97 -2016-02-16 03:00:00,1118.0,0.0,7.19,1.14 -2016-02-16 04:00:00,1142.0,0.0,7.11,1.33 -2016-02-16 05:00:00,1137.0,0.0,7.03,1.52 -2016-02-16 06:00:00,1162.0,0.0,6.96,1.71 -2016-02-16 07:00:00,1238.0,0.0,6.95,2.39 -2016-02-16 08:00:00,1343.0,29.3,6.95,3.07 -2016-02-16 09:00:00,1413.0,36.3,6.94,3.75 -2016-02-16 10:00:00,1377.0,84.04,7.05,4.36 -2016-02-16 11:00:00,1290.0,84.48,7.15,4.97 -2016-02-16 12:00:00,1222.0,432.16,7.26,5.59 -2016-02-16 13:00:00,1119.0,315.7,7.43,6.51 -2016-02-16 14:00:00,1058.0,116.3,7.6,7.43 -2016-02-16 15:00:00,1001.0,113.92,7.78,8.34 -2016-02-16 16:00:00,997.0,48.58,7.94,8.66 -2016-02-16 17:00:00,1056.0,33.47,8.1,8.98 -2016-02-16 18:00:00,1152.0,0.0,8.26,9.3 -2016-02-16 19:00:00,1320.0,0.0,8.39,9.61 -2016-02-16 20:00:00,1347.0,0.0,8.52,9.93 -2016-02-16 21:00:00,1452.0,0.0,8.65,10.25 -2016-02-16 22:00:00,1632.0,0.0,8.82,10.48 -2016-02-16 23:00:00,1479.0,0.0,8.98,10.72 -2016-02-17 00:00:00,1349.0,0.0,9.15,10.95 -2016-02-17 01:00:00,1261.0,0.0,9.0,11.85 -2016-02-17 02:00:00,1165.0,0.0,8.85,12.75 -2016-02-17 03:00:00,1120.0,0.0,8.7,13.66 -2016-02-17 04:00:00,1125.0,0.0,8.79,13.43 -2016-02-17 05:00:00,1105.0,0.0,8.88,13.21 -2016-02-17 06:00:00,1105.0,0.0,8.98,12.99 -2016-02-17 07:00:00,1175.0,0.0,8.98,12.75 -2016-02-17 08:00:00,1276.0,7.83,8.99,12.51 -2016-02-17 09:00:00,1361.0,82.97,9.0,12.28 -2016-02-17 10:00:00,1412.0,68.25,8.92,10.43 -2016-02-17 11:00:00,1403.0,53.45,8.84,8.58 -2016-02-17 12:00:00,1445.0,76.83,8.76,6.73 -2016-02-17 13:00:00,1423.0,53.5,8.55,7.44 -2016-02-17 14:00:00,1409.0,81.02,8.34,8.16 -2016-02-17 15:00:00,1339.0,27.36,8.12,8.87 -2016-02-17 16:00:00,1312.0,49.21,8.14,9.54 -2016-02-17 17:00:00,1335.0,18.82,8.15,10.21 -2016-02-17 18:00:00,1387.0,0.0,8.17,10.88 -2016-02-17 19:00:00,1446.0,0.0,8.27,10.57 -2016-02-17 20:00:00,1411.0,0.0,8.37,10.26 -2016-02-17 21:00:00,1501.0,0.0,8.48,9.94 -2016-02-17 22:00:00,1658.0,0.0,8.5,9.83 -2016-02-17 23:00:00,1521.0,0.0,8.52,9.71 -2016-02-18 00:00:00,1383.0,0.0,8.54,9.59 -2016-02-18 01:00:00,1288.0,0.0,8.61,9.3 -2016-02-18 02:00:00,1190.0,0.0,8.68,9.01 -2016-02-18 03:00:00,1149.0,0.0,8.75,8.72 -2016-02-18 04:00:00,1157.0,0.0,8.61,8.03 -2016-02-18 05:00:00,1164.0,0.0,8.48,7.35 -2016-02-18 06:00:00,1157.0,0.0,8.35,6.66 -2016-02-18 07:00:00,1218.0,0.0,8.3,6.01 -2016-02-18 08:00:00,1325.0,107.21,8.26,5.36 -2016-02-18 09:00:00,1356.0,80.87,8.21,4.7 -2016-02-18 10:00:00,1310.0,288.1,8.21,4.7 -2016-02-18 11:00:00,1264.0,689.94,8.22,4.7 -2016-02-18 12:00:00,1260.0,687.73,8.22,4.7 -2016-02-18 13:00:00,1200.0,448.67,8.11,5.2 -2016-02-18 14:00:00,1196.0,688.15,8.01,5.7 -2016-02-18 15:00:00,1126.0,442.19,7.91,6.19 -2016-02-18 16:00:00,1057.0,266.48,7.84,6.17 -2016-02-18 17:00:00,1033.0,68.19,7.77,6.15 -2016-02-18 18:00:00,1121.0,0.0,7.7,6.12 -2016-02-18 19:00:00,1280.0,0.0,7.79,6.23 -2016-02-18 20:00:00,1324.0,0.0,7.88,6.34 -2016-02-18 21:00:00,1421.0,0.0,7.97,6.44 -2016-02-18 22:00:00,1564.0,0.0,8.13,6.42 -2016-02-18 23:00:00,1432.0,0.0,8.28,6.4 -2016-02-19 00:00:00,1304.0,0.0,8.44,6.37 -2016-02-19 01:00:00,1207.0,0.0,8.51,6.83 -2016-02-19 02:00:00,1128.0,0.0,8.58,7.29 -2016-02-19 03:00:00,1106.0,0.0,8.65,7.75 -2016-02-19 04:00:00,1091.0,0.0,8.77,8.27 -2016-02-19 05:00:00,1063.0,0.0,8.89,8.78 -2016-02-19 06:00:00,1066.0,0.0,9.02,9.3 -2016-02-19 07:00:00,1138.0,0.0,9.11,9.85 -2016-02-19 08:00:00,1197.0,31.4,9.2,10.41 -2016-02-19 09:00:00,1271.0,106.01,9.29,10.97 -2016-02-19 10:00:00,1254.0,57.94,9.57,11.78 -2016-02-19 11:00:00,1223.0,104.12,9.86,12.59 -2016-02-19 12:00:00,1211.0,101.99,10.14,13.41 -2016-02-19 13:00:00,1187.0,141.91,10.27,13.54 -2016-02-19 14:00:00,1174.0,140.34,10.4,13.66 -2016-02-19 15:00:00,1163.0,125.37,10.53,13.79 -2016-02-19 16:00:00,1158.0,142.97,10.63,13.62 -2016-02-19 17:00:00,1118.0,50.01,10.72,13.45 -2016-02-19 18:00:00,1151.0,0.0,10.81,13.28 -2016-02-19 19:00:00,1249.0,0.0,10.94,12.19 -2016-02-19 20:00:00,1276.0,0.0,11.07,11.1 -2016-02-19 21:00:00,1387.0,0.0,11.2,10.01 -2016-02-19 22:00:00,1542.0,0.0,11.25,10.05 -2016-02-19 23:00:00,1344.0,0.0,11.29,10.08 -2016-02-20 00:00:00,1174.0,0.0,11.33,10.11 -2016-02-20 01:00:00,1087.0,0.0,11.32,10.48 -2016-02-20 02:00:00,1014.0,0.0,11.31,10.85 -2016-02-20 03:00:00,978.0,0.0,11.3,11.21 -2016-02-20 04:00:00,991.0,0.0,11.36,11.4 -2016-02-20 05:00:00,966.0,0.0,11.43,11.58 -2016-02-20 06:00:00,913.0,0.0,11.5,11.77 -2016-02-20 07:00:00,966.0,0.0,11.56,11.76 -2016-02-20 08:00:00,1033.0,32.68,11.63,11.76 -2016-02-20 09:00:00,1089.0,78.38,11.69,11.75 -2016-02-20 10:00:00,1134.0,33.35,11.71,11.95 -2016-02-20 11:00:00,1108.0,158.68,11.73,12.15 -2016-02-20 12:00:00,1091.0,169.11,11.75,12.34 -2016-02-20 13:00:00,1045.0,140.89,11.55,12.83 -2016-02-20 14:00:00,1037.0,74.53,11.36,13.32 -2016-02-20 15:00:00,1029.0,76.67,11.16,13.81 -2016-02-20 16:00:00,1057.0,87.53,11.23,13.56 -2016-02-20 17:00:00,1043.0,31.59,11.3,13.32 -2016-02-20 18:00:00,1054.0,0.0,11.37,13.08 -2016-02-20 19:00:00,1144.0,0.0,11.39,12.68 -2016-02-20 20:00:00,1137.0,0.0,11.41,12.28 -2016-02-20 21:00:00,1248.0,0.0,11.43,11.89 -2016-02-20 22:00:00,1436.0,0.0,11.46,11.53 -2016-02-20 23:00:00,1256.0,0.0,11.5,11.17 -2016-02-21 00:00:00,1065.0,0.0,11.54,10.81 -2016-02-21 01:00:00,985.0,0.0,11.3,10.99 -2016-02-21 02:00:00,898.0,0.0,11.06,11.16 -2016-02-21 03:00:00,873.0,0.0,10.83,11.34 -2016-02-21 04:00:00,908.0,0.0,10.9,10.94 -2016-02-21 05:00:00,900.0,0.0,10.97,10.55 -2016-02-21 06:00:00,836.0,0.0,11.04,10.15 -2016-02-21 07:00:00,856.0,0.0,11.08,10.25 -2016-02-21 08:00:00,913.0,19.24,11.13,10.35 -2016-02-21 09:00:00,970.0,55.59,11.18,10.46 -2016-02-21 10:00:00,1068.0,147.06,11.18,10.3 -2016-02-21 11:00:00,1094.0,141.87,11.18,10.15 -2016-02-21 12:00:00,1019.0,216.86,11.18,10.0 -2016-02-21 13:00:00,924.0,260.43,11.07,9.99 -2016-02-21 14:00:00,833.0,363.23,10.97,9.98 -2016-02-21 15:00:00,811.0,507.69,10.86,9.97 -2016-02-21 16:00:00,836.0,133.08,10.87,9.84 -2016-02-21 17:00:00,857.0,47.8,10.88,9.71 -2016-02-21 18:00:00,853.0,0.0,10.89,9.57 -2016-02-21 19:00:00,920.0,0.0,10.91,9.42 -2016-02-21 20:00:00,937.0,0.0,10.93,9.27 -2016-02-21 21:00:00,1025.0,0.0,10.94,9.12 -2016-02-21 22:00:00,1234.0,0.0,10.97,8.91 -2016-02-21 23:00:00,1160.0,0.0,11.0,8.71 -2016-02-22 00:00:00,926.0,0.0,11.03,8.51 -2016-02-22 01:00:00,808.0,0.0,10.96,8.78 -2016-02-22 02:00:00,737.0,0.0,10.9,9.05 -2016-02-22 03:00:00,735.0,0.0,10.84,9.32 -2016-02-22 04:00:00,763.0,0.0,10.85,9.23 -2016-02-22 05:00:00,782.0,0.0,10.87,9.14 -2016-02-22 06:00:00,750.0,0.0,10.89,9.05 -2016-02-22 07:00:00,774.0,0.0,10.92,9.24 -2016-02-22 08:00:00,854.0,43.06,10.96,9.43 -2016-02-22 09:00:00,887.0,71.36,10.99,9.63 -2016-02-22 10:00:00,924.0,148.27,10.98,9.34 -2016-02-22 11:00:00,1029.0,89.49,10.98,9.05 -2016-02-22 12:00:00,1022.0,226.44,10.97,8.76 -2016-02-22 13:00:00,898.0,115.21,11.01,8.85 -2016-02-22 14:00:00,851.0,163.84,11.05,8.93 -2016-02-22 15:00:00,830.0,141.31,11.09,9.02 -2016-02-22 16:00:00,855.0,115.8,11.02,8.55 -2016-02-22 17:00:00,856.0,13.18,10.95,8.07 -2016-02-22 18:00:00,908.0,0.0,10.89,7.6 -2016-02-22 19:00:00,941.0,0.0,10.79,7.35 -2016-02-22 20:00:00,973.0,0.0,10.69,7.09 -2016-02-22 21:00:00,1047.0,0.0,10.6,6.84 -2016-02-22 22:00:00,1222.0,0.0,10.42,6.53 -2016-02-22 23:00:00,1098.0,0.0,10.24,6.22 -2016-02-23 00:00:00,989.0,0.0,10.06,5.9 -2016-02-23 01:00:00,848.0,0.0,9.92,5.68 -2016-02-23 02:00:00,781.0,0.0,9.78,5.46 -2016-02-23 03:00:00,761.0,0.0,9.65,5.24 -2016-02-23 04:00:00,812.0,0.0,9.43,4.72 -2016-02-23 05:00:00,826.0,0.0,9.22,4.19 -2016-02-23 06:00:00,838.0,0.0,9.01,3.67 -2016-02-23 07:00:00,866.0,0.0,8.95,3.41 -2016-02-23 08:00:00,946.0,49.16,8.89,3.14 -2016-02-23 09:00:00,1021.0,128.99,8.83,2.88 -2016-02-23 10:00:00,1037.0,253.46,8.84,2.78 -2016-02-23 11:00:00,1067.0,275.92,8.84,2.68 -2016-02-23 12:00:00,1094.0,199.63,8.85,2.58 -2016-02-23 13:00:00,930.0,202.05,8.77,2.97 -2016-02-23 14:00:00,911.0,132.56,8.69,3.37 -2016-02-23 15:00:00,947.0,110.44,8.61,3.77 -2016-02-23 16:00:00,874.0,70.59,8.67,4.38 -2016-02-23 17:00:00,950.0,11.54,8.73,4.99 -2016-02-23 18:00:00,1055.0,0.0,8.79,5.6 -2016-02-23 19:00:00,1080.0,0.0,8.87,6.28 -2016-02-23 20:00:00,1090.0,0.0,8.95,6.96 -2016-02-23 21:00:00,1192.0,0.0,9.02,7.64 -2016-02-23 22:00:00,1372.0,0.0,9.13,8.05 -2016-02-23 23:00:00,1246.0,0.0,9.24,8.45 -2016-02-24 00:00:00,1160.0,0.0,9.35,8.86 -2016-02-24 01:00:00,1064.0,0.0,9.5,9.12 -2016-02-24 02:00:00,968.0,0.0,9.65,9.38 -2016-02-24 03:00:00,933.0,0.0,9.81,9.64 -2016-02-24 04:00:00,952.0,0.0,9.78,9.58 -2016-02-24 05:00:00,974.0,0.0,9.76,9.51 -2016-02-24 06:00:00,995.0,0.0,9.74,9.45 -2016-02-24 07:00:00,1034.0,0.0,9.65,9.54 -2016-02-24 08:00:00,1090.0,47.34,9.57,9.63 -2016-02-24 09:00:00,1204.0,71.76,9.49,9.72 -2016-02-24 10:00:00,1255.0,35.94,9.42,9.93 -2016-02-24 11:00:00,1199.0,22.94,9.34,10.14 -2016-02-24 12:00:00,1213.0,124.32,9.27,10.34 -2016-02-24 13:00:00,1110.0,25.08,9.16,10.32 -2016-02-24 14:00:00,1043.0,19.5,9.05,10.29 -2016-02-24 15:00:00,1067.0,82.06,8.94,10.26 -2016-02-24 16:00:00,997.0,57.73,8.83,10.32 -2016-02-24 17:00:00,1061.0,41.52,8.71,10.37 -2016-02-24 18:00:00,1118.0,0.0,8.59,10.43 -2016-02-24 19:00:00,1233.0,0.0,8.56,10.32 -2016-02-24 20:00:00,1195.0,0.0,8.53,10.21 -2016-02-24 21:00:00,1283.0,0.0,8.49,10.1 -2016-02-24 22:00:00,1446.0,0.0,8.48,9.78 -2016-02-24 23:00:00,1304.0,0.0,8.47,9.46 -2016-02-25 00:00:00,1201.0,0.0,8.46,9.14 -2016-02-25 01:00:00,1143.0,0.0,8.31,8.55 -2016-02-25 02:00:00,1017.0,0.0,8.16,7.96 -2016-02-25 03:00:00,984.0,0.0,8.02,7.37 -2016-02-25 04:00:00,1045.0,0.0,7.91,6.79 -2016-02-25 05:00:00,1040.0,0.0,7.8,6.22 -2016-02-25 06:00:00,1053.0,0.0,7.69,5.64 -2016-02-25 07:00:00,1132.0,0.0,7.65,5.19 -2016-02-25 08:00:00,1197.0,140.74,7.62,4.73 -2016-02-25 09:00:00,1223.0,112.41,7.58,4.28 -2016-02-25 10:00:00,1166.0,186.29,7.61,4.01 -2016-02-25 11:00:00,1074.0,177.96,7.64,3.75 -2016-02-25 12:00:00,1062.0,160.89,7.67,3.49 -2016-02-25 13:00:00,981.0,200.66,7.83,2.91 -2016-02-25 14:00:00,920.0,292.26,7.99,2.32 -2016-02-25 15:00:00,941.0,206.4,8.15,1.74 -2016-02-25 16:00:00,898.0,144.63,8.16,2.1 -2016-02-25 17:00:00,875.0,41.28,8.16,2.46 -2016-02-25 18:00:00,1004.0,0.0,8.17,2.83 -2016-02-25 19:00:00,1141.0,0.0,8.15,3.47 -2016-02-25 20:00:00,1144.0,0.0,8.13,4.11 -2016-02-25 21:00:00,1208.0,0.0,8.1,4.76 -2016-02-25 22:00:00,1361.0,0.0,8.11,5.45 -2016-02-25 23:00:00,1217.0,0.0,8.11,6.14 -2016-02-26 00:00:00,1116.0,0.0,8.11,6.83 -2016-02-26 01:00:00,1110.0,0.0,8.23,7.35 -2016-02-26 02:00:00,1015.0,0.0,8.35,7.88 -2016-02-26 03:00:00,946.0,0.0,8.47,8.4 -2016-02-26 04:00:00,968.0,0.0,8.48,9.13 -2016-02-26 05:00:00,978.0,0.0,8.49,9.85 -2016-02-26 06:00:00,1018.0,0.0,8.5,10.58 -2016-02-26 07:00:00,1114.0,0.0,8.57,11.12 -2016-02-26 08:00:00,1167.0,33.14,8.65,11.66 -2016-02-26 09:00:00,1151.0,65.34,8.72,12.19 -2016-02-26 10:00:00,1154.0,67.53,8.83,12.07 -2016-02-26 11:00:00,1225.0,285.36,8.93,11.95 -2016-02-26 12:00:00,1305.0,179.81,9.04,11.83 -2016-02-26 13:00:00,1290.0,166.45,8.99,10.39 -2016-02-26 14:00:00,1252.0,469.02,8.94,8.95 -2016-02-26 15:00:00,1214.0,131.45,8.9,7.5 -2016-02-26 16:00:00,1209.0,36.01,8.8,6.15 -2016-02-26 17:00:00,1184.0,30.41,8.7,4.79 -2016-02-26 18:00:00,1265.0,0.0,8.61,3.43 -2016-02-26 19:00:00,1414.0,0.0,8.32,3.66 -2016-02-26 20:00:00,1444.0,0.0,8.03,3.89 -2016-02-26 21:00:00,1488.0,0.0,7.74,4.12 -2016-02-26 22:00:00,1669.0,0.0,7.41,4.69 -2016-02-26 23:00:00,1491.0,0.0,7.07,5.26 -2016-02-27 00:00:00,1351.0,0.0,6.74,5.83 -2016-02-27 01:00:00,1307.0,0.0,6.43,6.46 -2016-02-27 02:00:00,1203.0,0.0,6.13,7.09 -2016-02-27 03:00:00,1112.0,0.0,5.83,7.72 -2016-02-27 04:00:00,1106.0,0.0,5.8,8.09 -2016-02-27 05:00:00,1101.0,0.0,5.77,8.46 -2016-02-27 06:00:00,1128.0,0.0,5.75,8.83 -2016-02-27 07:00:00,1203.0,0.0,5.87,9.59 -2016-02-27 08:00:00,1292.0,23.24,6.0,10.34 -2016-02-27 09:00:00,1332.0,83.14,6.12,11.1 -2016-02-27 10:00:00,1353.0,44.86,6.39,11.42 -2016-02-27 11:00:00,1338.0,49.42,6.65,11.74 -2016-02-27 12:00:00,1365.0,326.28,6.92,12.06 -2016-02-27 13:00:00,1345.0,42.32,7.57,11.88 -2016-02-27 14:00:00,1298.0,53.66,8.22,11.71 -2016-02-27 15:00:00,1250.0,44.31,8.88,11.53 -2016-02-27 16:00:00,1218.0,35.32,8.81,11.67 -2016-02-27 17:00:00,1248.0,65.66,8.75,11.81 -2016-02-27 18:00:00,1337.0,0.0,8.69,11.94 -2016-02-27 19:00:00,1458.0,0.0,8.69,11.82 -2016-02-27 20:00:00,1485.0,0.0,8.68,11.7 -2016-02-27 21:00:00,1575.0,0.0,8.68,11.57 -2016-02-27 22:00:00,1707.0,0.0,8.61,11.42 -2016-02-27 23:00:00,1607.0,0.0,8.54,11.26 -2016-02-28 00:00:00,1493.0,0.0,8.48,11.1 -2016-02-28 01:00:00,1399.0,0.0,8.22,11.08 -2016-02-28 02:00:00,1278.0,0.0,7.96,11.07 -2016-02-28 03:00:00,1225.0,0.0,7.7,11.05 -2016-02-28 04:00:00,1242.0,0.0,7.58,11.08 -2016-02-28 05:00:00,1229.0,0.0,7.46,11.12 -2016-02-28 06:00:00,1219.0,0.0,7.34,11.16 -2016-02-28 07:00:00,1250.0,0.0,7.34,11.49 -2016-02-28 08:00:00,1296.0,16.93,7.35,11.83 -2016-02-28 09:00:00,1360.0,21.07,7.35,12.17 -2016-02-28 10:00:00,1428.0,26.02,7.49,11.92 -2016-02-28 11:00:00,1436.0,30.36,7.64,11.68 -2016-02-28 12:00:00,1343.0,289.61,7.78,11.43 -2016-02-28 13:00:00,1217.0,66.26,7.87,11.79 -2016-02-28 14:00:00,1123.0,52.19,7.97,12.14 -2016-02-28 15:00:00,1080.0,76.5,8.06,12.5 -2016-02-28 16:00:00,1087.0,46.0,8.15,12.5 -2016-02-28 17:00:00,1140.0,32.04,8.23,12.51 -2016-02-28 18:00:00,1198.0,0.0,8.31,12.51 -2016-02-28 19:00:00,1284.0,0.0,8.32,12.01 -2016-02-28 20:00:00,1339.0,0.0,8.33,11.52 -2016-02-28 21:00:00,1422.0,0.0,8.35,11.02 -2016-02-28 22:00:00,1610.0,0.0,8.3,10.54 -2016-02-28 23:00:00,1485.0,0.0,8.25,10.06 -2016-02-29 00:00:00,1350.0,0.0,8.2,9.59 -2016-02-29 01:00:00,1232.0,0.0,7.99,8.81 -2016-02-29 02:00:00,1135.0,0.0,7.79,8.04 -2016-02-29 03:00:00,1119.0,0.0,7.59,7.27 -2016-02-29 04:00:00,1105.0,0.0,7.52,6.77 -2016-02-29 05:00:00,1098.0,0.0,7.46,6.28 -2016-02-29 06:00:00,1091.0,0.0,7.4,5.78 -2016-02-29 07:00:00,1149.0,0.0,7.46,5.47 -2016-02-29 08:00:00,1193.0,36.5,7.53,5.15 -2016-02-29 09:00:00,1244.0,86.88,7.6,4.84 -2016-02-29 10:00:00,1263.0,308.01,7.69,4.04 -2016-02-29 11:00:00,1275.0,527.2,7.77,3.24 -2016-02-29 12:00:00,1269.0,414.19,7.86,2.44 -2016-02-29 13:00:00,1133.0,244.77,7.96,2.77 -2016-02-29 14:00:00,1065.0,292.73,8.06,3.09 -2016-02-29 15:00:00,1029.0,277.52,8.16,3.42 -2016-02-29 16:00:00,1026.0,92.39,8.37,3.81 -2016-02-29 17:00:00,1027.0,24.49,8.58,4.2 -2016-02-29 18:00:00,1090.0,0.0,8.79,4.59 -2016-02-29 19:00:00,1194.0,0.0,9.02,5.4 -2016-02-29 20:00:00,1220.0,0.0,9.24,6.21 -2016-02-29 21:00:00,1331.0,0.0,9.47,7.02 -2016-02-29 22:00:00,1488.0,0.0,9.73,7.97 -2016-02-29 23:00:00,1321.0,0.0,10.0,8.92 -2016-03-01 00:00:00,1152.0,0.0,10.27,9.88 -2016-03-01 01:00:00,1087.0,0.0,10.61,10.85 -2016-03-01 02:00:00,1041.0,0.0,10.95,11.82 -2016-03-01 03:00:00,1018.0,0.0,11.29,12.79 -2016-03-01 04:00:00,1031.0,0.0,11.45,13.09 -2016-03-01 05:00:00,978.0,0.0,11.61,13.39 -2016-03-01 06:00:00,938.0,0.0,11.77,13.7 -2016-03-01 07:00:00,1035.0,0.0,11.84,13.91 -2016-03-01 08:00:00,1101.0,10.73,11.91,14.12 -2016-03-01 09:00:00,1132.0,15.86,11.98,14.33 -2016-03-01 10:00:00,1173.0,22.64,11.95,14.36 -2016-03-01 11:00:00,1184.0,61.92,11.92,14.4 -2016-03-01 12:00:00,1201.0,61.93,11.9,14.43 -2016-03-01 13:00:00,1178.0,140.66,11.96,14.41 -2016-03-01 14:00:00,1166.0,264.33,12.02,14.39 -2016-03-01 15:00:00,1143.0,461.76,12.08,14.37 -2016-03-01 16:00:00,1105.0,323.89,12.07,13.48 -2016-03-01 17:00:00,1096.0,52.85,12.07,12.6 -2016-03-01 18:00:00,1094.0,0.0,12.07,11.71 -2016-03-01 19:00:00,1166.0,0.0,11.95,11.72 -2016-03-01 20:00:00,1191.0,0.0,11.83,11.73 -2016-03-01 21:00:00,1272.0,0.0,11.71,11.74 -2016-03-01 22:00:00,1445.0,0.0,11.4,12.1 -2016-03-01 23:00:00,1296.0,0.0,11.1,12.46 -2016-03-02 00:00:00,1183.0,0.0,10.79,12.83 -2016-03-02 01:00:00,1092.0,0.0,10.51,13.44 -2016-03-02 02:00:00,1018.0,0.0,10.23,14.06 -2016-03-02 03:00:00,1009.0,0.0,9.94,14.68 -2016-03-02 04:00:00,1016.0,0.0,9.78,14.81 -2016-03-02 05:00:00,1026.0,0.0,9.61,14.94 -2016-03-02 06:00:00,1036.0,0.0,9.45,15.08 -2016-03-02 07:00:00,1109.0,0.0,9.39,15.95 -2016-03-02 08:00:00,1198.0,11.83,9.33,16.82 -2016-03-02 09:00:00,1238.0,254.11,9.27,17.7 -2016-03-02 10:00:00,1205.0,477.65,9.34,18.32 -2016-03-02 11:00:00,1176.0,90.46,9.41,18.95 -2016-03-02 12:00:00,1115.0,736.67,9.48,19.57 -2016-03-02 13:00:00,1036.0,253.5,9.41,18.96 -2016-03-02 14:00:00,1043.0,526.49,9.35,18.35 -2016-03-02 15:00:00,1034.0,335.39,9.29,17.74 -2016-03-02 16:00:00,1048.0,174.51,9.4,17.9 -2016-03-02 17:00:00,1071.0,90.98,9.51,18.06 -2016-03-02 18:00:00,1115.0,0.0,9.62,18.22 -2016-03-02 19:00:00,1208.0,0.0,9.71,17.7 -2016-03-02 20:00:00,1272.0,0.0,9.8,17.18 -2016-03-02 21:00:00,1366.0,0.0,9.9,16.66 -2016-03-02 22:00:00,1536.0,0.0,9.72,16.32 -2016-03-02 23:00:00,1421.0,0.0,9.55,15.97 -2016-03-03 00:00:00,1286.0,0.0,9.37,15.63 -2016-03-03 01:00:00,1171.0,0.0,9.23,15.23 -2016-03-03 02:00:00,1086.0,0.0,9.09,14.84 -2016-03-03 03:00:00,1045.0,0.0,8.95,14.44 -2016-03-03 04:00:00,1068.0,0.0,9.0,14.22 -2016-03-03 05:00:00,1044.0,0.0,9.04,13.99 -2016-03-03 06:00:00,1050.0,0.0,9.09,13.77 -2016-03-03 07:00:00,1115.0,0.0,8.78,13.01 -2016-03-03 08:00:00,1214.0,49.79,8.47,12.25 -2016-03-03 09:00:00,1239.0,75.97,8.16,11.49 -2016-03-03 10:00:00,1256.0,67.63,8.38,10.11 -2016-03-03 11:00:00,1244.0,87.31,8.6,8.73 -2016-03-03 12:00:00,1242.0,84.76,8.82,7.35 -2016-03-03 13:00:00,1175.0,87.05,9.2,8.61 -2016-03-03 14:00:00,1135.0,187.01,9.58,9.86 -2016-03-03 15:00:00,1140.0,260.75,9.97,11.12 -2016-03-03 16:00:00,1085.0,90.42,10.12,11.34 -2016-03-03 17:00:00,1080.0,7.78,10.28,11.57 -2016-03-03 18:00:00,1118.0,0.0,10.44,11.79 -2016-03-03 19:00:00,1218.0,0.0,10.22,11.94 -2016-03-03 20:00:00,1226.0,0.0,10.01,12.09 -2016-03-03 21:00:00,1323.0,0.0,9.8,12.23 -2016-03-03 22:00:00,1480.0,0.0,9.39,12.35 -2016-03-03 23:00:00,1342.0,0.0,8.99,12.47 -2016-03-04 00:00:00,1215.0,0.0,8.58,12.59 -2016-03-04 01:00:00,1150.0,0.0,8.36,11.71 -2016-03-04 02:00:00,1048.0,0.0,8.14,10.83 -2016-03-04 03:00:00,1023.0,0.0,7.91,9.94 -2016-03-04 04:00:00,1054.0,0.0,7.6,9.45 -2016-03-04 05:00:00,1045.0,0.0,7.29,8.96 -2016-03-04 06:00:00,1045.0,0.0,6.98,8.47 -2016-03-04 07:00:00,1128.0,0.0,6.77,8.32 -2016-03-04 08:00:00,1133.0,41.41,6.56,8.17 -2016-03-04 09:00:00,1159.0,90.31,6.36,8.01 -2016-03-04 10:00:00,1200.0,337.63,6.34,8.06 -2016-03-04 11:00:00,1215.0,563.3,6.33,8.11 -2016-03-04 12:00:00,1164.0,193.95,6.32,8.15 -2016-03-04 13:00:00,1114.0,813.61,6.61,8.4 -2016-03-04 14:00:00,1046.0,556.71,6.91,8.65 -2016-03-04 15:00:00,1029.0,44.64,7.21,8.9 -2016-03-04 16:00:00,1028.0,48.7,7.38,9.29 -2016-03-04 17:00:00,1056.0,7.62,7.55,9.68 -2016-03-04 18:00:00,1128.0,0.0,7.72,10.07 -2016-03-04 19:00:00,1283.0,0.0,7.65,10.9 -2016-03-04 20:00:00,1338.0,0.0,7.58,11.72 -2016-03-04 21:00:00,1430.0,0.0,7.51,12.55 -2016-03-04 22:00:00,1626.0,0.0,7.68,13.47 -2016-03-04 23:00:00,1492.0,0.0,7.85,14.39 -2016-03-05 00:00:00,1371.0,0.0,8.02,15.31 -2016-03-05 01:00:00,1268.0,0.0,8.0,15.32 -2016-03-05 02:00:00,1177.0,0.0,7.99,15.34 -2016-03-05 03:00:00,1145.0,0.0,7.97,15.35 -2016-03-05 04:00:00,1165.0,0.0,7.94,15.04 -2016-03-05 05:00:00,1174.0,0.0,7.9,14.74 -2016-03-05 06:00:00,1180.0,0.0,7.87,14.43 -2016-03-05 07:00:00,1240.0,0.0,7.75,14.17 -2016-03-05 08:00:00,1284.0,58.52,7.63,13.92 -2016-03-05 09:00:00,1336.0,153.11,7.52,13.67 -2016-03-05 10:00:00,1293.0,180.13,7.43,13.15 -2016-03-05 11:00:00,1265.0,244.54,7.35,12.63 -2016-03-05 12:00:00,1188.0,592.07,7.27,12.11 -2016-03-05 13:00:00,1104.0,488.67,7.42,11.71 -2016-03-05 14:00:00,1081.0,306.82,7.58,11.32 -2016-03-05 15:00:00,1055.0,385.74,7.74,10.92 -2016-03-05 16:00:00,1064.0,197.37,7.64,10.91 -2016-03-05 17:00:00,1082.0,71.93,7.55,10.89 -2016-03-05 18:00:00,1142.0,0.0,7.46,10.87 -2016-03-05 19:00:00,1261.0,0.0,7.5,10.66 -2016-03-05 20:00:00,1300.0,0.0,7.55,10.46 -2016-03-05 21:00:00,1421.0,0.0,7.6,10.25 -2016-03-05 22:00:00,1615.0,0.0,7.57,10.04 -2016-03-05 23:00:00,1493.0,0.0,7.55,9.83 -2016-03-06 00:00:00,1351.0,0.0,7.53,9.63 -2016-03-06 01:00:00,1265.0,0.0,7.45,9.42 -2016-03-06 02:00:00,1139.0,0.0,7.37,9.21 -2016-03-06 03:00:00,1103.0,0.0,7.28,9.01 -2016-03-06 04:00:00,1112.0,0.0,7.28,8.83 -2016-03-06 05:00:00,1112.0,0.0,7.28,8.66 -2016-03-06 06:00:00,1112.0,0.0,7.28,8.48 -2016-03-06 07:00:00,1154.0,0.0,7.24,8.45 -2016-03-06 08:00:00,1200.0,114.83,7.2,8.42 -2016-03-06 09:00:00,1223.0,97.85,7.16,8.39 -2016-03-06 10:00:00,1239.0,233.75,7.15,8.01 -2016-03-06 11:00:00,1192.0,396.18,7.14,7.64 -2016-03-06 12:00:00,1160.0,253.47,7.13,7.27 -2016-03-06 13:00:00,1096.0,111.95,7.29,7.14 -2016-03-06 14:00:00,1056.0,59.99,7.46,7.0 -2016-03-06 15:00:00,1031.0,66.99,7.63,6.87 -2016-03-06 16:00:00,1010.0,142.78,7.44,6.31 -2016-03-06 17:00:00,1057.0,17.43,7.25,5.75 -2016-03-06 18:00:00,1090.0,0.0,7.07,5.19 -2016-03-06 19:00:00,1142.0,0.0,7.12,4.17 -2016-03-06 20:00:00,1170.0,0.0,7.17,3.16 -2016-03-06 21:00:00,1290.0,0.0,7.22,2.15 -2016-03-06 22:00:00,1492.0,0.0,7.21,4.18 -2016-03-06 23:00:00,1364.0,0.0,7.21,6.21 -2016-03-07 00:00:00,1245.0,0.0,7.2,8.23 -2016-03-07 01:00:00,1122.0,0.0,7.17,8.51 -2016-03-07 02:00:00,1034.0,0.0,7.14,8.8 -2016-03-07 03:00:00,1030.0,0.0,7.1,9.08 -2016-03-07 04:00:00,1014.0,0.0,7.05,9.27 -2016-03-07 05:00:00,1026.0,0.0,6.99,9.46 -2016-03-07 06:00:00,1048.0,0.0,6.94,9.66 -2016-03-07 07:00:00,1110.0,0.0,6.97,9.47 -2016-03-07 08:00:00,1187.0,77.75,7.0,9.29 -2016-03-07 09:00:00,1251.0,257.84,7.03,9.1 -2016-03-07 10:00:00,1211.0,514.69,7.11,8.84 -2016-03-07 11:00:00,1126.0,472.65,7.19,8.57 -2016-03-07 12:00:00,1076.0,114.17,7.27,8.3 -2016-03-07 13:00:00,991.0,173.88,7.37,8.16 -2016-03-07 14:00:00,964.0,398.06,7.47,8.01 -2016-03-07 15:00:00,948.0,132.04,7.58,7.86 -2016-03-07 16:00:00,928.0,203.79,7.6,8.33 -2016-03-07 17:00:00,939.0,109.04,7.62,8.8 -2016-03-07 18:00:00,1015.0,0.0,7.64,9.27 -2016-03-07 19:00:00,1135.0,0.0,7.65,9.16 -2016-03-07 20:00:00,1161.0,0.0,7.66,9.05 -2016-03-07 21:00:00,1299.0,0.0,7.67,8.94 -2016-03-07 22:00:00,1476.0,0.0,7.62,8.51 -2016-03-07 23:00:00,1338.0,0.0,7.57,8.08 -2016-03-08 00:00:00,1211.0,0.0,7.52,7.66 -2016-03-08 01:00:00,1135.0,0.0,7.56,7.22 -2016-03-08 02:00:00,1051.0,0.0,7.6,6.78 -2016-03-08 03:00:00,1012.0,0.0,7.63,6.34 -2016-03-08 04:00:00,1030.0,0.0,7.84,6.65 -2016-03-08 05:00:00,1014.0,0.0,8.05,6.95 -2016-03-08 06:00:00,1011.0,0.0,8.26,7.26 -2016-03-08 07:00:00,1069.0,0.0,8.35,7.13 -2016-03-08 08:00:00,1130.0,12.85,8.44,7.0 -2016-03-08 09:00:00,1179.0,78.51,8.53,6.87 -2016-03-08 10:00:00,1200.0,117.59,8.74,7.72 -2016-03-08 11:00:00,1193.0,251.67,8.95,8.58 -2016-03-08 12:00:00,1195.0,67.36,9.16,9.43 -2016-03-08 13:00:00,1162.0,231.29,9.48,10.08 -2016-03-08 14:00:00,1119.0,179.56,9.81,10.72 -2016-03-08 15:00:00,1116.0,112.03,10.14,11.37 -2016-03-08 16:00:00,1041.0,77.04,10.29,11.05 -2016-03-08 17:00:00,1062.0,21.82,10.44,10.73 -2016-03-08 18:00:00,1111.0,0.0,10.59,10.41 -2016-03-08 19:00:00,1193.0,0.0,10.65,11.24 -2016-03-08 20:00:00,1174.0,0.0,10.71,12.06 -2016-03-08 21:00:00,1284.0,0.0,10.78,12.88 -2016-03-08 22:00:00,1436.0,0.0,10.69,14.42 -2016-03-08 23:00:00,1297.0,0.0,10.61,15.96 -2016-03-09 00:00:00,1178.0,0.0,10.52,17.5 -2016-03-09 01:00:00,1074.0,0.0,10.09,17.66 -2016-03-09 02:00:00,1004.0,0.0,9.67,17.81 -2016-03-09 03:00:00,976.0,0.0,9.24,17.96 -2016-03-09 04:00:00,1023.0,0.0,8.8,17.89 -2016-03-09 05:00:00,1037.0,0.0,8.35,17.82 -2016-03-09 06:00:00,1055.0,0.0,7.9,17.75 -2016-03-09 07:00:00,1154.0,0.0,7.93,18.06 -2016-03-09 08:00:00,1222.0,97.19,7.95,18.36 -2016-03-09 09:00:00,1272.0,285.19,7.98,18.66 -2016-03-09 10:00:00,1253.0,368.36,8.05,18.62 -2016-03-09 11:00:00,1205.0,376.41,8.13,18.58 -2016-03-09 12:00:00,1149.0,367.96,8.21,18.54 -2016-03-09 13:00:00,1071.0,557.36,8.21,18.8 -2016-03-09 14:00:00,1064.0,498.28,8.21,19.06 -2016-03-09 15:00:00,1048.0,434.93,8.22,19.32 -2016-03-09 16:00:00,1064.0,369.25,8.43,18.97 -2016-03-09 17:00:00,1097.0,23.37,8.64,18.63 -2016-03-09 18:00:00,1170.0,0.0,8.85,18.28 -2016-03-09 19:00:00,1256.0,0.0,9.12,17.69 -2016-03-09 20:00:00,1298.0,0.0,9.4,17.11 -2016-03-09 21:00:00,1387.0,0.0,9.68,16.52 -2016-03-09 22:00:00,1576.0,0.0,9.68,16.17 -2016-03-09 23:00:00,1439.0,0.0,9.68,15.83 -2016-03-10 00:00:00,1318.0,0.0,9.68,15.48 -2016-03-10 01:00:00,1233.0,0.0,9.54,14.84 -2016-03-10 02:00:00,1110.0,0.0,9.4,14.21 -2016-03-10 03:00:00,1070.0,0.0,9.26,13.57 -2016-03-10 04:00:00,1073.0,0.0,9.26,12.98 -2016-03-10 05:00:00,1066.0,0.0,9.25,12.39 -2016-03-10 06:00:00,1057.0,0.0,9.25,11.79 -2016-03-10 07:00:00,1108.0,3.93,9.25,11.13 -2016-03-10 08:00:00,1171.0,113.65,9.25,10.46 -2016-03-10 09:00:00,1244.0,210.34,9.25,9.79 -2016-03-10 10:00:00,1204.0,523.93,9.24,9.19 -2016-03-10 11:00:00,1122.0,431.12,9.24,8.59 -2016-03-10 12:00:00,1106.0,680.36,9.24,7.99 -2016-03-10 13:00:00,1000.0,509.1,9.24,7.93 -2016-03-10 14:00:00,950.0,307.98,9.25,7.87 -2016-03-10 15:00:00,912.0,313.25,9.26,7.81 -2016-03-10 16:00:00,898.0,392.34,9.21,7.63 -2016-03-10 17:00:00,928.0,141.81,9.17,7.45 -2016-03-10 18:00:00,961.0,0.0,9.13,7.27 -2016-03-10 19:00:00,1055.0,0.0,9.09,6.96 -2016-03-10 20:00:00,1095.0,0.0,9.06,6.65 -2016-03-10 21:00:00,1194.0,0.0,9.03,6.34 -2016-03-10 22:00:00,1401.0,0.0,8.91,5.99 -2016-03-10 23:00:00,1276.0,0.0,8.8,5.63 -2016-03-11 00:00:00,1132.0,0.0,8.69,5.27 -2016-03-11 01:00:00,1041.0,0.0,8.66,4.63 -2016-03-11 02:00:00,969.0,0.0,8.64,4.0 -2016-03-11 03:00:00,937.0,0.0,8.61,3.37 -2016-03-11 04:00:00,933.0,0.0,8.55,2.98 -2016-03-11 05:00:00,931.0,0.0,8.49,2.59 -2016-03-11 06:00:00,941.0,0.0,8.43,2.21 -2016-03-11 07:00:00,1044.0,0.65,8.42,1.91 -2016-03-11 08:00:00,1060.0,143.49,8.41,1.62 -2016-03-11 09:00:00,1051.0,317.52,8.4,1.32 -2016-03-11 10:00:00,985.0,376.35,8.45,1.34 -2016-03-11 11:00:00,947.0,488.34,8.5,1.35 -2016-03-11 12:00:00,942.0,564.1,8.55,1.37 -2016-03-11 13:00:00,877.0,502.52,8.68,1.45 -2016-03-11 14:00:00,874.0,450.74,8.82,1.53 -2016-03-11 15:00:00,853.0,262.94,8.96,1.61 -2016-03-11 16:00:00,840.0,267.75,8.99,1.43 -2016-03-11 17:00:00,859.0,57.51,9.03,1.25 -2016-03-11 18:00:00,900.0,0.0,9.07,1.06 -2016-03-11 19:00:00,994.0,0.0,9.06,1.02 -2016-03-11 20:00:00,1094.0,0.0,9.05,0.97 -2016-03-11 21:00:00,1205.0,0.0,9.05,0.92 -2016-03-11 22:00:00,1424.0,0.0,8.94,0.9 -2016-03-11 23:00:00,1304.0,0.0,8.84,0.87 -2016-03-12 00:00:00,1169.0,0.0,8.74,0.84 -2016-03-12 01:00:00,1061.0,0.0,8.62,0.83 -2016-03-12 02:00:00,973.0,0.0,8.5,0.81 -2016-03-12 03:00:00,955.0,0.0,8.37,0.8 -2016-03-12 04:00:00,975.0,0.0,8.32,0.91 -2016-03-12 05:00:00,969.0,0.0,8.28,1.01 -2016-03-12 06:00:00,980.0,0.0,8.23,1.12 -2016-03-12 07:00:00,1026.0,0.0,8.22,1.3 -2016-03-12 08:00:00,1102.0,12.2,8.21,1.49 -2016-03-12 09:00:00,1120.0,73.15,8.2,1.67 -2016-03-12 10:00:00,1106.0,203.64,8.19,1.54 -2016-03-12 11:00:00,995.0,275.03,8.18,1.41 -2016-03-12 12:00:00,967.0,452.05,8.17,1.28 -2016-03-12 13:00:00,865.0,378.29,8.34,1.31 -2016-03-12 14:00:00,830.0,632.29,8.51,1.33 -2016-03-12 15:00:00,819.0,547.68,8.68,1.35 -2016-03-12 16:00:00,814.0,405.38,8.69,2.01 -2016-03-12 17:00:00,825.0,173.64,8.7,2.68 -2016-03-12 18:00:00,879.0,0.0,8.71,3.34 -2016-03-12 19:00:00,1028.0,0.0,8.61,3.73 -2016-03-12 20:00:00,1122.0,0.0,8.51,4.12 -2016-03-12 21:00:00,1204.0,0.0,8.41,4.51 -2016-03-12 22:00:00,1428.0,0.0,8.22,4.81 -2016-03-12 23:00:00,1309.0,0.0,8.04,5.12 -2016-03-13 00:00:00,1157.0,0.0,7.86,5.42 -2016-03-13 01:00:00,1049.0,0.0,7.89,5.43 -2016-03-13 02:00:00,958.0,0.0,7.93,5.45 -2016-03-13 03:00:00,941.0,0.0,7.96,5.46 -2016-03-13 04:00:00,950.0,0.0,7.94,5.59 -2016-03-13 05:00:00,955.0,0.0,7.92,5.72 -2016-03-13 06:00:00,962.0,0.0,7.9,5.85 -2016-03-13 07:00:00,982.0,7.32,8.03,6.02 -2016-03-13 08:00:00,1039.0,109.23,8.16,6.2 -2016-03-13 09:00:00,1050.0,203.37,8.3,6.37 -2016-03-13 10:00:00,1015.0,474.22,8.57,6.71 -2016-03-13 11:00:00,982.0,674.36,8.85,7.05 -2016-03-13 12:00:00,914.0,821.41,9.13,7.39 -2016-03-13 13:00:00,828.0,784.97,9.28,7.86 -2016-03-13 14:00:00,778.0,681.55,9.43,8.33 -2016-03-13 15:00:00,764.0,410.36,9.59,8.8 -2016-03-13 16:00:00,752.0,292.43,9.42,9.23 -2016-03-13 17:00:00,772.0,101.23,9.26,9.66 -2016-03-13 18:00:00,854.0,0.0,9.1,10.08 -2016-03-13 19:00:00,962.0,0.0,8.92,10.18 -2016-03-13 20:00:00,1060.0,0.0,8.74,10.28 -2016-03-13 21:00:00,1177.0,0.0,8.57,10.39 -2016-03-13 22:00:00,1397.0,0.0,8.51,10.12 -2016-03-13 23:00:00,1291.0,0.0,8.46,9.85 -2016-03-14 00:00:00,1134.0,0.0,8.4,9.59 -2016-03-14 01:00:00,1019.0,0.0,8.43,9.34 -2016-03-14 02:00:00,950.0,0.0,8.47,9.09 -2016-03-14 03:00:00,934.0,0.0,8.5,8.84 -2016-03-14 04:00:00,932.0,0.0,8.51,9.09 -2016-03-14 05:00:00,942.0,0.0,8.51,9.35 -2016-03-14 06:00:00,955.0,0.0,8.51,9.6 -2016-03-14 07:00:00,1010.0,4.22,8.53,9.94 -2016-03-14 08:00:00,1067.0,42.88,8.56,10.27 -2016-03-14 09:00:00,1101.0,36.84,8.59,10.61 -2016-03-14 10:00:00,1087.0,78.44,8.78,10.58 -2016-03-14 11:00:00,1033.0,271.25,8.97,10.56 -2016-03-14 12:00:00,993.0,450.77,9.16,10.54 -2016-03-14 13:00:00,926.0,253.63,9.46,10.62 -2016-03-14 14:00:00,882.0,455.58,9.76,10.69 -2016-03-14 15:00:00,852.0,293.84,10.06,10.77 -2016-03-14 16:00:00,856.0,221.51,10.14,10.92 -2016-03-14 17:00:00,857.0,69.25,10.23,11.07 -2016-03-14 18:00:00,935.0,0.0,10.32,11.21 -2016-03-14 19:00:00,1063.0,0.0,10.31,11.06 -2016-03-14 20:00:00,1136.0,0.0,10.3,10.9 -2016-03-14 21:00:00,1248.0,0.0,10.3,10.74 -2016-03-14 22:00:00,1442.0,0.0,10.25,10.48 -2016-03-14 23:00:00,1299.0,0.0,10.2,10.22 -2016-03-15 00:00:00,1127.0,0.0,10.15,9.96 -2016-03-15 01:00:00,1039.0,0.0,9.68,9.21 -2016-03-15 02:00:00,966.0,0.0,9.22,8.47 -2016-03-15 03:00:00,946.0,0.0,8.75,7.72 -2016-03-15 04:00:00,967.0,0.0,8.61,7.72 -2016-03-15 05:00:00,960.0,0.0,8.47,7.71 -2016-03-15 06:00:00,972.0,0.0,8.33,7.71 -2016-03-15 07:00:00,1048.0,15.0,8.31,7.83 -2016-03-15 08:00:00,1123.0,84.05,8.29,7.95 -2016-03-15 09:00:00,1145.0,66.04,8.28,8.07 -2016-03-15 10:00:00,1074.0,83.3,8.59,8.01 -2016-03-15 11:00:00,1006.0,100.71,8.9,7.95 -2016-03-15 12:00:00,967.0,81.46,9.22,7.89 -2016-03-15 13:00:00,862.0,226.11,9.56,8.04 -2016-03-15 14:00:00,823.0,374.86,9.91,8.18 -2016-03-15 15:00:00,807.0,293.8,10.26,8.33 -2016-03-15 16:00:00,801.0,170.31,10.17,8.2 -2016-03-15 17:00:00,849.0,88.84,10.08,8.07 -2016-03-15 18:00:00,898.0,0.0,9.99,7.94 -2016-03-15 19:00:00,1032.0,0.0,9.75,7.84 -2016-03-15 20:00:00,1087.0,0.0,9.51,7.73 -2016-03-15 21:00:00,1176.0,0.0,9.27,7.63 -2016-03-15 22:00:00,1398.0,0.0,9.1,7.75 -2016-03-15 23:00:00,1255.0,0.0,8.93,7.88 -2016-03-16 00:00:00,1095.0,0.0,8.76,8.0 -2016-03-16 01:00:00,1018.0,0.0,8.81,8.43 -2016-03-16 02:00:00,945.0,0.0,8.86,8.86 -2016-03-16 03:00:00,899.0,0.0,8.91,9.28 -2016-03-16 04:00:00,912.0,0.0,8.8,9.39 -2016-03-16 05:00:00,895.0,0.0,8.69,9.49 -2016-03-16 06:00:00,888.0,0.0,8.58,9.6 -2016-03-16 07:00:00,969.0,1.92,8.57,9.75 -2016-03-16 08:00:00,1052.0,16.82,8.57,9.89 -2016-03-16 09:00:00,1165.0,62.87,8.57,10.04 -2016-03-16 10:00:00,1148.0,42.08,8.59,9.86 -2016-03-16 11:00:00,1099.0,249.7,8.61,9.67 -2016-03-16 12:00:00,995.0,193.91,8.64,9.49 -2016-03-16 13:00:00,880.0,230.35,8.87,9.36 -2016-03-16 14:00:00,839.0,35.25,9.11,9.23 -2016-03-16 15:00:00,825.0,221.03,9.35,9.1 -2016-03-16 16:00:00,843.0,412.59,9.21,9.25 -2016-03-16 17:00:00,861.0,189.3,9.07,9.39 -2016-03-16 18:00:00,941.0,0.0,8.94,9.53 -2016-03-16 19:00:00,1056.0,0.0,8.78,9.47 -2016-03-16 20:00:00,1124.0,0.0,8.62,9.41 -2016-03-16 21:00:00,1220.0,0.0,8.46,9.35 -2016-03-16 22:00:00,1429.0,0.0,8.38,9.27 -2016-03-16 23:00:00,1276.0,0.0,8.31,9.19 -2016-03-17 00:00:00,1121.0,0.0,8.24,9.1 -2016-03-17 01:00:00,1051.0,0.0,8.28,8.93 -2016-03-17 02:00:00,971.0,0.0,8.33,8.76 -2016-03-17 03:00:00,952.0,0.0,8.37,8.59 -2016-03-17 04:00:00,956.0,0.0,8.29,8.66 -2016-03-17 05:00:00,972.0,0.0,8.2,8.72 -2016-03-17 06:00:00,981.0,0.0,8.12,8.79 -2016-03-17 07:00:00,1026.0,17.01,8.17,8.81 -2016-03-17 08:00:00,1077.0,225.57,8.22,8.83 -2016-03-17 09:00:00,1090.0,142.33,8.28,8.86 -2016-03-17 10:00:00,1047.0,69.86,8.48,8.85 -2016-03-17 11:00:00,980.0,89.68,8.68,8.85 -2016-03-17 12:00:00,964.0,167.03,8.88,8.84 -2016-03-17 13:00:00,878.0,575.72,9.23,9.1 -2016-03-17 14:00:00,845.0,385.77,9.59,9.36 -2016-03-17 15:00:00,855.0,279.11,9.95,9.61 -2016-03-17 16:00:00,832.0,178.64,10.11,9.35 -2016-03-17 17:00:00,854.0,91.04,10.28,9.08 -2016-03-17 18:00:00,918.0,0.0,10.45,8.81 -2016-03-17 19:00:00,1033.0,0.0,10.51,8.37 -2016-03-17 20:00:00,1089.0,0.0,10.57,7.92 -2016-03-17 21:00:00,1144.0,0.0,10.63,7.48 -2016-03-17 22:00:00,1355.0,0.0,10.6,7.09 -2016-03-17 23:00:00,1215.0,0.0,10.58,6.69 -2016-03-18 00:00:00,1074.0,0.0,10.56,6.3 -2016-03-18 01:00:00,1007.0,0.0,10.32,6.54 -2016-03-18 02:00:00,902.0,0.0,10.08,6.77 -2016-03-18 03:00:00,890.0,0.0,9.84,7.01 -2016-03-18 04:00:00,903.0,0.0,9.81,7.02 -2016-03-18 05:00:00,902.0,0.0,9.79,7.03 -2016-03-18 06:00:00,920.0,0.0,9.76,7.05 -2016-03-18 07:00:00,1005.0,24.82,9.84,7.13 -2016-03-18 08:00:00,1008.0,202.48,9.91,7.2 -2016-03-18 09:00:00,1004.0,361.53,9.99,7.28 -2016-03-18 10:00:00,937.0,458.82,10.18,7.01 -2016-03-18 11:00:00,914.0,694.77,10.37,6.74 -2016-03-18 12:00:00,863.0,787.39,10.57,6.47 -2016-03-18 13:00:00,829.0,764.53,10.34,7.16 -2016-03-18 14:00:00,778.0,669.62,10.11,7.86 -2016-03-18 15:00:00,759.0,541.4,9.88,8.55 -2016-03-18 16:00:00,778.0,288.88,9.7,8.73 -2016-03-18 17:00:00,797.0,175.93,9.53,8.9 -2016-03-18 18:00:00,846.0,4.98,9.36,9.08 -2016-03-18 19:00:00,971.0,0.0,9.05,8.9 -2016-03-18 20:00:00,1117.0,0.0,8.75,8.72 -2016-03-18 21:00:00,1229.0,0.0,8.45,8.54 -2016-03-18 22:00:00,1432.0,0.0,8.27,8.44 -2016-03-18 23:00:00,1263.0,0.0,8.1,8.34 -2016-03-19 00:00:00,1098.0,0.0,7.93,8.25 -2016-03-19 01:00:00,1032.0,0.0,7.46,8.65 -2016-03-19 02:00:00,966.0,0.0,6.99,9.05 -2016-03-19 03:00:00,939.0,0.0,6.51,9.45 -2016-03-19 04:00:00,965.0,0.0,6.61,9.96 -2016-03-19 05:00:00,982.0,0.0,6.71,10.48 -2016-03-19 06:00:00,985.0,0.0,6.81,10.99 -2016-03-19 07:00:00,1032.0,8.77,7.11,10.81 -2016-03-19 08:00:00,1070.0,26.75,7.41,10.63 -2016-03-19 09:00:00,1088.0,31.81,7.71,10.44 -2016-03-19 10:00:00,1114.0,33.2,8.16,10.25 -2016-03-19 11:00:00,1135.0,42.08,8.61,10.06 -2016-03-19 12:00:00,1191.0,48.81,9.07,9.88 -2016-03-19 13:00:00,1144.0,60.24,9.54,9.94 -2016-03-19 14:00:00,1116.0,58.55,10.02,10.0 -2016-03-19 15:00:00,1036.0,57.64,10.5,10.07 -2016-03-19 16:00:00,1023.0,59.95,10.53,10.1 -2016-03-19 17:00:00,1041.0,41.36,10.56,10.12 -2016-03-19 18:00:00,1118.0,4.46,10.59,10.15 -2016-03-19 19:00:00,1189.0,0.0,10.43,10.14 -2016-03-19 20:00:00,1241.0,0.0,10.27,10.12 -2016-03-19 21:00:00,1358.0,0.0,10.12,10.11 -2016-03-19 22:00:00,1542.0,0.0,9.98,9.94 -2016-03-19 23:00:00,1393.0,0.0,9.84,9.78 -2016-03-20 00:00:00,1243.0,0.0,9.7,9.61 -2016-03-20 01:00:00,1129.0,0.0,9.55,9.88 -2016-03-20 02:00:00,1038.0,0.0,9.4,10.14 -2016-03-20 03:00:00,999.0,0.0,9.24,10.4 -2016-03-20 04:00:00,1005.0,0.0,9.12,10.38 -2016-03-20 05:00:00,1006.0,0.0,8.99,10.35 -2016-03-20 06:00:00,1011.0,0.0,8.87,10.33 -2016-03-20 07:00:00,1027.0,7.25,8.88,10.49 -2016-03-20 08:00:00,1106.0,22.29,8.89,10.65 -2016-03-20 09:00:00,1191.0,29.43,8.9,10.81 -2016-03-20 10:00:00,1255.0,41.23,9.1,10.79 -2016-03-20 11:00:00,1245.0,51.06,9.31,10.77 -2016-03-20 12:00:00,1238.0,63.37,9.52,10.74 -2016-03-20 13:00:00,1159.0,62.63,9.37,10.57 -2016-03-20 14:00:00,1108.0,49.55,9.22,10.4 -2016-03-20 15:00:00,1087.0,84.0,9.07,10.23 -2016-03-20 16:00:00,1086.0,59.63,9.0,10.28 -2016-03-20 17:00:00,1064.0,36.36,8.93,10.32 -2016-03-20 18:00:00,1101.0,0.91,8.87,10.36 -2016-03-20 19:00:00,1159.0,0.0,8.75,10.39 -2016-03-20 20:00:00,1199.0,0.0,8.64,10.41 -2016-03-20 21:00:00,1322.0,0.0,8.53,10.44 -2016-03-20 22:00:00,1487.0,0.0,8.47,10.08 -2016-03-20 23:00:00,1364.0,0.0,8.42,9.71 -2016-03-21 00:00:00,1213.0,0.0,8.37,9.35 -2016-03-21 01:00:00,1076.0,0.0,8.06,9.14 -2016-03-21 02:00:00,1011.0,0.0,7.75,8.94 -2016-03-21 03:00:00,975.0,0.0,7.43,8.73 -2016-03-21 04:00:00,970.0,0.0,7.45,8.88 -2016-03-21 05:00:00,982.0,0.0,7.46,9.03 -2016-03-21 06:00:00,996.0,0.0,7.47,9.19 -2016-03-21 07:00:00,1045.0,36.34,7.68,9.01 -2016-03-21 08:00:00,1103.0,276.56,7.89,8.84 -2016-03-21 09:00:00,1207.0,485.65,8.1,8.66 -2016-03-21 10:00:00,1200.0,669.06,8.43,8.41 -2016-03-21 11:00:00,1191.0,808.11,8.77,8.17 -2016-03-21 12:00:00,1206.0,879.72,9.11,7.92 -2016-03-21 13:00:00,1125.0,851.54,9.28,7.86 -2016-03-21 14:00:00,1017.0,760.18,9.45,7.8 -2016-03-21 15:00:00,973.0,621.64,9.62,7.74 -2016-03-21 16:00:00,945.0,430.74,9.67,7.75 -2016-03-21 17:00:00,919.0,201.96,9.72,7.77 -2016-03-21 18:00:00,962.0,10.47,9.77,7.78 -2016-03-21 19:00:00,1027.0,0.0,9.66,7.39 -2016-03-21 20:00:00,1130.0,0.0,9.56,7.0 -2016-03-21 21:00:00,1240.0,0.0,9.46,6.61 -2016-03-21 22:00:00,1438.0,0.0,9.3,6.06 -2016-03-21 23:00:00,1294.0,0.0,9.15,5.51 -2016-03-22 00:00:00,1137.0,0.0,9.0,4.97 -2016-03-22 01:00:00,1058.0,0.0,8.71,4.63 -2016-03-22 02:00:00,999.0,0.0,8.42,4.3 -2016-03-22 03:00:00,962.0,0.0,8.12,3.97 -2016-03-22 04:00:00,971.0,0.0,8.02,3.67 -2016-03-22 05:00:00,968.0,0.0,7.91,3.37 -2016-03-22 06:00:00,961.0,0.0,7.81,3.06 -2016-03-22 07:00:00,1017.0,32.35,7.84,3.11 -2016-03-22 08:00:00,1068.0,38.89,7.87,3.15 -2016-03-22 09:00:00,1068.0,38.35,7.9,3.2 -2016-03-22 10:00:00,1079.0,49.64,8.05,3.14 -2016-03-22 11:00:00,1071.0,51.13,8.21,3.08 -2016-03-22 12:00:00,1048.0,75.14,8.37,3.02 -2016-03-22 13:00:00,932.0,272.8,8.46,3.49 -2016-03-22 14:00:00,847.0,691.46,8.55,3.95 -2016-03-22 15:00:00,820.0,435.68,8.65,4.41 -2016-03-22 16:00:00,828.0,213.76,8.68,4.52 -2016-03-22 17:00:00,837.0,44.49,8.72,4.63 -2016-03-22 18:00:00,859.0,2.65,8.76,4.73 -2016-03-22 19:00:00,949.0,0.0,8.71,4.47 -2016-03-22 20:00:00,1040.0,0.0,8.66,4.22 -2016-03-22 21:00:00,1161.0,0.0,8.61,3.96 -2016-03-22 22:00:00,1367.0,0.0,8.49,3.8 -2016-03-22 23:00:00,1206.0,0.0,8.37,3.64 -2016-03-23 00:00:00,1055.0,0.0,8.25,3.48 -2016-03-23 01:00:00,974.0,0.0,8.2,3.6 -2016-03-23 02:00:00,901.0,0.0,8.15,3.71 -2016-03-23 03:00:00,864.0,0.0,8.1,3.83 -2016-03-23 04:00:00,885.0,0.0,8.12,4.1 -2016-03-23 05:00:00,869.0,0.0,8.15,4.37 -2016-03-23 06:00:00,867.0,0.0,8.17,4.63 -2016-03-23 07:00:00,913.0,13.82,8.31,4.65 -2016-03-23 08:00:00,966.0,59.03,8.44,4.67 -2016-03-23 09:00:00,1012.0,40.49,8.58,4.69 -2016-03-23 10:00:00,1008.0,65.71,8.72,4.04 -2016-03-23 11:00:00,985.0,85.22,8.86,3.38 -2016-03-23 12:00:00,958.0,174.88,9.01,2.73 -2016-03-23 13:00:00,922.0,275.61,9.01,3.27 -2016-03-23 14:00:00,844.0,741.81,9.01,3.82 -2016-03-23 15:00:00,804.0,622.2,9.02,4.36 -2016-03-23 16:00:00,771.0,424.47,9.0,4.19 -2016-03-23 17:00:00,772.0,139.68,8.98,4.03 -2016-03-23 18:00:00,832.0,11.85,8.96,3.86 -2016-03-23 19:00:00,946.0,0.0,8.94,3.75 -2016-03-23 20:00:00,1000.0,0.0,8.92,3.63 -2016-03-23 21:00:00,1101.0,0.0,8.9,3.52 -2016-03-23 22:00:00,1290.0,0.0,8.91,3.72 -2016-03-23 23:00:00,1135.0,0.0,8.93,3.93 -2016-03-24 00:00:00,1018.0,0.0,8.95,4.14 -2016-03-24 01:00:00,933.0,0.0,8.8,4.53 -2016-03-24 02:00:00,869.0,0.0,8.65,4.92 -2016-03-24 03:00:00,838.0,0.0,8.5,5.31 -2016-03-24 04:00:00,848.0,0.0,8.65,5.84 -2016-03-24 05:00:00,839.0,0.0,8.81,6.37 -2016-03-24 06:00:00,854.0,0.0,8.96,6.9 -2016-03-24 07:00:00,914.0,19.79,9.19,7.49 -2016-03-24 08:00:00,992.0,39.64,9.43,8.09 -2016-03-24 09:00:00,1014.0,86.07,9.67,8.69 -2016-03-24 10:00:00,989.0,100.28,9.74,9.51 -2016-03-24 11:00:00,1001.0,108.43,9.81,10.33 -2016-03-24 12:00:00,1015.0,135.82,9.89,11.14 -2016-03-24 13:00:00,933.0,175.3,9.64,11.66 -2016-03-24 14:00:00,917.0,67.35,9.39,12.17 -2016-03-24 15:00:00,931.0,93.06,9.15,12.68 -2016-03-24 16:00:00,917.0,30.85,9.2,13.21 -2016-03-24 17:00:00,947.0,24.37,9.25,13.75 -2016-03-24 18:00:00,1037.0,4.88,9.31,14.29 -2016-03-24 19:00:00,1099.0,0.0,9.52,13.27 -2016-03-24 20:00:00,1123.0,0.0,9.73,12.25 -2016-03-24 21:00:00,1219.0,0.0,9.95,11.23 -2016-03-24 22:00:00,1404.0,0.0,10.05,10.12 -2016-03-24 23:00:00,1249.0,0.0,10.16,9.01 -2016-03-25 00:00:00,1088.0,0.0,10.26,7.9 -2016-03-25 01:00:00,992.0,0.0,10.3,7.72 -2016-03-25 02:00:00,909.0,0.0,10.35,7.54 -2016-03-25 03:00:00,859.0,0.0,10.39,7.35 -2016-03-25 04:00:00,874.0,0.0,10.18,7.34 -2016-03-25 05:00:00,862.0,0.0,9.96,7.32 -2016-03-25 06:00:00,863.0,0.0,9.75,7.31 -2016-03-25 07:00:00,919.0,73.0,9.45,5.87 -2016-03-25 08:00:00,943.0,298.83,9.15,4.42 -2016-03-25 09:00:00,925.0,496.92,8.86,2.98 -2016-03-25 10:00:00,864.0,663.96,8.7,2.74 -2016-03-25 11:00:00,835.0,747.76,8.55,2.51 -2016-03-25 12:00:00,829.0,777.82,8.4,2.28 -2016-03-25 13:00:00,771.0,812.51,8.88,3.08 -2016-03-25 14:00:00,717.0,728.42,9.36,3.88 -2016-03-25 15:00:00,717.0,517.65,9.85,4.68 -2016-03-25 16:00:00,711.0,350.12,9.77,5.56 -2016-03-25 17:00:00,709.0,137.75,9.69,6.45 -2016-03-25 18:00:00,821.0,13.19,9.61,7.34 -2016-03-25 19:00:00,901.0,0.0,9.75,8.03 -2016-03-25 20:00:00,1034.0,0.0,9.9,8.73 -2016-03-25 21:00:00,1197.0,0.0,10.05,9.42 -2016-03-25 22:00:00,1398.0,0.0,10.24,9.89 -2016-03-25 23:00:00,1227.0,0.0,10.43,10.37 -2016-03-26 00:00:00,1054.0,0.0,10.62,10.84 -2016-03-26 01:00:00,949.0,0.0,10.77,11.11 -2016-03-26 02:00:00,865.0,0.0,10.92,11.38 -2016-03-26 03:00:00,837.0,0.0,11.07,11.66 -2016-03-26 04:00:00,839.0,0.0,11.19,12.0 -2016-03-26 05:00:00,840.0,0.0,11.3,12.34 -2016-03-26 06:00:00,842.0,0.0,11.41,12.69 -2016-03-26 07:00:00,878.0,6.0,11.41,13.26 -2016-03-26 08:00:00,968.0,71.1,11.42,13.82 -2016-03-26 09:00:00,1041.0,66.72,11.43,14.39 -2016-03-26 10:00:00,1081.0,56.69,11.39,14.83 -2016-03-26 11:00:00,1121.0,43.04,11.36,15.27 -2016-03-26 12:00:00,1154.0,52.89,11.33,15.71 -2016-03-26 13:00:00,1117.0,66.28,10.43,15.47 -2016-03-26 14:00:00,1124.0,41.93,9.53,15.23 -2016-03-26 15:00:00,1160.0,51.28,8.64,14.99 -2016-03-26 16:00:00,1163.0,160.54,8.52,14.46 -2016-03-26 17:00:00,1216.0,182.38,8.4,13.92 -2016-03-26 18:00:00,1267.0,17.21,8.29,13.38 -2016-03-26 19:00:00,1278.0,0.0,8.68,13.6 -2016-03-26 20:00:00,1352.0,0.0,9.08,13.82 -2016-03-26 21:00:00,1412.0,0.0,9.48,14.04 -2016-03-26 22:00:00,1586.0,0.0,9.22,14.17 -2016-03-26 23:00:00,1461.0,0.0,8.97,14.29 -2016-03-27 00:00:00,1285.0,0.0,8.72,14.41 -2016-03-27 01:00:00,1140.0,0.0,8.82,14.59 -2016-03-27 02:00:00,1116.0,0.0,8.93,14.77 -2016-03-27 03:00:00,1092.0,0.0,9.03,14.95 -2016-03-27 04:00:00,1066.0,0.0,9.1,14.97 -2016-03-27 05:00:00,1057.0,0.0,9.17,15.0 -2016-03-27 06:00:00,1061.0,0.0,9.24,15.02 -2016-03-27 07:00:00,1079.0,59.6,9.38,14.84 -2016-03-27 08:00:00,1181.0,9.02,9.52,14.66 -2016-03-27 09:00:00,1321.0,518.18,9.67,14.48 -2016-03-27 10:00:00,1370.0,654.01,9.78,14.62 -2016-03-27 11:00:00,1384.0,821.6,9.89,14.76 -2016-03-27 12:00:00,1249.0,863.32,10.0,14.9 -2016-03-27 13:00:00,1062.0,344.43,9.74,14.73 -2016-03-27 14:00:00,1000.0,169.16,9.48,14.57 -2016-03-27 15:00:00,960.0,65.09,9.23,14.4 -2016-03-27 16:00:00,967.0,68.21,9.3,14.81 -2016-03-27 17:00:00,1018.0,25.78,9.37,15.23 -2016-03-27 18:00:00,1135.0,9.63,9.45,15.64 -2016-03-27 19:00:00,1223.0,0.0,9.91,17.02 -2016-03-27 20:00:00,1285.0,0.0,10.38,18.39 -2016-03-27 21:00:00,1281.0,0.0,10.85,19.77 -2016-03-27 22:00:00,1510.0,0.0,10.95,19.38 -2016-03-27 23:00:00,1477.0,0.0,11.05,19.0 -2016-03-28 00:00:00,1299.0,0.0,11.15,18.62 -2016-03-28 01:00:00,1172.0,0.0,10.19,18.64 -2016-03-28 02:00:00,1072.0,0.0,9.24,18.66 -2016-03-28 03:00:00,1001.0,0.0,8.28,18.68 -2016-03-28 04:00:00,1009.0,0.0,8.49,17.26 -2016-03-28 05:00:00,998.0,0.0,8.7,15.83 -2016-03-28 06:00:00,993.0,0.0,8.91,14.41 -2016-03-28 07:00:00,1012.0,74.3,8.95,13.59 -2016-03-28 08:00:00,1111.0,21.59,9.0,12.76 -2016-03-28 09:00:00,1193.0,271.53,9.05,11.93 -2016-03-28 10:00:00,1218.0,691.94,8.82,12.97 -2016-03-28 11:00:00,1230.0,287.74,8.59,14.0 -2016-03-28 12:00:00,1145.0,262.37,8.36,15.03 -2016-03-28 13:00:00,1060.0,644.62,8.62,15.35 -2016-03-28 14:00:00,997.0,508.71,8.88,15.66 -2016-03-28 15:00:00,949.0,257.49,9.14,15.97 -2016-03-28 16:00:00,915.0,387.41,9.13,16.08 -2016-03-28 17:00:00,884.0,215.46,9.12,16.18 -2016-03-28 18:00:00,946.0,19.72,9.12,16.29 -2016-03-28 19:00:00,985.0,0.0,9.18,15.71 -2016-03-28 20:00:00,1015.0,0.0,9.24,15.13 -2016-03-28 21:00:00,1081.0,0.0,9.31,14.55 -2016-03-28 22:00:00,1322.0,0.0,9.21,14.06 -2016-03-28 23:00:00,1362.0,0.0,9.12,13.57 -2016-03-29 00:00:00,1221.0,0.0,9.03,13.08 -2016-03-29 01:00:00,1103.0,0.0,8.98,12.83 -2016-03-29 02:00:00,1007.0,0.0,8.93,12.59 -2016-03-29 03:00:00,971.0,0.0,8.88,12.34 -2016-03-29 04:00:00,955.0,0.0,8.89,11.6 -2016-03-29 05:00:00,940.0,0.0,8.89,10.86 -2016-03-29 06:00:00,945.0,0.0,8.89,10.12 -2016-03-29 07:00:00,993.0,81.92,9.1,10.09 -2016-03-29 08:00:00,1069.0,77.55,9.32,10.05 -2016-03-29 09:00:00,1119.0,256.58,9.54,10.01 -2016-03-29 10:00:00,1066.0,68.83,9.55,10.68 -2016-03-29 11:00:00,1026.0,708.59,9.56,11.36 -2016-03-29 12:00:00,1039.0,85.44,9.58,12.03 -2016-03-29 13:00:00,961.0,736.73,9.47,12.21 -2016-03-29 14:00:00,993.0,477.62,9.36,12.39 -2016-03-29 15:00:00,946.0,285.94,9.26,12.57 -2016-03-29 16:00:00,896.0,239.71,9.27,12.16 -2016-03-29 17:00:00,863.0,77.9,9.28,11.75 -2016-03-29 18:00:00,899.0,12.76,9.3,11.34 -2016-03-29 19:00:00,957.0,0.0,9.31,11.03 -2016-03-29 20:00:00,978.0,0.0,9.32,10.71 -2016-03-29 21:00:00,1027.0,0.0,9.34,10.4 -2016-03-29 22:00:00,1268.0,0.0,9.29,10.26 -2016-03-29 23:00:00,1272.0,0.0,9.25,10.12 -2016-03-30 00:00:00,1107.0,0.0,9.21,9.99 -2016-03-30 01:00:00,1041.0,0.0,9.28,9.52 -2016-03-30 02:00:00,960.0,0.0,9.36,9.05 -2016-03-30 03:00:00,892.0,0.0,9.43,8.58 -2016-03-30 04:00:00,898.0,0.0,9.32,7.81 -2016-03-30 05:00:00,884.0,0.0,9.22,7.04 -2016-03-30 06:00:00,877.0,0.0,9.11,6.28 -2016-03-30 07:00:00,934.0,49.34,8.93,5.77 -2016-03-30 08:00:00,1016.0,142.18,8.74,5.26 -2016-03-30 09:00:00,1099.0,229.89,8.56,4.76 -2016-03-30 10:00:00,1110.0,531.77,8.49,4.4 -2016-03-30 11:00:00,1093.0,623.69,8.42,4.03 -2016-03-30 12:00:00,1082.0,534.35,8.36,3.67 -2016-03-30 13:00:00,982.0,563.01,8.38,3.88 -2016-03-30 14:00:00,950.0,563.03,8.41,4.09 -2016-03-30 15:00:00,901.0,244.97,8.44,4.3 -2016-03-30 16:00:00,856.0,165.89,8.37,4.25 -2016-03-30 17:00:00,845.0,79.7,8.3,4.2 -2016-03-30 18:00:00,890.0,15.27,8.24,4.15 -2016-03-30 19:00:00,905.0,0.0,8.26,4.46 -2016-03-30 20:00:00,963.0,0.0,8.28,4.78 -2016-03-30 21:00:00,985.0,0.0,8.3,5.09 -2016-03-30 22:00:00,1270.0,0.0,8.25,5.6 -2016-03-30 23:00:00,1278.0,0.0,8.2,6.1 -2016-03-31 00:00:00,1105.0,0.0,8.15,6.61 -2016-03-31 01:00:00,1001.0,0.0,8.24,6.93 -2016-03-31 02:00:00,924.0,0.0,8.33,7.26 -2016-03-31 03:00:00,871.0,0.0,8.41,7.59 -2016-03-31 04:00:00,893.0,0.0,8.44,7.55 -2016-03-31 05:00:00,866.0,0.0,8.47,7.52 -2016-03-31 06:00:00,874.0,0.0,8.5,7.49 -2016-03-31 07:00:00,946.0,89.28,8.6,7.44 -2016-03-31 08:00:00,1043.0,284.72,8.71,7.39 -2016-03-31 09:00:00,1057.0,406.62,8.82,7.34 -2016-03-31 10:00:00,1036.0,693.43,8.93,7.32 -2016-03-31 11:00:00,975.0,705.6,9.04,7.31 -2016-03-31 12:00:00,941.0,854.51,9.16,7.3 -2016-03-31 13:00:00,850.0,841.85,9.11,6.97 -2016-03-31 14:00:00,782.0,777.42,9.06,6.65 -2016-03-31 15:00:00,758.0,654.13,9.01,6.33 -2016-03-31 16:00:00,742.0,464.15,9.01,6.23 -2016-03-31 17:00:00,736.0,226.48,9.01,6.13 -2016-03-31 18:00:00,776.0,20.14,9.01,6.03 -2016-03-31 19:00:00,872.0,0.0,8.92,5.0 -2016-03-31 20:00:00,867.0,0.0,8.84,3.98 -2016-03-31 21:00:00,926.0,0.0,8.76,2.95 -2016-03-31 22:00:00,1159.0,0.0,8.65,2.63 -2016-03-31 23:00:00,1170.0,0.0,8.55,2.3 -2016-04-01 00:00:00,1021.0,0.0,8.45,1.97 -2016-04-01 01:00:00,921.0,0.0,8.4,3.03 -2016-04-01 02:00:00,843.0,0.0,8.35,4.09 -2016-04-01 03:00:00,795.0,0.0,8.31,5.14 -2016-04-01 04:00:00,816.0,0.0,8.41,5.98 -2016-04-01 05:00:00,813.0,0.0,8.51,6.81 -2016-04-01 06:00:00,834.0,0.0,8.61,7.64 -2016-04-01 07:00:00,891.0,12.18,8.81,8.19 -2016-04-01 08:00:00,963.0,59.59,9.0,8.74 -2016-04-01 09:00:00,1009.0,45.73,9.19,9.28 -2016-04-01 10:00:00,966.0,121.48,9.37,9.56 -2016-04-01 11:00:00,947.0,359.47,9.54,9.84 -2016-04-01 12:00:00,951.0,139.89,9.72,10.12 -2016-04-01 13:00:00,883.0,169.67,9.76,10.46 -2016-04-01 14:00:00,835.0,206.63,9.8,10.8 -2016-04-01 15:00:00,825.0,204.24,9.85,11.13 -2016-04-01 16:00:00,827.0,75.0,9.89,11.0 -2016-04-01 17:00:00,824.0,83.75,9.94,10.87 -2016-04-01 18:00:00,904.0,0.0,9.98,10.74 -2016-04-01 19:00:00,907.0,0.0,10.02,10.92 -2016-04-01 20:00:00,984.0,0.0,10.06,11.09 -2016-04-01 21:00:00,1024.0,0.0,10.09,11.27 -2016-04-01 22:00:00,1298.0,0.0,10.02,11.14 -2016-04-01 23:00:00,1294.0,0.0,9.95,11.01 -2016-04-02 00:00:00,1146.0,0.0,9.88,10.88 -2016-04-02 01:00:00,1039.0,0.0,9.74,10.57 -2016-04-02 02:00:00,918.0,0.0,9.61,10.26 -2016-04-02 03:00:00,879.0,0.0,9.48,9.94 -2016-04-02 04:00:00,873.0,0.0,9.49,10.02 -2016-04-02 05:00:00,858.0,0.0,9.5,10.09 -2016-04-02 06:00:00,877.0,0.0,9.51,10.17 -2016-04-02 07:00:00,901.0,17.41,9.52,10.37 -2016-04-02 08:00:00,990.0,30.79,9.54,10.58 -2016-04-02 09:00:00,1043.0,51.76,9.56,10.79 -2016-04-02 10:00:00,1062.0,68.02,9.61,10.33 -2016-04-02 11:00:00,1081.0,102.06,9.67,9.87 -2016-04-02 12:00:00,1040.0,131.74,9.73,9.41 -2016-04-02 13:00:00,985.0,129.28,9.72,8.97 -2016-04-02 14:00:00,976.0,80.5,9.71,8.52 -2016-04-02 15:00:00,963.0,82.86,9.71,8.08 -2016-04-02 16:00:00,980.0,48.7,9.39,7.95 -2016-04-02 17:00:00,1007.0,32.3,9.07,7.82 -2016-04-02 18:00:00,1056.0,0.27,8.74,7.68 -2016-04-02 19:00:00,1146.0,0.0,8.83,7.99 -2016-04-02 20:00:00,1177.0,0.0,8.92,8.3 -2016-04-02 21:00:00,1198.0,0.0,9.01,8.61 -2016-04-02 22:00:00,1409.0,0.0,9.21,9.25 -2016-04-02 23:00:00,1400.0,0.0,9.4,9.88 -2016-04-03 00:00:00,1227.0,0.0,9.6,10.52 -2016-04-03 01:00:00,1133.0,0.0,9.81,10.56 -2016-04-03 02:00:00,1038.0,0.0,10.02,10.59 -2016-04-03 03:00:00,953.0,0.0,10.23,10.62 -2016-04-03 04:00:00,958.0,0.0,10.27,10.18 -2016-04-03 05:00:00,941.0,0.0,10.32,9.75 -2016-04-03 06:00:00,930.0,0.0,10.36,9.31 -2016-04-03 07:00:00,959.0,48.69,10.19,9.29 -2016-04-03 08:00:00,1033.0,250.71,10.02,9.26 -2016-04-03 09:00:00,1121.0,482.05,9.85,9.24 -2016-04-03 10:00:00,1174.0,715.53,9.91,9.24 -2016-04-03 11:00:00,1179.0,504.3,9.97,9.24 -2016-04-03 12:00:00,1085.0,848.53,10.03,9.24 -2016-04-03 13:00:00,921.0,834.86,10.04,9.12 -2016-04-03 14:00:00,840.0,710.47,10.05,9.0 -2016-04-03 15:00:00,776.0,583.65,10.06,8.88 -2016-04-03 16:00:00,769.0,294.26,9.89,8.7 -2016-04-03 17:00:00,747.0,65.59,9.73,8.51 -2016-04-03 18:00:00,807.0,25.35,9.56,8.33 -2016-04-03 19:00:00,889.0,0.0,9.6,8.02 -2016-04-03 20:00:00,938.0,0.0,9.64,7.71 -2016-04-03 21:00:00,1017.0,0.0,9.67,7.41 -2016-04-03 22:00:00,1284.0,0.0,9.48,7.09 -2016-04-03 23:00:00,1296.0,0.0,9.29,6.78 -2016-04-04 00:00:00,1120.0,0.0,9.1,6.47 -2016-04-04 01:00:00,991.0,0.0,9.27,6.22 -2016-04-04 02:00:00,907.0,0.0,9.44,5.96 -2016-04-04 03:00:00,843.0,0.0,9.61,5.71 -2016-04-04 04:00:00,849.0,0.0,9.61,5.9 -2016-04-04 05:00:00,839.0,0.0,9.61,6.1 -2016-04-04 06:00:00,840.0,0.0,9.61,6.29 -2016-04-04 07:00:00,885.0,48.63,9.67,6.4 -2016-04-04 08:00:00,971.0,238.98,9.73,6.51 -2016-04-04 09:00:00,1039.0,432.46,9.8,6.62 -2016-04-04 10:00:00,1046.0,710.22,9.88,6.73 -2016-04-04 11:00:00,1038.0,808.14,9.96,6.83 -2016-04-04 12:00:00,1027.0,635.45,10.05,6.94 -2016-04-04 13:00:00,931.0,791.79,10.1,6.79 -2016-04-04 14:00:00,856.0,687.25,10.15,6.63 -2016-04-04 15:00:00,789.0,305.8,10.2,6.48 -2016-04-04 16:00:00,821.0,401.13,10.18,6.21 -2016-04-04 17:00:00,831.0,144.95,10.17,5.93 -2016-04-04 18:00:00,921.0,21.48,10.15,5.66 -2016-04-04 19:00:00,997.0,0.0,10.12,5.84 -2016-04-04 20:00:00,1001.0,0.0,10.08,6.02 -2016-04-04 21:00:00,1041.0,0.0,10.04,6.21 -2016-04-04 22:00:00,1299.0,0.0,9.95,6.06 -2016-04-04 23:00:00,1274.0,0.0,9.85,5.92 -2016-04-05 00:00:00,1074.0,0.0,9.76,5.78 -2016-04-05 01:00:00,960.0,0.0,9.83,5.68 -2016-04-05 02:00:00,882.0,0.0,9.9,5.59 -2016-04-05 03:00:00,821.0,0.0,9.97,5.49 -2016-04-05 04:00:00,837.0,0.0,9.95,5.64 -2016-04-05 05:00:00,834.0,0.0,9.94,5.79 -2016-04-05 06:00:00,835.0,0.0,9.92,5.94 -2016-04-05 07:00:00,875.0,62.3,9.95,5.86 -2016-04-05 08:00:00,967.0,275.48,9.99,5.78 -2016-04-05 09:00:00,1025.0,469.87,10.02,5.7 -2016-04-05 10:00:00,995.0,692.93,9.95,5.71 -2016-04-05 11:00:00,960.0,733.45,9.87,5.72 -2016-04-05 12:00:00,928.0,851.68,9.8,5.74 -2016-04-05 13:00:00,839.0,835.35,9.85,6.09 -2016-04-05 14:00:00,764.0,768.21,9.91,6.44 -2016-04-05 15:00:00,723.0,420.87,9.97,6.79 -2016-04-05 16:00:00,686.0,451.65,9.84,6.8 -2016-04-05 17:00:00,714.0,221.49,9.71,6.8 -2016-04-05 18:00:00,803.0,29.58,9.57,6.81 -2016-04-05 19:00:00,871.0,0.0,9.47,7.0 -2016-04-05 20:00:00,929.0,0.0,9.37,7.18 -2016-04-05 21:00:00,944.0,0.0,9.27,7.37 -2016-04-05 22:00:00,1211.0,0.0,9.3,7.74 -2016-04-05 23:00:00,1230.0,0.0,9.32,8.12 -2016-04-06 00:00:00,1059.0,0.0,9.34,8.5 -2016-04-06 01:00:00,932.0,0.0,9.6,9.13 -2016-04-06 02:00:00,823.0,0.0,9.87,9.76 -2016-04-06 03:00:00,769.0,0.0,10.14,10.39 -2016-04-06 04:00:00,768.0,0.0,10.21,10.11 -2016-04-06 05:00:00,769.0,0.0,10.28,9.83 -2016-04-06 06:00:00,779.0,0.0,10.35,9.54 -2016-04-06 07:00:00,826.0,86.12,10.24,9.69 -2016-04-06 08:00:00,920.0,213.09,10.14,9.83 -2016-04-06 09:00:00,1024.0,142.3,10.03,9.97 -2016-04-06 10:00:00,1064.0,472.25,9.84,10.26 -2016-04-06 11:00:00,1067.0,770.19,9.64,10.55 -2016-04-06 12:00:00,1051.0,427.48,9.44,10.84 -2016-04-06 13:00:00,945.0,690.97,9.51,11.13 -2016-04-06 14:00:00,906.0,698.02,9.59,11.41 -2016-04-06 15:00:00,877.0,568.75,9.67,11.7 -2016-04-06 16:00:00,870.0,202.26,9.5,11.8 -2016-04-06 17:00:00,871.0,132.22,9.33,11.9 -2016-04-06 18:00:00,890.0,30.45,9.15,12.0 -2016-04-06 19:00:00,985.0,0.0,8.93,12.02 -2016-04-06 20:00:00,1013.0,0.0,8.71,12.04 -2016-04-06 21:00:00,1067.0,0.0,8.48,12.06 -2016-04-06 22:00:00,1326.0,0.0,8.39,12.14 -2016-04-06 23:00:00,1326.0,0.0,8.29,12.22 -2016-04-07 00:00:00,1173.0,0.0,8.2,12.3 -2016-04-07 01:00:00,1062.0,0.0,8.25,12.3 -2016-04-07 02:00:00,957.0,0.0,8.3,12.3 -2016-04-07 03:00:00,903.0,0.0,8.35,12.3 -2016-04-07 04:00:00,946.0,0.0,8.35,12.18 -2016-04-07 05:00:00,934.0,0.0,8.35,12.06 -2016-04-07 06:00:00,928.0,1.57,8.35,11.94 -2016-04-07 07:00:00,994.0,102.85,8.2,11.5 -2016-04-07 08:00:00,1111.0,331.12,8.05,11.05 -2016-04-07 09:00:00,1212.0,503.6,7.91,10.61 -2016-04-07 10:00:00,1214.0,473.8,8.06,10.5 -2016-04-07 11:00:00,1163.0,692.06,8.21,10.39 -2016-04-07 12:00:00,1151.0,816.07,8.37,10.28 -2016-04-07 13:00:00,1011.0,591.1,8.45,10.33 -2016-04-07 14:00:00,990.0,766.56,8.53,10.38 -2016-04-07 15:00:00,930.0,580.55,8.62,10.43 -2016-04-07 16:00:00,875.0,400.81,8.81,10.46 -2016-04-07 17:00:00,863.0,214.69,9.0,10.49 -2016-04-07 18:00:00,921.0,31.56,9.19,10.52 -2016-04-07 19:00:00,1017.0,0.0,9.32,10.29 -2016-04-07 20:00:00,1036.0,0.0,9.45,10.06 -2016-04-07 21:00:00,1097.0,0.0,9.58,9.82 -2016-04-07 22:00:00,1377.0,0.0,9.58,9.75 -2016-04-07 23:00:00,1403.0,0.0,9.58,9.67 -2016-04-08 00:00:00,1208.0,0.0,9.58,9.6 -2016-04-08 01:00:00,1041.0,0.0,9.63,9.37 -2016-04-08 02:00:00,943.0,0.0,9.68,9.14 -2016-04-08 03:00:00,877.0,0.0,9.73,8.91 -2016-04-08 04:00:00,895.0,0.0,9.63,8.91 -2016-04-08 05:00:00,894.0,0.0,9.53,8.91 -2016-04-08 06:00:00,910.0,0.0,9.43,8.91 -2016-04-08 07:00:00,991.0,10.87,9.39,8.4 -2016-04-08 08:00:00,1051.0,43.4,9.34,7.88 -2016-04-08 09:00:00,1138.0,158.48,9.3,7.37 -2016-04-08 10:00:00,1162.0,130.95,9.27,7.0 -2016-04-08 11:00:00,1133.0,107.64,9.25,6.64 -2016-04-08 12:00:00,1185.0,181.38,9.23,6.28 -2016-04-08 13:00:00,1087.0,109.28,9.19,6.72 -2016-04-08 14:00:00,1025.0,83.03,9.15,7.16 -2016-04-08 15:00:00,998.0,70.45,9.12,7.6 -2016-04-08 16:00:00,950.0,60.27,9.34,8.83 -2016-04-08 17:00:00,924.0,54.82,9.56,10.06 -2016-04-08 18:00:00,1022.0,17.53,9.78,11.3 -2016-04-08 19:00:00,1098.0,0.0,9.88,10.62 -2016-04-08 20:00:00,1173.0,0.0,9.97,9.94 -2016-04-08 21:00:00,1214.0,0.0,10.06,9.26 -2016-04-08 22:00:00,1452.0,0.0,9.75,9.52 -2016-04-08 23:00:00,1458.0,0.0,9.44,9.79 -2016-04-09 00:00:00,1306.0,0.0,9.13,10.06 -2016-04-09 01:00:00,1119.0,0.0,8.85,9.83 -2016-04-09 02:00:00,1006.0,0.0,8.58,9.6 -2016-04-09 03:00:00,957.0,0.0,8.31,9.38 -2016-04-09 04:00:00,949.0,0.0,8.0,9.51 -2016-04-09 05:00:00,945.0,0.0,7.69,9.64 -2016-04-09 06:00:00,964.0,2.9,7.38,9.77 -2016-04-09 07:00:00,1025.0,113.3,7.26,9.69 -2016-04-09 08:00:00,1134.0,341.48,7.14,9.61 -2016-04-09 09:00:00,1256.0,501.61,7.03,9.53 -2016-04-09 10:00:00,1309.0,708.02,7.08,9.35 -2016-04-09 11:00:00,1229.0,801.8,7.14,9.16 -2016-04-09 12:00:00,1152.0,788.16,7.2,8.98 -2016-04-09 13:00:00,1090.0,672.06,7.36,9.02 -2016-04-09 14:00:00,1000.0,626.84,7.53,9.06 -2016-04-09 15:00:00,970.0,567.84,7.7,9.1 -2016-04-09 16:00:00,926.0,141.82,7.72,8.86 -2016-04-09 17:00:00,1053.0,207.77,7.75,8.63 -2016-04-09 18:00:00,1117.0,27.22,7.77,8.39 -2016-04-09 19:00:00,1191.0,0.0,8.09,8.53 -2016-04-09 20:00:00,1215.0,0.0,8.4,8.68 -2016-04-09 21:00:00,1278.0,0.0,8.71,8.83 -2016-04-09 22:00:00,1540.0,0.0,8.57,10.04 -2016-04-09 23:00:00,1597.0,0.0,8.43,11.26 -2016-04-10 00:00:00,1408.0,0.0,8.29,12.47 -2016-04-10 01:00:00,1252.0,0.0,8.54,13.4 -2016-04-10 02:00:00,1154.0,0.0,8.79,14.33 -2016-04-10 03:00:00,1129.0,0.0,9.05,15.26 -2016-04-10 04:00:00,1119.0,0.0,9.26,15.2 -2016-04-10 05:00:00,1128.0,0.0,9.48,15.14 -2016-04-10 06:00:00,1113.0,1.15,9.7,15.08 -2016-04-10 07:00:00,1140.0,12.77,9.72,14.94 -2016-04-10 08:00:00,1257.0,26.45,9.73,14.81 -2016-04-10 09:00:00,1404.0,39.63,9.74,14.68 -2016-04-10 10:00:00,1440.0,64.13,9.78,14.2 -2016-04-10 11:00:00,1463.0,122.29,9.82,13.72 -2016-04-10 12:00:00,1359.0,140.04,9.86,13.24 -2016-04-10 13:00:00,1240.0,123.96,9.66,12.84 -2016-04-10 14:00:00,1094.0,95.08,9.46,12.43 -2016-04-10 15:00:00,1017.0,42.72,9.26,12.03 -2016-04-10 16:00:00,984.0,30.87,9.04,11.23 -2016-04-10 17:00:00,1033.0,99.8,8.82,10.43 -2016-04-10 18:00:00,1123.0,29.88,8.6,9.63 -2016-04-10 19:00:00,1174.0,0.0,8.69,8.91 -2016-04-10 20:00:00,1171.0,0.0,8.78,8.18 -2016-04-10 21:00:00,1190.0,0.0,8.86,7.46 -2016-04-10 22:00:00,1505.0,0.0,8.82,7.36 -2016-04-10 23:00:00,1514.0,0.0,8.77,7.26 -2016-04-11 00:00:00,1339.0,0.0,8.72,7.16 -2016-04-11 01:00:00,1206.0,0.0,9.06,6.28 -2016-04-11 02:00:00,1075.0,0.0,9.4,5.4 -2016-04-11 03:00:00,1034.0,0.0,9.74,4.52 -2016-04-11 04:00:00,1051.0,0.0,9.76,5.03 -2016-04-11 05:00:00,1044.0,0.0,9.78,5.53 -2016-04-11 06:00:00,1058.0,1.97,9.8,6.03 -2016-04-11 07:00:00,1108.0,113.14,9.98,6.22 -2016-04-11 08:00:00,1198.0,274.94,10.15,6.4 -2016-04-11 09:00:00,1296.0,434.32,10.32,6.59 -2016-04-11 10:00:00,1280.0,592.99,10.37,6.58 -2016-04-11 11:00:00,1148.0,831.48,10.41,6.57 -2016-04-11 12:00:00,1085.0,859.86,10.45,6.55 -2016-04-11 13:00:00,1006.0,837.37,10.52,5.4 -2016-04-11 14:00:00,896.0,751.82,10.59,4.24 -2016-04-11 15:00:00,852.0,633.28,10.67,3.09 -2016-04-11 16:00:00,827.0,444.76,10.6,2.79 -2016-04-11 17:00:00,835.0,224.2,10.53,2.49 -2016-04-11 18:00:00,884.0,33.51,10.46,2.19 -2016-04-11 19:00:00,995.0,0.0,10.36,3.02 -2016-04-11 20:00:00,1035.0,0.0,10.26,3.85 -2016-04-11 21:00:00,1053.0,0.0,10.16,4.68 -2016-04-11 22:00:00,1331.0,0.0,10.07,5.02 -2016-04-11 23:00:00,1354.0,0.0,9.98,5.36 -2016-04-12 00:00:00,1165.0,0.0,9.89,5.7 -2016-04-12 01:00:00,997.0,0.0,9.93,5.39 -2016-04-12 02:00:00,886.0,0.0,9.97,5.09 -2016-04-12 03:00:00,859.0,0.0,10.02,4.79 -2016-04-12 04:00:00,853.0,0.0,9.81,4.71 -2016-04-12 05:00:00,843.0,0.0,9.61,4.63 -2016-04-12 06:00:00,862.0,4.29,9.4,4.55 -2016-04-12 07:00:00,920.0,94.03,9.56,4.32 -2016-04-12 08:00:00,1020.0,325.3,9.72,4.09 -2016-04-12 09:00:00,1081.0,464.54,9.89,3.86 -2016-04-12 10:00:00,1047.0,683.54,10.03,4.06 -2016-04-12 11:00:00,1035.0,771.41,10.18,4.27 -2016-04-12 12:00:00,997.0,813.74,10.33,4.47 -2016-04-12 13:00:00,929.0,725.9,10.5,4.23 -2016-04-12 14:00:00,837.0,371.95,10.68,3.98 -2016-04-12 15:00:00,781.0,628.98,10.86,3.74 -2016-04-12 16:00:00,731.0,356.22,10.98,3.99 -2016-04-12 17:00:00,727.0,31.2,11.1,4.24 -2016-04-12 18:00:00,845.0,36.22,11.22,4.5 -2016-04-12 19:00:00,971.0,0.0,11.2,5.14 -2016-04-12 20:00:00,963.0,0.0,11.17,5.77 -2016-04-12 21:00:00,997.0,0.0,11.14,6.41 -2016-04-12 22:00:00,1270.0,0.0,10.99,6.71 -2016-04-12 23:00:00,1289.0,0.0,10.84,7.0 -2016-04-13 00:00:00,1095.0,0.0,10.69,7.3 -2016-04-13 01:00:00,915.0,0.0,10.73,6.94 -2016-04-13 02:00:00,835.0,0.0,10.77,6.59 -2016-04-13 03:00:00,781.0,0.0,10.82,6.23 -2016-04-13 04:00:00,781.0,0.0,10.73,5.57 -2016-04-13 05:00:00,755.0,0.0,10.64,4.91 -2016-04-13 06:00:00,776.0,0.0,10.55,4.25 -2016-04-13 07:00:00,835.0,17.29,10.48,4.14 -2016-04-13 08:00:00,966.0,159.11,10.41,4.04 -2016-04-13 09:00:00,1122.0,122.25,10.34,3.93 -2016-04-13 10:00:00,1093.0,488.05,10.26,4.23 -2016-04-13 11:00:00,1070.0,754.6,10.18,4.52 -2016-04-13 12:00:00,1088.0,851.67,10.11,4.81 -2016-04-13 13:00:00,968.0,778.71,10.28,5.12 -2016-04-13 14:00:00,894.0,770.13,10.46,5.43 -2016-04-13 15:00:00,784.0,646.27,10.64,5.74 -2016-04-13 16:00:00,726.0,447.46,10.75,5.94 -2016-04-13 17:00:00,713.0,164.1,10.86,6.13 -2016-04-13 18:00:00,808.0,37.66,10.96,6.33 -2016-04-13 19:00:00,913.0,0.0,10.88,6.48 -2016-04-13 20:00:00,967.0,0.0,10.8,6.63 -2016-04-13 21:00:00,973.0,0.0,10.71,6.79 -2016-04-13 22:00:00,1228.0,0.0,10.66,6.69 -2016-04-13 23:00:00,1250.0,0.0,10.6,6.59 -2016-04-14 00:00:00,1085.0,0.0,10.55,6.5 -2016-04-14 01:00:00,875.0,0.0,10.71,6.32 -2016-04-14 02:00:00,793.0,0.0,10.87,6.14 -2016-04-14 03:00:00,766.0,0.0,11.04,5.96 -2016-04-14 04:00:00,780.0,0.0,11.04,6.21 -2016-04-14 05:00:00,753.0,0.0,11.05,6.46 -2016-04-14 06:00:00,766.0,5.76,11.06,6.72 -2016-04-14 07:00:00,832.0,54.86,10.98,7.48 -2016-04-14 08:00:00,955.0,76.89,10.9,8.23 -2016-04-14 09:00:00,1065.0,86.39,10.82,8.99 -2016-04-14 10:00:00,1122.0,56.81,10.71,9.68 -2016-04-14 11:00:00,1224.0,67.7,10.59,10.37 -2016-04-14 12:00:00,1308.0,44.71,10.47,11.06 -2016-04-14 13:00:00,1278.0,40.15,10.72,10.48 -2016-04-14 14:00:00,1241.0,35.66,10.98,9.89 -2016-04-14 15:00:00,1182.0,194.72,11.24,9.31 -2016-04-14 16:00:00,1071.0,139.81,11.25,9.54 -2016-04-14 17:00:00,1001.0,128.62,11.27,9.77 -2016-04-14 18:00:00,1051.0,31.6,11.28,10.0 -2016-04-14 19:00:00,1111.0,0.0,11.23,10.43 -2016-04-14 20:00:00,1109.0,0.0,11.17,10.86 -2016-04-14 21:00:00,1118.0,0.0,11.11,11.28 -2016-04-14 22:00:00,1407.0,0.0,10.97,11.66 -2016-04-14 23:00:00,1409.0,0.0,10.82,12.03 -2016-04-15 00:00:00,1203.0,0.0,10.67,12.4 -2016-04-15 01:00:00,1044.0,0.0,10.62,11.92 -2016-04-15 02:00:00,966.0,0.0,10.57,11.43 -2016-04-15 03:00:00,932.0,0.0,10.53,10.95 -2016-04-15 04:00:00,925.0,0.0,10.54,10.89 -2016-04-15 05:00:00,929.0,0.0,10.55,10.83 -2016-04-15 06:00:00,932.0,10.18,10.56,10.77 -2016-04-15 07:00:00,986.0,66.9,10.5,10.04 -2016-04-15 08:00:00,1049.0,252.74,10.44,9.31 -2016-04-15 09:00:00,1126.0,173.07,10.39,8.58 -2016-04-15 10:00:00,1118.0,624.49,10.4,8.11 -2016-04-15 11:00:00,1153.0,165.66,10.41,7.65 -2016-04-15 12:00:00,1129.0,189.36,10.43,7.19 -2016-04-15 13:00:00,1066.0,114.27,10.42,7.26 -2016-04-15 14:00:00,951.0,211.77,10.41,7.34 -2016-04-15 15:00:00,849.0,172.77,10.4,7.42 -2016-04-15 16:00:00,784.0,139.27,10.31,7.26 -2016-04-15 17:00:00,762.0,101.03,10.22,7.1 -2016-04-15 18:00:00,860.0,39.99,10.13,6.94 -2016-04-15 19:00:00,923.0,0.0,9.99,6.62 -2016-04-15 20:00:00,976.0,0.0,9.84,6.29 -2016-04-15 21:00:00,972.0,0.0,9.69,5.97 -2016-04-15 22:00:00,1261.0,0.0,9.47,5.89 -2016-04-15 23:00:00,1261.0,0.0,9.25,5.8 -2016-04-16 00:00:00,1056.0,0.0,9.03,5.71 -2016-04-16 01:00:00,887.0,0.0,9.34,6.09 -2016-04-16 02:00:00,825.0,0.0,9.66,6.47 -2016-04-16 03:00:00,796.0,0.0,9.98,6.86 -2016-04-16 04:00:00,804.0,0.0,9.85,7.37 -2016-04-16 05:00:00,800.0,0.0,9.73,7.88 -2016-04-16 06:00:00,819.0,13.78,9.61,8.4 -2016-04-16 07:00:00,867.0,126.52,9.37,8.29 -2016-04-16 08:00:00,1001.0,321.57,9.13,8.19 -2016-04-16 09:00:00,1040.0,238.84,8.89,8.08 -2016-04-16 10:00:00,1046.0,429.32,8.72,7.85 -2016-04-16 11:00:00,1010.0,638.77,8.55,7.62 -2016-04-16 12:00:00,997.0,672.99,8.39,7.39 -2016-04-16 13:00:00,873.0,769.47,8.6,7.42 -2016-04-16 14:00:00,790.0,714.83,8.81,7.45 -2016-04-16 15:00:00,775.0,619.04,9.03,7.48 -2016-04-16 16:00:00,767.0,409.24,8.99,7.93 -2016-04-16 17:00:00,775.0,185.56,8.96,8.38 -2016-04-16 18:00:00,867.0,40.83,8.92,8.83 -2016-04-16 19:00:00,945.0,0.0,8.88,8.68 -2016-04-16 20:00:00,1002.0,0.0,8.84,8.54 -2016-04-16 21:00:00,1033.0,0.0,8.8,8.4 -2016-04-16 22:00:00,1339.0,0.0,8.73,7.97 -2016-04-16 23:00:00,1343.0,0.0,8.65,7.54 -2016-04-17 00:00:00,1189.0,0.0,8.58,7.12 -2016-04-17 01:00:00,1007.0,0.0,8.62,6.79 -2016-04-17 02:00:00,930.0,0.0,8.67,6.46 -2016-04-17 03:00:00,891.0,0.0,8.72,6.12 -2016-04-17 04:00:00,883.0,0.0,8.71,5.89 -2016-04-17 05:00:00,889.0,0.0,8.7,5.66 -2016-04-17 06:00:00,889.0,11.45,8.69,5.42 -2016-04-17 07:00:00,937.0,124.13,8.78,5.22 -2016-04-17 08:00:00,1027.0,193.13,8.87,5.02 -2016-04-17 09:00:00,1078.0,111.58,8.95,4.81 -2016-04-17 10:00:00,1065.0,146.77,9.02,4.67 -2016-04-17 11:00:00,1027.0,668.29,9.09,4.53 -2016-04-17 12:00:00,949.0,606.42,9.16,4.39 -2016-04-17 13:00:00,820.0,86.8,9.14,4.47 -2016-04-17 14:00:00,743.0,568.41,9.12,4.55 -2016-04-17 15:00:00,706.0,615.29,9.1,4.63 -2016-04-17 16:00:00,677.0,454.83,9.15,4.43 -2016-04-17 17:00:00,684.0,230.82,9.2,4.22 -2016-04-17 18:00:00,744.0,35.16,9.25,4.01 -2016-04-17 19:00:00,851.0,0.0,9.22,3.75 -2016-04-17 20:00:00,867.0,0.0,9.18,3.49 -2016-04-17 21:00:00,888.0,0.0,9.14,3.23 -2016-04-17 22:00:00,1176.0,0.0,9.06,2.97 -2016-04-17 23:00:00,1230.0,0.0,8.97,2.7 -2016-04-18 00:00:00,1083.0,0.0,8.88,2.44 -2016-04-18 01:00:00,929.0,0.0,8.59,2.42 -2016-04-18 02:00:00,844.0,0.0,8.3,2.4 -2016-04-18 03:00:00,804.0,0.0,8.01,2.37 -2016-04-18 04:00:00,807.0,0.0,8.01,2.19 -2016-04-18 05:00:00,799.0,0.0,8.01,2.0 -2016-04-18 06:00:00,796.0,13.88,8.01,1.82 -2016-04-18 07:00:00,827.0,117.22,8.1,1.55 -2016-04-18 08:00:00,930.0,107.25,8.19,1.28 -2016-04-18 09:00:00,965.0,126.53,8.27,1.01 -2016-04-18 10:00:00,955.0,249.17,8.43,1.15 -2016-04-18 11:00:00,924.0,368.3,8.58,1.29 -2016-04-18 12:00:00,928.0,458.3,8.74,1.43 -2016-04-18 13:00:00,913.0,512.72,8.85,1.31 -2016-04-18 14:00:00,840.0,547.29,8.96,1.2 -2016-04-18 15:00:00,784.0,526.48,9.07,1.08 -2016-04-18 16:00:00,770.0,344.55,9.23,1.39 -2016-04-18 17:00:00,742.0,227.77,9.39,1.71 -2016-04-18 18:00:00,783.0,36.18,9.54,2.03 -2016-04-18 19:00:00,868.0,0.0,9.63,2.55 -2016-04-18 20:00:00,891.0,0.0,9.72,3.07 -2016-04-18 21:00:00,917.0,0.0,9.8,3.59 -2016-04-18 22:00:00,1230.0,0.0,9.74,4.01 -2016-04-18 23:00:00,1177.0,0.0,9.67,4.44 -2016-04-19 00:00:00,1005.0,0.0,9.6,4.87 -2016-04-19 01:00:00,901.0,0.0,9.31,5.81 -2016-04-19 02:00:00,808.0,0.0,9.02,6.74 -2016-04-19 03:00:00,756.0,0.0,8.74,7.68 -2016-04-19 04:00:00,778.0,0.0,8.74,7.74 -2016-04-19 05:00:00,794.0,0.0,8.75,7.79 -2016-04-19 06:00:00,805.0,13.85,8.76,7.85 -2016-04-19 07:00:00,858.0,145.57,9.11,7.94 -2016-04-19 08:00:00,929.0,377.98,9.46,8.03 -2016-04-19 09:00:00,966.0,566.18,9.81,8.12 -2016-04-19 10:00:00,919.0,677.05,10.33,8.29 -2016-04-19 11:00:00,892.0,840.79,10.85,8.46 -2016-04-19 12:00:00,868.0,866.48,11.37,8.62 -2016-04-19 13:00:00,758.0,856.75,11.54,9.26 -2016-04-19 14:00:00,711.0,775.23,11.71,9.9 -2016-04-19 15:00:00,667.0,652.78,11.89,10.54 -2016-04-19 16:00:00,663.0,458.65,12.06,10.83 -2016-04-19 17:00:00,688.0,230.54,12.24,11.12 -2016-04-19 18:00:00,767.0,41.06,12.41,11.41 -2016-04-19 19:00:00,864.0,0.0,12.43,11.71 -2016-04-19 20:00:00,889.0,0.0,12.45,12.01 -2016-04-19 21:00:00,903.0,0.0,12.46,12.32 -2016-04-19 22:00:00,1200.0,0.0,12.4,12.28 -2016-04-19 23:00:00,1190.0,0.0,12.33,12.25 -2016-04-20 00:00:00,999.0,0.0,12.27,12.22 -2016-04-20 01:00:00,908.0,0.0,12.0,12.29 -2016-04-20 02:00:00,814.0,0.0,11.73,12.37 -2016-04-20 03:00:00,762.0,0.0,11.47,12.44 -2016-04-20 04:00:00,770.0,0.0,11.39,12.37 -2016-04-20 05:00:00,758.0,0.0,11.32,12.3 -2016-04-20 06:00:00,783.0,14.88,11.24,12.23 -2016-04-20 07:00:00,846.0,73.55,11.32,12.08 -2016-04-20 08:00:00,913.0,317.08,11.4,11.93 -2016-04-20 09:00:00,963.0,450.31,11.48,11.78 -2016-04-20 10:00:00,1000.0,355.09,11.79,11.68 -2016-04-20 11:00:00,1014.0,346.34,12.11,11.59 -2016-04-20 12:00:00,1006.0,484.87,12.43,11.49 -2016-04-20 13:00:00,868.0,503.58,12.73,11.65 -2016-04-20 14:00:00,838.0,285.65,13.04,11.8 -2016-04-20 15:00:00,832.0,118.56,13.35,11.96 -2016-04-20 16:00:00,827.0,59.18,13.25,11.64 -2016-04-20 17:00:00,877.0,91.09,13.16,11.32 -2016-04-20 18:00:00,945.0,4.84,13.06,11.01 -2016-04-20 19:00:00,1038.0,0.0,13.05,10.97 -2016-04-20 20:00:00,1054.0,0.0,13.03,10.92 -2016-04-20 21:00:00,1028.0,0.0,13.01,10.88 -2016-04-20 22:00:00,1316.0,0.0,12.89,10.3 -2016-04-20 23:00:00,1281.0,0.0,12.76,9.72 -2016-04-21 00:00:00,1087.0,0.0,12.64,9.14 -2016-04-21 01:00:00,963.0,0.0,12.4,8.56 -2016-04-21 02:00:00,858.0,0.0,12.16,7.98 -2016-04-21 03:00:00,788.0,0.0,11.92,7.39 -2016-04-21 04:00:00,803.0,0.0,11.79,6.83 -2016-04-21 05:00:00,790.0,0.0,11.67,6.26 -2016-04-21 06:00:00,808.0,0.09,11.54,5.7 -2016-04-21 07:00:00,858.0,10.74,11.45,5.11 -2016-04-21 08:00:00,921.0,61.06,11.36,4.53 -2016-04-21 09:00:00,988.0,65.67,11.27,3.94 -2016-04-21 10:00:00,1019.0,205.62,11.36,3.55 -2016-04-21 11:00:00,985.0,266.31,11.46,3.16 -2016-04-21 12:00:00,1018.0,109.06,11.56,2.77 -2016-04-21 13:00:00,933.0,207.19,11.65,2.97 -2016-04-21 14:00:00,847.0,246.85,11.74,3.17 -2016-04-21 15:00:00,784.0,368.54,11.83,3.37 -2016-04-21 16:00:00,704.0,182.84,11.7,2.84 -2016-04-21 17:00:00,677.0,51.0,11.57,2.32 -2016-04-21 18:00:00,723.0,19.88,11.44,1.79 -2016-04-21 19:00:00,826.0,0.0,11.41,1.55 -2016-04-21 20:00:00,832.0,0.0,11.38,1.31 -2016-04-21 21:00:00,821.0,0.0,11.34,1.06 -2016-04-21 22:00:00,1120.0,0.0,11.24,1.5 -2016-04-21 23:00:00,1121.0,0.0,11.14,1.94 -2016-04-22 00:00:00,951.0,0.0,11.04,2.39 -2016-04-22 01:00:00,835.0,0.0,10.99,2.39 -2016-04-22 02:00:00,727.0,0.0,10.94,2.4 -2016-04-22 03:00:00,681.0,0.0,10.9,2.4 -2016-04-22 04:00:00,694.0,0.0,10.9,2.73 -2016-04-22 05:00:00,681.0,0.0,10.9,3.05 -2016-04-22 06:00:00,674.0,21.12,10.9,3.38 -2016-04-22 07:00:00,772.0,116.44,10.94,3.31 -2016-04-22 08:00:00,804.0,117.44,10.97,3.23 -2016-04-22 09:00:00,860.0,115.73,11.01,3.16 -2016-04-22 10:00:00,862.0,129.98,11.04,3.35 -2016-04-22 11:00:00,866.0,203.35,11.08,3.54 -2016-04-22 12:00:00,863.0,370.14,11.12,3.74 -2016-04-22 13:00:00,788.0,297.78,11.13,4.11 -2016-04-22 14:00:00,705.0,171.1,11.14,4.47 -2016-04-22 15:00:00,682.0,203.06,11.16,4.84 -2016-04-22 16:00:00,647.0,139.51,11.09,4.3 -2016-04-22 17:00:00,649.0,69.58,11.02,3.77 -2016-04-22 18:00:00,724.0,26.2,10.95,3.23 -2016-04-22 19:00:00,762.0,0.0,10.89,2.91 -2016-04-22 20:00:00,846.0,0.0,10.82,2.6 -2016-04-22 21:00:00,895.0,0.0,10.75,2.29 -2016-04-22 22:00:00,1207.0,0.0,10.63,2.42 -2016-04-22 23:00:00,1196.0,0.0,10.51,2.55 -2016-04-23 00:00:00,957.0,0.0,10.39,2.68 -2016-04-23 01:00:00,826.0,0.0,10.46,2.97 -2016-04-23 02:00:00,732.0,0.0,10.53,3.26 -2016-04-23 03:00:00,678.0,0.0,10.61,3.56 -2016-04-23 04:00:00,663.0,0.0,10.55,4.35 -2016-04-23 05:00:00,667.0,0.0,10.49,5.15 -2016-04-23 06:00:00,678.0,10.76,10.43,5.94 -2016-04-23 07:00:00,710.0,52.84,10.34,6.46 -2016-04-23 08:00:00,802.0,65.37,10.25,6.98 -2016-04-23 09:00:00,850.0,70.09,10.15,7.5 -2016-04-23 10:00:00,865.0,78.74,10.08,7.53 -2016-04-23 11:00:00,903.0,178.03,10.01,7.55 -2016-04-23 12:00:00,950.0,327.38,9.94,7.57 -2016-04-23 13:00:00,895.0,835.87,9.93,7.83 -2016-04-23 14:00:00,825.0,780.46,9.93,8.1 -2016-04-23 15:00:00,820.0,650.12,9.93,8.36 -2016-04-23 16:00:00,824.0,455.67,9.89,8.19 -2016-04-23 17:00:00,818.0,230.32,9.86,8.03 -2016-04-23 18:00:00,848.0,40.55,9.82,7.86 -2016-04-23 19:00:00,914.0,0.0,9.79,7.67 -2016-04-23 20:00:00,937.0,0.0,9.75,7.49 -2016-04-23 21:00:00,970.0,0.0,9.71,7.3 -2016-04-23 22:00:00,1309.0,0.0,9.67,6.95 -2016-04-23 23:00:00,1348.0,0.0,9.62,6.6 -2016-04-24 00:00:00,1110.0,0.0,9.58,6.25 -2016-04-24 01:00:00,942.0,0.0,9.51,6.18 -2016-04-24 02:00:00,886.0,0.0,9.44,6.12 -2016-04-24 03:00:00,875.0,0.0,9.37,6.06 -2016-04-24 04:00:00,837.0,0.0,9.45,6.41 -2016-04-24 05:00:00,830.0,0.0,9.54,6.77 -2016-04-24 06:00:00,824.0,20.01,9.62,7.13 -2016-04-24 07:00:00,870.0,157.57,9.77,7.18 -2016-04-24 08:00:00,948.0,350.25,9.91,7.22 -2016-04-24 09:00:00,979.0,568.18,10.06,7.27 -2016-04-24 10:00:00,1022.0,656.41,10.03,7.12 -2016-04-24 11:00:00,1002.0,834.66,10.01,6.97 -2016-04-24 12:00:00,911.0,875.03,9.99,6.81 -2016-04-24 13:00:00,820.0,857.51,9.97,6.82 -2016-04-24 14:00:00,756.0,770.94,9.96,6.83 -2016-04-24 15:00:00,699.0,653.44,9.95,6.84 -2016-04-24 16:00:00,662.0,412.73,9.77,6.82 -2016-04-24 17:00:00,640.0,227.49,9.59,6.8 -2016-04-24 18:00:00,690.0,48.31,9.4,6.77 -2016-04-24 19:00:00,766.0,0.0,9.18,6.46 -2016-04-24 20:00:00,810.0,0.0,8.95,6.16 -2016-04-24 21:00:00,834.0,0.0,8.72,5.85 -2016-04-24 22:00:00,1187.0,0.0,8.51,5.64 -2016-04-24 23:00:00,1183.0,0.0,8.29,5.43 -2016-04-25 00:00:00,1027.0,0.0,8.08,5.21 -2016-04-25 01:00:00,895.0,0.0,8.32,5.56 -2016-04-25 02:00:00,773.0,0.0,8.56,5.91 -2016-04-25 03:00:00,740.0,0.0,8.81,6.26 -2016-04-25 04:00:00,750.0,0.0,8.92,6.85 -2016-04-25 05:00:00,738.0,0.0,9.04,7.43 -2016-04-25 06:00:00,735.0,26.24,9.15,8.01 -2016-04-25 07:00:00,796.0,149.59,9.29,8.4 -2016-04-25 08:00:00,903.0,361.16,9.43,8.79 -2016-04-25 09:00:00,976.0,497.44,9.57,9.17 -2016-04-25 10:00:00,995.0,712.31,9.63,9.29 -2016-04-25 11:00:00,953.0,591.24,9.68,9.4 -2016-04-25 12:00:00,953.0,189.16,9.73,9.52 -2016-04-25 13:00:00,851.0,380.48,9.74,9.71 -2016-04-25 14:00:00,774.0,684.93,9.75,9.89 -2016-04-25 15:00:00,769.0,624.94,9.77,10.08 -2016-04-25 16:00:00,767.0,306.96,9.74,9.87 -2016-04-25 17:00:00,774.0,123.11,9.71,9.65 -2016-04-25 18:00:00,829.0,30.76,9.68,9.43 -2016-04-25 19:00:00,938.0,1.15,9.68,9.12 -2016-04-25 20:00:00,940.0,0.0,9.68,8.81 -2016-04-25 21:00:00,941.0,0.0,9.68,8.5 -2016-04-25 22:00:00,1233.0,0.0,9.66,8.1 -2016-04-25 23:00:00,1227.0,0.0,9.64,7.7 -2016-04-26 00:00:00,1055.0,0.0,9.62,7.3 -2016-04-26 01:00:00,929.0,0.0,9.74,7.05 -2016-04-26 02:00:00,841.0,0.0,9.86,6.8 -2016-04-26 03:00:00,792.0,0.0,9.98,6.55 -2016-04-26 04:00:00,781.0,0.0,9.88,7.0 -2016-04-26 05:00:00,768.0,0.0,9.79,7.45 -2016-04-26 06:00:00,801.0,25.17,9.7,7.9 -2016-04-26 07:00:00,877.0,75.16,9.45,8.35 -2016-04-26 08:00:00,948.0,163.45,9.2,8.8 -2016-04-26 09:00:00,984.0,169.63,8.94,9.24 -2016-04-26 10:00:00,1008.0,627.48,8.65,8.99 -2016-04-26 11:00:00,1017.0,571.52,8.36,8.74 -2016-04-26 12:00:00,1035.0,808.43,8.07,8.48 -2016-04-26 13:00:00,983.0,809.6,8.11,8.81 -2016-04-26 14:00:00,910.0,541.0,8.15,9.14 -2016-04-26 15:00:00,860.0,467.04,8.19,9.48 -2016-04-26 16:00:00,840.0,433.69,8.17,9.46 -2016-04-26 17:00:00,820.0,224.18,8.15,9.44 -2016-04-26 18:00:00,849.0,43.78,8.12,9.42 -2016-04-26 19:00:00,936.0,1.17,8.24,9.21 -2016-04-26 20:00:00,959.0,0.0,8.36,9.0 -2016-04-26 21:00:00,992.0,0.0,8.47,8.79 -2016-04-26 22:00:00,1319.0,0.0,8.5,8.12 -2016-04-26 23:00:00,1320.0,0.0,8.53,7.46 -2016-04-27 00:00:00,1142.0,0.0,8.56,6.8 -2016-04-27 01:00:00,998.0,0.0,8.69,6.91 -2016-04-27 02:00:00,907.0,0.0,8.82,7.01 -2016-04-27 03:00:00,849.0,0.0,8.95,7.12 -2016-04-27 04:00:00,865.0,0.0,8.88,6.98 -2016-04-27 05:00:00,872.0,0.0,8.81,6.84 -2016-04-27 06:00:00,870.0,23.73,8.74,6.7 -2016-04-27 07:00:00,967.0,129.79,8.61,6.56 -2016-04-27 08:00:00,1034.0,387.07,8.48,6.41 -2016-04-27 09:00:00,1116.0,325.01,8.35,6.26 -2016-04-27 10:00:00,1071.0,608.09,8.26,6.38 -2016-04-27 11:00:00,1018.0,830.78,8.17,6.5 -2016-04-27 12:00:00,1046.0,795.36,8.08,6.62 -2016-04-27 13:00:00,1040.0,555.96,8.12,7.0 -2016-04-27 14:00:00,961.0,759.53,8.17,7.38 -2016-04-27 15:00:00,897.0,571.84,8.22,7.77 -2016-04-27 16:00:00,877.0,449.07,8.22,8.04 -2016-04-27 17:00:00,886.0,153.22,8.22,8.31 -2016-04-27 18:00:00,961.0,47.43,8.21,8.58 -2016-04-27 19:00:00,1033.0,1.17,8.22,8.4 -2016-04-27 20:00:00,1049.0,0.0,8.22,8.23 -2016-04-27 21:00:00,1051.0,0.0,8.22,8.06 -2016-04-27 22:00:00,1376.0,0.0,8.27,7.62 -2016-04-27 23:00:00,1378.0,0.0,8.31,7.18 -2016-04-28 00:00:00,1158.0,0.0,8.36,6.74 -2016-04-28 01:00:00,1050.0,0.0,8.52,6.16 -2016-04-28 02:00:00,954.0,0.0,8.68,5.58 -2016-04-28 03:00:00,900.0,0.0,8.84,4.99 -2016-04-28 04:00:00,904.0,0.0,8.93,5.12 -2016-04-28 05:00:00,903.0,0.0,9.03,5.25 -2016-04-28 06:00:00,923.0,20.01,9.12,5.38 -2016-04-28 07:00:00,992.0,165.33,9.25,5.52 -2016-04-28 08:00:00,1089.0,392.16,9.38,5.66 -2016-04-28 09:00:00,1149.0,585.67,9.52,5.81 -2016-04-28 10:00:00,1102.0,740.66,9.61,6.08 -2016-04-28 11:00:00,1072.0,843.96,9.7,6.36 -2016-04-28 12:00:00,1019.0,784.06,9.79,6.63 -2016-04-28 13:00:00,941.0,860.55,9.56,7.23 -2016-04-28 14:00:00,832.0,275.18,9.33,7.82 -2016-04-28 15:00:00,784.0,231.82,9.1,8.41 -2016-04-28 16:00:00,758.0,322.53,9.15,8.78 -2016-04-28 17:00:00,718.0,201.57,9.21,9.14 -2016-04-28 18:00:00,800.0,49.06,9.26,9.5 -2016-04-28 19:00:00,889.0,3.33,9.43,9.65 -2016-04-28 20:00:00,905.0,0.0,9.6,9.79 -2016-04-28 21:00:00,919.0,0.0,9.77,9.93 -2016-04-28 22:00:00,1210.0,0.0,9.67,9.64 -2016-04-28 23:00:00,1223.0,0.0,9.56,9.35 -2016-04-29 00:00:00,1048.0,0.0,9.46,9.06 -2016-04-29 01:00:00,929.0,0.0,9.27,8.95 -2016-04-29 02:00:00,840.0,0.0,9.08,8.83 -2016-04-29 03:00:00,783.0,0.0,8.9,8.72 -2016-04-29 04:00:00,794.0,0.0,8.96,8.57 -2016-04-29 05:00:00,813.0,0.0,9.02,8.43 -2016-04-29 06:00:00,806.0,22.88,9.08,8.29 -2016-04-29 07:00:00,884.0,82.71,9.11,7.57 -2016-04-29 08:00:00,911.0,61.05,9.14,6.85 -2016-04-29 09:00:00,935.0,98.04,9.18,6.12 -2016-04-29 10:00:00,938.0,185.46,9.19,5.67 -2016-04-29 11:00:00,951.0,305.81,9.2,5.22 -2016-04-29 12:00:00,951.0,144.26,9.21,4.77 -2016-04-29 13:00:00,943.0,222.21,8.9,5.28 -2016-04-29 14:00:00,873.0,300.33,8.6,5.79 -2016-04-29 15:00:00,881.0,392.08,8.3,6.3 -2016-04-29 16:00:00,872.0,67.59,8.14,6.47 -2016-04-29 17:00:00,897.0,43.68,7.98,6.63 -2016-04-29 18:00:00,973.0,45.35,7.81,6.8 -2016-04-29 19:00:00,1022.0,1.58,7.91,7.26 -2016-04-29 20:00:00,1078.0,0.0,8.01,7.72 -2016-04-29 21:00:00,1090.0,0.0,8.11,8.18 -2016-04-29 22:00:00,1368.0,0.0,8.28,8.14 -2016-04-29 23:00:00,1375.0,0.0,8.45,8.11 -2016-04-30 00:00:00,1234.0,0.0,8.62,8.07 -2016-04-30 01:00:00,1061.0,0.0,8.94,8.3 -2016-04-30 02:00:00,954.0,0.0,9.27,8.54 -2016-04-30 03:00:00,927.0,0.0,9.6,8.77 -2016-04-30 04:00:00,917.0,0.0,9.52,8.46 -2016-04-30 05:00:00,938.0,0.0,9.45,8.15 -2016-04-30 06:00:00,945.0,18.8,9.38,7.83 -2016-04-30 07:00:00,991.0,155.89,9.42,7.47 -2016-04-30 08:00:00,1090.0,379.26,9.46,7.1 -2016-04-30 09:00:00,1105.0,582.84,9.49,6.73 -2016-04-30 10:00:00,1062.0,735.88,9.56,6.63 -2016-04-30 11:00:00,1014.0,795.34,9.62,6.52 -2016-04-30 12:00:00,941.0,874.35,9.68,6.41 -2016-04-30 13:00:00,878.0,834.34,9.68,6.34 -2016-04-30 14:00:00,805.0,778.14,9.68,6.28 -2016-04-30 15:00:00,760.0,613.38,9.69,6.21 -2016-04-30 16:00:00,739.0,445.17,9.66,6.47 -2016-04-30 17:00:00,746.0,238.43,9.64,6.73 -2016-04-30 18:00:00,782.0,39.41,9.61,6.99 -2016-04-30 19:00:00,852.0,4.79,9.59,6.33 -2016-04-30 20:00:00,861.0,0.0,9.56,5.67 -2016-04-30 21:00:00,871.0,0.0,9.53,5.01 -2016-04-30 22:00:00,1232.0,0.0,9.51,4.55 -2016-04-30 23:00:00,1256.0,0.0,9.49,4.1 -2016-05-01 00:00:00,1103.0,0.0,9.47,3.64 -2016-05-01 01:00:00,926.0,0.0,9.66,4.13 -2016-05-01 02:00:00,810.0,0.0,9.84,4.62 -2016-05-01 03:00:00,795.0,0.0,10.03,5.1 -2016-05-01 04:00:00,783.0,0.0,10.11,5.45 -2016-05-01 05:00:00,778.0,0.0,10.19,5.8 -2016-05-01 06:00:00,780.0,8.97,10.27,6.15 -2016-05-01 07:00:00,801.0,40.16,10.41,6.56 -2016-05-01 08:00:00,886.0,103.17,10.56,6.97 -2016-05-01 09:00:00,972.0,154.76,10.7,7.38 -2016-05-01 10:00:00,1015.0,186.99,10.69,7.34 -2016-05-01 11:00:00,1045.0,282.65,10.69,7.3 -2016-05-01 12:00:00,1069.0,224.32,10.68,7.26 -2016-05-01 13:00:00,1013.0,310.36,10.78,7.26 -2016-05-01 14:00:00,924.0,314.89,10.89,7.26 -2016-05-01 15:00:00,864.0,453.53,10.99,7.26 -2016-05-01 16:00:00,796.0,298.74,11.06,7.1 -2016-05-01 17:00:00,747.0,166.94,11.14,6.95 -2016-05-01 18:00:00,769.0,57.52,11.22,6.8 -2016-05-01 19:00:00,806.0,2.38,11.24,6.83 -2016-05-01 20:00:00,838.0,0.0,11.27,6.86 -2016-05-01 21:00:00,850.0,0.0,11.3,6.9 -2016-05-01 22:00:00,1132.0,0.0,11.29,7.14 -2016-05-01 23:00:00,1135.0,0.0,11.28,7.39 -2016-05-02 00:00:00,981.0,0.0,11.27,7.64 -2016-05-02 01:00:00,863.0,0.0,11.24,7.83 -2016-05-02 02:00:00,784.0,0.0,11.21,8.01 -2016-05-02 03:00:00,723.0,0.0,11.18,8.19 -2016-05-02 04:00:00,728.0,0.0,11.19,8.43 -2016-05-02 05:00:00,701.0,0.0,11.2,8.67 -2016-05-02 06:00:00,697.0,26.14,11.21,8.91 -2016-05-02 07:00:00,733.0,93.44,11.22,9.12 -2016-05-02 08:00:00,821.0,135.7,11.24,9.33 -2016-05-02 09:00:00,890.0,75.25,11.25,9.54 -2016-05-02 10:00:00,881.0,98.14,11.19,9.77 -2016-05-02 11:00:00,898.0,87.88,11.14,10.0 -2016-05-02 12:00:00,925.0,63.65,11.09,10.22 -2016-05-02 13:00:00,875.0,221.38,11.06,9.63 -2016-05-02 14:00:00,795.0,433.49,11.03,9.03 -2016-05-02 15:00:00,776.0,569.67,11.0,8.44 -2016-05-02 16:00:00,789.0,447.36,10.93,8.24 -2016-05-02 17:00:00,799.0,221.82,10.85,8.04 -2016-05-02 18:00:00,894.0,44.98,10.77,7.83 -2016-05-02 19:00:00,952.0,6.3,10.66,7.74 -2016-05-02 20:00:00,932.0,0.0,10.54,7.64 -2016-05-02 21:00:00,895.0,0.0,10.42,7.54 -2016-05-02 22:00:00,1202.0,0.0,10.29,7.23 -2016-05-02 23:00:00,1192.0,0.0,10.16,6.91 -2016-05-03 00:00:00,1015.0,0.0,10.03,6.59 -2016-05-03 01:00:00,899.0,0.0,10.14,6.31 -2016-05-03 02:00:00,820.0,0.0,10.24,6.02 -2016-05-03 03:00:00,795.0,0.0,10.35,5.74 -2016-05-03 04:00:00,802.0,0.0,10.27,5.42 -2016-05-03 05:00:00,803.0,0.0,10.19,5.1 -2016-05-03 06:00:00,845.0,36.27,10.11,4.79 -2016-05-03 07:00:00,896.0,118.58,10.16,4.23 -2016-05-03 08:00:00,912.0,342.19,10.21,3.66 -2016-05-03 09:00:00,920.0,488.17,10.26,3.1 -2016-05-03 10:00:00,874.0,691.57,10.24,2.69 -2016-05-03 11:00:00,815.0,769.55,10.22,2.28 -2016-05-03 12:00:00,816.0,788.36,10.2,1.86 -2016-05-03 13:00:00,760.0,767.87,10.38,1.62 -2016-05-03 14:00:00,704.0,652.28,10.56,1.37 -2016-05-03 15:00:00,685.0,602.92,10.74,1.13 -2016-05-03 16:00:00,679.0,436.23,10.79,1.1 -2016-05-03 17:00:00,643.0,227.01,10.84,1.08 -2016-05-03 18:00:00,699.0,44.76,10.89,1.05 -2016-05-03 19:00:00,742.0,6.81,10.86,1.32 -2016-05-03 20:00:00,756.0,0.0,10.84,1.59 -2016-05-03 21:00:00,761.0,0.0,10.82,1.86 -2016-05-03 22:00:00,1104.0,0.0,10.75,2.44 -2016-05-03 23:00:00,1108.0,0.0,10.67,3.01 -2016-05-04 00:00:00,928.0,0.0,10.6,3.59 -2016-05-04 01:00:00,796.0,0.0,10.42,4.06 -2016-05-04 02:00:00,716.0,0.0,10.23,4.52 -2016-05-04 03:00:00,675.0,0.0,10.05,4.99 -2016-05-04 04:00:00,680.0,0.0,10.06,5.15 -2016-05-04 05:00:00,694.0,0.0,10.08,5.31 -2016-05-04 06:00:00,708.0,23.98,10.09,5.48 -2016-05-04 07:00:00,775.0,111.87,10.4,5.4 -2016-05-04 08:00:00,815.0,381.89,10.71,5.33 -2016-05-04 09:00:00,847.0,570.9,11.02,5.26 -2016-05-04 10:00:00,832.0,697.87,11.27,4.65 -2016-05-04 11:00:00,745.0,800.33,11.53,4.04 -2016-05-04 12:00:00,738.0,824.93,11.78,3.43 -2016-05-04 13:00:00,691.0,808.63,12.03,3.22 -2016-05-04 14:00:00,643.0,751.12,12.27,3.01 -2016-05-04 15:00:00,602.0,613.36,12.52,2.8 -2016-05-04 16:00:00,591.0,441.93,12.57,2.88 -2016-05-04 17:00:00,604.0,230.78,12.63,2.96 -2016-05-04 18:00:00,648.0,42.8,12.69,3.03 -2016-05-04 19:00:00,706.0,7.28,12.69,3.17 -2016-05-04 20:00:00,741.0,0.0,12.7,3.31 -2016-05-04 21:00:00,774.0,0.0,12.71,3.45 -2016-05-04 22:00:00,1155.0,0.0,12.67,3.59 -2016-05-04 23:00:00,1188.0,0.0,12.62,3.73 -2016-05-05 00:00:00,961.0,0.0,12.58,3.88 -2016-05-05 01:00:00,767.0,0.0,12.37,3.82 -2016-05-05 02:00:00,670.0,0.0,12.17,3.77 -2016-05-05 03:00:00,632.0,0.0,11.96,3.71 -2016-05-05 04:00:00,630.0,0.0,11.84,3.78 -2016-05-05 05:00:00,606.0,0.0,11.71,3.86 -2016-05-05 06:00:00,602.0,26.69,11.58,3.93 -2016-05-05 07:00:00,653.0,170.07,11.71,4.05 -2016-05-05 08:00:00,727.0,381.19,11.83,4.16 -2016-05-05 09:00:00,799.0,548.99,11.96,4.28 -2016-05-05 10:00:00,795.0,647.55,12.05,4.51 -2016-05-05 11:00:00,773.0,759.46,12.14,4.74 -2016-05-05 12:00:00,709.0,679.34,12.23,4.98 -2016-05-05 13:00:00,663.0,691.5,12.38,5.15 -2016-05-05 14:00:00,585.0,644.3,12.54,5.32 -2016-05-05 15:00:00,572.0,438.31,12.69,5.49 -2016-05-05 16:00:00,588.0,410.39,12.69,5.47 -2016-05-05 17:00:00,568.0,225.98,12.69,5.44 -2016-05-05 18:00:00,637.0,44.07,12.69,5.42 -2016-05-05 19:00:00,727.0,8.93,12.52,5.93 -2016-05-05 20:00:00,765.0,0.0,12.34,6.44 -2016-05-05 21:00:00,813.0,0.0,12.16,6.95 -2016-05-05 22:00:00,1166.0,0.0,12.07,7.13 -2016-05-05 23:00:00,1159.0,0.0,11.97,7.3 -2016-05-06 00:00:00,966.0,0.0,11.88,7.48 -2016-05-06 01:00:00,794.0,0.0,11.85,7.32 -2016-05-06 02:00:00,682.0,0.0,11.83,7.17 -2016-05-06 03:00:00,630.0,0.0,11.8,7.02 -2016-05-06 04:00:00,590.0,0.0,11.78,6.72 -2016-05-06 05:00:00,583.0,0.0,11.76,6.41 -2016-05-06 06:00:00,604.0,32.31,11.73,6.11 -2016-05-06 07:00:00,672.0,103.1,11.83,5.93 -2016-05-06 08:00:00,760.0,231.12,11.92,5.75 -2016-05-06 09:00:00,858.0,214.02,12.02,5.57 -2016-05-06 10:00:00,900.0,279.41,12.03,5.37 -2016-05-06 11:00:00,889.0,490.72,12.05,5.18 -2016-05-06 12:00:00,850.0,155.04,12.06,4.98 -2016-05-06 13:00:00,760.0,319.92,12.14,4.71 -2016-05-06 14:00:00,706.0,262.07,12.23,4.45 -2016-05-06 15:00:00,663.0,142.0,12.31,4.18 -2016-05-06 16:00:00,635.0,162.28,12.45,3.88 -2016-05-06 17:00:00,667.0,35.28,12.59,3.58 -2016-05-06 18:00:00,812.0,53.08,12.73,3.28 -2016-05-06 19:00:00,958.0,7.28,12.63,3.14 -2016-05-06 20:00:00,949.0,0.0,12.54,3.01 -2016-05-06 21:00:00,917.0,0.0,12.45,2.87 -2016-05-06 22:00:00,1215.0,0.0,12.32,2.6 -2016-05-06 23:00:00,1209.0,0.0,12.2,2.34 -2016-05-07 00:00:00,1007.0,0.0,12.07,2.07 -2016-05-07 01:00:00,820.0,0.0,12.16,2.99 -2016-05-07 02:00:00,702.0,0.0,12.24,3.91 -2016-05-07 03:00:00,645.0,0.0,12.33,4.83 -2016-05-07 04:00:00,623.0,0.0,12.27,4.67 -2016-05-07 05:00:00,609.0,0.0,12.21,4.51 -2016-05-07 06:00:00,610.0,34.38,12.14,4.36 -2016-05-07 07:00:00,669.0,138.58,12.15,4.43 -2016-05-07 08:00:00,824.0,139.85,12.17,4.5 -2016-05-07 09:00:00,922.0,266.14,12.18,4.57 -2016-05-07 10:00:00,989.0,128.74,12.16,4.42 -2016-05-07 11:00:00,977.0,201.69,12.15,4.28 -2016-05-07 12:00:00,967.0,331.32,12.13,4.14 -2016-05-07 13:00:00,921.0,395.81,12.11,4.29 -2016-05-07 14:00:00,896.0,370.67,12.08,4.45 -2016-05-07 15:00:00,801.0,255.25,12.06,4.61 -2016-05-07 16:00:00,712.0,215.49,12.07,4.4 -2016-05-07 17:00:00,673.0,136.08,12.09,4.2 -2016-05-07 18:00:00,730.0,58.57,12.1,4.0 -2016-05-07 19:00:00,822.0,8.95,12.11,3.72 -2016-05-07 20:00:00,815.0,0.0,12.11,3.45 -2016-05-07 21:00:00,812.0,0.0,12.12,3.17 -2016-05-07 22:00:00,1155.0,0.0,12.21,3.45 -2016-05-07 23:00:00,1157.0,0.0,12.3,3.72 -2016-05-08 00:00:00,971.0,0.0,12.39,4.0 -2016-05-08 01:00:00,772.0,0.0,12.62,4.64 -2016-05-08 02:00:00,658.0,0.0,12.85,5.28 -2016-05-08 03:00:00,613.0,0.0,13.08,5.92 -2016-05-08 04:00:00,599.0,0.0,13.34,6.99 -2016-05-08 05:00:00,577.0,0.0,13.59,8.07 -2016-05-08 06:00:00,585.0,9.39,13.84,9.14 -2016-05-08 07:00:00,600.0,30.81,13.9,9.59 -2016-05-08 08:00:00,747.0,87.47,13.97,10.03 -2016-05-08 09:00:00,814.0,67.33,14.03,10.47 -2016-05-08 10:00:00,865.0,296.9,14.02,10.29 -2016-05-08 11:00:00,901.0,240.44,14.02,10.11 -2016-05-08 12:00:00,831.0,155.26,14.02,9.93 -2016-05-08 13:00:00,710.0,116.33,14.22,9.6 -2016-05-08 14:00:00,670.0,115.41,14.42,9.28 -2016-05-08 15:00:00,631.0,79.39,14.62,8.95 -2016-05-08 16:00:00,605.0,39.36,14.57,9.04 -2016-05-08 17:00:00,604.0,21.64,14.52,9.13 -2016-05-08 18:00:00,642.0,7.58,14.48,9.21 -2016-05-08 19:00:00,689.0,2.77,14.12,9.33 -2016-05-08 20:00:00,704.0,0.0,13.77,9.44 -2016-05-08 21:00:00,679.0,0.0,13.42,9.56 -2016-05-08 22:00:00,981.0,0.0,13.47,9.44 -2016-05-08 23:00:00,999.0,0.0,13.52,9.33 -2016-05-09 00:00:00,827.0,0.0,13.57,9.21 -2016-05-09 01:00:00,696.0,0.0,13.67,8.76 -2016-05-09 02:00:00,591.0,0.0,13.76,8.31 -2016-05-09 03:00:00,549.0,0.0,13.86,7.86 -2016-05-09 04:00:00,551.0,0.0,13.76,7.9 -2016-05-09 05:00:00,534.0,0.0,13.65,7.94 -2016-05-09 06:00:00,526.0,8.21,13.54,7.97 -2016-05-09 07:00:00,579.0,125.29,13.53,7.78 -2016-05-09 08:00:00,636.0,183.69,13.53,7.6 -2016-05-09 09:00:00,706.0,507.2,13.52,7.41 -2016-05-09 10:00:00,676.0,626.64,13.38,6.79 -2016-05-09 11:00:00,650.0,423.16,13.25,6.17 -2016-05-09 12:00:00,666.0,416.37,13.11,5.56 -2016-05-09 13:00:00,602.0,732.76,13.05,5.29 -2016-05-09 14:00:00,573.0,736.62,13.0,5.02 -2016-05-09 15:00:00,560.0,557.19,12.94,4.74 -2016-05-09 16:00:00,529.0,344.94,12.86,4.32 -2016-05-09 17:00:00,514.0,148.86,12.78,3.89 -2016-05-09 18:00:00,520.0,57.61,12.69,3.46 -2016-05-09 19:00:00,561.0,11.77,12.68,3.31 -2016-05-09 20:00:00,580.0,0.0,12.67,3.16 -2016-05-09 21:00:00,545.0,0.0,12.66,3.01 -2016-05-09 22:00:00,887.0,0.0,12.61,2.75 -2016-05-09 23:00:00,877.0,0.0,12.56,2.5 -2016-05-10 00:00:00,713.0,0.0,12.51,2.25 -2016-05-10 01:00:00,615.0,0.0,12.51,1.64 -2016-05-10 02:00:00,509.0,0.0,12.51,1.03 -2016-05-10 03:00:00,476.0,0.0,12.51,0.43 -2016-05-10 04:00:00,472.0,0.0,12.39,1.14 -2016-05-10 05:00:00,478.0,1.12,12.27,1.85 -2016-05-10 06:00:00,469.0,15.81,12.15,2.57 -2016-05-10 07:00:00,521.0,15.14,12.31,3.01 -2016-05-10 08:00:00,594.0,144.77,12.48,3.45 -2016-05-10 09:00:00,639.0,138.56,12.64,3.89 -2016-05-10 10:00:00,643.0,132.95,12.63,3.48 -2016-05-10 11:00:00,641.0,333.72,12.63,3.07 -2016-05-10 12:00:00,630.0,455.58,12.62,2.66 -2016-05-10 13:00:00,564.0,428.89,12.73,1.98 -2016-05-10 14:00:00,518.0,506.1,12.84,1.29 -2016-05-10 15:00:00,497.0,328.17,12.95,0.61 -2016-05-10 16:00:00,486.0,212.87,13.16,1.41 -2016-05-10 17:00:00,459.0,126.05,13.37,2.22 -2016-05-10 18:00:00,494.0,54.33,13.57,3.02 -2016-05-10 19:00:00,532.0,12.31,13.26,3.27 -2016-05-10 20:00:00,544.0,0.0,12.94,3.53 -2016-05-10 21:00:00,539.0,0.0,12.63,3.78 -2016-05-10 22:00:00,856.0,0.0,12.38,3.5 -2016-05-10 23:00:00,855.0,0.0,12.13,3.23 -2016-05-11 00:00:00,702.0,0.0,11.88,2.95 -2016-05-11 01:00:00,544.0,0.0,11.94,2.96 -2016-05-11 02:00:00,467.0,0.0,12.0,2.97 -2016-05-11 03:00:00,431.0,0.0,12.06,2.98 -2016-05-11 04:00:00,442.0,0.0,11.91,3.26 -2016-05-11 05:00:00,433.0,1.94,11.77,3.54 -2016-05-11 06:00:00,436.0,40.23,11.62,3.82 -2016-05-11 07:00:00,487.0,82.55,11.72,3.45 -2016-05-11 08:00:00,560.0,160.66,11.82,3.09 -2016-05-11 09:00:00,617.0,181.01,11.92,2.72 -2016-05-11 10:00:00,632.0,176.31,11.77,3.26 -2016-05-11 11:00:00,654.0,246.06,11.61,3.81 -2016-05-11 12:00:00,656.0,214.58,11.46,4.36 -2016-05-11 13:00:00,589.0,248.42,11.69,4.41 -2016-05-11 14:00:00,557.0,177.93,11.93,4.46 -2016-05-11 15:00:00,518.0,233.19,12.16,4.51 -2016-05-11 16:00:00,489.0,162.36,12.07,4.51 -2016-05-11 17:00:00,492.0,49.49,11.98,4.5 -2016-05-11 18:00:00,551.0,26.09,11.9,4.5 -2016-05-11 19:00:00,616.0,0.0,11.85,4.13 -2016-05-11 20:00:00,649.0,0.0,11.8,3.76 -2016-05-11 21:00:00,618.0,0.0,11.75,3.39 -2016-05-11 22:00:00,900.0,0.0,11.69,3.66 -2016-05-11 23:00:00,898.0,0.0,11.63,3.94 -2016-05-12 00:00:00,719.0,0.0,11.57,4.21 -2016-05-12 01:00:00,608.0,0.0,11.65,4.15 -2016-05-12 02:00:00,532.0,0.0,11.73,4.1 -2016-05-12 03:00:00,477.0,0.0,11.81,4.04 -2016-05-12 04:00:00,479.0,0.0,11.8,3.54 -2016-05-12 05:00:00,471.0,2.37,11.78,3.04 -2016-05-12 06:00:00,460.0,39.17,11.76,2.54 -2016-05-12 07:00:00,503.0,96.92,11.8,2.24 -2016-05-12 08:00:00,602.0,74.53,11.84,1.95 -2016-05-12 09:00:00,645.0,128.93,11.88,1.66 -2016-05-12 10:00:00,670.0,111.73,11.87,1.62 -2016-05-12 11:00:00,708.0,193.19,11.87,1.59 -2016-05-12 12:00:00,722.0,216.75,11.86,1.56 -2016-05-12 13:00:00,656.0,250.17,11.94,2.06 -2016-05-12 14:00:00,639.0,265.37,12.02,2.55 -2016-05-12 15:00:00,612.0,255.13,12.1,3.05 -2016-05-12 16:00:00,584.0,73.04,12.12,3.37 -2016-05-12 17:00:00,552.0,33.18,12.15,3.68 -2016-05-12 18:00:00,597.0,21.88,12.18,4.0 -2016-05-12 19:00:00,643.0,4.71,12.15,3.96 -2016-05-12 20:00:00,626.0,0.0,12.12,3.92 -2016-05-12 21:00:00,624.0,0.0,12.1,3.88 -2016-05-12 22:00:00,931.0,0.0,12.05,4.0 -2016-05-12 23:00:00,939.0,0.0,11.99,4.11 -2016-05-13 00:00:00,751.0,0.0,11.94,4.23 -2016-05-13 01:00:00,625.0,0.0,11.94,4.11 -2016-05-13 02:00:00,526.0,0.0,11.93,3.98 -2016-05-13 03:00:00,498.0,0.0,11.93,3.85 -2016-05-13 04:00:00,500.0,0.0,11.92,4.0 -2016-05-13 05:00:00,482.0,0.75,11.91,4.14 -2016-05-13 06:00:00,483.0,40.29,11.89,4.29 -2016-05-13 07:00:00,559.0,102.58,12.01,3.96 -2016-05-13 08:00:00,622.0,162.08,12.12,3.63 -2016-05-13 09:00:00,661.0,286.88,12.24,3.3 -2016-05-13 10:00:00,663.0,269.52,12.26,3.23 -2016-05-13 11:00:00,694.0,358.59,12.29,3.17 -2016-05-13 12:00:00,716.0,364.76,12.31,3.1 -2016-05-13 13:00:00,668.0,374.91,12.46,3.29 -2016-05-13 14:00:00,632.0,701.84,12.62,3.48 -2016-05-13 15:00:00,590.0,580.01,12.77,3.67 -2016-05-13 16:00:00,558.0,408.81,12.76,3.66 -2016-05-13 17:00:00,546.0,221.58,12.75,3.64 -2016-05-13 18:00:00,590.0,66.38,12.73,3.63 -2016-05-13 19:00:00,640.0,15.34,12.7,3.7 -2016-05-13 20:00:00,650.0,0.0,12.66,3.77 -2016-05-13 21:00:00,662.0,0.0,12.63,3.85 -2016-05-13 22:00:00,1025.0,0.0,12.6,3.84 -2016-05-13 23:00:00,1035.0,0.0,12.57,3.84 -2016-05-14 00:00:00,830.0,0.0,12.54,3.83 -2016-05-14 01:00:00,662.0,0.0,12.52,3.88 -2016-05-14 02:00:00,579.0,0.0,12.5,3.93 -2016-05-14 03:00:00,546.0,0.0,12.48,3.97 -2016-05-14 04:00:00,534.0,0.0,12.47,4.45 -2016-05-14 05:00:00,529.0,3.73,12.46,4.93 -2016-05-14 06:00:00,532.0,46.61,12.44,5.41 -2016-05-14 07:00:00,579.0,132.83,12.5,5.55 -2016-05-14 08:00:00,647.0,211.04,12.55,5.7 -2016-05-14 09:00:00,751.0,230.63,12.61,5.85 -2016-05-14 10:00:00,781.0,246.86,12.66,5.72 -2016-05-14 11:00:00,772.0,327.7,12.71,5.59 -2016-05-14 12:00:00,751.0,302.53,12.76,5.46 -2016-05-14 13:00:00,700.0,342.64,12.8,5.2 -2016-05-14 14:00:00,636.0,442.95,12.83,4.93 -2016-05-14 15:00:00,594.0,396.14,12.87,4.66 -2016-05-14 16:00:00,555.0,330.73,12.82,4.36 -2016-05-14 17:00:00,533.0,221.32,12.77,4.06 -2016-05-14 18:00:00,597.0,73.28,12.73,3.75 -2016-05-14 19:00:00,701.0,16.57,12.55,3.42 -2016-05-14 20:00:00,759.0,0.0,12.38,3.08 -2016-05-14 21:00:00,781.0,0.0,12.21,2.74 -2016-05-14 22:00:00,1112.0,0.0,12.05,2.34 -2016-05-14 23:00:00,1163.0,0.0,11.89,1.93 -2016-05-15 00:00:00,899.0,0.0,11.73,1.52 -2016-05-15 01:00:00,715.0,0.0,11.67,1.3 -2016-05-15 02:00:00,642.0,0.0,11.61,1.09 -2016-05-15 03:00:00,575.0,0.0,11.55,0.87 -2016-05-15 04:00:00,548.0,0.0,11.48,0.99 -2016-05-15 05:00:00,532.0,1.94,11.41,1.12 -2016-05-15 06:00:00,540.0,37.46,11.34,1.24 -2016-05-15 07:00:00,567.0,84.73,11.4,1.78 -2016-05-15 08:00:00,673.0,193.98,11.46,2.32 -2016-05-15 09:00:00,792.0,359.41,11.52,2.86 -2016-05-15 10:00:00,892.0,355.71,11.48,3.04 -2016-05-15 11:00:00,892.0,310.69,11.45,3.22 -2016-05-15 12:00:00,841.0,208.98,11.41,3.41 -2016-05-15 13:00:00,756.0,642.55,11.65,3.11 -2016-05-15 14:00:00,718.0,426.96,11.89,2.81 -2016-05-15 15:00:00,676.0,150.24,12.13,2.51 -2016-05-15 16:00:00,619.0,74.52,12.11,2.65 -2016-05-15 17:00:00,620.0,72.23,12.09,2.8 -2016-05-15 18:00:00,660.0,52.45,12.06,2.94 -2016-05-15 19:00:00,782.0,10.08,11.99,3.09 -2016-05-15 20:00:00,816.0,0.0,11.91,3.25 -2016-05-15 21:00:00,781.0,0.0,11.84,3.41 -2016-05-15 22:00:00,1112.0,0.0,11.75,3.47 -2016-05-15 23:00:00,1116.0,0.0,11.67,3.53 -2016-05-16 00:00:00,919.0,0.0,11.58,3.59 -2016-05-16 01:00:00,735.0,0.0,11.54,3.26 -2016-05-16 02:00:00,651.0,0.0,11.5,2.94 -2016-05-16 03:00:00,591.0,0.0,11.46,2.62 -2016-05-16 04:00:00,573.0,0.0,11.46,2.61 -2016-05-16 05:00:00,545.0,0.76,11.46,2.6 -2016-05-16 06:00:00,546.0,27.54,11.45,2.59 -2016-05-16 07:00:00,570.0,71.4,11.5,2.73 -2016-05-16 08:00:00,670.0,110.71,11.56,2.86 -2016-05-16 09:00:00,739.0,194.32,11.61,2.99 -2016-05-16 10:00:00,766.0,294.29,11.59,3.13 -2016-05-16 11:00:00,730.0,538.54,11.58,3.27 -2016-05-16 12:00:00,701.0,817.43,11.56,3.41 -2016-05-16 13:00:00,637.0,800.99,11.74,3.59 -2016-05-16 14:00:00,607.0,499.76,11.91,3.77 -2016-05-16 15:00:00,578.0,576.84,12.09,3.96 -2016-05-16 16:00:00,511.0,231.71,12.08,3.94 -2016-05-16 17:00:00,476.0,130.19,12.07,3.92 -2016-05-16 18:00:00,502.0,25.36,12.06,3.9 -2016-05-16 19:00:00,534.0,17.85,12.04,3.71 -2016-05-16 20:00:00,562.0,0.0,12.02,3.52 -2016-05-16 21:00:00,575.0,0.0,12.0,3.32 -2016-05-16 22:00:00,906.0,0.0,11.95,3.43 -2016-05-16 23:00:00,944.0,0.0,11.91,3.54 -2016-05-17 00:00:00,761.0,0.0,11.86,3.66 -2016-05-17 01:00:00,609.0,0.0,11.8,3.81 -2016-05-17 02:00:00,511.0,0.0,11.75,3.97 -2016-05-17 03:00:00,463.0,0.0,11.69,4.12 -2016-05-17 04:00:00,458.0,0.0,11.74,4.57 -2016-05-17 05:00:00,442.0,0.0,11.79,5.01 -2016-05-17 06:00:00,432.0,11.34,11.84,5.45 -2016-05-17 07:00:00,478.0,90.12,11.99,5.66 -2016-05-17 08:00:00,547.0,259.58,12.13,5.87 -2016-05-17 09:00:00,620.0,364.3,12.28,6.08 -2016-05-17 10:00:00,673.0,477.85,12.31,6.38 -2016-05-17 11:00:00,643.0,460.79,12.33,6.67 -2016-05-17 12:00:00,605.0,372.6,12.36,6.97 -2016-05-17 13:00:00,577.0,615.1,12.38,7.2 -2016-05-17 14:00:00,556.0,575.85,12.4,7.43 -2016-05-17 15:00:00,509.0,291.86,12.42,7.66 -2016-05-17 16:00:00,489.0,101.33,12.5,7.69 -2016-05-17 17:00:00,477.0,157.09,12.58,7.73 -2016-05-17 18:00:00,477.0,71.52,12.65,7.77 -2016-05-17 19:00:00,526.0,16.58,12.68,8.0 -2016-05-17 20:00:00,547.0,0.0,12.7,8.23 -2016-05-17 21:00:00,545.0,0.0,12.73,8.46 -2016-05-17 22:00:00,869.0,0.0,12.73,9.09 -2016-05-17 23:00:00,856.0,0.0,12.74,9.73 -2016-05-18 00:00:00,661.0,0.0,12.74,10.37 -2016-05-18 01:00:00,546.0,0.0,12.66,10.65 -2016-05-18 02:00:00,470.0,0.0,12.59,10.92 -2016-05-18 03:00:00,432.0,0.0,12.51,11.2 -2016-05-18 04:00:00,434.0,0.0,12.54,11.54 -2016-05-18 05:00:00,429.0,0.0,12.57,11.88 -2016-05-18 06:00:00,422.0,49.55,12.6,12.22 -2016-05-18 07:00:00,491.0,184.39,12.44,12.22 -2016-05-18 08:00:00,545.0,386.53,12.29,12.22 -2016-05-18 09:00:00,648.0,488.58,12.13,12.22 -2016-05-18 10:00:00,673.0,729.58,11.96,12.42 -2016-05-18 11:00:00,645.0,799.04,11.8,12.63 -2016-05-18 12:00:00,649.0,579.03,11.63,12.83 -2016-05-18 13:00:00,583.0,785.57,11.93,12.13 -2016-05-18 14:00:00,542.0,653.64,12.24,11.43 -2016-05-18 15:00:00,528.0,565.94,12.54,10.73 -2016-05-18 16:00:00,519.0,451.56,12.56,10.5 -2016-05-18 17:00:00,486.0,241.18,12.57,10.27 -2016-05-18 18:00:00,498.0,48.51,12.59,10.04 -2016-05-18 19:00:00,559.0,17.83,12.62,9.84 -2016-05-18 20:00:00,549.0,0.0,12.65,9.65 -2016-05-18 21:00:00,543.0,0.0,12.69,9.45 -2016-05-18 22:00:00,869.0,0.0,12.64,9.18 -2016-05-18 23:00:00,858.0,0.0,12.58,8.91 -2016-05-19 00:00:00,681.0,0.0,12.53,8.65 -2016-05-19 01:00:00,572.0,0.0,12.41,8.56 -2016-05-19 02:00:00,489.0,0.0,12.3,8.46 -2016-05-19 03:00:00,461.0,0.0,12.18,8.37 -2016-05-19 04:00:00,468.0,0.0,12.18,7.93 -2016-05-19 05:00:00,447.0,0.75,12.18,7.49 -2016-05-19 06:00:00,430.0,51.61,12.18,7.05 -2016-05-19 07:00:00,482.0,36.1,12.35,6.68 -2016-05-19 08:00:00,548.0,75.42,12.52,6.31 -2016-05-19 09:00:00,603.0,133.43,12.69,5.94 -2016-05-19 10:00:00,620.0,191.9,12.69,6.31 -2016-05-19 11:00:00,636.0,105.87,12.7,6.67 -2016-05-19 12:00:00,657.0,191.53,12.7,7.03 -2016-05-19 13:00:00,623.0,154.25,12.82,7.77 -2016-05-19 14:00:00,603.0,172.58,12.94,8.51 -2016-05-19 15:00:00,616.0,120.13,13.06,9.24 -2016-05-19 16:00:00,590.0,106.69,13.05,9.29 -2016-05-19 17:00:00,577.0,61.49,13.04,9.34 -2016-05-19 18:00:00,621.0,6.5,13.02,9.39 -2016-05-19 19:00:00,644.0,15.94,13.04,9.12 -2016-05-19 20:00:00,662.0,0.0,13.06,8.85 -2016-05-19 21:00:00,638.0,0.0,13.08,8.58 -2016-05-19 22:00:00,956.0,0.0,13.08,8.33 -2016-05-19 23:00:00,899.0,0.0,13.07,8.07 -2016-05-20 00:00:00,710.0,0.0,13.07,7.82 -2016-05-20 01:00:00,591.0,0.0,13.09,7.45 -2016-05-20 02:00:00,510.0,0.0,13.12,7.09 -2016-05-20 03:00:00,475.0,0.0,13.14,6.72 -2016-05-20 04:00:00,472.0,0.0,13.09,6.34 -2016-05-20 05:00:00,461.0,4.19,13.03,5.97 -2016-05-20 06:00:00,454.0,56.32,12.97,5.6 -2016-05-20 07:00:00,504.0,165.82,12.95,5.65 -2016-05-20 08:00:00,573.0,157.45,12.93,5.7 -2016-05-20 09:00:00,596.0,341.32,12.91,5.75 -2016-05-20 10:00:00,623.0,229.74,12.86,6.11 -2016-05-20 11:00:00,602.0,389.52,12.82,6.48 -2016-05-20 12:00:00,627.0,368.51,12.77,6.84 -2016-05-20 13:00:00,584.0,344.51,12.89,7.54 -2016-05-20 14:00:00,554.0,91.7,13.01,8.25 -2016-05-20 15:00:00,533.0,102.69,13.13,8.95 -2016-05-20 16:00:00,525.0,102.7,13.14,9.25 -2016-05-20 17:00:00,518.0,117.37,13.15,9.54 -2016-05-20 18:00:00,570.0,8.23,13.15,9.83 -2016-05-20 19:00:00,627.0,17.16,13.16,10.1 -2016-05-20 20:00:00,625.0,0.0,13.16,10.37 -2016-05-20 21:00:00,626.0,0.0,13.17,10.63 -2016-05-20 22:00:00,958.0,0.0,13.16,10.7 -2016-05-20 23:00:00,925.0,0.0,13.16,10.77 -2016-05-21 00:00:00,719.0,0.0,13.15,10.84 -2016-05-21 01:00:00,603.0,0.0,13.01,11.09 -2016-05-21 02:00:00,509.0,0.0,12.88,11.35 -2016-05-21 03:00:00,470.0,0.0,12.74,11.6 -2016-05-21 04:00:00,494.0,0.0,12.71,11.2 -2016-05-21 05:00:00,473.0,0.0,12.68,10.79 -2016-05-21 06:00:00,495.0,35.3,12.64,10.39 -2016-05-21 07:00:00,524.0,63.16,12.66,10.29 -2016-05-21 08:00:00,608.0,49.45,12.67,10.2 -2016-05-21 09:00:00,667.0,125.97,12.69,10.11 -2016-05-21 10:00:00,713.0,119.61,12.66,9.84 -2016-05-21 11:00:00,723.0,125.96,12.64,9.57 -2016-05-21 12:00:00,804.0,131.51,12.62,9.3 -2016-05-21 13:00:00,750.0,200.5,12.51,9.17 -2016-05-21 14:00:00,769.0,257.55,12.4,9.05 -2016-05-21 15:00:00,754.0,236.03,12.29,8.92 -2016-05-21 16:00:00,742.0,428.16,12.43,8.15 -2016-05-21 17:00:00,735.0,205.81,12.57,7.37 -2016-05-21 18:00:00,745.0,80.29,12.72,6.59 -2016-05-21 19:00:00,781.0,21.57,12.74,5.88 -2016-05-21 20:00:00,724.0,0.0,12.76,5.17 -2016-05-21 21:00:00,724.0,0.0,12.78,4.46 -2016-05-21 22:00:00,1053.0,0.0,12.63,4.04 -2016-05-21 23:00:00,1011.0,0.0,12.49,3.63 -2016-05-22 00:00:00,818.0,0.0,12.34,3.21 -2016-05-22 01:00:00,665.0,0.0,12.41,4.41 -2016-05-22 02:00:00,577.0,0.0,12.49,5.6 -2016-05-22 03:00:00,526.0,0.0,12.56,6.8 -2016-05-22 04:00:00,538.0,0.0,12.52,6.62 -2016-05-22 05:00:00,541.0,8.38,12.47,6.44 -2016-05-22 06:00:00,541.0,32.83,12.42,6.26 -2016-05-22 07:00:00,553.0,185.36,12.19,6.19 -2016-05-22 08:00:00,640.0,367.93,11.95,6.11 -2016-05-22 09:00:00,723.0,575.77,11.72,6.04 -2016-05-22 10:00:00,739.0,720.68,11.51,6.17 -2016-05-22 11:00:00,680.0,824.83,11.3,6.3 -2016-05-22 12:00:00,653.0,820.83,11.09,6.43 -2016-05-22 13:00:00,591.0,820.12,11.12,7.15 -2016-05-22 14:00:00,531.0,741.4,11.14,7.87 -2016-05-22 15:00:00,493.0,614.82,11.17,8.59 -2016-05-22 16:00:00,466.0,451.88,11.41,8.53 -2016-05-22 17:00:00,447.0,245.37,11.65,8.47 -2016-05-22 18:00:00,486.0,56.02,11.89,8.41 -2016-05-22 19:00:00,542.0,22.96,11.99,8.34 -2016-05-22 20:00:00,552.0,0.0,12.09,8.26 -2016-05-22 21:00:00,524.0,0.0,12.19,8.18 -2016-05-22 22:00:00,849.0,0.0,12.19,8.14 -2016-05-22 23:00:00,881.0,0.0,12.2,8.11 -2016-05-23 00:00:00,693.0,0.0,12.2,8.07 -2016-05-23 01:00:00,560.0,0.0,12.31,7.82 -2016-05-23 02:00:00,476.0,0.0,12.41,7.57 -2016-05-23 03:00:00,444.0,0.0,12.52,7.32 -2016-05-23 04:00:00,455.0,0.0,12.48,7.08 -2016-05-23 05:00:00,444.0,9.5,12.45,6.83 -2016-05-23 06:00:00,428.0,41.9,12.41,6.58 -2016-05-23 07:00:00,472.0,129.43,12.4,6.01 -2016-05-23 08:00:00,546.0,299.46,12.39,5.44 -2016-05-23 09:00:00,589.0,412.21,12.38,4.87 -2016-05-23 10:00:00,596.0,706.85,12.3,4.82 -2016-05-23 11:00:00,590.0,802.28,12.21,4.78 -2016-05-23 12:00:00,595.0,837.72,12.13,4.73 -2016-05-23 13:00:00,553.0,815.47,12.1,4.76 -2016-05-23 14:00:00,497.0,756.83,12.07,4.79 -2016-05-23 15:00:00,482.0,622.08,12.04,4.81 -2016-05-23 16:00:00,479.0,445.23,12.02,4.87 -2016-05-23 17:00:00,428.0,236.85,12.0,4.92 -2016-05-23 18:00:00,482.0,59.13,11.98,4.98 -2016-05-23 19:00:00,535.0,23.58,11.93,4.88 -2016-05-23 20:00:00,536.0,0.0,11.88,4.79 -2016-05-23 21:00:00,523.0,0.0,11.83,4.69 -2016-05-23 22:00:00,857.0,0.0,11.78,4.7 -2016-05-23 23:00:00,844.0,0.0,11.74,4.71 -2016-05-24 00:00:00,649.0,0.0,11.69,4.72 -2016-05-24 01:00:00,526.0,0.0,11.55,4.87 -2016-05-24 02:00:00,442.0,0.0,11.41,5.03 -2016-05-24 03:00:00,422.0,0.0,11.27,5.19 -2016-05-24 04:00:00,429.0,0.0,11.33,5.62 -2016-05-24 05:00:00,427.0,10.13,11.39,6.06 -2016-05-24 06:00:00,426.0,36.4,11.44,6.5 -2016-05-24 07:00:00,495.0,150.19,11.71,6.69 -2016-05-24 08:00:00,583.0,343.18,11.99,6.88 -2016-05-24 09:00:00,598.0,528.44,12.26,7.08 -2016-05-24 10:00:00,603.0,633.28,12.51,7.37 -2016-05-24 11:00:00,571.0,535.3,12.77,7.67 -2016-05-24 12:00:00,629.0,499.72,13.02,7.97 -2016-05-24 13:00:00,551.0,557.45,13.27,8.03 -2016-05-24 14:00:00,491.0,572.2,13.52,8.08 -2016-05-24 15:00:00,480.0,537.8,13.77,8.14 -2016-05-24 16:00:00,464.0,260.02,13.84,8.78 -2016-05-24 17:00:00,473.0,157.95,13.91,9.42 -2016-05-24 18:00:00,526.0,67.4,13.98,10.06 -2016-05-24 19:00:00,583.0,11.12,13.98,10.17 -2016-05-24 20:00:00,602.0,0.0,13.98,10.28 -2016-05-24 21:00:00,576.0,0.0,13.97,10.39 -2016-05-24 22:00:00,875.0,0.0,13.9,10.47 -2016-05-24 23:00:00,878.0,0.0,13.82,10.56 -2016-05-25 00:00:00,691.0,0.0,13.75,10.65 -2016-05-25 01:00:00,560.0,0.0,13.58,10.48 -2016-05-25 02:00:00,488.0,0.0,13.41,10.31 -2016-05-25 03:00:00,432.0,0.0,13.24,10.14 -2016-05-25 04:00:00,450.0,0.0,13.04,10.17 -2016-05-25 05:00:00,444.0,5.19,12.84,10.2 -2016-05-25 06:00:00,445.0,51.26,12.63,10.23 -2016-05-25 07:00:00,509.0,113.94,12.67,10.27 -2016-05-25 08:00:00,592.0,124.41,12.71,10.31 -2016-05-25 09:00:00,616.0,347.1,12.75,10.34 -2016-05-25 10:00:00,641.0,400.18,12.9,10.2 -2016-05-25 11:00:00,643.0,655.31,13.04,10.05 -2016-05-25 12:00:00,643.0,796.59,13.19,9.9 -2016-05-25 13:00:00,579.0,570.84,13.45,9.85 -2016-05-25 14:00:00,548.0,506.78,13.7,9.8 -2016-05-25 15:00:00,516.0,446.65,13.96,9.75 -2016-05-25 16:00:00,486.0,292.35,13.86,9.63 -2016-05-25 17:00:00,492.0,236.45,13.77,9.51 -2016-05-25 18:00:00,520.0,61.74,13.68,9.39 -2016-05-25 19:00:00,576.0,21.5,13.55,9.03 -2016-05-25 20:00:00,591.0,0.0,13.43,8.68 -2016-05-25 21:00:00,561.0,0.0,13.31,8.32 -2016-05-25 22:00:00,857.0,0.0,13.2,7.87 -2016-05-25 23:00:00,856.0,0.0,13.1,7.42 -2016-05-26 00:00:00,666.0,0.0,12.99,6.97 -2016-05-26 01:00:00,562.0,0.0,12.88,6.54 -2016-05-26 02:00:00,480.0,0.0,12.78,6.11 -2016-05-26 03:00:00,438.0,0.0,12.67,5.68 -2016-05-26 04:00:00,446.0,0.0,12.62,5.51 -2016-05-26 05:00:00,434.0,5.71,12.58,5.34 -2016-05-26 06:00:00,433.0,60.6,12.53,5.17 -2016-05-26 07:00:00,489.0,170.94,12.67,5.25 -2016-05-26 08:00:00,576.0,273.43,12.81,5.32 -2016-05-26 09:00:00,603.0,191.14,12.95,5.39 -2016-05-26 10:00:00,576.0,92.28,12.94,5.12 -2016-05-26 11:00:00,602.0,253.39,12.92,4.84 -2016-05-26 12:00:00,615.0,291.94,12.91,4.57 -2016-05-26 13:00:00,551.0,143.86,12.93,3.88 -2016-05-26 14:00:00,527.0,146.0,12.94,3.2 -2016-05-26 15:00:00,490.0,235.43,12.96,2.51 -2016-05-26 16:00:00,482.0,65.79,12.94,2.62 -2016-05-26 17:00:00,468.0,24.54,12.92,2.73 -2016-05-26 18:00:00,532.0,47.7,12.9,2.84 -2016-05-26 19:00:00,569.0,5.18,12.86,2.6 -2016-05-26 20:00:00,545.0,0.0,12.82,2.36 -2016-05-26 21:00:00,516.0,0.0,12.78,2.12 -2016-05-26 22:00:00,834.0,0.0,12.68,1.71 -2016-05-26 23:00:00,842.0,0.0,12.58,1.29 -2016-05-27 00:00:00,655.0,0.0,12.48,0.87 -2016-05-27 01:00:00,529.0,0.0,12.4,0.85 -2016-05-27 02:00:00,435.0,0.0,12.31,0.82 -2016-05-27 03:00:00,401.0,0.0,12.23,0.8 -2016-05-27 04:00:00,405.0,0.0,12.19,1.04 -2016-05-27 05:00:00,413.0,11.21,12.16,1.28 -2016-05-27 06:00:00,403.0,60.5,12.12,1.52 -2016-05-27 07:00:00,462.0,139.54,12.25,1.53 -2016-05-27 08:00:00,523.0,341.05,12.37,1.54 -2016-05-27 09:00:00,541.0,514.85,12.5,1.54 -2016-05-27 10:00:00,564.0,417.39,12.51,1.63 -2016-05-27 11:00:00,576.0,654.63,12.53,1.71 -2016-05-27 12:00:00,596.0,644.35,12.54,1.79 -2016-05-27 13:00:00,530.0,728.73,12.77,1.88 -2016-05-27 14:00:00,484.0,695.36,13.0,1.97 -2016-05-27 15:00:00,467.0,581.28,13.23,2.06 -2016-05-27 16:00:00,420.0,416.35,13.25,2.3 -2016-05-27 17:00:00,405.0,225.67,13.27,2.54 -2016-05-27 18:00:00,429.0,84.79,13.28,2.79 -2016-05-27 19:00:00,456.0,11.73,13.21,2.76 -2016-05-27 20:00:00,494.0,0.0,13.14,2.73 -2016-05-27 21:00:00,467.0,0.0,13.06,2.7 -2016-05-27 22:00:00,815.0,0.0,12.7,2.78 -2016-05-27 23:00:00,824.0,0.0,12.34,2.85 -2016-05-28 00:00:00,663.0,0.0,11.98,2.92 -2016-05-28 01:00:00,506.0,0.0,11.8,2.96 -2016-05-28 02:00:00,413.0,0.0,11.62,2.99 -2016-05-28 03:00:00,396.0,0.0,11.44,3.02 -2016-05-28 04:00:00,387.0,0.0,11.44,3.18 -2016-05-28 05:00:00,382.0,6.8,11.43,3.33 -2016-05-28 06:00:00,381.0,61.35,11.42,3.49 -2016-05-28 07:00:00,408.0,116.49,11.69,3.71 -2016-05-28 08:00:00,491.0,261.3,11.95,3.94 -2016-05-28 09:00:00,541.0,304.57,12.22,4.17 -2016-05-28 10:00:00,531.0,312.43,12.39,4.5 -2016-05-28 11:00:00,545.0,768.42,12.56,4.84 -2016-05-28 12:00:00,573.0,579.69,12.73,5.17 -2016-05-28 13:00:00,522.0,732.27,12.94,5.39 -2016-05-28 14:00:00,463.0,706.73,13.14,5.6 -2016-05-28 15:00:00,439.0,578.88,13.35,5.82 -2016-05-28 16:00:00,434.0,406.79,13.12,6.43 -2016-05-28 17:00:00,435.0,100.88,12.89,7.04 -2016-05-28 18:00:00,480.0,67.78,12.66,7.66 -2016-05-28 19:00:00,565.0,1.12,12.42,7.64 -2016-05-28 20:00:00,554.0,0.0,12.17,7.62 -2016-05-28 21:00:00,560.0,0.0,11.92,7.6 -2016-05-28 22:00:00,870.0,0.0,11.87,8.25 -2016-05-28 23:00:00,862.0,0.0,11.83,8.9 -2016-05-29 00:00:00,671.0,0.0,11.78,9.54 -2016-05-29 01:00:00,534.0,0.0,12.04,8.32 -2016-05-29 02:00:00,453.0,0.0,12.29,7.09 -2016-05-29 03:00:00,430.0,0.0,12.55,5.86 -2016-05-29 04:00:00,421.0,0.0,12.54,5.9 -2016-05-29 05:00:00,389.0,1.12,12.53,5.94 -2016-05-29 06:00:00,390.0,60.09,12.51,5.99 -2016-05-29 07:00:00,407.0,140.59,12.66,5.84 -2016-05-29 08:00:00,510.0,243.38,12.81,5.69 -2016-05-29 09:00:00,575.0,257.57,12.96,5.54 -2016-05-29 10:00:00,594.0,240.57,13.04,5.57 -2016-05-29 11:00:00,566.0,601.36,13.12,5.59 -2016-05-29 12:00:00,549.0,558.85,13.2,5.61 -2016-05-29 13:00:00,499.0,730.52,13.43,5.28 -2016-05-29 14:00:00,476.0,674.05,13.65,4.95 -2016-05-29 15:00:00,458.0,545.5,13.88,4.62 -2016-05-29 16:00:00,416.0,318.19,13.89,4.61 -2016-05-29 17:00:00,392.0,113.56,13.9,4.6 -2016-05-29 18:00:00,409.0,84.09,13.91,4.59 -2016-05-29 19:00:00,454.0,7.23,13.87,5.05 -2016-05-29 20:00:00,454.0,0.0,13.83,5.51 -2016-05-29 21:00:00,453.0,0.0,13.79,5.97 -2016-05-29 22:00:00,776.0,0.0,13.79,6.1 -2016-05-29 23:00:00,773.0,0.0,13.78,6.23 -2016-05-30 00:00:00,582.0,0.0,13.78,6.36 -2016-05-30 01:00:00,479.0,0.0,13.71,6.17 -2016-05-30 02:00:00,406.0,0.0,13.65,5.97 -2016-05-30 03:00:00,368.0,0.0,13.58,5.78 -2016-05-30 04:00:00,377.0,0.0,13.52,5.52 -2016-05-30 05:00:00,364.0,11.73,13.46,5.26 -2016-05-30 06:00:00,345.0,34.58,13.4,4.99 -2016-05-30 07:00:00,388.0,184.46,13.46,4.94 -2016-05-30 08:00:00,464.0,351.44,13.53,4.89 -2016-05-30 09:00:00,515.0,512.34,13.59,4.84 -2016-05-30 10:00:00,514.0,680.09,13.56,4.9 -2016-05-30 11:00:00,523.0,778.6,13.54,4.96 -2016-05-30 12:00:00,563.0,818.27,13.51,5.02 -2016-05-30 13:00:00,499.0,779.8,13.55,5.32 -2016-05-30 14:00:00,481.0,739.55,13.6,5.62 -2016-05-30 15:00:00,444.0,602.08,13.64,5.92 -2016-05-30 16:00:00,419.0,435.89,13.62,5.94 -2016-05-30 17:00:00,394.0,242.63,13.6,5.96 -2016-05-30 18:00:00,436.0,54.36,13.59,5.99 -2016-05-30 19:00:00,500.0,20.22,13.52,6.19 -2016-05-30 20:00:00,482.0,0.0,13.46,6.39 -2016-05-30 21:00:00,477.0,0.0,13.4,6.59 -2016-05-30 22:00:00,767.0,0.0,13.4,6.81 -2016-05-30 23:00:00,754.0,0.0,13.4,7.03 -2016-05-31 00:00:00,570.0,0.0,13.4,7.24 -2016-05-31 01:00:00,458.0,0.0,13.46,7.3 -2016-05-31 02:00:00,410.0,0.0,13.53,7.35 -2016-05-31 03:00:00,360.0,0.0,13.59,7.41 -2016-05-31 04:00:00,366.0,0.0,13.62,7.63 -2016-05-31 05:00:00,360.0,11.72,13.66,7.86 -2016-05-31 06:00:00,334.0,28.37,13.69,8.08 -2016-05-31 07:00:00,383.0,184.99,13.75,8.02 -2016-05-31 08:00:00,449.0,394.23,13.82,7.96 -2016-05-31 09:00:00,514.0,574.58,13.88,7.9 -2016-05-31 10:00:00,515.0,723.3,13.81,8.03 -2016-05-31 11:00:00,526.0,819.87,13.75,8.15 -2016-05-31 12:00:00,555.0,854.05,13.68,8.28 -2016-05-31 13:00:00,498.0,842.32,13.75,8.89 -2016-05-31 14:00:00,474.0,773.68,13.81,9.51 -2016-05-31 15:00:00,467.0,639.08,13.88,10.12 -2016-05-31 16:00:00,429.0,460.79,13.94,10.24 -2016-05-31 17:00:00,396.0,249.88,13.99,10.36 -2016-05-31 18:00:00,434.0,55.79,14.05,10.48 -2016-05-31 19:00:00,459.0,22.08,14.1,10.38 -2016-05-31 20:00:00,460.0,0.0,14.16,10.27 -2016-05-31 21:00:00,442.0,0.0,14.22,10.17 -2016-05-31 22:00:00,732.0,0.0,14.24,9.94 -2016-05-31 23:00:00,742.0,0.0,14.27,9.71 -2016-06-01 00:00:00,560.0,0.0,14.29,9.49 -2016-06-01 01:00:00,452.0,0.0,14.27,9.39 -2016-06-01 02:00:00,382.0,0.0,14.26,9.29 -2016-06-01 03:00:00,344.0,0.0,14.24,9.19 -2016-06-01 04:00:00,353.0,0.0,14.19,9.11 -2016-06-01 05:00:00,338.0,11.69,14.14,9.04 -2016-06-01 06:00:00,339.0,33.81,14.09,8.97 -2016-06-01 07:00:00,392.0,182.3,13.99,8.85 -2016-06-01 08:00:00,464.0,384.3,13.89,8.74 -2016-06-01 09:00:00,520.0,356.39,13.78,8.62 -2016-06-01 10:00:00,499.0,346.5,13.72,8.41 -2016-06-01 11:00:00,490.0,269.49,13.65,8.2 -2016-06-01 12:00:00,530.0,352.17,13.58,7.99 -2016-06-01 13:00:00,491.0,362.88,13.72,7.54 -2016-06-01 14:00:00,455.0,687.59,13.85,7.09 -2016-06-01 15:00:00,430.0,392.71,13.98,6.63 -2016-06-01 16:00:00,419.0,236.24,14.04,6.48 -2016-06-01 17:00:00,414.0,130.65,14.1,6.32 -2016-06-01 18:00:00,441.0,49.01,14.16,6.17 -2016-06-01 19:00:00,503.0,7.22,14.22,5.97 -2016-06-01 20:00:00,501.0,0.0,14.27,5.77 -2016-06-01 21:00:00,483.0,0.0,14.32,5.57 -2016-06-01 22:00:00,792.0,0.0,14.32,5.42 -2016-06-01 23:00:00,804.0,0.0,14.32,5.27 -2016-06-02 00:00:00,617.0,0.0,14.32,5.12 -2016-06-02 01:00:00,450.0,0.0,14.2,5.01 -2016-06-02 02:00:00,391.0,0.0,14.08,4.91 -2016-06-02 03:00:00,378.0,0.0,13.97,4.8 -2016-06-02 04:00:00,379.0,0.0,13.97,4.97 -2016-06-02 05:00:00,340.0,5.15,13.98,5.14 -2016-06-02 06:00:00,333.0,16.98,13.98,5.31 -2016-06-02 07:00:00,392.0,128.09,13.97,5.6 -2016-06-02 08:00:00,478.0,154.7,13.96,5.9 -2016-06-02 09:00:00,525.0,190.37,13.94,6.19 -2016-06-02 10:00:00,524.0,256.18,13.94,6.25 -2016-06-02 11:00:00,526.0,188.63,13.93,6.3 -2016-06-02 12:00:00,564.0,204.68,13.92,6.36 -2016-06-02 13:00:00,520.0,181.4,14.06,6.18 -2016-06-02 14:00:00,462.0,369.87,14.19,6.01 -2016-06-02 15:00:00,428.0,302.57,14.32,5.83 -2016-06-02 16:00:00,430.0,219.02,14.25,5.58 -2016-06-02 17:00:00,426.0,119.53,14.18,5.32 -2016-06-02 18:00:00,452.0,37.93,14.11,5.06 -2016-06-02 19:00:00,514.0,12.27,14.02,5.14 -2016-06-02 20:00:00,495.0,0.0,13.92,5.21 -2016-06-02 21:00:00,476.0,0.0,13.82,5.28 -2016-06-02 22:00:00,773.0,0.0,13.72,5.27 -2016-06-02 23:00:00,779.0,0.0,13.61,5.26 -2016-06-03 00:00:00,611.0,0.0,13.5,5.26 -2016-06-03 01:00:00,458.0,0.0,13.31,5.6 -2016-06-03 02:00:00,394.0,0.0,13.12,5.95 -2016-06-03 03:00:00,383.0,0.0,12.94,6.3 -2016-06-03 04:00:00,373.0,0.0,12.89,6.2 -2016-06-03 05:00:00,370.0,11.77,12.85,6.09 -2016-06-03 06:00:00,354.0,45.52,12.81,5.99 -2016-06-03 07:00:00,416.0,38.19,12.88,6.12 -2016-06-03 08:00:00,503.0,196.89,12.95,6.25 -2016-06-03 09:00:00,521.0,543.91,13.02,6.39 -2016-06-03 10:00:00,499.0,679.05,13.15,6.4 -2016-06-03 11:00:00,543.0,786.46,13.28,6.4 -2016-06-03 12:00:00,568.0,727.68,13.4,6.41 -2016-06-03 13:00:00,543.0,696.82,13.7,6.32 -2016-06-03 14:00:00,489.0,516.64,14.0,6.23 -2016-06-03 15:00:00,472.0,317.33,14.31,6.14 -2016-06-03 16:00:00,453.0,376.65,14.31,6.0 -2016-06-03 17:00:00,435.0,231.96,14.31,5.85 -2016-06-03 18:00:00,464.0,89.74,14.31,5.71 -2016-06-03 19:00:00,507.0,30.46,14.22,5.54 -2016-06-03 20:00:00,516.0,0.0,14.13,5.38 -2016-06-03 21:00:00,510.0,0.0,14.03,5.21 -2016-06-03 22:00:00,859.0,0.0,13.87,5.08 -2016-06-03 23:00:00,855.0,0.0,13.71,4.95 -2016-06-04 00:00:00,660.0,0.0,13.55,4.81 -2016-06-04 01:00:00,497.0,0.0,13.5,4.61 -2016-06-04 02:00:00,444.0,0.0,13.44,4.4 -2016-06-04 03:00:00,406.0,0.0,13.39,4.19 -2016-06-04 04:00:00,397.0,0.0,13.34,4.16 -2016-06-04 05:00:00,387.0,4.18,13.29,4.12 -2016-06-04 06:00:00,377.0,22.44,13.24,4.08 -2016-06-04 07:00:00,402.0,51.38,13.36,3.94 -2016-06-04 08:00:00,508.0,109.02,13.48,3.8 -2016-06-04 09:00:00,548.0,192.94,13.6,3.66 -2016-06-04 10:00:00,563.0,293.08,13.67,3.3 -2016-06-04 11:00:00,581.0,254.79,13.74,2.94 -2016-06-04 12:00:00,583.0,165.4,13.81,2.58 -2016-06-04 13:00:00,552.0,314.88,13.93,2.54 -2016-06-04 14:00:00,503.0,448.49,14.05,2.51 -2016-06-04 15:00:00,498.0,358.5,14.18,2.47 -2016-06-04 16:00:00,479.0,371.32,14.15,2.5 -2016-06-04 17:00:00,475.0,243.81,14.13,2.52 -2016-06-04 18:00:00,517.0,77.13,14.11,2.55 -2016-06-04 19:00:00,562.0,28.46,14.09,2.51 -2016-06-04 20:00:00,530.0,0.0,14.07,2.48 -2016-06-04 21:00:00,556.0,0.0,14.05,2.44 -2016-06-04 22:00:00,839.0,0.0,14.0,2.28 -2016-06-04 23:00:00,853.0,0.0,13.95,2.12 -2016-06-05 00:00:00,682.0,0.0,13.89,1.96 -2016-06-05 01:00:00,495.0,0.0,13.86,2.01 -2016-06-05 02:00:00,446.0,0.0,13.83,2.06 -2016-06-05 03:00:00,421.0,0.0,13.8,2.11 -2016-06-05 04:00:00,391.0,0.0,13.8,2.07 -2016-06-05 05:00:00,372.0,5.66,13.81,2.03 -2016-06-05 06:00:00,363.0,27.19,13.81,1.99 -2016-06-05 07:00:00,398.0,96.33,13.89,2.09 -2016-06-05 08:00:00,471.0,220.17,13.96,2.19 -2016-06-05 09:00:00,527.0,261.3,14.03,2.29 -2016-06-05 10:00:00,582.0,225.92,14.05,2.1 -2016-06-05 11:00:00,604.0,194.42,14.07,1.9 -2016-06-05 12:00:00,628.0,160.85,14.08,1.71 -2016-06-05 13:00:00,546.0,194.19,14.33,1.33 -2016-06-05 14:00:00,485.0,207.23,14.58,0.96 -2016-06-05 15:00:00,467.0,203.29,14.84,0.58 -2016-06-05 16:00:00,428.0,190.51,14.76,1.0 -2016-06-05 17:00:00,389.0,123.5,14.69,1.42 -2016-06-05 18:00:00,419.0,70.6,14.62,1.83 -2016-06-05 19:00:00,429.0,22.59,14.63,2.27 -2016-06-05 20:00:00,450.0,0.0,14.64,2.71 -2016-06-05 21:00:00,435.0,0.0,14.64,3.14 -2016-06-05 22:00:00,744.0,0.0,14.6,3.26 -2016-06-05 23:00:00,782.0,0.0,14.55,3.37 -2016-06-06 00:00:00,581.0,0.0,14.5,3.48 -2016-06-06 01:00:00,455.0,0.0,14.47,3.61 -2016-06-06 02:00:00,405.0,0.0,14.44,3.75 -2016-06-06 03:00:00,367.0,0.0,14.4,3.89 -2016-06-06 04:00:00,356.0,0.0,14.4,3.97 -2016-06-06 05:00:00,338.0,14.0,14.4,4.05 -2016-06-06 06:00:00,335.0,51.5,14.39,4.12 -2016-06-06 07:00:00,376.0,119.37,14.54,3.82 -2016-06-06 08:00:00,450.0,264.24,14.69,3.51 -2016-06-06 09:00:00,478.0,524.83,14.85,3.2 -2016-06-06 10:00:00,486.0,660.07,14.88,2.74 -2016-06-06 11:00:00,508.0,454.21,14.92,2.29 -2016-06-06 12:00:00,531.0,476.44,14.95,1.83 -2016-06-06 13:00:00,456.0,734.86,14.94,1.44 -2016-06-06 14:00:00,433.0,653.87,14.92,1.05 -2016-06-06 15:00:00,413.0,336.29,14.9,0.66 -2016-06-06 16:00:00,407.0,145.14,14.87,0.69 -2016-06-06 17:00:00,390.0,195.73,14.84,0.72 -2016-06-06 18:00:00,406.0,8.72,14.81,0.74 -2016-06-06 19:00:00,461.0,29.61,14.81,0.73 -2016-06-06 20:00:00,455.0,0.0,14.81,0.71 -2016-06-06 21:00:00,451.0,0.0,14.81,0.69 -2016-06-06 22:00:00,748.0,0.0,14.75,0.87 -2016-06-06 23:00:00,724.0,0.0,14.68,1.05 -2016-06-07 00:00:00,531.0,0.0,14.61,1.23 -2016-06-07 01:00:00,438.0,0.0,14.61,1.07 -2016-06-07 02:00:00,376.0,0.0,14.6,0.91 -2016-06-07 03:00:00,343.0,0.0,14.6,0.74 -2016-06-07 04:00:00,346.0,0.0,14.55,0.85 -2016-06-07 05:00:00,321.0,3.67,14.51,0.96 -2016-06-07 06:00:00,333.0,62.97,14.47,1.06 -2016-06-07 07:00:00,408.0,132.29,14.52,1.13 -2016-06-07 08:00:00,444.0,99.39,14.57,1.19 -2016-06-07 09:00:00,503.0,207.88,14.62,1.26 -2016-06-07 10:00:00,505.0,549.35,14.61,1.41 -2016-06-07 11:00:00,532.0,659.82,14.59,1.57 -2016-06-07 12:00:00,563.0,439.7,14.57,1.72 -2016-06-07 13:00:00,484.0,722.92,14.61,1.92 -2016-06-07 14:00:00,427.0,691.41,14.65,2.11 -2016-06-07 15:00:00,450.0,597.52,14.69,2.3 -2016-06-07 16:00:00,411.0,435.74,14.68,2.47 -2016-06-07 17:00:00,380.0,243.5,14.67,2.63 -2016-06-07 18:00:00,432.0,92.44,14.65,2.8 -2016-06-07 19:00:00,469.0,10.5,14.62,2.81 -2016-06-07 20:00:00,436.0,0.0,14.59,2.82 -2016-06-07 21:00:00,444.0,0.0,14.55,2.83 -2016-06-07 22:00:00,761.0,0.0,14.46,2.87 -2016-06-07 23:00:00,754.0,0.0,14.37,2.92 -2016-06-08 00:00:00,531.0,0.0,14.28,2.97 -2016-06-08 01:00:00,440.0,0.0,14.25,2.8 -2016-06-08 02:00:00,372.0,0.0,14.22,2.63 -2016-06-08 03:00:00,343.0,0.0,14.19,2.47 -2016-06-08 04:00:00,343.0,0.0,14.14,2.81 -2016-06-08 05:00:00,331.0,15.22,14.08,3.15 -2016-06-08 06:00:00,336.0,48.51,14.02,3.49 -2016-06-08 07:00:00,405.0,136.68,14.1,3.65 -2016-06-08 08:00:00,448.0,129.68,14.18,3.81 -2016-06-08 09:00:00,513.0,207.84,14.27,3.97 -2016-06-08 10:00:00,533.0,283.01,14.31,3.92 -2016-06-08 11:00:00,540.0,366.71,14.36,3.86 -2016-06-08 12:00:00,556.0,684.8,14.41,3.81 -2016-06-08 13:00:00,523.0,796.52,14.54,4.0 -2016-06-08 14:00:00,485.0,581.84,14.66,4.18 -2016-06-08 15:00:00,487.0,618.18,14.78,4.37 -2016-06-08 16:00:00,459.0,451.62,14.66,4.39 -2016-06-08 17:00:00,419.0,251.43,14.54,4.4 -2016-06-08 18:00:00,451.0,61.49,14.42,4.41 -2016-06-08 19:00:00,489.0,27.17,14.2,4.33 -2016-06-08 20:00:00,478.0,0.0,13.98,4.25 -2016-06-08 21:00:00,466.0,0.0,13.76,4.17 -2016-06-08 22:00:00,747.0,0.0,13.62,4.17 -2016-06-08 23:00:00,727.0,0.0,13.48,4.17 -2016-06-09 00:00:00,527.0,0.0,13.33,4.17 -2016-06-09 01:00:00,452.0,0.0,13.56,4.01 -2016-06-09 02:00:00,386.0,0.0,13.79,3.85 -2016-06-09 03:00:00,352.0,0.0,14.02,3.7 -2016-06-09 04:00:00,347.0,0.0,14.09,3.97 -2016-06-09 05:00:00,323.0,14.62,14.17,4.24 -2016-06-09 06:00:00,337.0,62.42,14.25,4.51 -2016-06-09 07:00:00,385.0,168.3,14.53,4.42 -2016-06-09 08:00:00,478.0,161.32,14.81,4.33 -2016-06-09 09:00:00,525.0,212.88,15.1,4.23 -2016-06-09 10:00:00,507.0,574.0,15.25,4.15 -2016-06-09 11:00:00,521.0,662.21,15.41,4.06 -2016-06-09 12:00:00,581.0,265.32,15.57,3.97 -2016-06-09 13:00:00,520.0,280.06,15.86,4.02 -2016-06-09 14:00:00,445.0,424.82,16.14,4.06 -2016-06-09 15:00:00,455.0,224.73,16.42,4.11 -2016-06-09 16:00:00,421.0,168.68,16.64,3.89 -2016-06-09 17:00:00,415.0,49.0,16.86,3.67 -2016-06-09 18:00:00,473.0,53.36,17.08,3.45 -2016-06-09 19:00:00,512.0,1.46,16.88,3.1 -2016-06-09 20:00:00,511.0,0.0,16.67,2.76 -2016-06-09 21:00:00,475.0,0.0,16.46,2.41 -2016-06-09 22:00:00,770.0,0.0,16.18,2.58 -2016-06-09 23:00:00,765.0,0.0,15.89,2.75 -2016-06-10 00:00:00,569.0,0.0,15.6,2.92 -2016-06-10 01:00:00,446.0,0.0,15.52,2.04 -2016-06-10 02:00:00,397.0,0.0,15.44,1.16 -2016-06-10 03:00:00,349.0,0.0,15.36,0.28 -2016-06-10 04:00:00,355.0,0.0,15.19,0.25 -2016-06-10 05:00:00,341.0,0.07,15.02,0.23 -2016-06-10 06:00:00,325.0,36.18,14.85,0.21 -2016-06-10 07:00:00,385.0,74.02,14.75,0.58 -2016-06-10 08:00:00,454.0,71.85,14.65,0.95 -2016-06-10 09:00:00,478.0,344.4,14.55,1.32 -2016-06-10 10:00:00,495.0,256.06,14.45,2.19 -2016-06-10 11:00:00,527.0,269.97,14.35,3.06 -2016-06-10 12:00:00,554.0,636.82,14.25,3.93 -2016-06-10 13:00:00,510.0,715.81,14.17,3.36 -2016-06-10 14:00:00,470.0,416.82,14.08,2.78 -2016-06-10 15:00:00,467.0,346.65,13.99,2.21 -2016-06-10 16:00:00,443.0,239.39,14.11,2.63 -2016-06-10 17:00:00,407.0,136.42,14.23,3.04 -2016-06-10 18:00:00,455.0,92.81,14.35,3.46 -2016-06-10 19:00:00,490.0,7.21,14.42,3.67 -2016-06-10 20:00:00,470.0,0.0,14.48,3.89 -2016-06-10 21:00:00,461.0,0.0,14.54,4.1 -2016-06-10 22:00:00,771.0,0.0,14.52,4.39 -2016-06-10 23:00:00,746.0,0.0,14.49,4.69 -2016-06-11 00:00:00,572.0,0.0,14.46,4.98 -2016-06-11 01:00:00,431.0,0.0,14.51,5.26 -2016-06-11 02:00:00,407.0,0.0,14.56,5.54 -2016-06-11 03:00:00,364.0,0.0,14.6,5.82 -2016-06-11 04:00:00,353.0,0.0,14.56,5.86 -2016-06-11 05:00:00,342.0,9.95,14.52,5.9 -2016-06-11 06:00:00,329.0,57.91,14.47,5.94 -2016-06-11 07:00:00,333.0,151.37,14.45,5.9 -2016-06-11 08:00:00,427.0,259.91,14.43,5.85 -2016-06-11 09:00:00,492.0,441.74,14.4,5.81 -2016-06-11 10:00:00,548.0,654.23,14.41,5.91 -2016-06-11 11:00:00,539.0,724.86,14.41,6.01 -2016-06-11 12:00:00,560.0,817.64,14.41,6.11 -2016-06-11 13:00:00,518.0,628.22,14.51,5.96 -2016-06-11 14:00:00,439.0,379.77,14.61,5.81 -2016-06-11 15:00:00,446.0,184.16,14.7,5.66 -2016-06-11 16:00:00,427.0,100.8,14.84,5.73 -2016-06-11 17:00:00,416.0,136.58,14.98,5.81 -2016-06-11 18:00:00,463.0,13.01,15.11,5.89 -2016-06-11 19:00:00,533.0,17.58,15.08,6.22 -2016-06-11 20:00:00,512.0,0.0,15.05,6.54 -2016-06-11 21:00:00,506.0,0.0,15.01,6.87 -2016-06-11 22:00:00,786.0,0.0,14.95,6.98 -2016-06-11 23:00:00,766.0,0.0,14.89,7.09 -2016-06-12 00:00:00,561.0,0.0,14.82,7.2 -2016-06-12 01:00:00,480.0,0.0,14.91,7.69 -2016-06-12 02:00:00,423.0,0.0,15.0,8.18 -2016-06-12 03:00:00,371.0,0.0,15.1,8.68 -2016-06-12 04:00:00,351.0,0.0,15.09,8.36 -2016-06-12 05:00:00,340.0,0.0,15.08,8.05 -2016-06-12 06:00:00,330.0,46.42,15.07,7.74 -2016-06-12 07:00:00,337.0,41.44,15.06,7.4 -2016-06-12 08:00:00,415.0,297.17,15.04,7.07 -2016-06-12 09:00:00,481.0,353.16,15.02,6.73 -2016-06-12 10:00:00,562.0,626.76,14.96,6.54 -2016-06-12 11:00:00,598.0,459.97,14.9,6.34 -2016-06-12 12:00:00,612.0,695.11,14.83,6.15 -2016-06-12 13:00:00,546.0,421.89,14.77,6.98 -2016-06-12 14:00:00,478.0,507.26,14.71,7.82 -2016-06-12 15:00:00,453.0,213.69,14.65,8.65 -2016-06-12 16:00:00,418.0,139.66,14.63,8.46 -2016-06-12 17:00:00,385.0,29.27,14.62,8.26 -2016-06-12 18:00:00,418.0,82.81,14.61,8.07 -2016-06-12 19:00:00,462.0,8.83,14.62,7.86 -2016-06-12 20:00:00,446.0,0.0,14.62,7.65 -2016-06-12 21:00:00,435.0,0.0,14.62,7.43 -2016-06-12 22:00:00,726.0,0.0,14.69,7.31 -2016-06-12 23:00:00,741.0,0.0,14.76,7.18 -2016-06-13 00:00:00,536.0,0.0,14.82,7.05 -2016-06-13 01:00:00,449.0,0.0,14.67,6.95 -2016-06-13 02:00:00,408.0,0.0,14.52,6.85 -2016-06-13 03:00:00,345.0,0.0,14.36,6.74 -2016-06-13 04:00:00,342.0,0.0,14.42,6.61 -2016-06-13 05:00:00,330.0,0.07,14.47,6.48 -2016-06-13 06:00:00,301.0,5.33,14.52,6.34 -2016-06-13 07:00:00,341.0,51.89,14.58,6.15 -2016-06-13 08:00:00,421.0,47.4,14.64,5.96 -2016-06-13 09:00:00,484.0,147.79,14.69,5.77 -2016-06-13 10:00:00,487.0,66.27,14.6,6.88 -2016-06-13 11:00:00,531.0,521.69,14.51,8.0 -2016-06-13 12:00:00,557.0,532.78,14.42,9.12 -2016-06-13 13:00:00,524.0,383.81,14.38,9.78 -2016-06-13 14:00:00,493.0,479.41,14.34,10.45 -2016-06-13 15:00:00,478.0,321.44,14.3,11.12 -2016-06-13 16:00:00,471.0,275.81,14.27,11.17 -2016-06-13 17:00:00,437.0,258.16,14.25,11.23 -2016-06-13 18:00:00,445.0,90.46,14.23,11.28 -2016-06-13 19:00:00,502.0,7.22,14.37,11.02 -2016-06-13 20:00:00,486.0,0.0,14.51,10.75 -2016-06-13 21:00:00,464.0,0.0,14.65,10.48 -2016-06-13 22:00:00,743.0,0.0,14.67,10.25 -2016-06-13 23:00:00,735.0,0.0,14.68,10.02 -2016-06-14 00:00:00,530.0,0.0,14.69,9.79 -2016-06-14 01:00:00,448.0,0.0,14.58,9.54 -2016-06-14 02:00:00,389.0,0.0,14.46,9.3 -2016-06-14 03:00:00,344.0,0.0,14.35,9.05 -2016-06-14 04:00:00,345.0,0.0,14.3,9.07 -2016-06-14 05:00:00,330.0,6.69,14.25,9.09 -2016-06-14 06:00:00,320.0,28.63,14.2,9.12 -2016-06-14 07:00:00,380.0,115.58,14.11,8.9 -2016-06-14 08:00:00,463.0,309.25,14.02,8.68 -2016-06-14 09:00:00,520.0,537.21,13.93,8.47 -2016-06-14 10:00:00,529.0,527.72,13.81,8.09 -2016-06-14 11:00:00,519.0,372.95,13.7,7.71 -2016-06-14 12:00:00,558.0,672.5,13.59,7.32 -2016-06-14 13:00:00,488.0,448.9,13.51,7.33 -2016-06-14 14:00:00,436.0,695.59,13.43,7.34 -2016-06-14 15:00:00,459.0,605.27,13.35,7.35 -2016-06-14 16:00:00,431.0,345.88,13.49,7.26 -2016-06-14 17:00:00,391.0,228.34,13.63,7.18 -2016-06-14 18:00:00,426.0,92.76,13.77,7.09 -2016-06-14 19:00:00,478.0,27.25,13.84,7.03 -2016-06-14 20:00:00,443.0,0.0,13.91,6.96 -2016-06-14 21:00:00,445.0,0.0,13.98,6.9 -2016-06-14 22:00:00,739.0,0.0,13.91,6.85 -2016-06-14 23:00:00,730.0,0.0,13.84,6.8 -2016-06-15 00:00:00,547.0,0.0,13.77,6.74 -2016-06-15 01:00:00,465.0,0.0,13.67,6.62 -2016-06-15 02:00:00,413.0,0.0,13.57,6.49 -2016-06-15 03:00:00,352.0,0.0,13.48,6.36 -2016-06-15 04:00:00,357.0,0.0,13.43,6.21 -2016-06-15 05:00:00,331.0,15.29,13.39,6.06 -2016-06-15 06:00:00,314.0,64.31,13.35,5.9 -2016-06-15 07:00:00,382.0,176.74,13.42,5.87 -2016-06-15 08:00:00,456.0,355.2,13.49,5.83 -2016-06-15 09:00:00,516.0,573.72,13.56,5.79 -2016-06-15 10:00:00,500.0,706.51,13.61,5.9 -2016-06-15 11:00:00,504.0,776.98,13.66,6.0 -2016-06-15 12:00:00,522.0,810.96,13.71,6.11 -2016-06-15 13:00:00,473.0,803.35,13.85,5.91 -2016-06-15 14:00:00,422.0,714.83,13.98,5.71 -2016-06-15 15:00:00,437.0,522.69,14.11,5.52 -2016-06-15 16:00:00,416.0,411.61,14.13,5.43 -2016-06-15 17:00:00,393.0,224.84,14.15,5.34 -2016-06-15 18:00:00,441.0,80.61,14.17,5.26 -2016-06-15 19:00:00,477.0,10.53,14.19,5.03 -2016-06-15 20:00:00,457.0,0.0,14.21,4.8 -2016-06-15 21:00:00,441.0,0.0,14.22,4.58 -2016-06-15 22:00:00,715.0,0.0,14.18,4.23 -2016-06-15 23:00:00,722.0,0.0,14.13,3.88 -2016-06-16 00:00:00,536.0,0.0,14.08,3.53 -2016-06-16 01:00:00,445.0,0.0,14.05,3.44 -2016-06-16 02:00:00,395.0,0.0,14.01,3.36 -2016-06-16 03:00:00,337.0,0.0,13.98,3.27 -2016-06-16 04:00:00,347.0,0.0,14.01,3.12 -2016-06-16 05:00:00,318.0,15.83,14.05,2.97 -2016-06-16 06:00:00,306.0,32.68,14.09,2.81 -2016-06-16 07:00:00,382.0,174.59,14.2,2.69 -2016-06-16 08:00:00,454.0,367.1,14.31,2.56 -2016-06-16 09:00:00,489.0,552.9,14.43,2.43 -2016-06-16 10:00:00,505.0,657.95,14.39,2.88 -2016-06-16 11:00:00,498.0,731.48,14.36,3.33 -2016-06-16 12:00:00,515.0,769.71,14.33,3.78 -2016-06-16 13:00:00,477.0,751.42,14.39,4.35 -2016-06-16 14:00:00,452.0,701.76,14.45,4.92 -2016-06-16 15:00:00,467.0,593.62,14.52,5.49 -2016-06-16 16:00:00,429.0,446.67,14.49,5.63 -2016-06-16 17:00:00,406.0,251.81,14.47,5.77 -2016-06-16 18:00:00,439.0,83.19,14.45,5.92 -2016-06-16 19:00:00,479.0,34.41,14.45,5.99 -2016-06-16 20:00:00,454.0,0.0,14.45,6.06 -2016-06-16 21:00:00,426.0,0.0,14.44,6.14 -2016-06-16 22:00:00,710.0,0.0,14.31,6.4 -2016-06-16 23:00:00,726.0,0.0,14.18,6.67 -2016-06-17 00:00:00,527.0,0.0,14.05,6.94 -2016-06-17 01:00:00,447.0,0.0,14.0,7.04 -2016-06-17 02:00:00,384.0,0.0,13.95,7.14 -2016-06-17 03:00:00,332.0,0.0,13.9,7.24 -2016-06-17 04:00:00,326.0,0.0,13.74,7.37 -2016-06-17 05:00:00,308.0,8.88,13.58,7.5 -2016-06-17 06:00:00,316.0,54.81,13.42,7.63 -2016-06-17 07:00:00,374.0,107.01,13.42,7.78 -2016-06-17 08:00:00,431.0,221.16,13.42,7.94 -2016-06-17 09:00:00,472.0,365.72,13.43,8.1 -2016-06-17 10:00:00,483.0,168.14,13.47,8.28 -2016-06-17 11:00:00,481.0,168.13,13.52,8.46 -2016-06-17 12:00:00,526.0,207.27,13.56,8.63 -2016-06-17 13:00:00,496.0,122.92,13.63,8.81 -2016-06-17 14:00:00,442.0,115.0,13.69,8.98 -2016-06-17 15:00:00,443.0,380.55,13.76,9.16 -2016-06-17 16:00:00,428.0,368.73,13.84,9.05 -2016-06-17 17:00:00,417.0,257.58,13.93,8.94 -2016-06-17 18:00:00,485.0,70.74,14.01,8.83 -2016-06-17 19:00:00,527.0,33.84,14.1,8.69 -2016-06-17 20:00:00,508.0,0.0,14.18,8.56 -2016-06-17 21:00:00,478.0,0.0,14.26,8.43 -2016-06-17 22:00:00,771.0,0.0,14.33,8.26 -2016-06-17 23:00:00,746.0,0.0,14.4,8.1 -2016-06-18 00:00:00,590.0,0.0,14.46,7.93 -2016-06-18 01:00:00,451.0,0.0,14.44,7.66 -2016-06-18 02:00:00,399.0,0.0,14.42,7.4 -2016-06-18 03:00:00,363.0,0.0,14.4,7.13 -2016-06-18 04:00:00,359.0,0.0,14.36,6.86 -2016-06-18 05:00:00,342.0,14.62,14.33,6.6 -2016-06-18 06:00:00,332.0,46.65,14.3,6.33 -2016-06-18 07:00:00,359.0,131.94,14.31,6.17 -2016-06-18 08:00:00,433.0,44.51,14.32,6.02 -2016-06-18 09:00:00,487.0,276.33,14.32,5.86 -2016-06-18 10:00:00,524.0,546.0,14.31,5.93 -2016-06-18 11:00:00,527.0,756.46,14.29,5.99 -2016-06-18 12:00:00,542.0,695.68,14.27,6.06 -2016-06-18 13:00:00,508.0,367.3,14.42,5.82 -2016-06-18 14:00:00,451.0,733.27,14.57,5.58 -2016-06-18 15:00:00,468.0,616.0,14.73,5.34 -2016-06-18 16:00:00,435.0,330.59,14.72,5.35 -2016-06-18 17:00:00,403.0,152.44,14.71,5.36 -2016-06-18 18:00:00,438.0,76.38,14.7,5.37 -2016-06-18 19:00:00,510.0,18.85,14.68,5.29 -2016-06-18 20:00:00,487.0,0.0,14.66,5.22 -2016-06-18 21:00:00,482.0,0.0,14.64,5.14 -2016-06-18 22:00:00,757.0,0.0,14.6,4.92 -2016-06-18 23:00:00,781.0,0.0,14.56,4.69 -2016-06-19 00:00:00,613.0,0.0,14.52,4.47 -2016-06-19 01:00:00,447.0,0.0,14.46,4.48 -2016-06-19 02:00:00,396.0,0.0,14.4,4.49 -2016-06-19 03:00:00,360.0,0.0,14.34,4.5 -2016-06-19 04:00:00,345.0,0.0,14.41,4.81 -2016-06-19 05:00:00,307.0,9.39,14.49,5.12 -2016-06-19 06:00:00,308.0,47.95,14.57,5.43 -2016-06-19 07:00:00,331.0,66.21,14.64,5.94 -2016-06-19 08:00:00,413.0,60.9,14.71,6.46 -2016-06-19 09:00:00,481.0,107.96,14.78,6.97 -2016-06-19 10:00:00,561.0,108.01,14.73,7.22 -2016-06-19 11:00:00,580.0,120.6,14.67,7.47 -2016-06-19 12:00:00,586.0,104.21,14.61,7.72 -2016-06-19 13:00:00,515.0,108.89,14.67,7.96 -2016-06-19 14:00:00,478.0,145.82,14.73,8.2 -2016-06-19 15:00:00,479.0,224.06,14.8,8.44 -2016-06-19 16:00:00,436.0,58.66,14.82,8.86 -2016-06-19 17:00:00,402.0,40.04,14.85,9.27 -2016-06-19 18:00:00,449.0,11.16,14.88,9.68 -2016-06-19 19:00:00,479.0,1.89,14.91,9.73 -2016-06-19 20:00:00,484.0,0.0,14.94,9.78 -2016-06-19 21:00:00,477.0,0.0,14.96,9.83 -2016-06-19 22:00:00,757.0,0.0,14.94,10.16 -2016-06-19 23:00:00,733.0,0.0,14.92,10.48 -2016-06-20 00:00:00,564.0,0.0,14.89,10.8 -2016-06-20 01:00:00,444.0,0.0,14.86,11.04 -2016-06-20 02:00:00,399.0,0.0,14.83,11.28 -2016-06-20 03:00:00,369.0,0.0,14.8,11.52 -2016-06-20 04:00:00,343.0,0.0,14.94,11.7 -2016-06-20 05:00:00,321.0,6.14,15.08,11.88 -2016-06-20 06:00:00,323.0,18.21,15.22,12.06 -2016-06-20 07:00:00,352.0,47.35,15.28,11.61 -2016-06-20 08:00:00,439.0,66.15,15.34,11.17 -2016-06-20 09:00:00,497.0,96.21,15.4,10.73 -2016-06-20 10:00:00,528.0,103.22,15.32,9.9 -2016-06-20 11:00:00,572.0,447.95,15.24,9.08 -2016-06-20 12:00:00,620.0,237.54,15.15,8.25 -2016-06-20 13:00:00,569.0,164.26,15.2,7.54 -2016-06-20 14:00:00,530.0,260.28,15.24,6.83 -2016-06-20 15:00:00,547.0,312.04,15.28,6.12 -2016-06-20 16:00:00,511.0,173.52,15.3,5.83 -2016-06-20 17:00:00,440.0,45.75,15.32,5.53 -2016-06-20 18:00:00,479.0,99.8,15.33,5.23 -2016-06-20 19:00:00,549.0,6.12,15.3,4.77 -2016-06-20 20:00:00,534.0,0.0,15.27,4.32 -2016-06-20 21:00:00,490.0,0.0,15.24,3.86 -2016-06-20 22:00:00,779.0,0.0,15.22,3.9 -2016-06-20 23:00:00,756.0,0.0,15.2,3.94 -2016-06-21 00:00:00,582.0,0.0,15.17,3.97 -2016-06-21 01:00:00,467.0,0.0,15.1,4.01 -2016-06-21 02:00:00,394.0,0.0,15.03,4.06 -2016-06-21 03:00:00,347.0,0.0,14.95,4.1 -2016-06-21 04:00:00,361.0,0.0,14.91,4.02 -2016-06-21 05:00:00,326.0,1.89,14.86,3.95 -2016-06-21 06:00:00,318.0,10.54,14.81,3.88 -2016-06-21 07:00:00,382.0,146.21,14.83,3.85 -2016-06-21 08:00:00,474.0,245.44,14.85,3.83 -2016-06-21 09:00:00,515.0,356.23,14.88,3.81 -2016-06-21 10:00:00,543.0,676.05,14.92,4.19 -2016-06-21 11:00:00,533.0,563.71,14.96,4.58 -2016-06-21 12:00:00,564.0,462.96,15.0,4.97 -2016-06-21 13:00:00,506.0,190.23,15.08,4.88 -2016-06-21 14:00:00,465.0,621.83,15.16,4.8 -2016-06-21 15:00:00,471.0,487.25,15.23,4.72 -2016-06-21 16:00:00,417.0,378.6,15.27,4.74 -2016-06-21 17:00:00,411.0,170.02,15.3,4.75 -2016-06-21 18:00:00,445.0,29.13,15.33,4.77 -2016-06-21 19:00:00,486.0,27.66,15.33,4.74 -2016-06-21 20:00:00,505.0,0.0,15.33,4.7 -2016-06-21 21:00:00,481.0,0.0,15.33,4.66 -2016-06-21 22:00:00,750.0,0.0,15.3,4.78 -2016-06-21 23:00:00,736.0,0.0,15.27,4.89 -2016-06-22 00:00:00,572.0,0.0,15.23,5.01 -2016-06-22 01:00:00,439.0,0.0,15.3,5.11 -2016-06-22 02:00:00,400.0,0.0,15.37,5.22 -2016-06-22 03:00:00,359.0,0.0,15.44,5.32 -2016-06-22 04:00:00,351.0,0.0,15.39,4.8 -2016-06-22 05:00:00,313.0,3.65,15.35,4.27 -2016-06-22 06:00:00,335.0,18.83,15.31,3.74 -2016-06-22 07:00:00,401.0,34.79,15.26,3.25 -2016-06-22 08:00:00,427.0,65.07,15.21,2.76 -2016-06-22 09:00:00,485.0,148.87,15.16,2.28 -2016-06-22 10:00:00,497.0,102.33,15.21,1.89 -2016-06-22 11:00:00,532.0,90.73,15.25,1.5 -2016-06-22 12:00:00,562.0,127.37,15.29,1.12 -2016-06-22 13:00:00,524.0,357.69,15.37,1.54 -2016-06-22 14:00:00,474.0,307.25,15.45,1.95 -2016-06-22 15:00:00,457.0,307.25,15.52,2.37 -2016-06-22 16:00:00,428.0,381.53,15.55,2.86 -2016-06-22 17:00:00,403.0,253.88,15.57,3.35 -2016-06-22 18:00:00,465.0,70.12,15.59,3.83 -2016-06-22 19:00:00,509.0,25.69,15.56,4.23 -2016-06-22 20:00:00,522.0,0.0,15.53,4.63 -2016-06-22 21:00:00,482.0,0.0,15.49,5.02 -2016-06-22 22:00:00,752.0,0.0,15.39,5.19 -2016-06-22 23:00:00,755.0,0.0,15.28,5.36 -2016-06-23 00:00:00,556.0,0.0,15.17,5.53 -2016-06-23 01:00:00,438.0,0.0,15.05,5.66 -2016-06-23 02:00:00,397.0,0.0,14.93,5.78 -2016-06-23 03:00:00,352.0,0.0,14.81,5.9 -2016-06-23 04:00:00,361.0,0.0,14.73,5.84 -2016-06-23 05:00:00,332.0,13.4,14.65,5.77 -2016-06-23 06:00:00,315.0,51.05,14.57,5.71 -2016-06-23 07:00:00,373.0,168.13,14.6,5.83 -2016-06-23 08:00:00,473.0,300.34,14.63,5.94 -2016-06-23 09:00:00,537.0,481.43,14.66,6.06 -2016-06-23 10:00:00,546.0,677.4,14.62,5.88 -2016-06-23 11:00:00,534.0,777.58,14.58,5.7 -2016-06-23 12:00:00,581.0,815.58,14.53,5.52 -2016-06-23 13:00:00,547.0,439.36,14.59,5.49 -2016-06-23 14:00:00,514.0,604.01,14.65,5.47 -2016-06-23 15:00:00,502.0,340.58,14.7,5.45 -2016-06-23 16:00:00,446.0,451.56,14.71,5.26 -2016-06-23 17:00:00,429.0,261.8,14.72,5.08 -2016-06-23 18:00:00,480.0,91.06,14.73,4.9 -2016-06-23 19:00:00,528.0,5.63,14.79,5.14 -2016-06-23 20:00:00,488.0,0.0,14.85,5.38 -2016-06-23 21:00:00,459.0,0.0,14.91,5.63 -2016-06-23 22:00:00,744.0,0.0,14.94,5.52 -2016-06-23 23:00:00,733.0,0.0,14.96,5.42 -2016-06-24 00:00:00,552.0,0.0,14.98,5.31 -2016-06-24 01:00:00,446.0,0.0,15.03,5.65 -2016-06-24 02:00:00,408.0,0.0,15.07,5.98 -2016-06-24 03:00:00,356.0,0.0,15.11,6.32 -2016-06-24 04:00:00,347.0,0.0,15.13,6.43 -2016-06-24 05:00:00,315.0,11.62,15.15,6.55 -2016-06-24 06:00:00,319.0,19.53,15.17,6.66 -2016-06-24 07:00:00,395.0,66.8,15.24,6.36 -2016-06-24 08:00:00,443.0,199.79,15.31,6.06 -2016-06-24 09:00:00,484.0,291.45,15.39,5.75 -2016-06-24 10:00:00,511.0,465.54,15.34,5.83 -2016-06-24 11:00:00,550.0,771.83,15.3,5.92 -2016-06-24 12:00:00,551.0,793.57,15.25,6.0 -2016-06-24 13:00:00,554.0,366.58,15.19,6.34 -2016-06-24 14:00:00,477.0,443.26,15.13,6.69 -2016-06-24 15:00:00,481.0,392.48,15.07,7.03 -2016-06-24 16:00:00,458.0,265.77,15.04,7.02 -2016-06-24 17:00:00,414.0,234.08,15.0,7.01 -2016-06-24 18:00:00,432.0,88.37,14.96,6.99 -2016-06-24 19:00:00,467.0,29.71,14.91,6.71 -2016-06-24 20:00:00,478.0,0.0,14.85,6.42 -2016-06-24 21:00:00,447.0,0.0,14.79,6.14 -2016-06-24 22:00:00,755.0,0.0,14.69,6.18 -2016-06-24 23:00:00,761.0,0.0,14.58,6.23 -2016-06-25 00:00:00,576.0,0.0,14.47,6.28 -2016-06-25 01:00:00,462.0,0.0,14.41,6.49 -2016-06-25 02:00:00,406.0,0.0,14.35,6.71 -2016-06-25 03:00:00,359.0,0.0,14.28,6.92 -2016-06-25 04:00:00,354.0,0.0,14.18,6.95 -2016-06-25 05:00:00,336.0,12.27,14.08,6.97 -2016-06-25 06:00:00,332.0,17.65,13.98,6.99 -2016-06-25 07:00:00,372.0,169.1,14.0,7.24 -2016-06-25 08:00:00,448.0,332.54,14.02,7.49 -2016-06-25 09:00:00,502.0,297.42,14.03,7.74 -2016-06-25 10:00:00,550.0,225.19,14.05,7.79 -2016-06-25 11:00:00,550.0,584.08,14.07,7.84 -2016-06-25 12:00:00,567.0,816.77,14.09,7.89 -2016-06-25 13:00:00,536.0,823.68,14.38,7.76 -2016-06-25 14:00:00,479.0,743.46,14.67,7.62 -2016-06-25 15:00:00,512.0,624.9,14.95,7.49 -2016-06-25 16:00:00,475.0,453.55,14.95,7.52 -2016-06-25 17:00:00,428.0,147.4,14.95,7.54 -2016-06-25 18:00:00,464.0,81.15,14.95,7.57 -2016-06-25 19:00:00,512.0,12.22,14.86,7.47 -2016-06-25 20:00:00,510.0,0.0,14.76,7.37 -2016-06-25 21:00:00,485.0,0.0,14.66,7.27 -2016-06-25 22:00:00,757.0,0.0,14.46,7.17 -2016-06-25 23:00:00,757.0,0.0,14.26,7.08 -2016-06-26 00:00:00,599.0,0.0,14.06,6.98 -2016-06-26 01:00:00,475.0,0.0,14.08,6.84 -2016-06-26 02:00:00,431.0,0.0,14.09,6.69 -2016-06-26 03:00:00,391.0,0.0,14.1,6.55 -2016-06-26 04:00:00,357.0,0.0,14.06,6.43 -2016-06-26 05:00:00,340.0,10.54,14.02,6.3 -2016-06-26 06:00:00,308.0,57.66,13.97,6.18 -2016-06-26 07:00:00,344.0,119.5,13.97,5.89 -2016-06-26 08:00:00,452.0,52.03,13.97,5.6 -2016-06-26 09:00:00,521.0,157.77,13.97,5.31 -2016-06-26 10:00:00,577.0,119.17,14.08,5.84 -2016-06-26 11:00:00,599.0,170.31,14.2,6.37 -2016-06-26 12:00:00,594.0,116.81,14.32,6.9 -2016-06-26 13:00:00,551.0,104.15,14.61,7.02 -2016-06-26 14:00:00,505.0,83.83,14.9,7.14 -2016-06-26 15:00:00,539.0,97.65,15.19,7.27 -2016-06-26 16:00:00,469.0,115.6,15.19,7.43 -2016-06-26 17:00:00,438.0,134.36,15.18,7.59 -2016-06-26 18:00:00,452.0,49.54,15.17,7.75 -2016-06-26 19:00:00,502.0,32.97,15.17,7.54 -2016-06-26 20:00:00,494.0,0.0,15.17,7.34 -2016-06-26 21:00:00,475.0,0.0,15.16,7.13 -2016-06-26 22:00:00,744.0,0.0,15.11,7.15 -2016-06-26 23:00:00,743.0,0.0,15.06,7.17 -2016-06-27 00:00:00,545.0,0.0,15.01,7.19 -2016-06-27 01:00:00,446.0,0.0,15.09,7.04 -2016-06-27 02:00:00,422.0,0.0,15.16,6.89 -2016-06-27 03:00:00,381.0,0.0,15.23,6.74 -2016-06-27 04:00:00,355.0,0.0,15.2,6.49 -2016-06-27 05:00:00,333.0,5.11,15.16,6.23 -2016-06-27 06:00:00,317.0,31.3,15.13,5.97 -2016-06-27 07:00:00,379.0,134.59,15.09,5.71 -2016-06-27 08:00:00,436.0,230.97,15.05,5.45 -2016-06-27 09:00:00,511.0,409.91,15.01,5.19 -2016-06-27 10:00:00,517.0,534.59,14.88,5.09 -2016-06-27 11:00:00,517.0,700.11,14.76,4.98 -2016-06-27 12:00:00,565.0,336.33,14.64,4.88 -2016-06-27 13:00:00,513.0,572.67,14.79,4.66 -2016-06-27 14:00:00,450.0,331.25,14.94,4.43 -2016-06-27 15:00:00,474.0,328.69,15.09,4.21 -2016-06-27 16:00:00,440.0,371.96,15.04,3.96 -2016-06-27 17:00:00,411.0,225.23,15.0,3.72 -2016-06-27 18:00:00,424.0,85.5,14.96,3.48 -2016-06-27 19:00:00,496.0,16.36,14.94,2.96 -2016-06-27 20:00:00,494.0,0.0,14.91,2.45 -2016-06-27 21:00:00,449.0,0.0,14.88,1.93 -2016-06-27 22:00:00,726.0,0.0,14.8,2.22 -2016-06-27 23:00:00,721.0,0.0,14.72,2.5 -2016-06-28 00:00:00,510.0,0.0,14.64,2.79 -2016-06-28 01:00:00,422.0,0.0,14.5,3.03 -2016-06-28 02:00:00,398.0,0.0,14.36,3.27 -2016-06-28 03:00:00,345.0,0.0,14.23,3.52 -2016-06-28 04:00:00,344.0,0.0,14.18,4.16 -2016-06-28 05:00:00,331.0,7.76,14.13,4.8 -2016-06-28 06:00:00,307.0,59.1,14.08,5.43 -2016-06-28 07:00:00,346.0,128.88,14.19,6.32 -2016-06-28 08:00:00,414.0,236.42,14.29,7.21 -2016-06-28 09:00:00,486.0,120.83,14.4,8.1 -2016-06-28 10:00:00,513.0,63.31,14.54,8.89 -2016-06-28 11:00:00,521.0,123.16,14.68,9.68 -2016-06-28 12:00:00,529.0,121.56,14.82,10.47 -2016-06-28 13:00:00,487.0,469.35,15.01,10.67 -2016-06-28 14:00:00,476.0,756.26,15.2,10.86 -2016-06-28 15:00:00,499.0,615.46,15.4,11.06 -2016-06-28 16:00:00,487.0,344.52,15.4,10.57 -2016-06-28 17:00:00,456.0,249.75,15.41,10.08 -2016-06-28 18:00:00,466.0,86.06,15.42,9.59 -2016-06-28 19:00:00,514.0,37.65,15.27,8.86 -2016-06-28 20:00:00,493.0,0.0,15.12,8.14 -2016-06-28 21:00:00,453.0,0.0,14.96,7.42 -2016-06-28 22:00:00,746.0,0.0,14.74,7.53 -2016-06-28 23:00:00,760.0,0.0,14.52,7.64 -2016-06-29 00:00:00,549.0,0.0,14.3,7.75 -2016-06-29 01:00:00,440.0,0.0,14.16,7.84 -2016-06-29 02:00:00,403.0,0.0,14.01,7.93 -2016-06-29 03:00:00,348.0,0.0,13.86,8.01 -2016-06-29 04:00:00,357.0,0.0,14.04,8.91 -2016-06-29 05:00:00,326.0,0.07,14.22,9.81 -2016-06-29 06:00:00,317.0,54.9,14.39,10.7 -2016-06-29 07:00:00,368.0,43.04,14.65,10.91 -2016-06-29 08:00:00,434.0,50.41,14.91,11.12 -2016-06-29 09:00:00,506.0,76.18,15.18,11.32 -2016-06-29 10:00:00,555.0,60.14,15.14,11.28 -2016-06-29 11:00:00,569.0,192.59,15.1,11.23 -2016-06-29 12:00:00,612.0,305.94,15.06,11.19 -2016-06-29 13:00:00,585.0,592.18,15.06,10.89 -2016-06-29 14:00:00,506.0,507.92,15.05,10.6 -2016-06-29 15:00:00,535.0,468.79,15.05,10.3 -2016-06-29 16:00:00,496.0,423.94,15.16,9.82 -2016-06-29 17:00:00,443.0,188.24,15.27,9.34 -2016-06-29 18:00:00,450.0,73.2,15.38,8.86 -2016-06-29 19:00:00,485.0,2.74,15.47,8.62 -2016-06-29 20:00:00,505.0,0.0,15.56,8.38 -2016-06-29 21:00:00,458.0,0.0,15.65,8.14 -2016-06-29 22:00:00,762.0,0.0,15.62,8.36 -2016-06-29 23:00:00,738.0,0.0,15.59,8.58 -2016-06-30 00:00:00,526.0,0.0,15.55,8.8 -2016-06-30 01:00:00,435.0,0.0,15.49,8.27 -2016-06-30 02:00:00,417.0,0.0,15.43,7.73 -2016-06-30 03:00:00,371.0,0.0,15.38,7.2 -2016-06-30 04:00:00,362.0,0.0,15.37,7.03 -2016-06-30 05:00:00,337.0,7.17,15.36,6.87 -2016-06-30 06:00:00,332.0,60.32,15.35,6.7 -2016-06-30 07:00:00,369.0,59.96,15.35,6.67 -2016-06-30 08:00:00,461.0,95.98,15.35,6.64 -2016-06-30 09:00:00,515.0,145.14,15.35,6.61 -2016-06-30 10:00:00,540.0,298.96,15.36,6.91 -2016-06-30 11:00:00,538.0,452.46,15.36,7.2 -2016-06-30 12:00:00,575.0,535.8,15.36,7.5 -2016-06-30 13:00:00,534.0,630.59,15.51,7.77 -2016-06-30 14:00:00,479.0,399.55,15.65,8.05 -2016-06-30 15:00:00,523.0,245.69,15.8,8.32 -2016-06-30 16:00:00,458.0,131.0,15.81,8.54 -2016-06-30 17:00:00,427.0,118.69,15.82,8.76 -2016-06-30 18:00:00,474.0,30.5,15.83,8.98 -2016-06-30 19:00:00,530.0,29.59,15.77,8.77 -2016-06-30 20:00:00,505.0,0.0,15.71,8.56 -2016-06-30 21:00:00,492.0,0.0,15.64,8.34 -2016-06-30 22:00:00,734.0,0.0,15.57,8.35 -2016-06-30 23:00:00,747.0,0.0,15.5,8.36 -2016-07-01 00:00:00,531.0,0.0,15.42,8.37 -2016-07-01 01:00:00,437.0,0.0,15.36,8.31 -2016-07-01 02:00:00,415.0,0.0,15.3,8.25 -2016-07-01 03:00:00,361.0,0.0,15.23,8.19 -2016-07-01 04:00:00,359.0,0.0,15.19,8.1 -2016-07-01 05:00:00,331.0,10.48,15.15,8.01 -2016-07-01 06:00:00,317.0,55.7,15.1,7.92 -2016-07-01 07:00:00,410.0,112.0,14.98,7.98 -2016-07-01 08:00:00,430.0,241.39,14.86,8.05 -2016-07-01 09:00:00,487.0,298.18,14.73,8.11 -2016-07-01 10:00:00,516.0,629.25,14.63,8.55 -2016-07-01 11:00:00,550.0,615.18,14.53,8.98 -2016-07-01 12:00:00,560.0,409.97,14.43,9.42 -2016-07-01 13:00:00,520.0,427.34,14.63,9.37 -2016-07-01 14:00:00,484.0,417.98,14.84,9.32 -2016-07-01 15:00:00,496.0,465.66,15.04,9.27 -2016-07-01 16:00:00,457.0,219.43,14.93,9.54 -2016-07-01 17:00:00,403.0,209.31,14.83,9.82 -2016-07-01 18:00:00,454.0,91.28,14.72,10.1 -2016-07-01 19:00:00,501.0,15.82,14.51,10.03 -2016-07-01 20:00:00,503.0,0.0,14.3,9.96 -2016-07-01 21:00:00,499.0,0.0,14.1,9.89 -2016-07-01 22:00:00,773.0,0.0,14.07,9.93 -2016-07-01 23:00:00,765.0,0.0,14.05,9.97 -2016-07-02 00:00:00,577.0,0.0,14.03,10.01 -2016-07-02 01:00:00,460.0,0.0,14.06,10.12 -2016-07-02 02:00:00,414.0,0.0,14.08,10.23 -2016-07-02 03:00:00,364.0,0.0,14.1,10.34 -2016-07-02 04:00:00,373.0,0.0,14.08,10.07 -2016-07-02 05:00:00,366.0,11.7,14.05,9.8 -2016-07-02 06:00:00,337.0,30.79,14.02,9.53 -2016-07-02 07:00:00,364.0,149.67,14.02,9.31 -2016-07-02 08:00:00,439.0,269.88,14.01,9.09 -2016-07-02 09:00:00,478.0,330.88,14.01,8.87 -2016-07-02 10:00:00,552.0,496.66,14.03,8.73 -2016-07-02 11:00:00,555.0,106.13,14.05,8.59 -2016-07-02 12:00:00,549.0,400.08,14.07,8.46 -2016-07-02 13:00:00,543.0,766.27,14.22,8.44 -2016-07-02 14:00:00,502.0,720.31,14.38,8.43 -2016-07-02 15:00:00,488.0,266.33,14.53,8.41 -2016-07-02 16:00:00,452.0,356.51,14.59,8.21 -2016-07-02 17:00:00,429.0,203.3,14.66,8.01 -2016-07-02 18:00:00,458.0,80.66,14.72,7.81 -2016-07-02 19:00:00,525.0,13.4,14.8,7.84 -2016-07-02 20:00:00,529.0,0.0,14.88,7.88 -2016-07-02 21:00:00,510.0,0.0,14.96,7.92 -2016-07-02 22:00:00,762.0,0.0,15.04,8.48 -2016-07-02 23:00:00,784.0,0.0,15.12,9.05 -2016-07-03 00:00:00,619.0,0.0,15.2,9.61 -2016-07-03 01:00:00,458.0,0.0,15.2,9.6 -2016-07-03 02:00:00,442.0,0.0,15.2,9.59 -2016-07-03 03:00:00,406.0,0.0,15.19,9.57 -2016-07-03 04:00:00,384.0,0.0,15.28,9.61 -2016-07-03 05:00:00,365.0,6.64,15.36,9.66 -2016-07-03 06:00:00,346.0,48.29,15.44,9.7 -2016-07-03 07:00:00,353.0,148.22,15.41,9.24 -2016-07-03 08:00:00,434.0,316.13,15.37,8.78 -2016-07-03 09:00:00,495.0,513.53,15.34,8.32 -2016-07-03 10:00:00,562.0,470.59,15.3,7.69 -2016-07-03 11:00:00,584.0,741.39,15.26,7.07 -2016-07-03 12:00:00,550.0,796.91,15.22,6.44 -2016-07-03 13:00:00,510.0,796.23,15.2,5.94 -2016-07-03 14:00:00,447.0,710.36,15.18,5.45 -2016-07-03 15:00:00,472.0,402.95,15.16,4.95 -2016-07-03 16:00:00,418.0,278.95,15.15,4.51 -2016-07-03 17:00:00,382.0,127.25,15.15,4.07 -2016-07-03 18:00:00,404.0,50.16,15.15,3.63 -2016-07-03 19:00:00,457.0,23.18,15.11,3.27 -2016-07-03 20:00:00,469.0,0.0,15.07,2.92 -2016-07-03 21:00:00,430.0,0.0,15.04,2.57 -2016-07-03 22:00:00,714.0,0.0,15.01,2.66 -2016-07-03 23:00:00,724.0,0.0,14.99,2.75 -2016-07-04 00:00:00,518.0,0.0,14.96,2.84 -2016-07-04 01:00:00,437.0,0.0,15.04,3.71 -2016-07-04 02:00:00,402.0,0.0,15.12,4.59 -2016-07-04 03:00:00,340.0,0.0,15.2,5.46 -2016-07-04 04:00:00,342.0,0.0,15.23,5.39 -2016-07-04 05:00:00,332.0,9.35,15.26,5.32 -2016-07-04 06:00:00,302.0,58.1,15.29,5.26 -2016-07-04 07:00:00,341.0,138.89,15.28,5.07 -2016-07-04 08:00:00,435.0,215.35,15.27,4.88 -2016-07-04 09:00:00,502.0,287.37,15.27,4.69 -2016-07-04 10:00:00,512.0,91.22,15.26,4.82 -2016-07-04 11:00:00,538.0,196.59,15.25,4.95 -2016-07-04 12:00:00,574.0,221.04,15.24,5.08 -2016-07-04 13:00:00,536.0,328.63,15.23,5.16 -2016-07-04 14:00:00,483.0,407.7,15.23,5.25 -2016-07-04 15:00:00,478.0,276.11,15.22,5.34 -2016-07-04 16:00:00,427.0,230.17,15.23,5.51 -2016-07-04 17:00:00,399.0,122.23,15.25,5.69 -2016-07-04 18:00:00,442.0,70.0,15.27,5.86 -2016-07-04 19:00:00,502.0,25.75,15.27,6.06 -2016-07-04 20:00:00,513.0,0.0,15.27,6.25 -2016-07-04 21:00:00,486.0,0.0,15.27,6.44 -2016-07-04 22:00:00,748.0,0.0,15.25,6.59 -2016-07-04 23:00:00,736.0,0.0,15.24,6.74 -2016-07-05 00:00:00,550.0,0.0,15.22,6.88 -2016-07-05 01:00:00,417.0,0.0,15.16,7.02 -2016-07-05 02:00:00,410.0,0.0,15.1,7.15 -2016-07-05 03:00:00,352.0,0.0,15.04,7.28 -2016-07-05 04:00:00,328.0,0.0,14.83,7.28 -2016-07-05 05:00:00,308.0,8.83,14.62,7.27 -2016-07-05 06:00:00,310.0,58.07,14.41,7.27 -2016-07-05 07:00:00,363.0,106.76,14.26,6.88 -2016-07-05 08:00:00,459.0,330.25,14.11,6.49 -2016-07-05 09:00:00,515.0,491.45,13.97,6.1 -2016-07-05 10:00:00,546.0,662.85,13.97,5.78 -2016-07-05 11:00:00,564.0,763.88,13.98,5.46 -2016-07-05 12:00:00,543.0,793.81,13.98,5.14 -2016-07-05 13:00:00,501.0,795.29,14.22,4.56 -2016-07-05 14:00:00,454.0,725.06,14.47,3.98 -2016-07-05 15:00:00,476.0,605.06,14.71,3.39 -2016-07-05 16:00:00,429.0,434.91,14.73,3.28 -2016-07-05 17:00:00,393.0,259.52,14.75,3.17 -2016-07-05 18:00:00,450.0,80.6,14.77,3.06 -2016-07-05 19:00:00,481.0,34.31,14.72,3.3 -2016-07-05 20:00:00,478.0,0.0,14.67,3.53 -2016-07-05 21:00:00,463.0,0.0,14.62,3.77 -2016-07-05 22:00:00,730.0,0.0,14.55,3.46 -2016-07-05 23:00:00,729.0,0.0,14.48,3.16 -2016-07-06 00:00:00,559.0,0.0,14.41,2.86 -2016-07-06 01:00:00,433.0,0.0,14.26,2.61 -2016-07-06 02:00:00,410.0,0.0,14.11,2.36 -2016-07-06 03:00:00,363.0,0.0,13.95,2.11 -2016-07-06 04:00:00,350.0,0.0,13.93,1.72 -2016-07-06 05:00:00,323.0,10.53,13.9,1.34 -2016-07-06 06:00:00,315.0,47.15,13.87,0.95 -2016-07-06 07:00:00,359.0,158.44,13.96,0.83 -2016-07-06 08:00:00,442.0,341.72,14.05,0.71 -2016-07-06 09:00:00,480.0,509.4,14.15,0.59 -2016-07-06 10:00:00,520.0,631.59,14.31,0.54 -2016-07-06 11:00:00,525.0,708.82,14.47,0.48 -2016-07-06 12:00:00,549.0,675.67,14.63,0.43 -2016-07-06 13:00:00,515.0,716.95,14.83,0.62 -2016-07-06 14:00:00,461.0,558.59,15.02,0.81 -2016-07-06 15:00:00,488.0,493.9,15.22,1.01 -2016-07-06 16:00:00,416.0,374.73,15.31,1.26 -2016-07-06 17:00:00,392.0,259.48,15.41,1.5 -2016-07-06 18:00:00,426.0,76.19,15.5,1.75 -2016-07-06 19:00:00,477.0,34.79,15.4,1.32 -2016-07-06 20:00:00,504.0,0.0,15.3,0.89 -2016-07-06 21:00:00,496.0,0.0,15.2,0.46 -2016-07-06 22:00:00,737.0,0.0,15.11,0.57 -2016-07-06 23:00:00,733.0,0.0,15.02,0.68 -2016-07-07 00:00:00,566.0,0.0,14.93,0.79 -2016-07-07 01:00:00,418.0,0.0,14.8,1.17 -2016-07-07 02:00:00,416.0,0.0,14.67,1.56 -2016-07-07 03:00:00,357.0,0.0,14.54,1.94 -2016-07-07 04:00:00,349.0,0.0,14.5,1.82 -2016-07-07 05:00:00,327.0,5.64,14.46,1.7 -2016-07-07 06:00:00,339.0,29.69,14.42,1.57 -2016-07-07 07:00:00,367.0,95.25,14.59,1.29 -2016-07-07 08:00:00,433.0,187.57,14.75,1.0 -2016-07-07 09:00:00,492.0,292.09,14.91,0.72 -2016-07-07 10:00:00,539.0,253.79,15.23,0.95 -2016-07-07 11:00:00,541.0,641.91,15.54,1.19 -2016-07-07 12:00:00,572.0,649.81,15.85,1.42 -2016-07-07 13:00:00,549.0,592.87,15.83,2.12 -2016-07-07 14:00:00,513.0,469.55,15.8,2.82 -2016-07-07 15:00:00,493.0,148.0,15.78,3.52 -2016-07-07 16:00:00,434.0,88.61,15.67,3.8 -2016-07-07 17:00:00,393.0,68.84,15.57,4.09 -2016-07-07 18:00:00,452.0,59.18,15.46,4.37 -2016-07-07 19:00:00,532.0,36.22,15.34,4.14 -2016-07-07 20:00:00,513.0,0.0,15.22,3.91 -2016-07-07 21:00:00,490.0,0.0,15.11,3.68 -2016-07-07 22:00:00,713.0,0.0,15.06,3.88 -2016-07-07 23:00:00,712.0,0.0,15.0,4.08 -2016-07-08 00:00:00,554.0,0.0,14.95,4.28 -2016-07-08 01:00:00,437.0,0.0,14.92,4.67 -2016-07-08 02:00:00,427.0,0.0,14.88,5.06 -2016-07-08 03:00:00,369.0,0.0,14.84,5.45 -2016-07-08 04:00:00,361.0,0.0,15.01,5.78 -2016-07-08 05:00:00,329.0,9.91,15.18,6.12 -2016-07-08 06:00:00,324.0,30.29,15.35,6.46 -2016-07-08 07:00:00,383.0,91.26,15.55,6.19 -2016-07-08 08:00:00,428.0,56.06,15.74,5.92 -2016-07-08 09:00:00,495.0,187.65,15.94,5.66 -2016-07-08 10:00:00,521.0,226.66,15.94,5.41 -2016-07-08 11:00:00,544.0,631.53,15.95,5.17 -2016-07-08 12:00:00,553.0,664.91,15.96,4.92 -2016-07-08 13:00:00,544.0,689.56,16.04,4.97 -2016-07-08 14:00:00,533.0,698.44,16.12,5.01 -2016-07-08 15:00:00,523.0,467.81,16.2,5.05 -2016-07-08 16:00:00,456.0,450.69,16.19,5.21 -2016-07-08 17:00:00,431.0,262.45,16.19,5.38 -2016-07-08 18:00:00,467.0,71.18,16.19,5.54 -2016-07-08 19:00:00,505.0,32.12,16.19,5.79 -2016-07-08 20:00:00,540.0,0.0,16.19,6.03 -2016-07-08 21:00:00,507.0,0.0,16.2,6.28 -2016-07-08 22:00:00,740.0,0.0,16.19,6.51 -2016-07-08 23:00:00,741.0,0.0,16.19,6.74 -2016-07-09 00:00:00,555.0,0.0,16.19,6.98 -2016-07-09 01:00:00,435.0,0.0,16.18,7.59 -2016-07-09 02:00:00,409.0,0.0,16.17,8.2 -2016-07-09 03:00:00,366.0,0.0,16.16,8.81 -2016-07-09 04:00:00,359.0,0.0,16.12,8.62 -2016-07-09 05:00:00,329.0,7.14,16.07,8.43 -2016-07-09 06:00:00,325.0,35.49,16.02,8.23 -2016-07-09 07:00:00,347.0,111.86,15.95,8.19 -2016-07-09 08:00:00,445.0,115.25,15.88,8.15 -2016-07-09 09:00:00,483.0,143.62,15.81,8.11 -2016-07-09 10:00:00,546.0,180.62,15.81,8.31 -2016-07-09 11:00:00,570.0,141.19,15.81,8.51 -2016-07-09 12:00:00,587.0,354.16,15.81,8.7 -2016-07-09 13:00:00,562.0,321.0,15.84,8.91 -2016-07-09 14:00:00,535.0,342.84,15.87,9.13 -2016-07-09 15:00:00,523.0,240.52,15.9,9.34 -2016-07-09 16:00:00,459.0,131.02,15.91,9.37 -2016-07-09 17:00:00,435.0,62.13,15.93,9.39 -2016-07-09 18:00:00,498.0,31.2,15.95,9.42 -2016-07-09 19:00:00,568.0,1.47,15.97,9.36 -2016-07-09 20:00:00,570.0,0.0,15.99,9.3 -2016-07-09 21:00:00,507.0,0.0,16.02,9.24 -2016-07-09 22:00:00,773.0,0.0,16.07,9.21 -2016-07-09 23:00:00,755.0,0.0,16.13,9.19 -2016-07-10 00:00:00,580.0,0.0,16.18,9.16 -2016-07-10 01:00:00,450.0,0.0,16.27,9.13 -2016-07-10 02:00:00,408.0,0.0,16.36,9.1 -2016-07-10 03:00:00,377.0,0.0,16.45,9.08 -2016-07-10 04:00:00,351.0,0.0,16.5,8.84 -2016-07-10 05:00:00,332.0,8.73,16.55,8.6 -2016-07-10 06:00:00,325.0,43.46,16.6,8.36 -2016-07-10 07:00:00,339.0,149.36,16.58,8.32 -2016-07-10 08:00:00,424.0,277.37,16.56,8.28 -2016-07-10 09:00:00,514.0,503.74,16.53,8.23 -2016-07-10 10:00:00,599.0,236.63,16.49,8.48 -2016-07-10 11:00:00,651.0,616.35,16.45,8.72 -2016-07-10 12:00:00,637.0,515.48,16.41,8.97 -2016-07-10 13:00:00,560.0,748.21,16.49,9.31 -2016-07-10 14:00:00,521.0,627.3,16.56,9.66 -2016-07-10 15:00:00,514.0,416.74,16.64,10.01 -2016-07-10 16:00:00,448.0,419.81,16.64,10.11 -2016-07-10 17:00:00,418.0,230.08,16.65,10.22 -2016-07-10 18:00:00,459.0,90.84,16.66,10.32 -2016-07-10 19:00:00,534.0,7.12,16.56,10.41 -2016-07-10 20:00:00,540.0,0.0,16.45,10.5 -2016-07-10 21:00:00,472.0,0.0,16.35,10.59 -2016-07-10 22:00:00,703.0,0.0,16.31,10.59 -2016-07-10 23:00:00,721.0,0.0,16.26,10.58 -2016-07-11 00:00:00,601.0,0.0,16.22,10.58 -2016-07-11 01:00:00,440.0,0.0,16.16,10.39 -2016-07-11 02:00:00,428.0,0.0,16.1,10.19 -2016-07-11 03:00:00,382.0,0.0,16.04,10.0 -2016-07-11 04:00:00,361.0,0.0,15.92,9.77 -2016-07-11 05:00:00,317.0,8.23,15.8,9.55 -2016-07-11 06:00:00,298.0,52.62,15.68,9.32 -2016-07-11 07:00:00,351.0,155.12,15.62,8.97 -2016-07-11 08:00:00,435.0,282.0,15.56,8.61 -2016-07-11 09:00:00,519.0,389.6,15.51,8.25 -2016-07-11 10:00:00,549.0,307.41,15.54,7.92 -2016-07-11 11:00:00,572.0,172.61,15.57,7.6 -2016-07-11 12:00:00,597.0,439.81,15.6,7.27 -2016-07-11 13:00:00,560.0,388.58,15.71,7.22 -2016-07-11 14:00:00,541.0,249.41,15.83,7.18 -2016-07-11 15:00:00,528.0,245.65,15.94,7.13 -2016-07-11 16:00:00,469.0,206.02,15.81,6.81 -2016-07-11 17:00:00,425.0,227.65,15.69,6.49 -2016-07-11 18:00:00,472.0,16.84,15.57,6.17 -2016-07-11 19:00:00,553.0,32.26,15.4,6.15 -2016-07-11 20:00:00,542.0,0.0,15.23,6.14 -2016-07-11 21:00:00,490.0,0.0,15.06,6.12 -2016-07-11 22:00:00,748.0,0.0,14.92,6.32 -2016-07-11 23:00:00,736.0,0.0,14.78,6.52 -2016-07-12 00:00:00,561.0,0.0,14.64,6.72 -2016-07-12 01:00:00,428.0,0.0,14.85,6.51 -2016-07-12 02:00:00,413.0,0.0,15.06,6.29 -2016-07-12 03:00:00,381.0,0.0,15.27,6.08 -2016-07-12 04:00:00,354.0,0.0,15.24,6.19 -2016-07-12 05:00:00,333.0,6.65,15.21,6.29 -2016-07-12 06:00:00,318.0,51.3,15.18,6.4 -2016-07-12 07:00:00,365.0,154.07,15.19,6.23 -2016-07-12 08:00:00,457.0,211.86,15.19,6.06 -2016-07-12 09:00:00,530.0,450.55,15.19,5.89 -2016-07-12 10:00:00,572.0,255.94,15.19,5.89 -2016-07-12 11:00:00,588.0,612.16,15.18,5.9 -2016-07-12 12:00:00,612.0,277.95,15.17,5.9 -2016-07-12 13:00:00,586.0,764.89,14.82,6.26 -2016-07-12 14:00:00,560.0,584.08,14.46,6.61 -2016-07-12 15:00:00,539.0,350.35,14.11,6.97 -2016-07-12 16:00:00,467.0,433.43,14.26,7.07 -2016-07-12 17:00:00,444.0,258.62,14.41,7.18 -2016-07-12 18:00:00,518.0,99.49,14.56,7.28 -2016-07-12 19:00:00,582.0,27.8,14.6,7.47 -2016-07-12 20:00:00,568.0,0.0,14.64,7.65 -2016-07-12 21:00:00,529.0,0.0,14.69,7.83 -2016-07-12 22:00:00,766.0,0.0,14.55,7.96 -2016-07-12 23:00:00,778.0,0.0,14.42,8.08 -2016-07-13 00:00:00,578.0,0.0,14.28,8.21 -2016-07-13 01:00:00,434.0,0.0,14.12,8.16 -2016-07-13 02:00:00,435.0,0.0,13.95,8.11 -2016-07-13 03:00:00,370.0,0.0,13.78,8.06 -2016-07-13 04:00:00,345.0,0.0,13.9,8.02 -2016-07-13 05:00:00,335.0,5.66,14.01,7.99 -2016-07-13 06:00:00,313.0,51.61,14.12,7.96 -2016-07-13 07:00:00,368.0,154.11,14.33,8.17 -2016-07-13 08:00:00,478.0,348.82,14.54,8.39 -2016-07-13 09:00:00,550.0,530.19,14.74,8.61 -2016-07-13 10:00:00,606.0,611.78,14.78,8.46 -2016-07-13 11:00:00,629.0,438.48,14.82,8.3 -2016-07-13 12:00:00,636.0,542.54,14.86,8.15 -2016-07-13 13:00:00,612.0,786.74,14.82,8.0 -2016-07-13 14:00:00,558.0,673.92,14.77,7.84 -2016-07-13 15:00:00,529.0,364.79,14.73,7.68 -2016-07-13 16:00:00,491.0,319.89,14.77,7.72 -2016-07-13 17:00:00,433.0,266.74,14.81,7.76 -2016-07-13 18:00:00,487.0,76.13,14.85,7.79 -2016-07-13 19:00:00,569.0,29.73,14.85,7.73 -2016-07-13 20:00:00,585.0,0.0,14.84,7.66 -2016-07-13 21:00:00,532.0,0.0,14.84,7.6 -2016-07-13 22:00:00,820.0,0.0,14.81,7.55 -2016-07-13 23:00:00,784.0,0.0,14.78,7.5 -2016-07-14 00:00:00,580.0,0.0,14.75,7.45 -2016-07-14 01:00:00,464.0,0.0,14.8,7.43 -2016-07-14 02:00:00,455.0,0.0,14.85,7.4 -2016-07-14 03:00:00,391.0,0.0,14.89,7.38 -2016-07-14 04:00:00,356.0,0.0,14.9,7.27 -2016-07-14 05:00:00,318.0,7.19,14.91,7.17 -2016-07-14 06:00:00,313.0,49.99,14.92,7.06 -2016-07-14 07:00:00,347.0,111.52,14.98,6.66 -2016-07-14 08:00:00,470.0,345.48,15.03,6.26 -2016-07-14 09:00:00,557.0,491.64,15.09,5.86 -2016-07-14 10:00:00,607.0,663.93,15.11,5.58 -2016-07-14 11:00:00,644.0,754.13,15.14,5.3 -2016-07-14 12:00:00,656.0,793.33,15.17,5.02 -2016-07-14 13:00:00,625.0,794.16,15.23,4.59 -2016-07-14 14:00:00,555.0,722.42,15.28,4.16 -2016-07-14 15:00:00,537.0,597.92,15.34,3.72 -2016-07-14 16:00:00,505.0,454.5,15.33,3.55 -2016-07-14 17:00:00,440.0,258.39,15.33,3.37 -2016-07-14 18:00:00,489.0,76.91,15.33,3.2 -2016-07-14 19:00:00,550.0,26.35,15.25,2.97 -2016-07-14 20:00:00,567.0,0.0,15.17,2.73 -2016-07-14 21:00:00,551.0,0.0,15.1,2.5 -2016-07-14 22:00:00,847.0,0.0,15.02,2.02 -2016-07-14 23:00:00,846.0,0.0,14.95,1.54 -2016-07-15 00:00:00,634.0,0.0,14.88,1.06 -2016-07-15 01:00:00,466.0,0.0,14.63,1.37 -2016-07-15 02:00:00,439.0,0.0,14.38,1.68 -2016-07-15 03:00:00,384.0,0.0,14.12,1.99 -2016-07-15 04:00:00,363.0,0.0,14.16,2.35 -2016-07-15 05:00:00,334.0,0.0,14.2,2.72 -2016-07-15 06:00:00,327.0,48.65,14.24,3.09 -2016-07-15 07:00:00,371.0,122.33,14.38,3.26 -2016-07-15 08:00:00,434.0,205.37,14.52,3.44 -2016-07-15 09:00:00,551.0,298.43,14.66,3.61 -2016-07-15 10:00:00,602.0,335.17,14.8,3.54 -2016-07-15 11:00:00,643.0,488.14,14.94,3.48 -2016-07-15 12:00:00,661.0,705.96,15.07,3.41 -2016-07-15 13:00:00,640.0,754.89,15.28,3.48 -2016-07-15 14:00:00,588.0,705.66,15.48,3.55 -2016-07-15 15:00:00,570.0,609.62,15.69,3.63 -2016-07-15 16:00:00,497.0,455.68,15.73,3.38 -2016-07-15 17:00:00,459.0,264.72,15.78,3.13 -2016-07-15 18:00:00,489.0,71.49,15.83,2.88 -2016-07-15 19:00:00,586.0,24.35,15.79,2.46 -2016-07-15 20:00:00,569.0,0.0,15.75,2.04 -2016-07-15 21:00:00,565.0,0.0,15.71,1.61 -2016-07-15 22:00:00,825.0,0.0,15.68,1.5 -2016-07-15 23:00:00,805.0,0.0,15.66,1.38 -2016-07-16 00:00:00,635.0,0.0,15.63,1.27 -2016-07-16 01:00:00,505.0,0.0,15.55,0.97 -2016-07-16 02:00:00,454.0,0.0,15.46,0.68 -2016-07-16 03:00:00,379.0,0.0,15.37,0.39 -2016-07-16 04:00:00,359.0,0.0,15.37,0.51 -2016-07-16 05:00:00,322.0,5.6,15.37,0.64 -2016-07-16 06:00:00,329.0,32.79,15.37,0.77 -2016-07-16 07:00:00,344.0,147.67,15.6,1.17 -2016-07-16 08:00:00,448.0,340.87,15.83,1.57 -2016-07-16 09:00:00,550.0,511.36,16.07,1.97 -2016-07-16 10:00:00,600.0,640.71,16.24,1.91 -2016-07-16 11:00:00,619.0,724.75,16.42,1.85 -2016-07-16 12:00:00,614.0,761.61,16.6,1.79 -2016-07-16 13:00:00,604.0,762.19,16.92,1.65 -2016-07-16 14:00:00,594.0,695.73,17.23,1.51 -2016-07-16 15:00:00,577.0,592.58,17.55,1.37 -2016-07-16 16:00:00,496.0,446.45,17.38,1.88 -2016-07-16 17:00:00,441.0,262.88,17.22,2.39 -2016-07-16 18:00:00,492.0,71.16,17.05,2.9 -2016-07-16 19:00:00,583.0,25.48,17.04,3.26 -2016-07-16 20:00:00,609.0,0.0,17.02,3.61 -2016-07-16 21:00:00,588.0,0.0,17.01,3.97 -2016-07-16 22:00:00,866.0,0.0,16.91,4.1 -2016-07-16 23:00:00,846.0,0.0,16.8,4.22 -2016-07-17 00:00:00,611.0,0.0,16.7,4.34 -2016-07-17 01:00:00,509.0,0.0,16.64,4.17 -2016-07-17 02:00:00,458.0,0.0,16.58,4.0 -2016-07-17 03:00:00,375.0,0.0,16.51,3.82 -2016-07-17 04:00:00,365.0,0.0,16.48,3.71 -2016-07-17 05:00:00,337.0,5.57,16.45,3.59 -2016-07-17 06:00:00,308.0,32.7,16.41,3.48 -2016-07-17 07:00:00,342.0,147.56,16.61,3.64 -2016-07-17 08:00:00,439.0,343.23,16.81,3.81 -2016-07-17 09:00:00,535.0,517.21,17.01,3.97 -2016-07-17 10:00:00,570.0,657.51,17.22,4.22 -2016-07-17 11:00:00,634.0,744.95,17.43,4.46 -2016-07-17 12:00:00,663.0,786.99,17.63,4.7 -2016-07-17 13:00:00,610.0,790.95,17.88,4.91 -2016-07-17 14:00:00,572.0,722.88,18.13,5.13 -2016-07-17 15:00:00,540.0,612.98,18.38,5.34 -2016-07-17 16:00:00,468.0,455.37,18.4,5.55 -2016-07-17 17:00:00,428.0,264.22,18.42,5.76 -2016-07-17 18:00:00,476.0,78.55,18.44,5.97 -2016-07-17 19:00:00,528.0,31.11,18.41,6.12 -2016-07-17 20:00:00,575.0,0.0,18.37,6.27 -2016-07-17 21:00:00,527.0,0.0,18.34,6.41 -2016-07-17 22:00:00,766.0,0.0,18.35,6.46 -2016-07-17 23:00:00,782.0,0.0,18.37,6.51 -2016-07-18 00:00:00,560.0,0.0,18.39,6.55 -2016-07-18 01:00:00,429.0,0.0,18.4,6.46 -2016-07-18 02:00:00,420.0,0.0,18.4,6.36 -2016-07-18 03:00:00,367.0,0.0,18.4,6.26 -2016-07-18 04:00:00,356.0,0.0,18.37,6.37 -2016-07-18 05:00:00,318.0,5.01,18.34,6.48 -2016-07-18 06:00:00,306.0,34.4,18.3,6.59 -2016-07-18 07:00:00,353.0,146.46,18.47,6.69 -2016-07-18 08:00:00,437.0,341.46,18.63,6.78 -2016-07-18 09:00:00,518.0,517.63,18.8,6.87 -2016-07-18 10:00:00,542.0,659.22,18.98,6.79 -2016-07-18 11:00:00,565.0,751.05,19.16,6.71 -2016-07-18 12:00:00,621.0,787.51,19.34,6.63 -2016-07-18 13:00:00,638.0,791.23,19.62,6.7 -2016-07-18 14:00:00,570.0,724.51,19.91,6.77 -2016-07-18 15:00:00,547.0,609.87,20.19,6.84 -2016-07-18 16:00:00,486.0,451.29,20.2,6.8 -2016-07-18 17:00:00,456.0,261.83,20.22,6.75 -2016-07-18 18:00:00,474.0,71.37,20.23,6.7 -2016-07-18 19:00:00,550.0,25.1,20.14,6.64 -2016-07-18 20:00:00,553.0,0.0,20.04,6.58 -2016-07-18 21:00:00,508.0,0.0,19.95,6.52 -2016-07-18 22:00:00,759.0,0.0,19.89,6.38 -2016-07-18 23:00:00,792.0,0.0,19.83,6.24 -2016-07-19 00:00:00,560.0,0.0,19.77,6.1 -2016-07-19 01:00:00,424.0,0.0,19.84,6.1 -2016-07-19 02:00:00,420.0,0.0,19.91,6.1 -2016-07-19 03:00:00,370.0,0.0,19.97,6.1 -2016-07-19 04:00:00,352.0,0.0,19.84,5.73 -2016-07-19 05:00:00,325.0,4.48,19.71,5.37 -2016-07-19 06:00:00,309.0,32.2,19.57,5.01 -2016-07-19 07:00:00,359.0,144.6,19.63,4.92 -2016-07-19 08:00:00,448.0,254.77,19.69,4.84 -2016-07-19 09:00:00,529.0,503.97,19.75,4.76 -2016-07-19 10:00:00,586.0,640.41,19.91,4.72 -2016-07-19 11:00:00,586.0,572.45,20.07,4.69 -2016-07-19 12:00:00,607.0,395.3,20.23,4.65 -2016-07-19 13:00:00,604.0,190.54,20.14,3.37 -2016-07-19 14:00:00,576.0,72.33,20.06,2.1 -2016-07-19 15:00:00,557.0,66.28,19.97,0.83 -2016-07-19 16:00:00,500.0,327.52,20.24,1.6 -2016-07-19 17:00:00,463.0,50.21,20.52,2.36 -2016-07-19 18:00:00,489.0,80.85,20.79,3.13 -2016-07-19 19:00:00,569.0,30.16,20.13,3.89 -2016-07-19 20:00:00,580.0,0.0,19.47,4.66 -2016-07-19 21:00:00,530.0,0.0,18.82,5.42 -2016-07-19 22:00:00,816.0,0.0,18.66,5.33 -2016-07-19 23:00:00,820.0,0.0,18.5,5.25 -2016-07-20 00:00:00,588.0,0.0,18.34,5.16 -2016-07-20 01:00:00,438.0,0.0,17.99,5.8 -2016-07-20 02:00:00,434.0,0.0,17.63,6.45 -2016-07-20 03:00:00,380.0,0.0,17.27,7.09 -2016-07-20 04:00:00,350.0,0.0,17.15,6.95 -2016-07-20 05:00:00,328.0,1.86,17.02,6.81 -2016-07-20 06:00:00,323.0,2.71,16.89,6.68 -2016-07-20 07:00:00,397.0,146.36,16.93,6.38 -2016-07-20 08:00:00,449.0,255.48,16.97,6.09 -2016-07-20 09:00:00,509.0,113.63,17.0,5.79 -2016-07-20 10:00:00,571.0,232.72,17.16,5.68 -2016-07-20 11:00:00,601.0,420.83,17.32,5.56 -2016-07-20 12:00:00,638.0,447.81,17.47,5.45 -2016-07-20 13:00:00,661.0,787.8,17.62,5.43 -2016-07-20 14:00:00,629.0,726.26,17.78,5.42 -2016-07-20 15:00:00,597.0,608.88,17.93,5.41 -2016-07-20 16:00:00,513.0,403.77,17.79,5.45 -2016-07-20 17:00:00,474.0,146.94,17.66,5.49 -2016-07-20 18:00:00,528.0,65.61,17.53,5.53 -2016-07-20 19:00:00,567.0,22.93,17.33,5.23 -2016-07-20 20:00:00,562.0,0.0,17.12,4.92 -2016-07-20 21:00:00,557.0,0.0,16.92,4.62 -2016-07-20 22:00:00,830.0,0.0,16.77,4.26 -2016-07-20 23:00:00,836.0,0.0,16.63,3.9 -2016-07-21 00:00:00,588.0,0.0,16.49,3.54 -2016-07-21 01:00:00,445.0,0.0,16.42,3.3 -2016-07-21 02:00:00,430.0,0.0,16.35,3.06 -2016-07-21 03:00:00,386.0,0.0,16.27,2.81 -2016-07-21 04:00:00,360.0,0.0,16.22,3.06 -2016-07-21 05:00:00,329.0,0.38,16.17,3.3 -2016-07-21 06:00:00,314.0,23.06,16.12,3.54 -2016-07-21 07:00:00,366.0,41.13,16.13,3.66 -2016-07-21 08:00:00,461.0,103.77,16.14,3.77 -2016-07-21 09:00:00,541.0,129.27,16.15,3.88 -2016-07-21 10:00:00,571.0,290.04,16.28,4.27 -2016-07-21 11:00:00,612.0,411.53,16.41,4.66 -2016-07-21 12:00:00,653.0,448.65,16.54,5.05 -2016-07-21 13:00:00,650.0,761.33,16.84,4.99 -2016-07-21 14:00:00,645.0,620.6,17.13,4.93 -2016-07-21 15:00:00,608.0,465.99,17.43,4.87 -2016-07-21 16:00:00,512.0,424.99,17.54,4.86 -2016-07-21 17:00:00,460.0,215.01,17.66,4.84 -2016-07-21 18:00:00,494.0,25.31,17.77,4.83 -2016-07-21 19:00:00,561.0,16.72,17.77,4.48 -2016-07-21 20:00:00,592.0,0.0,17.77,4.14 -2016-07-21 21:00:00,564.0,0.0,17.77,3.79 -2016-07-21 22:00:00,804.0,0.0,17.75,3.68 -2016-07-21 23:00:00,835.0,0.0,17.72,3.57 -2016-07-22 00:00:00,592.0,0.0,17.7,3.46 -2016-07-22 01:00:00,459.0,0.0,17.69,3.63 -2016-07-22 02:00:00,442.0,0.0,17.67,3.8 -2016-07-22 03:00:00,377.0,0.0,17.65,3.97 -2016-07-22 04:00:00,362.0,0.0,17.62,3.7 -2016-07-22 05:00:00,328.0,3.14,17.59,3.43 -2016-07-22 06:00:00,317.0,35.13,17.56,3.16 -2016-07-22 07:00:00,398.0,142.98,17.59,2.82 -2016-07-22 08:00:00,451.0,223.58,17.62,2.48 -2016-07-22 09:00:00,516.0,337.62,17.66,2.14 -2016-07-22 10:00:00,604.0,475.96,17.65,2.48 -2016-07-22 11:00:00,596.0,550.83,17.65,2.83 -2016-07-22 12:00:00,677.0,189.39,17.65,3.17 -2016-07-22 13:00:00,646.0,312.22,17.64,3.54 -2016-07-22 14:00:00,614.0,297.95,17.62,3.9 -2016-07-22 15:00:00,590.0,421.98,17.61,4.26 -2016-07-22 16:00:00,508.0,202.08,17.56,4.5 -2016-07-22 17:00:00,442.0,181.96,17.51,4.74 -2016-07-22 18:00:00,515.0,83.81,17.46,4.98 -2016-07-22 19:00:00,602.0,28.02,17.37,5.14 -2016-07-22 20:00:00,614.0,0.0,17.27,5.31 -2016-07-22 21:00:00,592.0,0.0,17.18,5.48 -2016-07-22 22:00:00,844.0,0.0,17.09,5.12 -2016-07-22 23:00:00,841.0,0.0,17.01,4.77 -2016-07-23 00:00:00,611.0,0.0,16.93,4.41 -2016-07-23 01:00:00,456.0,0.0,16.75,4.27 -2016-07-23 02:00:00,456.0,0.0,16.57,4.13 -2016-07-23 03:00:00,392.0,0.0,16.38,3.99 -2016-07-23 04:00:00,376.0,0.0,16.31,3.96 -2016-07-23 05:00:00,337.0,1.87,16.24,3.94 -2016-07-23 06:00:00,330.0,39.41,16.16,3.92 -2016-07-23 07:00:00,384.0,140.44,16.17,3.71 -2016-07-23 08:00:00,494.0,201.93,16.18,3.5 -2016-07-23 09:00:00,580.0,274.51,16.18,3.3 -2016-07-23 10:00:00,593.0,135.21,16.37,3.49 -2016-07-23 11:00:00,639.0,399.07,16.56,3.67 -2016-07-23 12:00:00,629.0,463.86,16.74,3.86 -2016-07-23 13:00:00,608.0,273.73,16.97,4.18 -2016-07-23 14:00:00,602.0,127.97,17.2,4.5 -2016-07-23 15:00:00,603.0,139.47,17.43,4.81 -2016-07-23 16:00:00,548.0,93.3,17.44,4.77 -2016-07-23 17:00:00,498.0,201.84,17.46,4.72 -2016-07-23 18:00:00,518.0,84.87,17.47,4.68 -2016-07-23 19:00:00,606.0,14.36,17.41,4.62 -2016-07-23 20:00:00,633.0,0.0,17.35,4.57 -2016-07-23 21:00:00,591.0,0.0,17.29,4.51 -2016-07-23 22:00:00,863.0,0.0,17.13,4.38 -2016-07-23 23:00:00,848.0,0.0,16.98,4.25 -2016-07-24 00:00:00,619.0,0.0,16.82,4.12 -2016-07-24 01:00:00,474.0,0.0,16.67,4.42 -2016-07-24 02:00:00,461.0,0.0,16.52,4.72 -2016-07-24 03:00:00,400.0,0.0,16.37,5.02 -2016-07-24 04:00:00,379.0,0.0,16.44,5.63 -2016-07-24 05:00:00,345.0,0.0,16.51,6.23 -2016-07-24 06:00:00,315.0,39.39,16.57,6.84 -2016-07-24 07:00:00,339.0,18.05,16.74,7.31 -2016-07-24 08:00:00,438.0,219.97,16.91,7.79 -2016-07-24 09:00:00,524.0,96.83,17.08,8.26 -2016-07-24 10:00:00,595.0,71.51,17.2,7.93 -2016-07-24 11:00:00,667.0,166.49,17.32,7.59 -2016-07-24 12:00:00,674.0,193.83,17.44,7.26 -2016-07-24 13:00:00,638.0,278.64,17.4,6.94 -2016-07-24 14:00:00,610.0,157.71,17.37,6.63 -2016-07-24 15:00:00,584.0,145.26,17.33,6.32 -2016-07-24 16:00:00,502.0,163.05,17.28,6.04 -2016-07-24 17:00:00,471.0,39.51,17.23,5.76 -2016-07-24 18:00:00,510.0,88.27,17.18,5.48 -2016-07-24 19:00:00,572.0,26.14,17.04,5.39 -2016-07-24 20:00:00,588.0,0.0,16.89,5.3 -2016-07-24 21:00:00,569.0,0.0,16.75,5.21 -2016-07-24 22:00:00,845.0,0.0,16.62,4.85 -2016-07-24 23:00:00,787.0,0.0,16.49,4.48 -2016-07-25 00:00:00,573.0,0.0,16.36,4.11 -2016-07-25 01:00:00,454.0,0.0,16.23,4.49 -2016-07-25 02:00:00,440.0,0.0,16.1,4.86 -2016-07-25 03:00:00,384.0,0.0,15.96,5.24 -2016-07-25 04:00:00,371.0,0.0,15.92,5.05 -2016-07-25 05:00:00,339.0,1.87,15.87,4.86 -2016-07-25 06:00:00,308.0,31.51,15.82,4.68 -2016-07-25 07:00:00,356.0,141.71,15.89,4.49 -2016-07-25 08:00:00,457.0,73.27,15.96,4.31 -2016-07-25 09:00:00,548.0,171.62,16.03,4.12 -2016-07-25 10:00:00,609.0,245.64,16.18,4.21 -2016-07-25 11:00:00,646.0,382.56,16.33,4.3 -2016-07-25 12:00:00,643.0,416.59,16.47,4.39 -2016-07-25 13:00:00,634.0,385.83,16.66,4.79 -2016-07-25 14:00:00,600.0,566.29,16.85,5.19 -2016-07-25 15:00:00,604.0,344.51,17.04,5.59 -2016-07-25 16:00:00,505.0,335.78,17.0,5.55 -2016-07-25 17:00:00,454.0,137.4,16.97,5.51 -2016-07-25 18:00:00,499.0,29.59,16.94,5.48 -2016-07-25 19:00:00,569.0,22.36,16.85,5.45 -2016-07-25 20:00:00,622.0,0.0,16.75,5.42 -2016-07-25 21:00:00,577.0,0.0,16.66,5.39 -2016-07-25 22:00:00,850.0,0.0,16.56,5.17 -2016-07-25 23:00:00,828.0,0.0,16.45,4.95 -2016-07-26 00:00:00,588.0,0.0,16.35,4.73 -2016-07-26 01:00:00,459.0,0.0,16.31,4.52 -2016-07-26 02:00:00,439.0,0.0,16.26,4.32 -2016-07-26 03:00:00,376.0,0.0,16.21,4.11 -2016-07-26 04:00:00,365.0,0.0,16.18,4.05 -2016-07-26 05:00:00,332.0,0.0,16.15,3.99 -2016-07-26 06:00:00,315.0,17.47,16.12,3.93 -2016-07-26 07:00:00,359.0,37.51,16.17,3.89 -2016-07-26 08:00:00,457.0,61.07,16.22,3.86 -2016-07-26 09:00:00,573.0,189.35,16.26,3.82 -2016-07-26 10:00:00,616.0,180.76,16.33,3.95 -2016-07-26 11:00:00,617.0,524.58,16.4,4.09 -2016-07-26 12:00:00,676.0,539.06,16.46,4.22 -2016-07-26 13:00:00,671.0,172.54,16.58,4.67 -2016-07-26 14:00:00,662.0,100.59,16.71,5.12 -2016-07-26 15:00:00,620.0,232.7,16.83,5.57 -2016-07-26 16:00:00,534.0,272.91,16.88,5.94 -2016-07-26 17:00:00,470.0,73.04,16.94,6.31 -2016-07-26 18:00:00,531.0,49.8,16.99,6.68 -2016-07-26 19:00:00,639.0,24.25,16.99,7.09 -2016-07-26 20:00:00,631.0,0.0,16.99,7.51 -2016-07-26 21:00:00,582.0,0.0,16.99,7.93 -2016-07-26 22:00:00,859.0,0.0,17.02,7.93 -2016-07-26 23:00:00,811.0,0.0,17.05,7.92 -2016-07-27 00:00:00,573.0,0.0,17.08,7.92 -2016-07-27 01:00:00,464.0,0.0,17.11,7.98 -2016-07-27 02:00:00,452.0,0.0,17.13,8.05 -2016-07-27 03:00:00,389.0,0.0,17.15,8.11 -2016-07-27 04:00:00,358.0,0.0,17.2,8.12 -2016-07-27 05:00:00,343.0,0.0,17.24,8.13 -2016-07-27 06:00:00,320.0,29.36,17.28,8.14 -2016-07-27 07:00:00,370.0,86.61,17.26,7.98 -2016-07-27 08:00:00,461.0,173.79,17.24,7.83 -2016-07-27 09:00:00,542.0,261.21,17.23,7.67 -2016-07-27 10:00:00,654.0,62.45,17.15,7.56 -2016-07-27 11:00:00,667.0,304.09,17.08,7.45 -2016-07-27 12:00:00,696.0,185.38,17.01,7.34 -2016-07-27 13:00:00,702.0,386.69,17.04,7.26 -2016-07-27 14:00:00,691.0,302.93,17.08,7.19 -2016-07-27 15:00:00,617.0,424.7,17.11,7.12 -2016-07-27 16:00:00,512.0,179.87,17.08,6.74 -2016-07-27 17:00:00,475.0,144.86,17.06,6.35 -2016-07-27 18:00:00,560.0,73.3,17.04,5.97 -2016-07-27 19:00:00,674.0,9.8,17.03,5.8 -2016-07-27 20:00:00,654.0,0.0,17.02,5.62 -2016-07-27 21:00:00,615.0,0.0,17.02,5.45 -2016-07-27 22:00:00,867.0,0.0,17.01,5.46 -2016-07-27 23:00:00,788.0,0.0,17.0,5.48 -2016-07-28 00:00:00,603.0,0.0,16.99,5.49 -2016-07-28 01:00:00,484.0,0.0,17.01,5.6 -2016-07-28 02:00:00,465.0,0.0,17.03,5.71 -2016-07-28 03:00:00,423.0,0.0,17.04,5.82 -2016-07-28 04:00:00,394.0,0.0,17.07,6.24 -2016-07-28 05:00:00,341.0,0.0,17.1,6.67 -2016-07-28 06:00:00,339.0,31.98,17.13,7.09 -2016-07-28 07:00:00,375.0,90.99,17.2,7.83 -2016-07-28 08:00:00,475.0,73.02,17.27,8.57 -2016-07-28 09:00:00,572.0,293.97,17.34,9.31 -2016-07-28 10:00:00,655.0,96.77,17.34,9.24 -2016-07-28 11:00:00,719.0,185.81,17.34,9.17 -2016-07-28 12:00:00,739.0,400.98,17.33,9.1 -2016-07-28 13:00:00,738.0,216.83,17.35,8.92 -2016-07-28 14:00:00,685.0,190.36,17.38,8.74 -2016-07-28 15:00:00,649.0,344.43,17.4,8.57 -2016-07-28 16:00:00,558.0,242.08,17.38,8.39 -2016-07-28 17:00:00,525.0,93.51,17.36,8.22 -2016-07-28 18:00:00,584.0,48.41,17.34,8.04 -2016-07-28 19:00:00,661.0,9.79,17.28,7.86 -2016-07-28 20:00:00,659.0,0.0,17.22,7.68 -2016-07-28 21:00:00,615.0,0.0,17.17,7.5 -2016-07-28 22:00:00,892.0,0.0,17.04,7.48 -2016-07-28 23:00:00,836.0,0.0,16.91,7.46 -2016-07-29 00:00:00,632.0,0.0,16.78,7.43 -2016-07-29 01:00:00,475.0,0.0,16.75,7.31 -2016-07-29 02:00:00,465.0,0.0,16.71,7.2 -2016-07-29 03:00:00,399.0,0.0,16.67,7.08 -2016-07-29 04:00:00,386.0,0.0,16.62,6.94 -2016-07-29 05:00:00,344.0,0.0,16.57,6.8 -2016-07-29 06:00:00,327.0,35.38,16.52,6.66 -2016-07-29 07:00:00,375.0,136.44,16.51,6.55 -2016-07-29 08:00:00,484.0,218.46,16.5,6.44 -2016-07-29 09:00:00,596.0,352.76,16.5,6.33 -2016-07-29 10:00:00,653.0,541.32,16.45,6.32 -2016-07-29 11:00:00,715.0,315.08,16.41,6.31 -2016-07-29 12:00:00,708.0,294.51,16.37,6.3 -2016-07-29 13:00:00,711.0,605.58,16.48,6.26 -2016-07-29 14:00:00,681.0,145.76,16.6,6.22 -2016-07-29 15:00:00,638.0,376.24,16.71,6.18 -2016-07-29 16:00:00,562.0,238.67,16.71,6.06 -2016-07-29 17:00:00,475.0,137.04,16.72,5.95 -2016-07-29 18:00:00,561.0,61.4,16.72,5.83 -2016-07-29 19:00:00,667.0,23.0,16.72,5.41 -2016-07-29 20:00:00,660.0,0.0,16.72,4.98 -2016-07-29 21:00:00,635.0,0.0,16.73,4.55 -2016-07-29 22:00:00,861.0,0.0,16.72,4.3 -2016-07-29 23:00:00,849.0,0.0,16.71,4.05 -2016-07-30 00:00:00,648.0,0.0,16.7,3.79 -2016-07-30 01:00:00,500.0,0.0,16.69,3.92 -2016-07-30 02:00:00,450.0,0.0,16.68,4.05 -2016-07-30 03:00:00,396.0,0.0,16.66,4.18 -2016-07-30 04:00:00,368.0,0.0,16.63,4.37 -2016-07-30 05:00:00,347.0,0.0,16.59,4.57 -2016-07-30 06:00:00,347.0,35.35,16.55,4.76 -2016-07-30 07:00:00,388.0,112.14,16.53,4.51 -2016-07-30 08:00:00,479.0,64.76,16.51,4.27 -2016-07-30 09:00:00,610.0,191.21,16.5,4.03 -2016-07-30 10:00:00,648.0,462.61,16.48,4.05 -2016-07-30 11:00:00,663.0,604.95,16.46,4.07 -2016-07-30 12:00:00,697.0,363.25,16.44,4.1 -2016-07-30 13:00:00,691.0,510.84,16.52,4.25 -2016-07-30 14:00:00,673.0,595.03,16.59,4.4 -2016-07-30 15:00:00,624.0,597.89,16.67,4.55 -2016-07-30 16:00:00,532.0,447.09,16.68,4.74 -2016-07-30 17:00:00,497.0,245.91,16.7,4.93 -2016-07-30 18:00:00,542.0,80.65,16.71,5.12 -2016-07-30 19:00:00,616.0,19.88,16.69,5.27 -2016-07-30 20:00:00,642.0,0.0,16.67,5.43 -2016-07-30 21:00:00,652.0,0.0,16.65,5.59 -2016-07-30 22:00:00,867.0,0.0,16.59,5.66 -2016-07-30 23:00:00,825.0,0.0,16.53,5.73 -2016-07-31 00:00:00,646.0,0.0,16.47,5.81 -2016-07-31 01:00:00,512.0,0.0,16.38,5.87 -2016-07-31 02:00:00,472.0,0.0,16.28,5.93 -2016-07-31 03:00:00,408.0,0.0,16.18,5.99 -2016-07-31 04:00:00,382.0,0.0,16.21,5.83 -2016-07-31 05:00:00,343.0,0.0,16.23,5.66 -2016-07-31 06:00:00,357.0,32.77,16.25,5.5 -2016-07-31 07:00:00,376.0,138.29,16.3,5.49 -2016-07-31 08:00:00,448.0,334.96,16.35,5.48 -2016-07-31 09:00:00,569.0,512.28,16.4,5.46 -2016-07-31 10:00:00,669.0,652.84,16.36,5.49 -2016-07-31 11:00:00,695.0,434.54,16.33,5.52 -2016-07-31 12:00:00,674.0,572.12,16.29,5.54 -2016-07-31 13:00:00,646.0,382.67,16.42,5.37 -2016-07-31 14:00:00,600.0,451.13,16.55,5.19 -2016-07-31 15:00:00,578.0,548.52,16.68,5.01 -2016-07-31 16:00:00,494.0,296.71,16.65,4.76 -2016-07-31 17:00:00,436.0,206.84,16.62,4.51 -2016-07-31 18:00:00,474.0,81.01,16.59,4.26 -2016-07-31 19:00:00,553.0,20.51,16.52,4.01 -2016-07-31 20:00:00,595.0,0.0,16.45,3.76 -2016-07-31 21:00:00,568.0,0.0,16.39,3.5 -2016-07-31 22:00:00,856.0,0.0,16.34,3.66 -2016-07-31 23:00:00,825.0,0.0,16.3,3.81 -2016-08-01 00:00:00,605.0,0.0,16.25,3.96 -2016-08-01 01:00:00,490.0,0.0,16.19,3.57 -2016-08-01 02:00:00,436.0,0.0,16.13,3.18 -2016-08-01 03:00:00,413.0,0.0,16.07,2.79 -2016-08-01 04:00:00,380.0,0.0,16.08,3.64 -2016-08-01 05:00:00,320.0,0.0,16.09,4.5 -2016-08-01 06:00:00,315.0,15.66,16.1,5.35 -2016-08-01 07:00:00,348.0,57.43,16.21,6.09 -2016-08-01 08:00:00,444.0,41.17,16.31,6.82 -2016-08-01 09:00:00,572.0,94.01,16.42,7.56 -2016-08-01 10:00:00,629.0,104.85,16.51,8.34 -2016-08-01 11:00:00,662.0,177.52,16.6,9.12 -2016-08-01 12:00:00,700.0,114.97,16.7,9.9 -2016-08-01 13:00:00,702.0,82.49,16.7,10.89 -2016-08-01 14:00:00,695.0,74.86,16.71,11.88 -2016-08-01 15:00:00,675.0,68.78,16.72,12.87 -2016-08-01 16:00:00,592.0,55.15,16.84,12.36 -2016-08-01 17:00:00,600.0,42.55,16.97,11.85 -2016-08-01 18:00:00,649.0,16.07,17.09,11.34 -2016-08-01 19:00:00,718.0,6.06,17.25,11.23 -2016-08-01 20:00:00,726.0,0.0,17.41,11.12 -2016-08-01 21:00:00,691.0,0.0,17.58,11.01 -2016-08-01 22:00:00,889.0,0.0,17.66,11.21 -2016-08-01 23:00:00,817.0,0.0,17.73,11.42 -2016-08-02 00:00:00,617.0,0.0,17.81,11.63 -2016-08-02 01:00:00,525.0,0.0,17.8,11.71 -2016-08-02 02:00:00,469.0,0.0,17.78,11.79 -2016-08-02 03:00:00,411.0,0.0,17.76,11.88 -2016-08-02 04:00:00,392.0,0.0,17.8,11.68 -2016-08-02 05:00:00,336.0,0.0,17.83,11.49 -2016-08-02 06:00:00,342.0,8.13,17.86,11.3 -2016-08-02 07:00:00,392.0,96.53,17.91,11.25 -2016-08-02 08:00:00,511.0,50.38,17.97,11.2 -2016-08-02 09:00:00,618.0,78.16,18.02,11.14 -2016-08-02 10:00:00,688.0,95.0,18.04,11.15 -2016-08-02 11:00:00,690.0,111.97,18.06,11.16 -2016-08-02 12:00:00,743.0,125.15,18.08,11.17 -2016-08-02 13:00:00,744.0,258.99,18.05,11.2 -2016-08-02 14:00:00,721.0,85.03,18.02,11.23 -2016-08-02 15:00:00,683.0,218.7,17.99,11.26 -2016-08-02 16:00:00,591.0,154.21,18.04,11.18 -2016-08-02 17:00:00,555.0,139.87,18.09,11.1 -2016-08-02 18:00:00,598.0,54.3,18.14,11.02 -2016-08-02 19:00:00,747.0,11.43,18.12,10.8 -2016-08-02 20:00:00,731.0,0.0,18.09,10.57 -2016-08-02 21:00:00,699.0,0.0,18.07,10.34 -2016-08-02 22:00:00,898.0,0.0,18.09,10.04 -2016-08-02 23:00:00,821.0,0.0,18.11,9.74 -2016-08-03 00:00:00,638.0,0.0,18.13,9.43 -2016-08-03 01:00:00,529.0,0.0,18.02,9.38 -2016-08-03 02:00:00,489.0,0.0,17.91,9.32 -2016-08-03 03:00:00,417.0,0.0,17.79,9.27 -2016-08-03 04:00:00,396.0,0.0,17.79,9.04 -2016-08-03 05:00:00,365.0,0.0,17.79,8.81 -2016-08-03 06:00:00,339.0,32.55,17.78,8.58 -2016-08-03 07:00:00,394.0,104.23,17.69,8.39 -2016-08-03 08:00:00,516.0,181.5,17.59,8.19 -2016-08-03 09:00:00,606.0,486.09,17.5,8.0 -2016-08-03 10:00:00,717.0,681.92,17.32,8.24 -2016-08-03 11:00:00,737.0,774.5,17.14,8.49 -2016-08-03 12:00:00,724.0,802.06,16.96,8.73 -2016-08-03 13:00:00,723.0,676.97,16.84,8.97 -2016-08-03 14:00:00,698.0,753.8,16.72,9.21 -2016-08-03 15:00:00,661.0,612.69,16.61,9.45 -2016-08-03 16:00:00,528.0,439.2,16.45,9.41 -2016-08-03 17:00:00,499.0,204.33,16.3,9.37 -2016-08-03 18:00:00,574.0,70.34,16.14,9.34 -2016-08-03 19:00:00,681.0,10.42,16.19,9.2 -2016-08-03 20:00:00,701.0,0.0,16.23,9.07 -2016-08-03 21:00:00,640.0,0.0,16.28,8.94 -2016-08-03 22:00:00,917.0,0.0,16.43,8.65 -2016-08-03 23:00:00,846.0,0.0,16.59,8.36 -2016-08-04 00:00:00,617.0,0.0,16.74,8.07 -2016-08-04 01:00:00,503.0,0.0,16.67,8.06 -2016-08-04 02:00:00,476.0,0.0,16.6,8.06 -2016-08-04 03:00:00,424.0,0.0,16.53,8.06 -2016-08-04 04:00:00,401.0,0.0,16.54,7.55 -2016-08-04 05:00:00,346.0,0.0,16.55,7.04 -2016-08-04 06:00:00,334.0,22.4,16.56,6.54 -2016-08-04 07:00:00,394.0,120.38,16.54,6.0 -2016-08-04 08:00:00,512.0,152.38,16.53,5.47 -2016-08-04 09:00:00,630.0,119.2,16.51,4.94 -2016-08-04 10:00:00,706.0,97.59,16.54,5.08 -2016-08-04 11:00:00,741.0,55.8,16.57,5.21 -2016-08-04 12:00:00,732.0,305.96,16.6,5.35 -2016-08-04 13:00:00,738.0,390.64,16.61,5.71 -2016-08-04 14:00:00,703.0,460.29,16.63,6.08 -2016-08-04 15:00:00,662.0,302.93,16.65,6.44 -2016-08-04 16:00:00,609.0,320.46,16.65,6.32 -2016-08-04 17:00:00,571.0,257.11,16.66,6.19 -2016-08-04 18:00:00,552.0,71.11,16.67,6.07 -2016-08-04 19:00:00,638.0,17.43,16.69,5.98 -2016-08-04 20:00:00,667.0,0.0,16.71,5.89 -2016-08-04 21:00:00,653.0,0.0,16.73,5.81 -2016-08-04 22:00:00,900.0,0.0,16.73,5.68 -2016-08-04 23:00:00,853.0,0.0,16.72,5.56 -2016-08-05 00:00:00,661.0,0.0,16.72,5.43 -2016-08-05 01:00:00,519.0,0.0,16.55,5.29 -2016-08-05 02:00:00,478.0,0.0,16.38,5.14 -2016-08-05 03:00:00,423.0,0.0,16.2,4.99 -2016-08-05 04:00:00,407.0,0.0,16.12,4.89 -2016-08-05 05:00:00,351.0,0.0,16.04,4.78 -2016-08-05 06:00:00,340.0,30.83,15.95,4.68 -2016-08-05 07:00:00,393.0,131.86,15.99,4.9 -2016-08-05 08:00:00,535.0,323.41,16.02,5.13 -2016-08-05 09:00:00,642.0,437.78,16.06,5.35 -2016-08-05 10:00:00,714.0,642.78,16.16,5.29 -2016-08-05 11:00:00,728.0,346.77,16.27,5.22 -2016-08-05 12:00:00,703.0,743.75,16.38,5.16 -2016-08-05 13:00:00,717.0,796.17,16.56,5.31 -2016-08-05 14:00:00,731.0,733.77,16.75,5.45 -2016-08-05 15:00:00,683.0,623.29,16.94,5.6 -2016-08-05 16:00:00,568.0,449.07,16.95,5.48 -2016-08-05 17:00:00,520.0,163.46,16.97,5.36 -2016-08-05 18:00:00,572.0,65.89,16.98,5.24 -2016-08-05 19:00:00,669.0,5.06,16.95,5.0 -2016-08-05 20:00:00,678.0,0.0,16.92,4.76 -2016-08-05 21:00:00,676.0,0.0,16.89,4.52 -2016-08-05 22:00:00,922.0,0.0,16.91,4.46 -2016-08-05 23:00:00,852.0,0.0,16.94,4.4 -2016-08-06 00:00:00,642.0,0.0,16.96,4.34 -2016-08-06 01:00:00,527.0,0.0,16.88,4.37 -2016-08-06 02:00:00,480.0,0.0,16.8,4.39 -2016-08-06 03:00:00,412.0,0.0,16.71,4.41 -2016-08-06 04:00:00,395.0,0.0,16.72,3.99 -2016-08-06 05:00:00,366.0,0.0,16.73,3.56 -2016-08-06 06:00:00,367.0,23.61,16.73,3.13 -2016-08-06 07:00:00,415.0,127.09,16.78,3.22 -2016-08-06 08:00:00,534.0,229.28,16.84,3.31 -2016-08-06 09:00:00,635.0,339.61,16.89,3.39 -2016-08-06 10:00:00,691.0,553.0,16.93,3.78 -2016-08-06 11:00:00,716.0,593.98,16.98,4.17 -2016-08-06 12:00:00,716.0,811.97,17.03,4.57 -2016-08-06 13:00:00,717.0,801.42,17.11,5.09 -2016-08-06 14:00:00,746.0,715.39,17.19,5.6 -2016-08-06 15:00:00,681.0,626.86,17.28,6.12 -2016-08-06 16:00:00,547.0,456.19,17.27,6.24 -2016-08-06 17:00:00,485.0,234.66,17.27,6.36 -2016-08-06 18:00:00,523.0,70.76,17.27,6.48 -2016-08-06 19:00:00,611.0,14.98,17.24,6.45 -2016-08-06 20:00:00,586.0,0.0,17.2,6.41 -2016-08-06 21:00:00,552.0,0.0,17.17,6.37 -2016-08-06 22:00:00,844.0,0.0,17.18,6.39 -2016-08-06 23:00:00,804.0,0.0,17.2,6.41 -2016-08-07 00:00:00,611.0,0.0,17.21,6.43 -2016-08-07 01:00:00,507.0,0.0,17.16,6.61 -2016-08-07 02:00:00,466.0,0.0,17.11,6.8 -2016-08-07 03:00:00,407.0,0.0,17.05,6.98 -2016-08-07 04:00:00,388.0,0.0,17.09,6.79 -2016-08-07 05:00:00,359.0,0.0,17.12,6.6 -2016-08-07 06:00:00,351.0,22.96,17.15,6.41 -2016-08-07 07:00:00,373.0,56.12,17.21,6.2 -2016-08-07 08:00:00,462.0,127.27,17.26,5.98 -2016-08-07 09:00:00,555.0,205.72,17.32,5.77 -2016-08-07 10:00:00,690.0,628.18,17.3,5.7 -2016-08-07 11:00:00,790.0,697.31,17.29,5.64 -2016-08-07 12:00:00,799.0,794.95,17.28,5.57 -2016-08-07 13:00:00,779.0,785.63,17.3,5.6 -2016-08-07 14:00:00,733.0,736.33,17.33,5.62 -2016-08-07 15:00:00,695.0,620.81,17.36,5.64 -2016-08-07 16:00:00,590.0,452.21,17.41,5.91 -2016-08-07 17:00:00,516.0,224.94,17.46,6.18 -2016-08-07 18:00:00,533.0,61.79,17.51,6.46 -2016-08-07 19:00:00,591.0,14.37,17.43,6.77 -2016-08-07 20:00:00,652.0,0.0,17.35,7.09 -2016-08-07 21:00:00,661.0,0.0,17.28,7.41 -2016-08-07 22:00:00,939.0,0.0,17.13,7.18 -2016-08-07 23:00:00,846.0,0.0,16.98,6.95 -2016-08-08 00:00:00,612.0,0.0,16.83,6.72 -2016-08-08 01:00:00,517.0,0.0,16.84,6.56 -2016-08-08 02:00:00,455.0,0.0,16.85,6.4 -2016-08-08 03:00:00,409.0,0.0,16.86,6.23 -2016-08-08 04:00:00,398.0,0.0,16.95,6.23 -2016-08-08 05:00:00,343.0,0.0,17.03,6.23 -2016-08-08 06:00:00,331.0,18.61,17.11,6.22 -2016-08-08 07:00:00,387.0,132.13,17.14,6.14 -2016-08-08 08:00:00,503.0,216.26,17.18,6.06 -2016-08-08 09:00:00,650.0,467.69,17.21,5.97 -2016-08-08 10:00:00,741.0,246.08,17.07,6.05 -2016-08-08 11:00:00,719.0,646.34,16.94,6.12 -2016-08-08 12:00:00,743.0,391.04,16.81,6.19 -2016-08-08 13:00:00,744.0,618.12,16.66,6.1 -2016-08-08 14:00:00,725.0,379.23,16.51,6.01 -2016-08-08 15:00:00,676.0,464.43,16.37,5.92 -2016-08-08 16:00:00,600.0,398.89,16.25,6.39 -2016-08-08 17:00:00,557.0,210.45,16.14,6.86 -2016-08-08 18:00:00,583.0,72.58,16.02,7.34 -2016-08-08 19:00:00,682.0,0.0,15.89,7.39 -2016-08-08 20:00:00,679.0,0.0,15.75,7.44 -2016-08-08 21:00:00,631.0,0.0,15.62,7.49 -2016-08-08 22:00:00,896.0,0.0,15.58,7.45 -2016-08-08 23:00:00,845.0,0.0,15.55,7.41 -2016-08-09 00:00:00,632.0,0.0,15.51,7.37 -2016-08-09 01:00:00,502.0,0.0,15.81,7.06 -2016-08-09 02:00:00,499.0,0.0,16.11,6.76 -2016-08-09 03:00:00,439.0,0.0,16.41,6.46 -2016-08-09 04:00:00,407.0,0.0,16.38,6.46 -2016-08-09 05:00:00,363.0,0.0,16.34,6.46 -2016-08-09 06:00:00,351.0,23.06,16.3,6.46 -2016-08-09 07:00:00,390.0,130.02,16.33,6.47 -2016-08-09 08:00:00,527.0,212.53,16.35,6.48 -2016-08-09 09:00:00,626.0,365.09,16.38,6.5 -2016-08-09 10:00:00,719.0,163.57,16.31,6.8 -2016-08-09 11:00:00,714.0,681.82,16.24,7.1 -2016-08-09 12:00:00,726.0,760.03,16.17,7.41 -2016-08-09 13:00:00,719.0,768.34,16.22,7.25 -2016-08-09 14:00:00,729.0,733.02,16.27,7.09 -2016-08-09 15:00:00,693.0,604.85,16.33,6.92 -2016-08-09 16:00:00,574.0,412.3,16.21,7.34 -2016-08-09 17:00:00,521.0,215.19,16.1,7.75 -2016-08-09 18:00:00,612.0,71.68,15.98,8.17 -2016-08-09 19:00:00,721.0,12.16,15.78,8.06 -2016-08-09 20:00:00,734.0,0.0,15.57,7.95 -2016-08-09 21:00:00,672.0,0.0,15.37,7.85 -2016-08-09 22:00:00,927.0,0.0,15.27,7.46 -2016-08-09 23:00:00,857.0,0.0,15.18,7.07 -2016-08-10 00:00:00,650.0,0.0,15.08,6.68 -2016-08-10 01:00:00,519.0,0.0,15.38,6.69 -2016-08-10 02:00:00,495.0,0.0,15.68,6.7 -2016-08-10 03:00:00,416.0,0.0,15.97,6.72 -2016-08-10 04:00:00,403.0,0.0,15.98,6.79 -2016-08-10 05:00:00,358.0,0.0,15.99,6.86 -2016-08-10 06:00:00,363.0,25.66,16.0,6.94 -2016-08-10 07:00:00,426.0,117.84,16.06,6.84 -2016-08-10 08:00:00,524.0,287.62,16.11,6.74 -2016-08-10 09:00:00,659.0,426.66,16.17,6.65 -2016-08-10 10:00:00,736.0,445.01,16.22,6.44 -2016-08-10 11:00:00,750.0,518.77,16.27,6.23 -2016-08-10 12:00:00,769.0,664.01,16.32,6.03 -2016-08-10 13:00:00,743.0,687.57,16.37,6.13 -2016-08-10 14:00:00,760.0,422.6,16.42,6.23 -2016-08-10 15:00:00,720.0,217.01,16.48,6.33 -2016-08-10 16:00:00,588.0,423.59,16.52,6.53 -2016-08-10 17:00:00,530.0,246.02,16.57,6.74 -2016-08-10 18:00:00,576.0,70.27,16.62,6.94 -2016-08-10 19:00:00,732.0,11.52,16.61,6.86 -2016-08-10 20:00:00,745.0,0.0,16.6,6.78 -2016-08-10 21:00:00,700.0,0.0,16.59,6.7 -2016-08-10 22:00:00,923.0,0.0,16.53,6.52 -2016-08-10 23:00:00,877.0,0.0,16.47,6.34 -2016-08-11 00:00:00,631.0,0.0,16.41,6.17 -2016-08-11 01:00:00,525.0,0.0,16.38,5.97 -2016-08-11 02:00:00,481.0,0.0,16.35,5.78 -2016-08-11 03:00:00,417.0,0.0,16.31,5.59 -2016-08-11 04:00:00,403.0,0.0,16.29,5.67 -2016-08-11 05:00:00,360.0,0.0,16.26,5.75 -2016-08-11 06:00:00,365.0,24.34,16.23,5.83 -2016-08-11 07:00:00,398.0,124.41,16.26,5.98 -2016-08-11 08:00:00,517.0,327.44,16.28,6.12 -2016-08-11 09:00:00,665.0,520.29,16.31,6.26 -2016-08-11 10:00:00,709.0,609.27,16.25,6.26 -2016-08-11 11:00:00,705.0,717.81,16.19,6.25 -2016-08-11 12:00:00,730.0,778.49,16.13,6.25 -2016-08-11 13:00:00,725.0,789.79,16.07,6.35 -2016-08-11 14:00:00,750.0,735.36,16.01,6.45 -2016-08-11 15:00:00,670.0,614.09,15.96,6.55 -2016-08-11 16:00:00,578.0,361.1,15.89,6.57 -2016-08-11 17:00:00,539.0,218.12,15.82,6.58 -2016-08-11 18:00:00,583.0,68.87,15.75,6.59 -2016-08-11 19:00:00,699.0,8.23,15.7,6.38 -2016-08-11 20:00:00,705.0,0.0,15.64,6.16 -2016-08-11 21:00:00,637.0,0.0,15.59,5.94 -2016-08-11 22:00:00,930.0,0.0,15.62,5.53 -2016-08-11 23:00:00,856.0,0.0,15.65,5.12 -2016-08-12 00:00:00,638.0,0.0,15.68,4.7 -2016-08-12 01:00:00,502.0,0.0,15.68,4.17 -2016-08-12 02:00:00,470.0,0.0,15.67,3.64 -2016-08-12 03:00:00,428.0,0.0,15.66,3.1 -2016-08-12 04:00:00,408.0,0.0,15.58,2.88 -2016-08-12 05:00:00,360.0,0.0,15.5,2.65 -2016-08-12 06:00:00,360.0,18.75,15.41,2.43 -2016-08-12 07:00:00,390.0,81.04,15.33,2.34 -2016-08-12 08:00:00,506.0,136.31,15.24,2.24 -2016-08-12 09:00:00,662.0,198.2,15.16,2.15 -2016-08-12 10:00:00,706.0,145.48,15.24,1.99 -2016-08-12 11:00:00,739.0,265.9,15.33,1.83 -2016-08-12 12:00:00,735.0,446.29,15.42,1.67 -2016-08-12 13:00:00,710.0,417.26,15.55,1.73 -2016-08-12 14:00:00,722.0,385.42,15.68,1.8 -2016-08-12 15:00:00,681.0,417.76,15.81,1.86 -2016-08-12 16:00:00,574.0,364.27,15.98,1.49 -2016-08-12 17:00:00,510.0,200.07,16.16,1.11 -2016-08-12 18:00:00,576.0,62.2,16.34,0.73 -2016-08-12 19:00:00,693.0,8.72,16.4,0.71 -2016-08-12 20:00:00,701.0,0.0,16.46,0.69 -2016-08-12 21:00:00,678.0,0.0,16.52,0.66 -2016-08-12 22:00:00,932.0,0.0,16.48,1.11 -2016-08-12 23:00:00,881.0,0.0,16.45,1.56 -2016-08-13 00:00:00,660.0,0.0,16.41,2.01 -2016-08-13 01:00:00,538.0,0.0,16.25,1.71 -2016-08-13 02:00:00,484.0,0.0,16.09,1.4 -2016-08-13 03:00:00,437.0,0.0,15.93,1.09 -2016-08-13 04:00:00,392.0,0.0,15.91,1.08 -2016-08-13 05:00:00,353.0,0.0,15.88,1.06 -2016-08-13 06:00:00,364.0,21.79,15.85,1.05 -2016-08-13 07:00:00,412.0,127.68,16.02,1.6 -2016-08-13 08:00:00,547.0,318.09,16.19,2.16 -2016-08-13 09:00:00,673.0,496.43,16.36,2.72 -2016-08-13 10:00:00,706.0,649.43,16.52,3.02 -2016-08-13 11:00:00,733.0,738.7,16.68,3.31 -2016-08-13 12:00:00,750.0,800.11,16.85,3.61 -2016-08-13 13:00:00,714.0,776.69,16.99,3.66 -2016-08-13 14:00:00,706.0,706.46,17.13,3.71 -2016-08-13 15:00:00,670.0,586.79,17.27,3.75 -2016-08-13 16:00:00,563.0,426.53,17.26,4.07 -2016-08-13 17:00:00,512.0,231.29,17.25,4.39 -2016-08-13 18:00:00,579.0,62.1,17.24,4.7 -2016-08-13 19:00:00,670.0,6.57,17.14,4.8 -2016-08-13 20:00:00,674.0,0.0,17.03,4.89 -2016-08-13 21:00:00,673.0,0.0,16.93,4.98 -2016-08-13 22:00:00,898.0,0.0,16.81,5.1 -2016-08-13 23:00:00,855.0,0.0,16.7,5.23 -2016-08-14 00:00:00,683.0,0.0,16.58,5.35 -2016-08-14 01:00:00,526.0,0.0,16.42,5.53 -2016-08-14 02:00:00,499.0,0.0,16.25,5.71 -2016-08-14 03:00:00,435.0,0.0,16.08,5.89 -2016-08-14 04:00:00,395.0,0.0,16.02,5.94 -2016-08-14 05:00:00,352.0,0.0,15.96,5.99 -2016-08-14 06:00:00,354.0,19.35,15.9,6.04 -2016-08-14 07:00:00,373.0,128.03,16.05,6.04 -2016-08-14 08:00:00,481.0,291.92,16.21,6.04 -2016-08-14 09:00:00,605.0,509.63,16.36,6.04 -2016-08-14 10:00:00,731.0,664.56,16.68,6.03 -2016-08-14 11:00:00,775.0,745.49,17.0,6.01 -2016-08-14 12:00:00,738.0,614.2,17.33,6.0 -2016-08-14 13:00:00,714.0,713.59,17.57,6.25 -2016-08-14 14:00:00,688.0,731.7,17.82,6.5 -2016-08-14 15:00:00,650.0,613.57,18.07,6.74 -2016-08-14 16:00:00,521.0,443.96,18.09,6.79 -2016-08-14 17:00:00,468.0,240.2,18.12,6.83 -2016-08-14 18:00:00,562.0,53.27,18.14,6.87 -2016-08-14 19:00:00,647.0,7.58,18.07,6.74 -2016-08-14 20:00:00,690.0,0.0,18.0,6.61 -2016-08-14 21:00:00,716.0,0.0,17.93,6.48 -2016-08-14 22:00:00,922.0,0.0,17.9,6.42 -2016-08-14 23:00:00,842.0,0.0,17.88,6.35 -2016-08-15 00:00:00,652.0,0.0,17.85,6.29 -2016-08-15 01:00:00,538.0,0.0,17.74,6.41 -2016-08-15 02:00:00,492.0,0.0,17.62,6.53 -2016-08-15 03:00:00,434.0,0.0,17.5,6.65 -2016-08-15 04:00:00,403.0,0.0,17.52,6.67 -2016-08-15 05:00:00,348.0,0.0,17.54,6.69 -2016-08-15 06:00:00,331.0,17.35,17.56,6.72 -2016-08-15 07:00:00,354.0,128.09,17.74,6.69 -2016-08-15 08:00:00,519.0,336.49,17.93,6.67 -2016-08-15 09:00:00,624.0,519.36,18.11,6.65 -2016-08-15 10:00:00,689.0,676.73,18.37,6.57 -2016-08-15 11:00:00,732.0,756.49,18.63,6.5 -2016-08-15 12:00:00,713.0,516.37,18.9,6.43 -2016-08-15 13:00:00,666.0,796.86,19.13,6.5 -2016-08-15 14:00:00,636.0,730.11,19.36,6.57 -2016-08-15 15:00:00,626.0,607.79,19.59,6.63 -2016-08-15 16:00:00,535.0,439.45,19.65,6.81 -2016-08-15 17:00:00,485.0,229.85,19.71,6.98 -2016-08-15 18:00:00,520.0,61.17,19.77,7.16 -2016-08-15 19:00:00,613.0,6.47,19.75,7.31 -2016-08-15 20:00:00,667.0,0.0,19.73,7.45 -2016-08-15 21:00:00,662.0,0.0,19.72,7.6 -2016-08-15 22:00:00,881.0,0.0,19.68,7.5 -2016-08-15 23:00:00,818.0,0.0,19.65,7.4 -2016-08-16 00:00:00,606.0,0.0,19.61,7.3 -2016-08-16 01:00:00,502.0,0.0,19.39,7.31 -2016-08-16 02:00:00,463.0,0.0,19.17,7.33 -2016-08-16 03:00:00,426.0,0.0,18.94,7.35 -2016-08-16 04:00:00,406.0,0.0,18.89,7.26 -2016-08-16 05:00:00,335.0,0.0,18.83,7.17 -2016-08-16 06:00:00,325.0,19.06,18.77,7.08 -2016-08-16 07:00:00,382.0,122.98,18.87,7.02 -2016-08-16 08:00:00,482.0,315.76,18.98,6.96 -2016-08-16 09:00:00,589.0,427.23,19.08,6.9 -2016-08-16 10:00:00,678.0,568.69,19.19,6.6 -2016-08-16 11:00:00,707.0,729.48,19.3,6.3 -2016-08-16 12:00:00,719.0,773.82,19.42,6.0 -2016-08-16 13:00:00,704.0,553.87,19.54,5.78 -2016-08-16 14:00:00,706.0,448.92,19.66,5.57 -2016-08-16 15:00:00,640.0,564.81,19.79,5.35 -2016-08-16 16:00:00,553.0,402.77,19.93,5.03 -2016-08-16 17:00:00,502.0,105.65,20.08,4.7 -2016-08-16 18:00:00,571.0,49.13,20.23,4.37 -2016-08-16 19:00:00,625.0,5.96,19.77,4.55 -2016-08-16 20:00:00,640.0,0.0,19.31,4.73 -2016-08-16 21:00:00,621.0,0.0,18.86,4.91 -2016-08-16 22:00:00,910.0,0.0,18.66,4.43 -2016-08-16 23:00:00,820.0,0.0,18.46,3.94 -2016-08-17 00:00:00,619.0,0.0,18.26,3.46 -2016-08-17 01:00:00,512.0,0.0,18.23,3.51 -2016-08-17 02:00:00,478.0,0.0,18.19,3.56 -2016-08-17 03:00:00,431.0,0.0,18.15,3.61 -2016-08-17 04:00:00,401.0,0.0,18.17,3.64 -2016-08-17 05:00:00,370.0,0.0,18.18,3.66 -2016-08-17 06:00:00,348.0,8.1,18.19,3.68 -2016-08-17 07:00:00,399.0,102.8,18.24,3.58 -2016-08-17 08:00:00,515.0,163.27,18.29,3.48 -2016-08-17 09:00:00,599.0,158.31,18.34,3.38 -2016-08-17 10:00:00,671.0,306.65,18.34,3.41 -2016-08-17 11:00:00,712.0,104.13,18.34,3.44 -2016-08-17 12:00:00,759.0,67.85,18.34,3.48 -2016-08-17 13:00:00,740.0,157.71,18.21,3.42 -2016-08-17 14:00:00,770.0,78.45,18.08,3.36 -2016-08-17 15:00:00,722.0,154.74,17.95,3.3 -2016-08-17 16:00:00,626.0,49.51,17.85,4.26 -2016-08-17 17:00:00,553.0,70.41,17.75,5.23 -2016-08-17 18:00:00,604.0,53.19,17.65,6.19 -2016-08-17 19:00:00,658.0,2.7,17.51,6.7 -2016-08-17 20:00:00,695.0,0.0,17.37,7.21 -2016-08-17 21:00:00,649.0,0.0,17.24,7.72 -2016-08-17 22:00:00,916.0,0.0,17.15,7.83 -2016-08-17 23:00:00,809.0,0.0,17.05,7.94 -2016-08-18 00:00:00,601.0,0.0,16.96,8.06 -2016-08-18 01:00:00,471.0,0.0,17.08,8.2 -2016-08-18 02:00:00,469.0,0.0,17.19,8.34 -2016-08-18 03:00:00,434.0,0.0,17.3,8.48 -2016-08-18 04:00:00,410.0,0.0,17.22,8.58 -2016-08-18 05:00:00,356.0,0.0,17.14,8.68 -2016-08-18 06:00:00,329.0,12.07,17.06,8.77 -2016-08-18 07:00:00,377.0,98.92,16.96,8.35 -2016-08-18 08:00:00,519.0,145.64,16.85,7.93 -2016-08-18 09:00:00,641.0,201.1,16.75,7.5 -2016-08-18 10:00:00,705.0,393.29,16.66,7.13 -2016-08-18 11:00:00,735.0,313.05,16.57,6.76 -2016-08-18 12:00:00,736.0,476.43,16.48,6.39 -2016-08-18 13:00:00,725.0,685.86,16.56,5.83 -2016-08-18 14:00:00,700.0,466.44,16.64,5.26 -2016-08-18 15:00:00,650.0,299.72,16.73,4.7 -2016-08-18 16:00:00,558.0,184.31,16.84,4.46 -2016-08-18 17:00:00,484.0,85.82,16.96,4.22 -2016-08-18 18:00:00,547.0,15.4,17.07,3.97 -2016-08-18 19:00:00,664.0,2.7,17.17,4.17 -2016-08-18 20:00:00,660.0,0.0,17.27,4.37 -2016-08-18 21:00:00,619.0,0.0,17.38,4.57 -2016-08-18 22:00:00,909.0,0.0,17.47,5.95 -2016-08-18 23:00:00,823.0,0.0,17.55,7.34 -2016-08-19 00:00:00,622.0,0.0,17.64,8.73 -2016-08-19 01:00:00,504.0,0.0,17.81,9.66 -2016-08-19 02:00:00,473.0,0.0,17.98,10.58 -2016-08-19 03:00:00,420.0,0.0,18.15,11.5 -2016-08-19 04:00:00,397.0,0.0,18.09,11.75 -2016-08-19 05:00:00,351.0,0.0,18.03,12.0 -2016-08-19 06:00:00,330.0,17.93,17.97,12.25 -2016-08-19 07:00:00,374.0,125.48,17.85,11.83 -2016-08-19 08:00:00,513.0,335.96,17.74,11.42 -2016-08-19 09:00:00,608.0,512.79,17.62,11.01 -2016-08-19 10:00:00,687.0,679.73,17.53,11.37 -2016-08-19 11:00:00,726.0,620.3,17.44,11.72 -2016-08-19 12:00:00,714.0,675.57,17.36,12.08 -2016-08-19 13:00:00,723.0,774.99,17.42,12.13 -2016-08-19 14:00:00,706.0,727.93,17.48,12.17 -2016-08-19 15:00:00,668.0,245.72,17.54,12.22 -2016-08-19 16:00:00,559.0,342.23,17.57,12.34 -2016-08-19 17:00:00,511.0,220.74,17.6,12.46 -2016-08-19 18:00:00,574.0,50.91,17.63,12.58 -2016-08-19 19:00:00,627.0,2.7,17.6,12.59 -2016-08-19 20:00:00,639.0,0.0,17.57,12.61 -2016-08-19 21:00:00,625.0,0.0,17.54,12.62 -2016-08-19 22:00:00,903.0,0.0,17.48,12.73 -2016-08-19 23:00:00,805.0,0.0,17.42,12.84 -2016-08-20 00:00:00,592.0,0.0,17.36,12.95 -2016-08-20 01:00:00,491.0,0.0,17.28,13.25 -2016-08-20 02:00:00,466.0,0.0,17.19,13.54 -2016-08-20 03:00:00,405.0,0.0,17.1,13.83 -2016-08-20 04:00:00,390.0,0.0,17.06,13.66 -2016-08-20 05:00:00,352.0,0.0,17.02,13.48 -2016-08-20 06:00:00,333.0,15.01,16.98,13.3 -2016-08-20 07:00:00,382.0,122.47,16.97,13.15 -2016-08-20 08:00:00,490.0,315.36,16.96,13.01 -2016-08-20 09:00:00,625.0,488.34,16.95,12.87 -2016-08-20 10:00:00,689.0,652.64,16.87,13.01 -2016-08-20 11:00:00,674.0,232.62,16.8,13.14 -2016-08-20 12:00:00,672.0,742.83,16.73,13.28 -2016-08-20 13:00:00,683.0,703.11,16.74,13.28 -2016-08-20 14:00:00,697.0,409.15,16.76,13.27 -2016-08-20 15:00:00,666.0,450.74,16.78,13.27 -2016-08-20 16:00:00,575.0,175.97,16.8,13.03 -2016-08-20 17:00:00,494.0,67.22,16.82,12.78 -2016-08-20 18:00:00,547.0,25.48,16.84,12.54 -2016-08-20 19:00:00,630.0,1.86,16.86,12.26 -2016-08-20 20:00:00,604.0,0.0,16.87,11.99 -2016-08-20 21:00:00,600.0,0.0,16.89,11.71 -2016-08-20 22:00:00,870.0,0.0,16.92,11.46 -2016-08-20 23:00:00,799.0,0.0,16.96,11.2 -2016-08-21 00:00:00,600.0,0.0,17.0,10.95 -2016-08-21 01:00:00,483.0,0.0,16.88,10.72 -2016-08-21 02:00:00,471.0,0.0,16.76,10.48 -2016-08-21 03:00:00,407.0,0.0,16.64,10.25 -2016-08-21 04:00:00,391.0,0.0,16.72,9.82 -2016-08-21 05:00:00,343.0,0.0,16.8,9.38 -2016-08-21 06:00:00,327.0,13.83,16.87,8.95 -2016-08-21 07:00:00,367.0,112.37,16.97,8.47 -2016-08-21 08:00:00,473.0,127.97,17.07,8.0 -2016-08-21 09:00:00,599.0,165.97,17.17,7.52 -2016-08-21 10:00:00,703.0,207.11,17.23,7.52 -2016-08-21 11:00:00,737.0,222.74,17.3,7.52 -2016-08-21 12:00:00,706.0,171.89,17.37,7.52 -2016-08-21 13:00:00,673.0,367.38,17.25,7.94 -2016-08-21 14:00:00,665.0,272.55,17.13,8.37 -2016-08-21 15:00:00,612.0,609.76,17.01,8.8 -2016-08-21 16:00:00,527.0,214.4,17.01,8.59 -2016-08-21 17:00:00,424.0,229.81,17.02,8.39 -2016-08-21 18:00:00,482.0,42.07,17.03,8.18 -2016-08-21 19:00:00,562.0,1.46,17.02,8.02 -2016-08-21 20:00:00,583.0,0.0,17.01,7.86 -2016-08-21 21:00:00,588.0,0.0,17.0,7.7 -2016-08-21 22:00:00,846.0,0.0,17.03,7.77 -2016-08-21 23:00:00,824.0,0.0,17.07,7.83 -2016-08-22 00:00:00,591.0,0.0,17.1,7.9 -2016-08-22 01:00:00,465.0,0.0,17.06,8.04 -2016-08-22 02:00:00,444.0,0.0,17.02,8.18 -2016-08-22 03:00:00,406.0,0.0,16.97,8.32 -2016-08-22 04:00:00,392.0,0.0,17.0,8.01 -2016-08-22 05:00:00,331.0,0.0,17.03,7.71 -2016-08-22 06:00:00,317.0,12.07,17.05,7.41 -2016-08-22 07:00:00,373.0,124.04,17.13,7.11 -2016-08-22 08:00:00,476.0,334.8,17.21,6.82 -2016-08-22 09:00:00,580.0,518.83,17.29,6.52 -2016-08-22 10:00:00,631.0,674.12,17.34,6.14 -2016-08-22 11:00:00,687.0,756.18,17.39,5.76 -2016-08-22 12:00:00,683.0,801.21,17.44,5.38 -2016-08-22 13:00:00,665.0,788.54,17.47,5.36 -2016-08-22 14:00:00,680.0,721.16,17.5,5.33 -2016-08-22 15:00:00,650.0,604.74,17.53,5.31 -2016-08-22 16:00:00,568.0,431.13,17.63,4.76 -2016-08-22 17:00:00,506.0,226.13,17.74,4.21 -2016-08-22 18:00:00,539.0,41.82,17.85,3.66 -2016-08-22 19:00:00,630.0,0.0,17.95,3.67 -2016-08-22 20:00:00,634.0,0.0,18.05,3.69 -2016-08-22 21:00:00,624.0,0.0,18.16,3.71 -2016-08-22 22:00:00,844.0,0.0,18.33,4.06 -2016-08-22 23:00:00,808.0,0.0,18.51,4.42 -2016-08-23 00:00:00,596.0,0.0,18.68,4.77 -2016-08-23 01:00:00,476.0,0.0,18.77,4.59 -2016-08-23 02:00:00,437.0,0.0,18.85,4.41 -2016-08-23 03:00:00,413.0,0.0,18.93,4.23 -2016-08-23 04:00:00,405.0,0.0,18.88,4.35 -2016-08-23 05:00:00,355.0,0.0,18.82,4.46 -2016-08-23 06:00:00,349.0,13.67,18.76,4.58 -2016-08-23 07:00:00,372.0,27.24,18.61,4.04 -2016-08-23 08:00:00,497.0,210.16,18.47,3.49 -2016-08-23 09:00:00,596.0,492.18,18.32,2.95 -2016-08-23 10:00:00,667.0,614.11,18.1,2.46 -2016-08-23 11:00:00,676.0,627.96,17.89,1.96 -2016-08-23 12:00:00,710.0,733.43,17.68,1.46 -2016-08-23 13:00:00,683.0,735.56,17.66,2.1 -2016-08-23 14:00:00,678.0,662.39,17.65,2.73 -2016-08-23 15:00:00,628.0,564.36,17.64,3.37 -2016-08-23 16:00:00,529.0,383.98,17.59,3.65 -2016-08-23 17:00:00,483.0,84.17,17.54,3.93 -2016-08-23 18:00:00,571.0,39.34,17.49,4.21 -2016-08-23 19:00:00,674.0,0.0,17.37,4.67 -2016-08-23 20:00:00,639.0,0.0,17.25,5.14 -2016-08-23 21:00:00,624.0,0.0,17.14,5.6 -2016-08-23 22:00:00,846.0,0.0,17.02,5.88 -2016-08-23 23:00:00,813.0,0.0,16.91,6.16 -2016-08-24 00:00:00,596.0,0.0,16.8,6.44 -2016-08-24 01:00:00,471.0,0.0,16.6,6.62 -2016-08-24 02:00:00,449.0,0.0,16.4,6.79 -2016-08-24 03:00:00,426.0,0.0,16.19,6.97 -2016-08-24 04:00:00,410.0,0.0,16.15,6.97 -2016-08-24 05:00:00,347.0,0.0,16.1,6.97 -2016-08-24 06:00:00,352.0,12.14,16.05,6.97 -2016-08-24 07:00:00,394.0,108.96,16.17,7.05 -2016-08-24 08:00:00,524.0,156.7,16.3,7.14 -2016-08-24 09:00:00,598.0,130.42,16.42,7.23 -2016-08-24 10:00:00,657.0,139.7,16.57,7.15 -2016-08-24 11:00:00,688.0,61.81,16.73,7.07 -2016-08-24 12:00:00,724.0,277.37,16.89,6.99 -2016-08-24 13:00:00,722.0,508.87,16.81,6.46 -2016-08-24 14:00:00,696.0,725.46,16.74,5.92 -2016-08-24 15:00:00,677.0,577.51,16.67,5.38 -2016-08-24 16:00:00,574.0,188.35,16.69,5.48 -2016-08-24 17:00:00,504.0,83.97,16.71,5.57 -2016-08-24 18:00:00,561.0,22.05,16.73,5.67 -2016-08-24 19:00:00,628.0,0.0,16.74,5.39 -2016-08-24 20:00:00,658.0,0.0,16.75,5.11 -2016-08-24 21:00:00,632.0,0.0,16.77,4.83 -2016-08-24 22:00:00,839.0,0.0,16.77,4.8 -2016-08-24 23:00:00,783.0,0.0,16.78,4.77 -2016-08-25 00:00:00,556.0,0.0,16.79,4.74 -2016-08-25 01:00:00,444.0,0.0,16.69,4.28 -2016-08-25 02:00:00,438.0,0.0,16.59,3.81 -2016-08-25 03:00:00,398.0,0.0,16.48,3.34 -2016-08-25 04:00:00,395.0,0.0,16.46,3.18 -2016-08-25 05:00:00,328.0,0.0,16.44,3.02 -2016-08-25 06:00:00,345.0,10.95,16.42,2.86 -2016-08-25 07:00:00,404.0,93.77,16.49,3.14 -2016-08-25 08:00:00,486.0,190.87,16.57,3.42 -2016-08-25 09:00:00,604.0,341.46,16.64,3.7 -2016-08-25 10:00:00,644.0,89.67,16.72,3.62 -2016-08-25 11:00:00,681.0,161.15,16.8,3.54 -2016-08-25 12:00:00,685.0,507.73,16.88,3.46 -2016-08-25 13:00:00,693.0,132.59,16.93,3.71 -2016-08-25 14:00:00,679.0,253.34,16.98,3.97 -2016-08-25 15:00:00,622.0,197.33,17.04,4.22 -2016-08-25 16:00:00,560.0,137.58,16.97,4.28 -2016-08-25 17:00:00,505.0,23.37,16.91,4.33 -2016-08-25 18:00:00,571.0,11.64,16.84,4.39 -2016-08-25 19:00:00,622.0,0.0,16.77,4.24 -2016-08-25 20:00:00,624.0,0.0,16.7,4.09 -2016-08-25 21:00:00,607.0,0.0,16.64,3.94 -2016-08-25 22:00:00,825.0,0.0,16.58,4.34 -2016-08-25 23:00:00,789.0,0.0,16.51,4.74 -2016-08-26 00:00:00,580.0,0.0,16.45,5.14 -2016-08-26 01:00:00,459.0,0.0,16.38,5.24 -2016-08-26 02:00:00,434.0,0.0,16.31,5.33 -2016-08-26 03:00:00,396.0,0.0,16.23,5.42 -2016-08-26 04:00:00,389.0,0.0,16.09,5.18 -2016-08-26 05:00:00,346.0,0.0,15.95,4.94 -2016-08-26 06:00:00,329.0,10.43,15.81,4.7 -2016-08-26 07:00:00,388.0,121.53,15.87,4.23 -2016-08-26 08:00:00,508.0,329.8,15.92,3.75 -2016-08-26 09:00:00,575.0,518.32,15.98,3.27 -2016-08-26 10:00:00,614.0,669.41,16.17,3.18 -2016-08-26 11:00:00,618.0,744.68,16.36,3.09 -2016-08-26 12:00:00,646.0,787.69,16.55,3.01 -2016-08-26 13:00:00,659.0,777.76,16.67,3.35 -2016-08-26 14:00:00,639.0,677.85,16.8,3.7 -2016-08-26 15:00:00,590.0,560.38,16.93,4.04 -2016-08-26 16:00:00,495.0,421.41,16.93,4.44 -2016-08-26 17:00:00,446.0,165.91,16.94,4.84 -2016-08-26 18:00:00,511.0,42.04,16.94,5.24 -2016-08-26 19:00:00,584.0,0.0,16.93,5.31 -2016-08-26 20:00:00,584.0,0.0,16.92,5.37 -2016-08-26 21:00:00,595.0,0.0,16.92,5.43 -2016-08-26 22:00:00,832.0,0.0,16.93,5.41 -2016-08-26 23:00:00,779.0,0.0,16.95,5.39 -2016-08-27 00:00:00,574.0,0.0,16.96,5.37 -2016-08-27 01:00:00,471.0,0.0,16.99,5.67 -2016-08-27 02:00:00,440.0,0.0,17.02,5.98 -2016-08-27 03:00:00,396.0,0.0,17.05,6.29 -2016-08-27 04:00:00,398.0,0.0,17.14,5.99 -2016-08-27 05:00:00,339.0,0.0,17.23,5.69 -2016-08-27 06:00:00,320.0,10.35,17.31,5.39 -2016-08-27 07:00:00,394.0,62.07,17.35,4.86 -2016-08-27 08:00:00,492.0,204.53,17.38,4.33 -2016-08-27 09:00:00,559.0,472.16,17.42,3.79 -2016-08-27 10:00:00,579.0,635.59,17.46,3.66 -2016-08-27 11:00:00,613.0,724.99,17.51,3.53 -2016-08-27 12:00:00,591.0,226.2,17.56,3.39 -2016-08-27 13:00:00,589.0,730.91,17.47,4.6 -2016-08-27 14:00:00,597.0,700.45,17.38,5.81 -2016-08-27 15:00:00,533.0,599.27,17.3,7.02 -2016-08-27 16:00:00,481.0,425.46,17.31,7.22 -2016-08-27 17:00:00,442.0,109.91,17.32,7.42 -2016-08-27 18:00:00,478.0,36.77,17.33,7.61 -2016-08-27 19:00:00,547.0,0.0,17.34,8.02 -2016-08-27 20:00:00,558.0,0.0,17.35,8.43 -2016-08-27 21:00:00,552.0,0.0,17.37,8.84 -2016-08-27 22:00:00,781.0,0.0,17.22,9.14 -2016-08-27 23:00:00,760.0,0.0,17.08,9.45 -2016-08-28 00:00:00,553.0,0.0,16.93,9.75 -2016-08-28 01:00:00,457.0,0.0,16.89,10.21 -2016-08-28 02:00:00,426.0,0.0,16.85,10.66 -2016-08-28 03:00:00,388.0,0.0,16.81,11.12 -2016-08-28 04:00:00,391.0,0.0,16.93,11.23 -2016-08-28 05:00:00,331.0,0.0,17.05,11.34 -2016-08-28 06:00:00,314.0,9.25,17.17,11.45 -2016-08-28 07:00:00,353.0,86.5,17.21,10.94 -2016-08-28 08:00:00,407.0,164.19,17.26,10.43 -2016-08-28 09:00:00,521.0,300.89,17.3,9.92 -2016-08-28 10:00:00,604.0,202.81,17.23,9.56 -2016-08-28 11:00:00,661.0,664.52,17.16,9.2 -2016-08-28 12:00:00,658.0,544.46,17.09,8.84 -2016-08-28 13:00:00,640.0,343.52,16.97,8.7 -2016-08-28 14:00:00,607.0,224.16,16.85,8.57 -2016-08-28 15:00:00,572.0,81.61,16.74,8.43 -2016-08-28 16:00:00,469.0,96.53,16.78,8.33 -2016-08-28 17:00:00,434.0,78.9,16.82,8.23 -2016-08-28 18:00:00,464.0,38.25,16.86,8.14 -2016-08-28 19:00:00,511.0,0.0,16.83,7.89 -2016-08-28 20:00:00,527.0,0.0,16.8,7.65 -2016-08-28 21:00:00,510.0,0.0,16.78,7.41 -2016-08-28 22:00:00,727.0,0.0,16.77,7.23 -2016-08-28 23:00:00,666.0,0.0,16.77,7.06 -2016-08-29 00:00:00,490.0,0.0,16.76,6.88 -2016-08-29 01:00:00,416.0,0.0,16.72,6.77 -2016-08-29 02:00:00,391.0,0.0,16.68,6.65 -2016-08-29 03:00:00,391.0,0.0,16.64,6.54 -2016-08-29 04:00:00,374.0,0.0,16.51,6.44 -2016-08-29 05:00:00,330.0,0.0,16.38,6.34 -2016-08-29 06:00:00,312.0,8.2,16.25,6.25 -2016-08-29 07:00:00,381.0,114.56,16.2,6.0 -2016-08-29 08:00:00,430.0,325.71,16.14,5.76 -2016-08-29 09:00:00,516.0,502.93,16.09,5.52 -2016-08-29 10:00:00,521.0,253.43,16.12,5.17 -2016-08-29 11:00:00,531.0,372.09,16.15,4.83 -2016-08-29 12:00:00,574.0,502.08,16.18,4.48 -2016-08-29 13:00:00,610.0,444.56,16.39,4.23 -2016-08-29 14:00:00,615.0,105.87,16.61,3.98 -2016-08-29 15:00:00,565.0,261.01,16.83,3.72 -2016-08-29 16:00:00,461.0,196.53,16.8,3.5 -2016-08-29 17:00:00,421.0,161.56,16.78,3.28 -2016-08-29 18:00:00,432.0,34.07,16.76,3.06 -2016-08-29 19:00:00,491.0,0.0,16.71,2.62 -2016-08-29 20:00:00,493.0,0.0,16.65,2.18 -2016-08-29 21:00:00,471.0,0.0,16.6,1.74 -2016-08-29 22:00:00,657.0,0.0,16.56,1.44 -2016-08-29 23:00:00,571.0,0.0,16.51,1.15 -2016-08-30 00:00:00,498.0,0.0,16.47,0.86 -2016-08-30 01:00:00,432.0,0.0,16.37,0.97 -2016-08-30 02:00:00,371.0,0.0,16.27,1.09 -2016-08-30 03:00:00,415.0,0.0,16.17,1.2 -2016-08-30 04:00:00,410.0,0.0,16.09,1.66 -2016-08-30 05:00:00,331.0,0.0,16.01,2.12 -2016-08-30 06:00:00,314.0,7.67,15.92,2.58 -2016-08-30 07:00:00,361.0,99.44,16.09,2.77 -2016-08-30 08:00:00,450.0,323.31,16.25,2.96 -2016-08-30 09:00:00,487.0,505.28,16.42,3.14 -2016-08-30 10:00:00,521.0,484.53,16.56,2.79 -2016-08-30 11:00:00,526.0,465.59,16.7,2.44 -2016-08-30 12:00:00,576.0,422.85,16.84,2.08 -2016-08-30 13:00:00,602.0,235.49,16.86,2.12 -2016-08-30 14:00:00,605.0,116.08,16.88,2.17 -2016-08-30 15:00:00,539.0,366.27,16.9,2.21 -2016-08-30 16:00:00,457.0,188.21,16.84,1.92 -2016-08-30 17:00:00,408.0,202.07,16.79,1.64 -2016-08-30 18:00:00,451.0,31.01,16.73,1.35 -2016-08-30 19:00:00,489.0,0.0,16.68,1.43 -2016-08-30 20:00:00,466.0,0.0,16.63,1.52 -2016-08-30 21:00:00,488.0,0.0,16.59,1.6 -2016-08-30 22:00:00,628.0,0.0,16.56,1.95 -2016-08-30 23:00:00,561.0,0.0,16.54,2.3 -2016-08-31 00:00:00,473.0,0.0,16.51,2.65 -2016-08-31 01:00:00,404.0,0.0,16.49,3.09 -2016-08-31 02:00:00,371.0,0.0,16.46,3.53 -2016-08-31 03:00:00,402.0,0.0,16.43,3.97 -2016-08-31 04:00:00,399.0,0.0,16.47,4.29 -2016-08-31 05:00:00,329.0,0.0,16.5,4.62 -2016-08-31 06:00:00,314.0,7.12,16.53,4.94 -2016-08-31 07:00:00,389.0,72.56,16.6,4.67 -2016-08-31 08:00:00,438.0,98.21,16.66,4.4 -2016-08-31 09:00:00,445.0,105.06,16.73,4.12 -2016-08-31 10:00:00,475.0,238.52,16.76,4.03 -2016-08-31 11:00:00,517.0,347.98,16.8,3.93 -2016-08-31 12:00:00,565.0,416.92,16.84,3.83 -2016-08-31 13:00:00,578.0,748.84,16.86,3.94 -2016-08-31 14:00:00,572.0,682.23,16.89,4.06 -2016-08-31 15:00:00,540.0,576.18,16.92,4.17 -2016-08-31 16:00:00,448.0,389.28,16.84,4.2 -2016-08-31 17:00:00,429.0,174.19,16.76,4.23 -2016-08-31 18:00:00,490.0,29.66,16.68,4.26 -2016-08-31 19:00:00,497.0,0.0,16.55,4.12 -2016-08-31 20:00:00,500.0,0.0,16.42,3.98 -2016-08-31 21:00:00,483.0,0.0,16.3,3.83 -2016-08-31 22:00:00,605.0,0.0,16.15,3.68 -2016-08-31 23:00:00,529.0,0.0,16.0,3.52 -2016-09-01 00:00:00,475.0,0.0,15.85,3.37 -2016-09-01 01:00:00,397.0,0.0,15.83,3.26 -2016-09-01 02:00:00,372.0,0.0,15.81,3.16 -2016-09-01 03:00:00,413.0,0.0,15.79,3.06 -2016-09-01 04:00:00,414.0,0.0,15.71,2.47 -2016-09-01 05:00:00,313.0,0.0,15.62,1.89 -2016-09-01 06:00:00,308.0,6.11,15.54,1.3 -2016-09-01 07:00:00,366.0,115.94,15.5,1.19 -2016-09-01 08:00:00,442.0,293.12,15.47,1.09 -2016-09-01 09:00:00,469.0,415.84,15.43,0.98 -2016-09-01 10:00:00,479.0,484.36,15.5,1.1 -2016-09-01 11:00:00,513.0,595.91,15.57,1.22 -2016-09-01 12:00:00,539.0,723.58,15.65,1.34 -2016-09-01 13:00:00,565.0,687.38,15.74,1.82 -2016-09-01 14:00:00,590.0,616.24,15.82,2.3 -2016-09-01 15:00:00,536.0,498.87,15.91,2.79 -2016-09-01 16:00:00,445.0,326.28,15.91,2.91 -2016-09-01 17:00:00,379.0,146.44,15.92,3.03 -2016-09-01 18:00:00,431.0,30.34,15.92,3.16 -2016-09-01 19:00:00,463.0,0.0,15.81,3.21 -2016-09-01 20:00:00,455.0,0.0,15.69,3.26 -2016-09-01 21:00:00,443.0,0.0,15.58,3.31 -2016-09-01 22:00:00,600.0,0.0,15.54,3.65 -2016-09-01 23:00:00,538.0,0.0,15.49,3.98 -2016-09-02 00:00:00,470.0,0.0,15.45,4.32 -2016-09-02 01:00:00,393.0,0.0,15.47,4.79 -2016-09-02 02:00:00,362.0,0.0,15.49,5.26 -2016-09-02 03:00:00,414.0,0.0,15.51,5.72 -2016-09-02 04:00:00,391.0,0.0,15.57,6.17 -2016-09-02 05:00:00,308.0,0.0,15.62,6.62 -2016-09-02 06:00:00,306.0,4.12,15.68,7.06 -2016-09-02 07:00:00,378.0,24.89,15.86,6.91 -2016-09-02 08:00:00,396.0,20.11,16.05,6.76 -2016-09-02 09:00:00,449.0,76.36,16.23,6.61 -2016-09-02 10:00:00,500.0,123.35,16.39,6.33 -2016-09-02 11:00:00,500.0,81.55,16.56,6.05 -2016-09-02 12:00:00,529.0,426.71,16.73,5.77 -2016-09-02 13:00:00,559.0,514.23,16.73,5.66 -2016-09-02 14:00:00,596.0,633.88,16.74,5.56 -2016-09-02 15:00:00,551.0,520.1,16.74,5.46 -2016-09-02 16:00:00,471.0,340.0,16.77,5.32 -2016-09-02 17:00:00,412.0,176.62,16.79,5.19 -2016-09-02 18:00:00,438.0,26.37,16.82,5.05 -2016-09-02 19:00:00,463.0,0.0,16.78,4.84 -2016-09-02 20:00:00,489.0,0.0,16.74,4.63 -2016-09-02 21:00:00,474.0,0.0,16.7,4.43 -2016-09-02 22:00:00,624.0,0.0,16.66,3.96 -2016-09-02 23:00:00,590.0,0.0,16.61,3.5 -2016-09-03 00:00:00,488.0,0.0,16.57,3.03 -2016-09-03 01:00:00,407.0,0.0,16.61,3.64 -2016-09-03 02:00:00,381.0,0.0,16.64,4.25 -2016-09-03 03:00:00,408.0,0.0,16.68,4.86 -2016-09-03 04:00:00,408.0,0.0,16.74,5.54 -2016-09-03 05:00:00,320.0,0.0,16.79,6.23 -2016-09-03 06:00:00,308.0,4.57,16.85,6.91 -2016-09-03 07:00:00,359.0,5.79,16.88,7.32 -2016-09-03 08:00:00,432.0,27.53,16.92,7.74 -2016-09-03 09:00:00,495.0,150.5,16.95,8.15 -2016-09-03 10:00:00,515.0,149.28,16.96,8.69 -2016-09-03 11:00:00,505.0,132.68,16.97,9.23 -2016-09-03 12:00:00,523.0,113.25,16.98,9.77 -2016-09-03 13:00:00,565.0,96.1,17.18,9.78 -2016-09-03 14:00:00,582.0,46.82,17.38,9.79 -2016-09-03 15:00:00,554.0,40.22,17.58,9.81 -2016-09-03 16:00:00,461.0,43.12,17.59,9.71 -2016-09-03 17:00:00,410.0,26.06,17.6,9.6 -2016-09-03 18:00:00,483.0,6.88,17.61,9.5 -2016-09-03 19:00:00,548.0,0.0,17.64,9.26 -2016-09-03 20:00:00,523.0,0.0,17.66,9.02 -2016-09-03 21:00:00,509.0,0.0,17.69,8.77 -2016-09-03 22:00:00,623.0,0.0,17.73,8.82 -2016-09-03 23:00:00,576.0,0.0,17.77,8.87 -2016-09-04 00:00:00,520.0,0.0,17.81,8.92 -2016-09-04 01:00:00,447.0,0.0,17.71,8.89 -2016-09-04 02:00:00,378.0,0.0,17.6,8.85 -2016-09-04 03:00:00,414.0,0.0,17.5,8.81 -2016-09-04 04:00:00,410.0,0.0,17.45,8.53 -2016-09-04 05:00:00,332.0,0.0,17.39,8.24 -2016-09-04 06:00:00,315.0,1.85,17.34,7.96 -2016-09-04 07:00:00,339.0,13.49,17.27,7.18 -2016-09-04 08:00:00,413.0,76.33,17.2,6.4 -2016-09-04 09:00:00,468.0,70.67,17.13,5.63 -2016-09-04 10:00:00,531.0,183.59,17.13,5.2 -2016-09-04 11:00:00,560.0,238.71,17.13,4.77 -2016-09-04 12:00:00,558.0,259.26,17.14,4.34 -2016-09-04 13:00:00,569.0,214.73,17.15,4.23 -2016-09-04 14:00:00,581.0,188.03,17.16,4.11 -2016-09-04 15:00:00,543.0,112.49,17.17,4.0 -2016-09-04 16:00:00,453.0,82.69,17.23,4.59 -2016-09-04 17:00:00,370.0,32.35,17.3,5.19 -2016-09-04 18:00:00,402.0,0.22,17.36,5.78 -2016-09-04 19:00:00,476.0,0.0,17.46,6.12 -2016-09-04 20:00:00,463.0,0.0,17.55,6.47 -2016-09-04 21:00:00,459.0,0.0,17.65,6.81 -2016-09-04 22:00:00,583.0,0.0,17.72,7.37 -2016-09-04 23:00:00,557.0,0.0,17.79,7.92 -2016-09-05 00:00:00,474.0,0.0,17.86,8.47 -2016-09-05 01:00:00,397.0,0.0,17.88,8.84 -2016-09-05 02:00:00,374.0,0.0,17.9,9.2 -2016-09-05 03:00:00,397.0,0.0,17.92,9.57 -2016-09-05 04:00:00,398.0,0.0,17.91,8.99 -2016-09-05 05:00:00,303.0,0.0,17.9,8.41 -2016-09-05 06:00:00,294.0,1.06,17.89,7.83 -2016-09-05 07:00:00,350.0,53.4,17.81,7.14 -2016-09-05 08:00:00,412.0,91.74,17.72,6.46 -2016-09-05 09:00:00,457.0,90.25,17.64,5.77 -2016-09-05 10:00:00,504.0,202.85,17.59,5.65 -2016-09-05 11:00:00,536.0,378.08,17.54,5.53 -2016-09-05 12:00:00,554.0,414.73,17.49,5.41 -2016-09-05 13:00:00,549.0,552.06,17.44,5.69 -2016-09-05 14:00:00,597.0,607.32,17.39,5.98 -2016-09-05 15:00:00,545.0,500.15,17.34,6.26 -2016-09-05 16:00:00,501.0,267.23,17.28,5.72 -2016-09-05 17:00:00,424.0,86.63,17.23,5.18 -2016-09-05 18:00:00,458.0,19.58,17.17,4.63 -2016-09-05 19:00:00,511.0,0.0,17.14,4.37 -2016-09-05 20:00:00,488.0,0.0,17.11,4.1 -2016-09-05 21:00:00,472.0,0.0,17.08,3.83 -2016-09-05 22:00:00,611.0,0.0,17.09,3.9 -2016-09-05 23:00:00,540.0,0.0,17.1,3.96 -2016-09-06 00:00:00,458.0,0.0,17.11,4.03 -2016-09-06 01:00:00,410.0,0.0,17.05,3.81 -2016-09-06 02:00:00,364.0,0.0,17.0,3.6 -2016-09-06 03:00:00,400.0,0.0,16.94,3.38 -2016-09-06 04:00:00,396.0,0.0,16.94,3.77 -2016-09-06 05:00:00,304.0,0.0,16.93,4.15 -2016-09-06 06:00:00,301.0,1.46,16.93,4.54 -2016-09-06 07:00:00,389.0,56.74,16.99,4.48 -2016-09-06 08:00:00,472.0,85.4,17.04,4.42 -2016-09-06 09:00:00,512.0,143.95,17.1,4.36 -2016-09-06 10:00:00,575.0,145.1,17.15,3.8 -2016-09-06 11:00:00,572.0,282.82,17.21,3.25 -2016-09-06 12:00:00,574.0,293.51,17.27,2.69 -2016-09-06 13:00:00,612.0,251.17,17.75,2.14 -2016-09-06 14:00:00,606.0,159.44,18.23,1.59 -2016-09-06 15:00:00,566.0,244.51,18.71,1.03 -2016-09-06 16:00:00,487.0,269.87,18.5,1.7 -2016-09-06 17:00:00,417.0,181.25,18.28,2.36 -2016-09-06 18:00:00,457.0,21.31,18.07,3.02 -2016-09-06 19:00:00,510.0,0.0,17.96,3.47 -2016-09-06 20:00:00,479.0,0.0,17.84,3.92 -2016-09-06 21:00:00,486.0,0.0,17.73,4.37 -2016-09-06 22:00:00,643.0,0.0,17.82,4.74 -2016-09-06 23:00:00,554.0,0.0,17.9,5.1 -2016-09-07 00:00:00,467.0,0.0,17.99,5.46 -2016-09-07 01:00:00,393.0,0.0,18.12,5.56 -2016-09-07 02:00:00,376.0,0.0,18.24,5.66 -2016-09-07 03:00:00,400.0,0.0,18.37,5.77 -2016-09-07 04:00:00,395.0,0.0,18.5,6.06 -2016-09-07 05:00:00,315.0,0.0,18.64,6.35 -2016-09-07 06:00:00,330.0,2.24,18.77,6.65 -2016-09-07 07:00:00,408.0,71.31,18.82,6.68 -2016-09-07 08:00:00,468.0,298.02,18.86,6.7 -2016-09-07 09:00:00,514.0,374.98,18.91,6.73 -2016-09-07 10:00:00,510.0,650.11,19.0,6.61 -2016-09-07 11:00:00,546.0,749.15,19.09,6.49 -2016-09-07 12:00:00,591.0,575.3,19.18,6.37 -2016-09-07 13:00:00,590.0,764.3,19.34,6.22 -2016-09-07 14:00:00,582.0,546.17,19.49,6.06 -2016-09-07 15:00:00,555.0,549.46,19.65,5.9 -2016-09-07 16:00:00,484.0,327.3,19.33,5.49 -2016-09-07 17:00:00,423.0,96.8,19.01,5.09 -2016-09-07 18:00:00,490.0,19.99,18.69,4.68 -2016-09-07 19:00:00,540.0,0.0,18.31,5.54 -2016-09-07 20:00:00,500.0,0.0,17.92,6.4 -2016-09-07 21:00:00,527.0,0.0,17.54,7.27 -2016-09-07 22:00:00,634.0,0.0,17.32,8.1 -2016-09-07 23:00:00,572.0,0.0,17.1,8.93 -2016-09-08 00:00:00,484.0,0.0,16.88,9.77 -2016-09-08 01:00:00,418.0,0.0,16.87,9.67 -2016-09-08 02:00:00,400.0,0.0,16.86,9.57 -2016-09-08 03:00:00,422.0,0.0,16.85,9.48 -2016-09-08 04:00:00,407.0,0.0,16.88,9.44 -2016-09-08 05:00:00,323.0,0.0,16.91,9.41 -2016-09-08 06:00:00,325.0,1.46,16.94,9.38 -2016-09-08 07:00:00,391.0,110.09,17.04,8.96 -2016-09-08 08:00:00,481.0,326.53,17.14,8.54 -2016-09-08 09:00:00,497.0,504.68,17.24,8.12 -2016-09-08 10:00:00,498.0,668.49,17.35,8.18 -2016-09-08 11:00:00,538.0,751.78,17.47,8.24 -2016-09-08 12:00:00,576.0,797.11,17.59,8.3 -2016-09-08 13:00:00,603.0,757.13,17.68,8.37 -2016-09-08 14:00:00,583.0,265.91,17.76,8.44 -2016-09-08 15:00:00,543.0,326.18,17.85,8.51 -2016-09-08 16:00:00,477.0,172.13,17.91,8.81 -2016-09-08 17:00:00,418.0,58.32,17.98,9.12 -2016-09-08 18:00:00,474.0,3.66,18.04,9.42 -2016-09-08 19:00:00,511.0,0.0,18.07,9.42 -2016-09-08 20:00:00,517.0,0.0,18.09,9.41 -2016-09-08 21:00:00,517.0,0.0,18.12,9.41 -2016-09-08 22:00:00,659.0,0.0,18.04,9.01 -2016-09-08 23:00:00,584.0,0.0,17.95,8.62 -2016-09-09 00:00:00,475.0,0.0,17.87,8.22 -2016-09-09 01:00:00,414.0,0.0,17.83,8.33 -2016-09-09 02:00:00,383.0,0.0,17.8,8.43 -2016-09-09 03:00:00,401.0,0.0,17.76,8.54 -2016-09-09 04:00:00,392.0,0.0,17.75,8.55 -2016-09-09 05:00:00,342.0,0.0,17.75,8.56 -2016-09-09 06:00:00,339.0,0.0,17.74,8.57 -2016-09-09 07:00:00,397.0,67.45,17.79,9.01 -2016-09-09 08:00:00,470.0,165.12,17.83,9.45 -2016-09-09 09:00:00,522.0,169.06,17.88,9.89 -2016-09-09 10:00:00,564.0,216.83,17.91,10.32 -2016-09-09 11:00:00,580.0,576.28,17.94,10.74 -2016-09-09 12:00:00,610.0,815.34,17.97,11.17 -2016-09-09 13:00:00,625.0,773.64,17.98,11.22 -2016-09-09 14:00:00,647.0,646.17,17.98,11.26 -2016-09-09 15:00:00,611.0,499.44,17.99,11.31 -2016-09-09 16:00:00,512.0,123.09,17.99,11.24 -2016-09-09 17:00:00,463.0,92.58,17.99,11.17 -2016-09-09 18:00:00,488.0,13.19,17.99,11.1 -2016-09-09 19:00:00,539.0,0.0,18.01,10.94 -2016-09-09 20:00:00,506.0,0.0,18.02,10.77 -2016-09-09 21:00:00,504.0,0.0,18.04,10.61 -2016-09-09 22:00:00,639.0,0.0,18.03,10.45 -2016-09-09 23:00:00,614.0,0.0,18.02,10.28 -2016-09-10 00:00:00,498.0,0.0,18.01,10.12 -2016-09-10 01:00:00,449.0,0.0,17.94,9.43 -2016-09-10 02:00:00,463.0,0.0,17.88,8.73 -2016-09-10 03:00:00,460.0,0.0,17.81,8.03 -2016-09-10 04:00:00,419.0,0.0,17.73,6.88 -2016-09-10 05:00:00,349.0,0.0,17.64,5.73 -2016-09-10 06:00:00,352.0,0.0,17.56,4.58 -2016-09-10 07:00:00,393.0,38.27,17.45,4.12 -2016-09-10 08:00:00,469.0,123.93,17.34,3.67 -2016-09-10 09:00:00,558.0,98.54,17.23,3.21 -2016-09-10 10:00:00,622.0,132.19,17.08,3.3 -2016-09-10 11:00:00,656.0,699.92,16.93,3.39 -2016-09-10 12:00:00,709.0,701.38,16.78,3.48 -2016-09-10 13:00:00,705.0,567.61,16.65,4.13 -2016-09-10 14:00:00,744.0,678.49,16.51,4.79 -2016-09-10 15:00:00,687.0,561.99,16.38,5.45 -2016-09-10 16:00:00,597.0,377.04,16.26,5.41 -2016-09-10 17:00:00,494.0,167.98,16.15,5.37 -2016-09-10 18:00:00,484.0,12.77,16.03,5.32 -2016-09-10 19:00:00,507.0,0.0,15.96,5.05 -2016-09-10 20:00:00,512.0,0.0,15.9,4.77 -2016-09-10 21:00:00,511.0,0.0,15.83,4.5 -2016-09-10 22:00:00,684.0,0.0,15.76,3.71 -2016-09-10 23:00:00,618.0,0.0,15.7,2.93 -2016-09-11 00:00:00,502.0,0.0,15.63,2.15 -2016-09-11 01:00:00,478.0,0.0,15.52,1.92 -2016-09-11 02:00:00,465.0,0.0,15.41,1.69 -2016-09-11 03:00:00,457.0,0.0,15.31,1.46 -2016-09-11 04:00:00,451.0,0.0,15.3,2.25 -2016-09-11 05:00:00,369.0,0.0,15.3,3.04 -2016-09-11 06:00:00,347.0,0.0,15.29,3.83 -2016-09-11 07:00:00,379.0,102.49,15.42,4.48 -2016-09-11 08:00:00,406.0,316.16,15.55,5.13 -2016-09-11 09:00:00,536.0,516.83,15.68,5.78 -2016-09-11 10:00:00,620.0,552.85,15.97,6.04 -2016-09-11 11:00:00,703.0,468.25,16.26,6.3 -2016-09-11 12:00:00,652.0,449.41,16.55,6.57 -2016-09-11 13:00:00,621.0,521.14,16.75,6.77 -2016-09-11 14:00:00,652.0,608.66,16.94,6.98 -2016-09-11 15:00:00,588.0,552.83,17.14,7.19 -2016-09-11 16:00:00,485.0,255.79,17.2,7.18 -2016-09-11 17:00:00,402.0,86.72,17.27,7.18 -2016-09-11 18:00:00,419.0,11.66,17.33,7.17 -2016-09-11 19:00:00,457.0,0.0,17.37,7.6 -2016-09-11 20:00:00,460.0,0.0,17.4,8.04 -2016-09-11 21:00:00,471.0,0.0,17.44,8.47 -2016-09-11 22:00:00,610.0,0.0,17.48,8.85 -2016-09-11 23:00:00,553.0,0.0,17.51,9.22 -2016-09-12 00:00:00,473.0,0.0,17.55,9.6 -2016-09-12 01:00:00,428.0,0.0,17.57,9.87 -2016-09-12 02:00:00,368.0,0.0,17.59,10.14 -2016-09-12 03:00:00,404.0,0.0,17.62,10.41 -2016-09-12 04:00:00,391.0,0.0,17.69,10.41 -2016-09-12 05:00:00,307.0,0.0,17.75,10.41 -2016-09-12 06:00:00,308.0,0.0,17.82,10.41 -2016-09-12 07:00:00,362.0,56.39,17.85,10.16 -2016-09-12 08:00:00,412.0,140.92,17.89,9.91 -2016-09-12 09:00:00,506.0,109.21,17.92,9.66 -2016-09-12 10:00:00,491.0,259.87,17.97,9.52 -2016-09-12 11:00:00,544.0,344.99,18.03,9.38 -2016-09-12 12:00:00,590.0,475.82,18.09,9.24 -2016-09-12 13:00:00,580.0,548.48,18.1,8.77 -2016-09-12 14:00:00,612.0,521.54,18.1,8.29 -2016-09-12 15:00:00,581.0,451.47,18.11,7.82 -2016-09-12 16:00:00,478.0,134.73,18.05,7.64 -2016-09-12 17:00:00,407.0,27.37,17.99,7.46 -2016-09-12 18:00:00,463.0,9.36,17.93,7.28 -2016-09-12 19:00:00,530.0,0.0,17.86,7.19 -2016-09-12 20:00:00,533.0,0.0,17.8,7.1 -2016-09-12 21:00:00,504.0,0.0,17.73,7.01 -2016-09-12 22:00:00,608.0,0.0,17.68,6.39 -2016-09-12 23:00:00,510.0,0.0,17.64,5.77 -2016-09-13 00:00:00,435.0,0.0,17.59,5.14 -2016-09-13 01:00:00,422.0,0.0,17.44,4.76 -2016-09-13 02:00:00,376.0,0.0,17.29,4.38 -2016-09-13 03:00:00,408.0,0.0,17.14,4.0 -2016-09-13 04:00:00,400.0,0.0,17.02,4.24 -2016-09-13 05:00:00,346.0,0.0,16.89,4.49 -2016-09-13 06:00:00,314.0,0.0,16.77,4.73 -2016-09-13 07:00:00,363.0,44.41,16.63,5.66 -2016-09-13 08:00:00,446.0,55.87,16.49,6.59 -2016-09-13 09:00:00,504.0,64.96,16.35,7.52 -2016-09-13 10:00:00,514.0,44.1,16.31,7.8 -2016-09-13 11:00:00,546.0,31.84,16.27,8.09 -2016-09-13 12:00:00,556.0,38.3,16.24,8.37 -2016-09-13 13:00:00,577.0,47.75,16.51,8.43 -2016-09-13 14:00:00,589.0,46.95,16.77,8.49 -2016-09-13 15:00:00,563.0,41.04,17.04,8.55 -2016-09-13 16:00:00,497.0,12.88,16.99,8.38 -2016-09-13 17:00:00,449.0,20.02,16.94,8.21 -2016-09-13 18:00:00,506.0,0.22,16.89,8.04 -2016-09-13 19:00:00,561.0,0.0,16.88,8.12 -2016-09-13 20:00:00,542.0,0.0,16.86,8.21 -2016-09-13 21:00:00,513.0,0.0,16.85,8.29 -2016-09-13 22:00:00,624.0,0.0,16.96,7.32 -2016-09-13 23:00:00,551.0,0.0,17.07,6.35 -2016-09-14 00:00:00,467.0,0.0,17.18,5.38 -2016-09-14 01:00:00,406.0,0.0,17.12,5.65 -2016-09-14 02:00:00,363.0,0.0,17.06,5.91 -2016-09-14 03:00:00,397.0,0.0,17.0,6.18 -2016-09-14 04:00:00,408.0,0.0,17.07,6.26 -2016-09-14 05:00:00,331.0,0.0,17.13,6.34 -2016-09-14 06:00:00,317.0,0.0,17.2,6.43 -2016-09-14 07:00:00,385.0,79.94,17.15,7.01 -2016-09-14 08:00:00,459.0,195.44,17.09,7.6 -2016-09-14 09:00:00,478.0,460.85,17.04,8.18 -2016-09-14 10:00:00,504.0,668.58,17.08,8.28 -2016-09-14 11:00:00,492.0,735.04,17.13,8.38 -2016-09-14 12:00:00,520.0,823.57,17.18,8.48 -2016-09-14 13:00:00,543.0,775.11,17.13,8.16 -2016-09-14 14:00:00,573.0,694.43,17.07,7.84 -2016-09-14 15:00:00,539.0,558.59,17.02,7.52 -2016-09-14 16:00:00,439.0,367.0,17.1,7.4 -2016-09-14 17:00:00,381.0,103.0,17.18,7.28 -2016-09-14 18:00:00,426.0,7.06,17.26,7.16 -2016-09-14 19:00:00,481.0,0.0,17.17,6.86 -2016-09-14 20:00:00,472.0,0.0,17.08,6.56 -2016-09-14 21:00:00,477.0,0.0,16.99,6.26 -2016-09-14 22:00:00,607.0,0.0,16.93,5.77 -2016-09-14 23:00:00,540.0,0.0,16.88,5.27 -2016-09-15 00:00:00,445.0,0.0,16.82,4.77 -2016-09-15 01:00:00,393.0,0.0,16.82,4.75 -2016-09-15 02:00:00,359.0,0.0,16.81,4.73 -2016-09-15 03:00:00,384.0,0.0,16.81,4.7 -2016-09-15 04:00:00,397.0,0.0,16.75,4.42 -2016-09-15 05:00:00,316.0,0.0,16.69,4.13 -2016-09-15 06:00:00,294.0,0.0,16.63,3.85 -2016-09-15 07:00:00,358.0,45.92,16.47,4.2 -2016-09-15 08:00:00,459.0,179.75,16.32,4.56 -2016-09-15 09:00:00,503.0,61.9,16.16,4.91 -2016-09-15 10:00:00,526.0,91.73,15.99,5.57 -2016-09-15 11:00:00,594.0,59.8,15.83,6.23 -2016-09-15 12:00:00,616.0,72.0,15.67,6.88 -2016-09-15 13:00:00,625.0,85.6,16.03,6.09 -2016-09-15 14:00:00,658.0,66.37,16.38,5.29 -2016-09-15 15:00:00,613.0,39.58,16.74,4.5 -2016-09-15 16:00:00,522.0,50.59,16.67,5.5 -2016-09-15 17:00:00,437.0,22.74,16.6,6.51 -2016-09-15 18:00:00,486.0,0.0,16.53,7.52 -2016-09-15 19:00:00,557.0,0.0,16.34,7.63 -2016-09-15 20:00:00,533.0,0.0,16.16,7.75 -2016-09-15 21:00:00,498.0,0.0,15.97,7.86 -2016-09-15 22:00:00,617.0,0.0,15.91,8.08 -2016-09-15 23:00:00,542.0,0.0,15.85,8.29 -2016-09-16 00:00:00,443.0,0.0,15.79,8.51 -2016-09-16 01:00:00,413.0,0.0,15.76,8.88 -2016-09-16 02:00:00,371.0,0.0,15.72,9.25 -2016-09-16 03:00:00,412.0,0.0,15.69,9.61 -2016-09-16 04:00:00,407.0,0.0,15.77,9.89 -2016-09-16 05:00:00,342.0,0.0,15.86,10.17 -2016-09-16 06:00:00,328.0,0.0,15.94,10.44 -2016-09-16 07:00:00,426.0,49.39,15.9,10.4 -2016-09-16 08:00:00,462.0,49.42,15.86,10.35 -2016-09-16 09:00:00,499.0,308.38,15.82,10.3 -2016-09-16 10:00:00,506.0,425.73,15.78,10.12 -2016-09-16 11:00:00,537.0,341.56,15.74,9.94 -2016-09-16 12:00:00,525.0,433.67,15.71,9.77 -2016-09-16 13:00:00,565.0,258.96,15.67,9.6 -2016-09-16 14:00:00,573.0,605.51,15.64,9.43 -2016-09-16 15:00:00,521.0,141.44,15.6,9.26 -2016-09-16 16:00:00,442.0,168.24,15.52,9.5 -2016-09-16 17:00:00,387.0,66.67,15.45,9.75 -2016-09-16 18:00:00,448.0,4.55,15.37,10.0 -2016-09-16 19:00:00,488.0,0.0,15.3,10.03 -2016-09-16 20:00:00,494.0,0.0,15.24,10.06 -2016-09-16 21:00:00,511.0,0.0,15.17,10.08 -2016-09-16 22:00:00,617.0,0.0,15.2,9.73 -2016-09-16 23:00:00,561.0,0.0,15.23,9.37 -2016-09-17 00:00:00,461.0,0.0,15.26,9.02 -2016-09-17 01:00:00,398.0,0.0,15.27,8.79 -2016-09-17 02:00:00,363.0,0.0,15.27,8.56 -2016-09-17 03:00:00,399.0,0.0,15.28,8.33 -2016-09-17 04:00:00,399.0,0.0,15.25,8.1 -2016-09-17 05:00:00,332.0,0.0,15.21,7.87 -2016-09-17 06:00:00,316.0,0.0,15.18,7.64 -2016-09-17 07:00:00,369.0,51.91,15.16,7.4 -2016-09-17 08:00:00,452.0,103.93,15.15,7.15 -2016-09-17 09:00:00,515.0,166.1,15.13,6.91 -2016-09-17 10:00:00,511.0,102.33,15.13,6.99 -2016-09-17 11:00:00,535.0,428.32,15.13,7.08 -2016-09-17 12:00:00,525.0,280.56,15.13,7.16 -2016-09-17 13:00:00,567.0,109.29,15.24,7.08 -2016-09-17 14:00:00,606.0,127.97,15.35,7.0 -2016-09-17 15:00:00,557.0,128.18,15.46,6.92 -2016-09-17 16:00:00,449.0,24.95,15.39,6.89 -2016-09-17 17:00:00,402.0,26.35,15.33,6.85 -2016-09-17 18:00:00,454.0,0.0,15.26,6.81 -2016-09-17 19:00:00,490.0,0.0,15.21,6.49 -2016-09-17 20:00:00,500.0,0.0,15.16,6.17 -2016-09-17 21:00:00,518.0,0.0,15.11,5.85 -2016-09-17 22:00:00,629.0,0.0,15.03,5.59 -2016-09-17 23:00:00,571.0,0.0,14.95,5.33 -2016-09-18 00:00:00,474.0,0.0,14.87,5.08 -2016-09-18 01:00:00,431.0,0.0,15.05,4.91 -2016-09-18 02:00:00,390.0,0.0,15.23,4.74 -2016-09-18 03:00:00,419.0,0.0,15.42,4.58 -2016-09-18 04:00:00,413.0,0.0,15.39,4.35 -2016-09-18 05:00:00,348.0,0.0,15.35,4.13 -2016-09-18 06:00:00,312.0,0.0,15.32,3.9 -2016-09-18 07:00:00,360.0,31.32,15.37,3.75 -2016-09-18 08:00:00,434.0,166.67,15.42,3.6 -2016-09-18 09:00:00,515.0,61.27,15.47,3.45 -2016-09-18 10:00:00,553.0,35.46,15.57,3.64 -2016-09-18 11:00:00,582.0,112.51,15.68,3.83 -2016-09-18 12:00:00,581.0,87.06,15.79,4.03 -2016-09-18 13:00:00,582.0,115.65,15.61,3.87 -2016-09-18 14:00:00,572.0,226.67,15.42,3.71 -2016-09-18 15:00:00,547.0,127.77,15.24,3.56 -2016-09-18 16:00:00,451.0,157.82,15.32,3.38 -2016-09-18 17:00:00,378.0,30.5,15.41,3.2 -2016-09-18 18:00:00,400.0,1.38,15.49,3.02 -2016-09-18 19:00:00,445.0,0.0,15.53,2.93 -2016-09-18 20:00:00,454.0,0.0,15.56,2.84 -2016-09-18 21:00:00,451.0,0.0,15.6,2.74 -2016-09-18 22:00:00,580.0,0.0,15.63,2.78 -2016-09-18 23:00:00,527.0,0.0,15.65,2.82 -2016-09-19 00:00:00,454.0,0.0,15.68,2.86 -2016-09-19 01:00:00,406.0,0.0,15.79,2.75 -2016-09-19 02:00:00,370.0,0.0,15.9,2.65 -2016-09-19 03:00:00,391.0,0.0,16.02,2.55 -2016-09-19 04:00:00,402.0,0.0,15.92,2.62 -2016-09-19 05:00:00,318.0,0.0,15.82,2.68 -2016-09-19 06:00:00,304.0,0.0,15.72,2.74 -2016-09-19 07:00:00,350.0,47.9,15.67,2.98 -2016-09-19 08:00:00,408.0,222.74,15.61,3.21 -2016-09-19 09:00:00,476.0,137.93,15.56,3.45 -2016-09-19 10:00:00,529.0,158.68,15.56,3.83 -2016-09-19 11:00:00,562.0,367.94,15.56,4.21 -2016-09-19 12:00:00,582.0,480.69,15.56,4.59 -2016-09-19 13:00:00,579.0,334.29,15.42,5.28 -2016-09-19 14:00:00,567.0,193.61,15.27,5.97 -2016-09-19 15:00:00,565.0,137.73,15.13,6.66 -2016-09-19 16:00:00,486.0,133.42,15.1,6.96 -2016-09-19 17:00:00,393.0,53.42,15.07,7.26 -2016-09-19 18:00:00,458.0,0.0,15.04,7.56 -2016-09-19 19:00:00,500.0,0.0,15.02,7.5 -2016-09-19 20:00:00,474.0,0.0,15.0,7.44 -2016-09-19 21:00:00,470.0,0.0,14.98,7.38 -2016-09-19 22:00:00,583.0,0.0,14.99,7.13 -2016-09-19 23:00:00,528.0,0.0,15.01,6.88 -2016-09-20 00:00:00,440.0,0.0,15.02,6.63 -2016-09-20 01:00:00,404.0,0.0,14.98,6.29 -2016-09-20 02:00:00,380.0,0.0,14.93,5.94 -2016-09-20 03:00:00,400.0,0.0,14.89,5.59 -2016-09-20 04:00:00,412.0,0.0,14.89,5.6 -2016-09-20 05:00:00,330.0,0.0,14.89,5.6 -2016-09-20 06:00:00,314.0,0.0,14.89,5.61 -2016-09-20 07:00:00,373.0,35.15,14.89,5.46 -2016-09-20 08:00:00,470.0,86.55,14.89,5.3 -2016-09-20 09:00:00,492.0,56.27,14.89,5.14 -2016-09-20 10:00:00,534.0,127.06,14.93,5.05 -2016-09-20 11:00:00,542.0,180.68,14.98,4.96 -2016-09-20 12:00:00,590.0,253.31,15.03,4.87 -2016-09-20 13:00:00,614.0,470.56,15.15,4.74 -2016-09-20 14:00:00,609.0,359.09,15.26,4.6 -2016-09-20 15:00:00,568.0,134.89,15.38,4.47 -2016-09-20 16:00:00,483.0,83.74,15.43,4.11 -2016-09-20 17:00:00,412.0,50.75,15.49,3.76 -2016-09-20 18:00:00,445.0,0.0,15.54,3.41 -2016-09-20 19:00:00,495.0,0.0,15.49,3.19 -2016-09-20 20:00:00,508.0,0.0,15.44,2.97 -2016-09-20 21:00:00,501.0,0.0,15.39,2.76 -2016-09-20 22:00:00,601.0,0.0,15.28,2.33 -2016-09-20 23:00:00,536.0,0.0,15.18,1.9 -2016-09-21 00:00:00,451.0,0.0,15.07,1.48 -2016-09-21 01:00:00,416.0,0.0,15.14,1.63 -2016-09-21 02:00:00,387.0,0.0,15.2,1.78 -2016-09-21 03:00:00,423.0,0.0,15.27,1.93 -2016-09-21 04:00:00,422.0,0.0,15.21,2.23 -2016-09-21 05:00:00,334.0,0.0,15.14,2.52 -2016-09-21 06:00:00,313.0,0.0,15.08,2.81 -2016-09-21 07:00:00,389.0,17.52,15.1,2.96 -2016-09-21 08:00:00,468.0,100.4,15.11,3.1 -2016-09-21 09:00:00,539.0,110.91,15.13,3.24 -2016-09-21 10:00:00,539.0,446.54,15.15,3.42 -2016-09-21 11:00:00,536.0,494.45,15.17,3.59 -2016-09-21 12:00:00,574.0,307.74,15.2,3.77 -2016-09-21 13:00:00,584.0,250.95,15.14,4.51 -2016-09-21 14:00:00,589.0,211.03,15.08,5.26 -2016-09-21 15:00:00,537.0,276.52,15.02,6.01 -2016-09-21 16:00:00,449.0,141.48,15.16,5.93 -2016-09-21 17:00:00,398.0,48.06,15.31,5.85 -2016-09-21 18:00:00,448.0,0.0,15.45,5.77 -2016-09-21 19:00:00,520.0,0.0,15.55,5.49 -2016-09-21 20:00:00,518.0,0.0,15.65,5.2 -2016-09-21 21:00:00,478.0,0.0,15.75,4.92 -2016-09-21 22:00:00,605.0,0.0,15.75,4.9 -2016-09-21 23:00:00,542.0,0.0,15.75,4.88 -2016-09-22 00:00:00,462.0,0.0,15.75,4.86 -2016-09-22 01:00:00,405.0,0.0,15.53,4.24 -2016-09-22 02:00:00,380.0,0.0,15.31,3.62 -2016-09-22 03:00:00,399.0,0.0,15.09,3.01 -2016-09-22 04:00:00,412.0,0.0,15.02,2.91 -2016-09-22 05:00:00,335.0,0.0,14.94,2.82 -2016-09-22 06:00:00,321.0,0.0,14.87,2.73 -2016-09-22 07:00:00,390.0,48.43,14.88,2.71 -2016-09-22 08:00:00,471.0,291.97,14.88,2.69 -2016-09-22 09:00:00,527.0,464.19,14.89,2.68 -2016-09-22 10:00:00,554.0,624.42,14.93,2.39 -2016-09-22 11:00:00,556.0,720.45,14.97,2.11 -2016-09-22 12:00:00,566.0,708.34,15.01,1.82 -2016-09-22 13:00:00,581.0,724.59,15.0,2.34 -2016-09-22 14:00:00,594.0,642.13,14.99,2.87 -2016-09-22 15:00:00,592.0,347.66,14.98,3.39 -2016-09-22 16:00:00,477.0,174.71,14.91,3.46 -2016-09-22 17:00:00,442.0,59.2,14.83,3.53 -2016-09-22 18:00:00,478.0,0.0,14.76,3.6 -2016-09-22 19:00:00,519.0,0.0,14.69,3.59 -2016-09-22 20:00:00,520.0,0.0,14.62,3.58 -2016-09-22 21:00:00,518.0,0.0,14.55,3.57 -2016-09-22 22:00:00,613.0,0.0,14.48,3.17 -2016-09-22 23:00:00,542.0,0.0,14.4,2.76 -2016-09-23 00:00:00,492.0,0.0,14.33,2.36 -2016-09-23 01:00:00,436.0,0.0,14.5,2.19 -2016-09-23 02:00:00,413.0,0.0,14.66,2.02 -2016-09-23 03:00:00,423.0,0.0,14.83,1.85 -2016-09-23 04:00:00,438.0,0.0,14.81,1.95 -2016-09-23 05:00:00,368.0,0.0,14.8,2.06 -2016-09-23 06:00:00,363.0,0.0,14.78,2.17 -2016-09-23 07:00:00,460.0,37.63,14.91,3.12 -2016-09-23 08:00:00,459.0,275.52,15.04,4.07 -2016-09-23 09:00:00,493.0,484.54,15.17,5.02 -2016-09-23 10:00:00,504.0,623.81,15.44,5.59 -2016-09-23 11:00:00,502.0,558.17,15.71,6.15 -2016-09-23 12:00:00,522.0,613.22,15.98,6.72 -2016-09-23 13:00:00,545.0,737.0,16.06,6.9 -2016-09-23 14:00:00,564.0,556.91,16.15,7.08 -2016-09-23 15:00:00,508.0,423.69,16.23,7.26 -2016-09-23 16:00:00,435.0,183.44,16.33,7.37 -2016-09-23 17:00:00,382.0,22.77,16.44,7.49 -2016-09-23 18:00:00,431.0,0.0,16.54,7.6 -2016-09-23 19:00:00,490.0,0.0,16.55,8.0 -2016-09-23 20:00:00,497.0,0.0,16.57,8.41 -2016-09-23 21:00:00,530.0,0.0,16.58,8.81 -2016-09-23 22:00:00,652.0,0.0,16.62,9.28 -2016-09-23 23:00:00,565.0,0.0,16.67,9.74 -2016-09-24 00:00:00,497.0,0.0,16.71,10.21 -2016-09-24 01:00:00,429.0,0.0,16.76,10.78 -2016-09-24 02:00:00,386.0,0.0,16.82,11.36 -2016-09-24 03:00:00,411.0,0.0,16.87,11.93 -2016-09-24 04:00:00,417.0,0.0,16.96,11.9 -2016-09-24 05:00:00,358.0,0.0,17.04,11.88 -2016-09-24 06:00:00,311.0,0.0,17.13,11.85 -2016-09-24 07:00:00,363.0,36.16,17.12,11.84 -2016-09-24 08:00:00,450.0,119.42,17.11,11.84 -2016-09-24 09:00:00,526.0,204.13,17.1,11.83 -2016-09-24 10:00:00,555.0,341.18,17.11,11.82 -2016-09-24 11:00:00,545.0,131.25,17.12,11.8 -2016-09-24 12:00:00,552.0,91.58,17.13,11.78 -2016-09-24 13:00:00,576.0,113.34,17.04,11.7 -2016-09-24 14:00:00,586.0,45.48,16.95,11.62 -2016-09-24 15:00:00,562.0,34.62,16.86,11.54 -2016-09-24 16:00:00,491.0,18.06,16.78,11.17 -2016-09-24 17:00:00,422.0,16.75,16.69,10.8 -2016-09-24 18:00:00,495.0,0.0,16.61,10.43 -2016-09-24 19:00:00,547.0,0.0,16.43,9.63 -2016-09-24 20:00:00,548.0,0.0,16.24,8.83 -2016-09-24 21:00:00,535.0,0.0,16.06,8.03 -2016-09-24 22:00:00,638.0,0.0,15.68,8.84 -2016-09-24 23:00:00,586.0,0.0,15.3,9.66 -2016-09-25 00:00:00,516.0,0.0,14.92,10.47 -2016-09-25 01:00:00,463.0,0.0,15.09,10.28 -2016-09-25 02:00:00,431.0,0.0,15.27,10.1 -2016-09-25 03:00:00,448.0,0.0,15.44,9.92 -2016-09-25 04:00:00,436.0,0.0,15.28,9.84 -2016-09-25 05:00:00,357.0,0.0,15.13,9.77 -2016-09-25 06:00:00,340.0,0.0,14.97,9.7 -2016-09-25 07:00:00,373.0,41.87,14.91,9.47 -2016-09-25 08:00:00,433.0,299.21,14.84,9.24 -2016-09-25 09:00:00,484.0,476.96,14.78,9.01 -2016-09-25 10:00:00,533.0,505.24,14.83,8.74 -2016-09-25 11:00:00,544.0,428.94,14.88,8.46 -2016-09-25 12:00:00,522.0,633.83,14.93,8.19 -2016-09-25 13:00:00,540.0,291.59,15.04,8.16 -2016-09-25 14:00:00,557.0,312.38,15.14,8.12 -2016-09-25 15:00:00,533.0,163.25,15.25,8.08 -2016-09-25 16:00:00,451.0,29.16,15.34,7.79 -2016-09-25 17:00:00,388.0,38.99,15.43,7.49 -2016-09-25 18:00:00,400.0,0.0,15.52,7.2 -2016-09-25 19:00:00,446.0,0.0,15.62,7.05 -2016-09-25 20:00:00,470.0,0.0,15.72,6.91 -2016-09-25 21:00:00,463.0,0.0,15.82,6.76 -2016-09-25 22:00:00,578.0,0.0,15.85,6.77 -2016-09-25 23:00:00,523.0,0.0,15.89,6.78 -2016-09-26 00:00:00,450.0,0.0,15.92,6.79 -2016-09-26 01:00:00,436.0,0.0,15.98,6.86 -2016-09-26 02:00:00,390.0,0.0,16.05,6.93 -2016-09-26 03:00:00,416.0,0.0,16.11,7.01 -2016-09-26 04:00:00,420.0,0.0,16.09,7.47 -2016-09-26 05:00:00,336.0,0.0,16.06,7.93 -2016-09-26 06:00:00,323.0,0.0,16.04,8.39 -2016-09-26 07:00:00,383.0,37.28,16.07,8.5 -2016-09-26 08:00:00,428.0,17.46,16.09,8.61 -2016-09-26 09:00:00,471.0,31.87,16.12,8.72 -2016-09-26 10:00:00,514.0,105.86,16.15,9.0 -2016-09-26 11:00:00,515.0,64.32,16.18,9.29 -2016-09-26 12:00:00,560.0,92.67,16.21,9.57 -2016-09-26 13:00:00,585.0,43.42,16.21,9.88 -2016-09-26 14:00:00,591.0,39.78,16.21,10.18 -2016-09-26 15:00:00,593.0,67.37,16.21,10.48 -2016-09-26 16:00:00,519.0,68.11,16.16,11.02 -2016-09-26 17:00:00,443.0,22.65,16.1,11.55 -2016-09-26 18:00:00,503.0,0.0,16.05,12.08 -2016-09-26 19:00:00,546.0,0.0,16.16,11.43 -2016-09-26 20:00:00,535.0,0.0,16.27,10.79 -2016-09-26 21:00:00,478.0,0.0,16.38,10.14 -2016-09-26 22:00:00,600.0,0.0,16.44,9.6 -2016-09-26 23:00:00,537.0,0.0,16.51,9.05 -2016-09-27 00:00:00,452.0,0.0,16.57,8.51 -2016-09-27 01:00:00,412.0,0.0,16.45,7.97 -2016-09-27 02:00:00,387.0,0.0,16.33,7.43 -2016-09-27 03:00:00,427.0,0.0,16.22,6.9 -2016-09-27 04:00:00,440.0,0.0,16.22,6.88 -2016-09-27 05:00:00,343.0,0.0,16.22,6.87 -2016-09-27 06:00:00,344.0,0.0,16.22,6.86 -2016-09-27 07:00:00,398.0,11.69,16.2,6.25 -2016-09-27 08:00:00,459.0,64.15,16.19,5.65 -2016-09-27 09:00:00,518.0,164.24,16.17,5.05 -2016-09-27 10:00:00,513.0,211.99,16.16,5.12 -2016-09-27 11:00:00,546.0,332.42,16.16,5.2 -2016-09-27 12:00:00,573.0,172.44,16.16,5.27 -2016-09-27 13:00:00,595.0,496.18,16.04,5.54 -2016-09-27 14:00:00,612.0,504.69,15.92,5.82 -2016-09-27 15:00:00,548.0,430.28,15.8,6.1 -2016-09-27 16:00:00,473.0,283.86,15.5,5.93 -2016-09-27 17:00:00,431.0,90.42,15.19,5.76 -2016-09-27 18:00:00,469.0,0.0,14.89,5.59 -2016-09-27 19:00:00,525.0,0.0,14.65,4.93 -2016-09-27 20:00:00,527.0,0.0,14.41,4.27 -2016-09-27 21:00:00,494.0,0.0,14.17,3.61 -2016-09-27 22:00:00,604.0,0.0,14.04,3.66 -2016-09-27 23:00:00,538.0,0.0,13.92,3.71 -2016-09-28 00:00:00,482.0,0.0,13.79,3.75 -2016-09-28 01:00:00,417.0,0.0,13.84,4.06 -2016-09-28 02:00:00,384.0,0.0,13.88,4.38 -2016-09-28 03:00:00,434.0,0.0,13.93,4.69 -2016-09-28 04:00:00,423.0,0.0,13.96,4.75 -2016-09-28 05:00:00,341.0,0.0,14.0,4.82 -2016-09-28 06:00:00,343.0,0.0,14.03,4.88 -2016-09-28 07:00:00,407.0,55.04,14.12,5.06 -2016-09-28 08:00:00,468.0,280.48,14.21,5.24 -2016-09-28 09:00:00,546.0,396.82,14.3,5.42 -2016-09-28 10:00:00,558.0,239.11,14.4,5.78 -2016-09-28 11:00:00,538.0,263.15,14.5,6.15 -2016-09-28 12:00:00,564.0,145.66,14.61,6.51 -2016-09-28 13:00:00,560.0,129.2,14.57,6.69 -2016-09-28 14:00:00,593.0,89.39,14.53,6.87 -2016-09-28 15:00:00,544.0,109.02,14.49,7.05 -2016-09-28 16:00:00,459.0,70.97,14.55,7.11 -2016-09-28 17:00:00,405.0,28.3,14.6,7.17 -2016-09-28 18:00:00,448.0,0.0,14.66,7.23 -2016-09-28 19:00:00,508.0,0.0,14.75,7.44 -2016-09-28 20:00:00,518.0,0.0,14.85,7.65 -2016-09-28 21:00:00,499.0,0.0,14.94,7.86 -2016-09-28 22:00:00,612.0,0.0,15.06,8.11 -2016-09-28 23:00:00,532.0,0.0,15.18,8.35 -2016-09-29 00:00:00,469.0,0.0,15.3,8.59 -2016-09-29 01:00:00,430.0,0.0,15.31,8.96 -2016-09-29 02:00:00,403.0,0.0,15.31,9.32 -2016-09-29 03:00:00,430.0,0.0,15.32,9.68 -2016-09-29 04:00:00,433.0,0.0,15.47,9.38 -2016-09-29 05:00:00,338.0,0.0,15.61,9.08 -2016-09-29 06:00:00,331.0,0.0,15.76,8.77 -2016-09-29 07:00:00,413.0,12.97,15.75,8.33 -2016-09-29 08:00:00,468.0,120.72,15.74,7.89 -2016-09-29 09:00:00,499.0,325.06,15.73,7.45 -2016-09-29 10:00:00,545.0,622.03,15.55,7.19 -2016-09-29 11:00:00,560.0,641.23,15.37,6.93 -2016-09-29 12:00:00,574.0,737.22,15.2,6.68 -2016-09-29 13:00:00,588.0,583.96,15.19,6.63 -2016-09-29 14:00:00,611.0,549.08,15.18,6.57 -2016-09-29 15:00:00,562.0,450.32,15.17,6.52 -2016-09-29 16:00:00,478.0,180.47,15.0,6.34 -2016-09-29 17:00:00,407.0,48.87,14.84,6.15 -2016-09-29 18:00:00,444.0,0.0,14.67,5.96 -2016-09-29 19:00:00,524.0,0.0,14.54,6.03 -2016-09-29 20:00:00,515.0,0.0,14.4,6.1 -2016-09-29 21:00:00,455.0,0.0,14.27,6.17 -2016-09-29 22:00:00,576.0,0.0,14.2,6.18 -2016-09-29 23:00:00,531.0,0.0,14.13,6.19 -2016-09-30 00:00:00,465.0,0.0,14.06,6.21 -2016-09-30 01:00:00,414.0,0.0,14.19,6.06 -2016-09-30 02:00:00,379.0,0.0,14.31,5.91 -2016-09-30 03:00:00,426.0,0.0,14.44,5.77 -2016-09-30 04:00:00,428.0,0.0,14.41,6.0 -2016-09-30 05:00:00,341.0,0.0,14.37,6.24 -2016-09-30 06:00:00,347.0,0.0,14.34,6.48 -2016-09-30 07:00:00,449.0,60.75,14.28,6.63 -2016-09-30 08:00:00,448.0,249.76,14.22,6.77 -2016-09-30 09:00:00,458.0,486.31,14.16,6.91 -2016-09-30 10:00:00,494.0,590.63,14.15,6.51 -2016-09-30 11:00:00,510.0,725.31,14.14,6.11 -2016-09-30 12:00:00,513.0,768.71,14.13,5.71 -2016-09-30 13:00:00,534.0,604.28,14.27,5.56 -2016-09-30 14:00:00,581.0,322.56,14.41,5.41 -2016-09-30 15:00:00,520.0,419.97,14.55,5.26 -2016-09-30 16:00:00,417.0,249.31,14.45,4.99 -2016-09-30 17:00:00,378.0,94.33,14.34,4.72 -2016-09-30 18:00:00,414.0,0.0,14.24,4.46 -2016-09-30 19:00:00,485.0,0.0,14.3,4.72 -2016-09-30 20:00:00,514.0,0.0,14.36,4.98 -2016-09-30 21:00:00,504.0,0.0,14.42,5.24 -2016-09-30 22:00:00,599.0,0.0,14.46,6.09 -2016-09-30 23:00:00,541.0,0.0,14.5,6.94 -2016-10-01 00:00:00,464.0,0.0,14.54,7.79 -2016-10-01 01:00:00,411.0,0.0,14.5,9.01 -2016-10-01 02:00:00,377.0,0.0,14.46,10.22 -2016-10-01 03:00:00,429.0,0.0,14.42,11.43 -2016-10-01 04:00:00,420.0,0.0,14.1,11.24 -2016-10-01 05:00:00,348.0,0.0,13.77,11.05 -2016-10-01 06:00:00,325.0,0.0,13.45,10.86 -2016-10-01 07:00:00,380.0,69.38,13.65,10.86 -2016-10-01 08:00:00,441.0,211.11,13.85,10.86 -2016-10-01 09:00:00,503.0,351.53,14.06,10.86 -2016-10-01 10:00:00,488.0,618.08,14.06,10.78 -2016-10-01 11:00:00,507.0,602.92,14.07,10.71 -2016-10-01 12:00:00,532.0,815.57,14.09,10.63 -2016-10-01 13:00:00,590.0,628.75,14.09,10.28 -2016-10-01 14:00:00,591.0,642.92,14.1,9.92 -2016-10-01 15:00:00,546.0,230.28,14.1,9.56 -2016-10-01 16:00:00,474.0,206.31,14.12,9.43 -2016-10-01 17:00:00,417.0,53.24,14.13,9.3 -2016-10-01 18:00:00,479.0,0.0,14.15,9.17 -2016-10-01 19:00:00,523.0,0.0,14.14,8.8 -2016-10-01 20:00:00,540.0,0.0,14.12,8.43 -2016-10-01 21:00:00,541.0,0.0,14.1,8.06 -2016-10-01 22:00:00,626.0,0.0,14.2,7.81 -2016-10-01 23:00:00,567.0,0.0,14.3,7.57 -2016-10-02 00:00:00,500.0,0.0,14.4,7.32 -2016-10-02 01:00:00,439.0,0.0,14.41,6.72 -2016-10-02 02:00:00,414.0,0.0,14.42,6.11 -2016-10-02 03:00:00,453.0,0.0,14.43,5.5 -2016-10-02 04:00:00,454.0,0.0,14.36,4.92 -2016-10-02 05:00:00,356.0,0.0,14.29,4.34 -2016-10-02 06:00:00,342.0,0.0,14.22,3.75 -2016-10-02 07:00:00,385.0,75.08,14.27,3.06 -2016-10-02 08:00:00,424.0,289.36,14.32,2.37 -2016-10-02 09:00:00,501.0,483.16,14.36,1.68 -2016-10-02 10:00:00,556.0,639.73,14.45,1.63 -2016-10-02 11:00:00,564.0,707.85,14.55,1.57 -2016-10-02 12:00:00,556.0,662.46,14.64,1.52 -2016-10-02 13:00:00,575.0,668.16,14.85,1.85 -2016-10-02 14:00:00,571.0,641.84,15.07,2.18 -2016-10-02 15:00:00,499.0,483.28,15.28,2.51 -2016-10-02 16:00:00,420.0,239.23,15.43,3.02 -2016-10-02 17:00:00,365.0,66.47,15.57,3.52 -2016-10-02 18:00:00,381.0,0.0,15.72,4.03 -2016-10-02 19:00:00,415.0,0.0,15.75,4.24 -2016-10-02 20:00:00,457.0,0.0,15.77,4.46 -2016-10-02 21:00:00,453.0,0.0,15.79,4.68 -2016-10-02 22:00:00,571.0,0.0,15.86,4.99 -2016-10-02 23:00:00,515.0,0.0,15.92,5.31 -2016-10-03 00:00:00,433.0,0.0,15.99,5.63 -2016-10-03 01:00:00,417.0,0.0,15.86,6.04 -2016-10-03 02:00:00,403.0,0.0,15.74,6.46 -2016-10-03 03:00:00,421.0,0.0,15.61,6.87 -2016-10-03 04:00:00,437.0,0.0,15.62,7.14 -2016-10-03 05:00:00,369.0,0.0,15.62,7.4 -2016-10-03 06:00:00,341.0,0.0,15.62,7.67 -2016-10-03 07:00:00,414.0,65.83,15.66,7.94 -2016-10-03 08:00:00,447.0,281.79,15.69,8.22 -2016-10-03 09:00:00,472.0,491.76,15.73,8.5 -2016-10-03 10:00:00,476.0,653.16,15.73,8.57 -2016-10-03 11:00:00,476.0,739.53,15.73,8.65 -2016-10-03 12:00:00,499.0,792.34,15.73,8.73 -2016-10-03 13:00:00,526.0,708.55,15.89,8.37 -2016-10-03 14:00:00,546.0,249.68,16.06,8.0 -2016-10-03 15:00:00,540.0,363.32,16.23,7.64 -2016-10-03 16:00:00,440.0,311.47,16.24,7.9 -2016-10-03 17:00:00,386.0,78.71,16.24,8.17 -2016-10-03 18:00:00,411.0,0.0,16.25,8.43 -2016-10-03 19:00:00,449.0,0.0,16.18,9.08 -2016-10-03 20:00:00,500.0,0.0,16.11,9.72 -2016-10-03 21:00:00,455.0,0.0,16.04,10.37 -2016-10-03 22:00:00,561.0,0.0,16.07,10.54 -2016-10-03 23:00:00,516.0,0.0,16.09,10.7 -2016-10-04 00:00:00,461.0,0.0,16.12,10.87 -2016-10-04 01:00:00,406.0,0.0,16.05,10.82 -2016-10-04 02:00:00,391.0,0.0,15.98,10.77 -2016-10-04 03:00:00,426.0,0.0,15.91,10.72 -2016-10-04 04:00:00,434.0,0.0,15.86,10.51 -2016-10-04 05:00:00,343.0,0.0,15.81,10.3 -2016-10-04 06:00:00,333.0,0.0,15.75,10.1 -2016-10-04 07:00:00,409.0,51.88,15.75,10.01 -2016-10-04 08:00:00,465.0,146.28,15.75,9.93 -2016-10-04 09:00:00,503.0,241.16,15.74,9.85 -2016-10-04 10:00:00,497.0,598.17,15.85,9.72 -2016-10-04 11:00:00,513.0,490.23,15.95,9.59 -2016-10-04 12:00:00,543.0,286.29,16.06,9.46 -2016-10-04 13:00:00,563.0,233.27,16.23,8.89 -2016-10-04 14:00:00,587.0,59.69,16.41,8.31 -2016-10-04 15:00:00,534.0,37.5,16.58,7.74 -2016-10-04 16:00:00,459.0,92.42,16.48,7.58 -2016-10-04 17:00:00,400.0,33.45,16.37,7.43 -2016-10-04 18:00:00,448.0,0.0,16.27,7.27 -2016-10-04 19:00:00,523.0,0.0,16.03,7.35 -2016-10-04 20:00:00,525.0,0.0,15.79,7.43 -2016-10-04 21:00:00,475.0,0.0,15.54,7.52 -2016-10-04 22:00:00,605.0,0.0,15.4,7.43 -2016-10-04 23:00:00,546.0,0.0,15.26,7.33 -2016-10-05 00:00:00,492.0,0.0,15.12,7.24 -2016-10-05 01:00:00,443.0,0.0,15.06,6.26 -2016-10-05 02:00:00,407.0,0.0,14.99,5.28 -2016-10-05 03:00:00,437.0,0.0,14.93,4.3 -2016-10-05 04:00:00,451.0,0.0,14.71,4.69 -2016-10-05 05:00:00,354.0,0.0,14.48,5.07 -2016-10-05 06:00:00,364.0,0.0,14.26,5.45 -2016-10-05 07:00:00,440.0,37.93,14.37,5.54 -2016-10-05 08:00:00,476.0,259.4,14.47,5.63 -2016-10-05 09:00:00,503.0,462.74,14.57,5.72 -2016-10-05 10:00:00,502.0,631.84,14.73,5.59 -2016-10-05 11:00:00,526.0,614.06,14.9,5.45 -2016-10-05 12:00:00,529.0,750.12,15.06,5.31 -2016-10-05 13:00:00,554.0,750.32,15.2,5.42 -2016-10-05 14:00:00,577.0,624.64,15.35,5.53 -2016-10-05 15:00:00,513.0,488.61,15.49,5.64 -2016-10-05 16:00:00,438.0,195.91,15.47,5.51 -2016-10-05 17:00:00,384.0,75.65,15.45,5.38 -2016-10-05 18:00:00,419.0,0.0,15.43,5.26 -2016-10-05 19:00:00,479.0,0.0,15.42,5.72 -2016-10-05 20:00:00,494.0,0.0,15.4,6.18 -2016-10-05 21:00:00,458.0,0.0,15.38,6.65 -2016-10-05 22:00:00,588.0,0.0,15.45,7.14 -2016-10-05 23:00:00,525.0,0.0,15.51,7.63 -2016-10-06 00:00:00,481.0,0.0,15.57,8.12 -2016-10-06 01:00:00,425.0,0.0,15.1,7.99 -2016-10-06 02:00:00,381.0,0.0,14.63,7.85 -2016-10-06 03:00:00,431.0,0.0,14.16,7.71 -2016-10-06 04:00:00,442.0,0.0,13.93,8.35 -2016-10-06 05:00:00,363.0,0.0,13.7,9.0 -2016-10-06 06:00:00,343.0,0.0,13.47,9.64 -2016-10-06 07:00:00,421.0,58.81,13.22,10.0 -2016-10-06 08:00:00,490.0,282.43,12.97,10.37 -2016-10-06 09:00:00,502.0,494.01,12.72,10.73 -2016-10-06 10:00:00,518.0,657.76,12.83,10.27 -2016-10-06 11:00:00,504.0,750.95,12.95,9.81 -2016-10-06 12:00:00,520.0,801.18,13.06,9.35 -2016-10-06 13:00:00,545.0,770.23,13.71,9.62 -2016-10-06 14:00:00,570.0,581.55,14.36,9.89 -2016-10-06 15:00:00,537.0,196.51,15.01,10.17 -2016-10-06 16:00:00,457.0,178.62,15.03,10.23 -2016-10-06 17:00:00,394.0,68.3,15.05,10.29 -2016-10-06 18:00:00,413.0,0.0,15.07,10.36 -2016-10-06 19:00:00,491.0,0.0,14.91,9.89 -2016-10-06 20:00:00,523.0,0.0,14.75,9.43 -2016-10-06 21:00:00,474.0,0.0,14.58,8.97 -2016-10-06 22:00:00,589.0,0.0,14.48,8.68 -2016-10-06 23:00:00,555.0,0.0,14.38,8.4 -2016-10-07 00:00:00,486.0,0.0,14.28,8.12 -2016-10-07 01:00:00,462.0,0.0,14.13,7.65 -2016-10-07 02:00:00,416.0,0.0,13.99,7.17 -2016-10-07 03:00:00,454.0,0.0,13.84,6.69 -2016-10-07 04:00:00,471.0,0.0,13.73,6.29 -2016-10-07 05:00:00,395.0,0.0,13.62,5.88 -2016-10-07 06:00:00,387.0,0.0,13.51,5.48 -2016-10-07 07:00:00,474.0,14.44,13.5,5.62 -2016-10-07 08:00:00,497.0,100.37,13.48,5.76 -2016-10-07 09:00:00,502.0,333.63,13.47,5.9 -2016-10-07 10:00:00,533.0,412.54,13.56,5.81 -2016-10-07 11:00:00,520.0,570.92,13.65,5.72 -2016-10-07 12:00:00,525.0,673.38,13.74,5.63 -2016-10-07 13:00:00,547.0,631.68,13.86,4.9 -2016-10-07 14:00:00,582.0,178.25,13.98,4.17 -2016-10-07 15:00:00,550.0,112.63,14.09,3.45 -2016-10-07 16:00:00,447.0,245.09,14.2,3.43 -2016-10-07 17:00:00,413.0,46.73,14.3,3.41 -2016-10-07 18:00:00,443.0,0.0,14.41,3.39 -2016-10-07 19:00:00,483.0,0.0,14.41,3.58 -2016-10-07 20:00:00,552.0,0.0,14.41,3.77 -2016-10-07 21:00:00,560.0,0.0,14.41,3.96 -2016-10-07 22:00:00,648.0,0.0,14.45,3.57 -2016-10-07 23:00:00,601.0,0.0,14.49,3.19 -2016-10-08 00:00:00,517.0,0.0,14.53,2.8 -2016-10-08 01:00:00,464.0,0.0,14.33,2.81 -2016-10-08 02:00:00,455.0,0.0,14.14,2.83 -2016-10-08 03:00:00,487.0,0.0,13.94,2.84 -2016-10-08 04:00:00,474.0,0.0,13.95,3.27 -2016-10-08 05:00:00,429.0,0.0,13.95,3.71 -2016-10-08 06:00:00,394.0,0.0,13.96,4.14 -2016-10-08 07:00:00,457.0,26.78,14.1,4.68 -2016-10-08 08:00:00,523.0,87.09,14.23,5.22 -2016-10-08 09:00:00,554.0,58.7,14.36,5.77 -2016-10-08 10:00:00,545.0,50.39,14.53,5.51 -2016-10-08 11:00:00,547.0,165.59,14.7,5.26 -2016-10-08 12:00:00,531.0,687.69,14.88,5.01 -2016-10-08 13:00:00,588.0,674.99,15.09,4.72 -2016-10-08 14:00:00,634.0,525.73,15.31,4.43 -2016-10-08 15:00:00,573.0,367.21,15.53,4.14 -2016-10-08 16:00:00,486.0,236.42,15.49,4.46 -2016-10-08 17:00:00,450.0,66.51,15.45,4.77 -2016-10-08 18:00:00,478.0,0.0,15.41,5.09 -2016-10-08 19:00:00,534.0,0.0,15.29,5.13 -2016-10-08 20:00:00,611.0,0.0,15.17,5.17 -2016-10-08 21:00:00,603.0,0.0,15.05,5.21 -2016-10-08 22:00:00,684.0,0.0,15.0,5.13 -2016-10-08 23:00:00,633.0,0.0,14.95,5.04 -2016-10-09 00:00:00,554.0,0.0,14.9,4.95 -2016-10-09 01:00:00,500.0,0.0,14.78,5.05 -2016-10-09 02:00:00,483.0,0.0,14.66,5.14 -2016-10-09 03:00:00,469.0,0.0,14.54,5.24 -2016-10-09 04:00:00,479.0,0.0,14.47,5.25 -2016-10-09 05:00:00,412.0,0.0,14.39,5.26 -2016-10-09 06:00:00,387.0,0.0,14.31,5.27 -2016-10-09 07:00:00,421.0,21.38,14.35,5.28 -2016-10-09 08:00:00,511.0,148.39,14.38,5.29 -2016-10-09 09:00:00,564.0,301.57,14.41,5.3 -2016-10-09 10:00:00,605.0,322.25,14.52,5.49 -2016-10-09 11:00:00,599.0,443.54,14.61,5.68 -2016-10-09 12:00:00,548.0,533.79,14.72,5.88 -2016-10-09 13:00:00,560.0,553.33,14.75,5.82 -2016-10-09 14:00:00,589.0,353.41,14.79,5.76 -2016-10-09 15:00:00,538.0,397.11,14.83,5.7 -2016-10-09 16:00:00,444.0,198.99,14.78,5.94 -2016-10-09 17:00:00,411.0,55.85,14.72,6.18 -2016-10-09 18:00:00,440.0,0.0,14.67,6.43 -2016-10-09 19:00:00,496.0,0.0,14.53,6.37 -2016-10-09 20:00:00,567.0,0.0,14.39,6.32 -2016-10-09 21:00:00,518.0,0.0,14.25,6.26 -2016-10-09 22:00:00,639.0,0.0,14.19,5.96 -2016-10-09 23:00:00,590.0,0.0,14.12,5.66 -2016-10-10 00:00:00,498.0,0.0,14.06,5.35 -2016-10-10 01:00:00,453.0,0.0,14.05,5.13 -2016-10-10 02:00:00,457.0,0.0,14.05,4.91 -2016-10-10 03:00:00,477.0,0.0,14.04,4.69 -2016-10-10 04:00:00,484.0,0.0,14.03,4.81 -2016-10-10 05:00:00,414.0,0.0,14.02,4.94 -2016-10-10 06:00:00,391.0,0.0,14.01,5.06 -2016-10-10 07:00:00,451.0,15.54,14.04,5.36 -2016-10-10 08:00:00,525.0,93.38,14.06,5.66 -2016-10-10 09:00:00,574.0,114.77,14.09,5.96 -2016-10-10 10:00:00,545.0,198.86,14.1,5.95 -2016-10-10 11:00:00,535.0,226.97,14.12,5.95 -2016-10-10 12:00:00,547.0,217.1,14.15,5.94 -2016-10-10 13:00:00,578.0,387.84,14.13,6.0 -2016-10-10 14:00:00,598.0,216.35,14.12,6.06 -2016-10-10 15:00:00,564.0,240.52,14.1,6.12 -2016-10-10 16:00:00,454.0,100.03,14.11,6.39 -2016-10-10 17:00:00,419.0,21.33,14.12,6.66 -2016-10-10 18:00:00,452.0,0.0,14.13,6.92 -2016-10-10 19:00:00,525.0,0.0,14.06,7.1 -2016-10-10 20:00:00,572.0,0.0,13.99,7.27 -2016-10-10 21:00:00,536.0,0.0,13.92,7.45 -2016-10-10 22:00:00,639.0,0.0,13.91,7.66 -2016-10-10 23:00:00,582.0,0.0,13.9,7.88 -2016-10-11 00:00:00,509.0,0.0,13.89,8.1 -2016-10-11 01:00:00,465.0,0.0,13.96,8.44 -2016-10-11 02:00:00,481.0,0.0,14.03,8.78 -2016-10-11 03:00:00,469.0,0.0,14.1,9.12 -2016-10-11 04:00:00,487.0,0.0,14.04,9.2 -2016-10-11 05:00:00,427.0,0.0,13.97,9.27 -2016-10-11 06:00:00,412.0,0.0,13.9,9.35 -2016-10-11 07:00:00,471.0,10.36,13.94,9.32 -2016-10-11 08:00:00,549.0,13.74,13.97,9.3 -2016-10-11 09:00:00,606.0,109.47,14.01,9.27 -2016-10-11 10:00:00,606.0,243.88,14.03,9.24 -2016-10-11 11:00:00,597.0,301.56,14.06,9.2 -2016-10-11 12:00:00,618.0,535.55,14.1,9.17 -2016-10-11 13:00:00,620.0,654.12,14.22,9.01 -2016-10-11 14:00:00,613.0,548.63,14.35,8.85 -2016-10-11 15:00:00,577.0,383.03,14.47,8.69 -2016-10-11 16:00:00,494.0,149.57,14.5,9.0 -2016-10-11 17:00:00,438.0,30.1,14.53,9.31 -2016-10-11 18:00:00,471.0,0.0,14.56,9.61 -2016-10-11 19:00:00,569.0,0.0,14.4,9.83 -2016-10-11 20:00:00,597.0,0.0,14.24,10.06 -2016-10-11 21:00:00,545.0,0.0,14.08,10.28 -2016-10-11 22:00:00,668.0,0.0,13.92,10.46 -2016-10-11 23:00:00,603.0,0.0,13.75,10.65 -2016-10-12 00:00:00,522.0,0.0,13.58,10.84 -2016-10-12 01:00:00,508.0,0.0,13.43,10.64 -2016-10-12 02:00:00,511.0,0.0,13.29,10.45 -2016-10-12 03:00:00,516.0,0.0,13.14,10.25 -2016-10-12 04:00:00,522.0,0.0,12.94,10.16 -2016-10-12 05:00:00,468.0,0.0,12.73,10.06 -2016-10-12 06:00:00,442.0,0.0,12.52,9.97 -2016-10-12 07:00:00,528.0,29.28,12.47,10.33 -2016-10-12 08:00:00,615.0,74.08,12.41,10.68 -2016-10-12 09:00:00,637.0,231.72,12.35,11.03 -2016-10-12 10:00:00,632.0,481.55,12.61,11.36 -2016-10-12 11:00:00,622.0,718.64,12.87,11.69 -2016-10-12 12:00:00,626.0,739.38,13.12,12.01 -2016-10-12 13:00:00,628.0,731.5,13.54,12.03 -2016-10-12 14:00:00,659.0,633.38,13.96,12.05 -2016-10-12 15:00:00,602.0,497.03,14.38,12.07 -2016-10-12 16:00:00,526.0,278.48,14.41,11.92 -2016-10-12 17:00:00,479.0,40.6,14.44,11.77 -2016-10-12 18:00:00,521.0,0.0,14.47,11.61 -2016-10-12 19:00:00,595.0,0.0,14.28,11.6 -2016-10-12 20:00:00,630.0,0.0,14.09,11.59 -2016-10-12 21:00:00,600.0,0.0,13.9,11.57 -2016-10-12 22:00:00,711.0,0.0,13.79,11.28 -2016-10-12 23:00:00,648.0,0.0,13.67,10.99 -2016-10-13 00:00:00,561.0,0.0,13.55,10.7 -2016-10-13 01:00:00,530.0,0.0,13.58,10.15 -2016-10-13 02:00:00,534.0,0.0,13.62,9.6 -2016-10-13 03:00:00,547.0,0.0,13.65,9.05 -2016-10-13 04:00:00,555.0,0.0,13.5,9.31 -2016-10-13 05:00:00,480.0,0.0,13.35,9.58 -2016-10-13 06:00:00,462.0,0.0,13.19,9.85 -2016-10-13 07:00:00,529.0,18.11,13.15,10.12 -2016-10-13 08:00:00,593.0,56.2,13.11,10.4 -2016-10-13 09:00:00,617.0,104.51,13.07,10.68 -2016-10-13 10:00:00,610.0,95.93,13.11,10.36 -2016-10-13 11:00:00,579.0,215.2,13.15,10.04 -2016-10-13 12:00:00,575.0,439.72,13.19,9.72 -2016-10-13 13:00:00,600.0,200.39,13.5,8.89 -2016-10-13 14:00:00,612.0,409.49,13.82,8.06 -2016-10-13 15:00:00,585.0,151.18,14.13,7.23 -2016-10-13 16:00:00,501.0,219.25,14.02,7.18 -2016-10-13 17:00:00,457.0,19.99,13.91,7.14 -2016-10-13 18:00:00,507.0,0.0,13.8,7.09 -2016-10-13 19:00:00,600.0,0.0,13.63,6.69 -2016-10-13 20:00:00,666.0,0.0,13.46,6.28 -2016-10-13 21:00:00,581.0,0.0,13.28,5.88 -2016-10-13 22:00:00,684.0,0.0,13.23,5.17 -2016-10-13 23:00:00,618.0,0.0,13.17,4.47 -2016-10-14 00:00:00,537.0,0.0,13.11,3.77 -2016-10-14 01:00:00,502.0,0.0,13.33,3.13 -2016-10-14 02:00:00,507.0,0.0,13.54,2.49 -2016-10-14 03:00:00,540.0,0.0,13.76,1.85 -2016-10-14 04:00:00,532.0,0.0,13.7,1.54 -2016-10-14 05:00:00,484.0,0.0,13.63,1.22 -2016-10-14 06:00:00,479.0,0.0,13.56,0.91 -2016-10-14 07:00:00,579.0,22.37,13.59,1.43 -2016-10-14 08:00:00,605.0,213.17,13.62,1.95 -2016-10-14 09:00:00,631.0,308.08,13.65,2.47 -2016-10-14 10:00:00,594.0,599.63,13.84,3.23 -2016-10-14 11:00:00,575.0,682.57,14.02,3.99 -2016-10-14 12:00:00,553.0,236.71,14.2,4.74 -2016-10-14 13:00:00,569.0,173.56,14.34,5.52 -2016-10-14 14:00:00,604.0,49.68,14.47,6.3 -2016-10-14 15:00:00,567.0,54.16,14.6,7.08 -2016-10-14 16:00:00,494.0,94.79,14.43,7.24 -2016-10-14 17:00:00,433.0,5.8,14.26,7.4 -2016-10-14 18:00:00,494.0,0.0,14.09,7.56 -2016-10-14 19:00:00,582.0,0.0,13.89,7.18 -2016-10-14 20:00:00,663.0,0.0,13.69,6.8 -2016-10-14 21:00:00,615.0,0.0,13.48,6.41 -2016-10-14 22:00:00,738.0,0.0,13.59,6.43 -2016-10-14 23:00:00,686.0,0.0,13.69,6.45 -2016-10-15 00:00:00,583.0,0.0,13.79,6.47 -2016-10-15 01:00:00,563.0,0.0,13.52,6.74 -2016-10-15 02:00:00,529.0,0.0,13.24,7.02 -2016-10-15 03:00:00,536.0,0.0,12.97,7.3 -2016-10-15 04:00:00,563.0,0.0,13.13,7.34 -2016-10-15 05:00:00,486.0,0.0,13.28,7.38 -2016-10-15 06:00:00,474.0,0.0,13.43,7.42 -2016-10-15 07:00:00,511.0,2.31,13.25,7.87 -2016-10-15 08:00:00,591.0,8.83,13.07,8.32 -2016-10-15 09:00:00,642.0,30.25,12.9,8.77 -2016-10-15 10:00:00,695.0,44.12,12.99,9.08 -2016-10-15 11:00:00,724.0,672.67,13.08,9.4 -2016-10-15 12:00:00,767.0,730.06,13.18,9.71 -2016-10-15 13:00:00,714.0,398.0,13.91,9.53 -2016-10-15 14:00:00,707.0,343.96,14.65,9.34 -2016-10-15 15:00:00,665.0,246.79,15.39,9.16 -2016-10-15 16:00:00,585.0,127.95,15.57,9.51 -2016-10-15 17:00:00,519.0,11.79,15.76,9.87 -2016-10-15 18:00:00,564.0,0.0,15.94,10.22 -2016-10-15 19:00:00,616.0,0.0,15.95,10.57 -2016-10-15 20:00:00,688.0,0.0,15.96,10.93 -2016-10-15 21:00:00,644.0,0.0,15.96,11.28 -2016-10-15 22:00:00,734.0,0.0,15.62,11.37 -2016-10-15 23:00:00,690.0,0.0,15.28,11.46 -2016-10-16 00:00:00,635.0,0.0,14.94,11.54 -2016-10-16 01:00:00,558.0,0.0,14.99,12.03 -2016-10-16 02:00:00,522.0,0.0,15.05,12.52 -2016-10-16 03:00:00,551.0,0.0,15.1,13.01 -2016-10-16 04:00:00,573.0,0.0,15.04,12.54 -2016-10-16 05:00:00,499.0,0.0,14.98,12.08 -2016-10-16 06:00:00,478.0,0.0,14.91,11.61 -2016-10-16 07:00:00,506.0,5.17,14.95,11.19 -2016-10-16 08:00:00,570.0,111.78,14.99,10.76 -2016-10-16 09:00:00,604.0,226.6,15.03,10.33 -2016-10-16 10:00:00,633.0,308.04,15.12,10.11 -2016-10-16 11:00:00,597.0,440.83,15.22,9.9 -2016-10-16 12:00:00,597.0,399.47,15.31,9.68 -2016-10-16 13:00:00,601.0,425.97,15.09,10.03 -2016-10-16 14:00:00,609.0,362.96,14.88,10.38 -2016-10-16 15:00:00,594.0,244.57,14.66,10.73 -2016-10-16 16:00:00,482.0,128.44,14.71,10.54 -2016-10-16 17:00:00,425.0,3.1,14.76,10.34 -2016-10-16 18:00:00,439.0,0.0,14.81,10.15 -2016-10-16 19:00:00,516.0,0.0,14.82,9.7 -2016-10-16 20:00:00,595.0,0.0,14.83,9.25 -2016-10-16 21:00:00,554.0,0.0,14.84,8.8 -2016-10-16 22:00:00,655.0,0.0,14.85,8.53 -2016-10-16 23:00:00,611.0,0.0,14.85,8.26 -2016-10-17 00:00:00,529.0,0.0,14.86,7.99 -2016-10-17 01:00:00,516.0,0.0,14.82,8.0 -2016-10-17 02:00:00,471.0,0.0,14.79,8.02 -2016-10-17 03:00:00,494.0,0.0,14.75,8.04 -2016-10-17 04:00:00,506.0,0.0,14.77,7.64 -2016-10-17 05:00:00,450.0,0.0,14.78,7.23 -2016-10-17 06:00:00,411.0,0.0,14.8,6.83 -2016-10-17 07:00:00,472.0,2.86,14.82,6.75 -2016-10-17 08:00:00,531.0,108.22,14.84,6.68 -2016-10-17 09:00:00,556.0,192.04,14.86,6.61 -2016-10-17 10:00:00,547.0,321.01,14.83,6.63 -2016-10-17 11:00:00,556.0,403.92,14.79,6.64 -2016-10-17 12:00:00,558.0,387.9,14.76,6.66 -2016-10-17 13:00:00,557.0,706.57,14.78,6.86 -2016-10-17 14:00:00,578.0,600.94,14.8,7.05 -2016-10-17 15:00:00,553.0,420.26,14.82,7.24 -2016-10-17 16:00:00,484.0,67.86,14.81,7.07 -2016-10-17 17:00:00,415.0,2.28,14.81,6.9 -2016-10-17 18:00:00,457.0,0.0,14.8,6.73 -2016-10-17 19:00:00,543.0,0.0,14.75,6.65 -2016-10-17 20:00:00,600.0,0.0,14.69,6.57 -2016-10-17 21:00:00,562.0,0.0,14.63,6.48 -2016-10-17 22:00:00,656.0,0.0,14.63,6.61 -2016-10-17 23:00:00,593.0,0.0,14.62,6.74 -2016-10-18 00:00:00,514.0,0.0,14.62,6.87 -2016-10-18 01:00:00,508.0,0.0,14.59,7.6 -2016-10-18 02:00:00,445.0,0.0,14.55,8.32 -2016-10-18 03:00:00,495.0,0.0,14.52,9.05 -2016-10-18 04:00:00,503.0,0.0,14.47,9.35 -2016-10-18 05:00:00,423.0,0.0,14.41,9.65 -2016-10-18 06:00:00,411.0,0.0,14.36,9.94 -2016-10-18 07:00:00,478.0,0.24,14.19,9.7 -2016-10-18 08:00:00,547.0,81.22,14.02,9.46 -2016-10-18 09:00:00,566.0,66.24,13.85,9.21 -2016-10-18 10:00:00,576.0,290.06,13.69,8.67 -2016-10-18 11:00:00,599.0,189.42,13.52,8.12 -2016-10-18 12:00:00,637.0,503.33,13.36,7.57 -2016-10-18 13:00:00,631.0,638.81,13.37,7.48 -2016-10-18 14:00:00,647.0,181.98,13.38,7.39 -2016-10-18 15:00:00,612.0,441.35,13.38,7.3 -2016-10-18 16:00:00,524.0,219.49,13.28,7.23 -2016-10-18 17:00:00,478.0,0.0,13.18,7.16 -2016-10-18 18:00:00,537.0,0.0,13.08,7.09 -2016-10-18 19:00:00,608.0,0.0,12.98,6.95 -2016-10-18 20:00:00,627.0,0.0,12.88,6.81 -2016-10-18 21:00:00,591.0,0.0,12.77,6.68 -2016-10-18 22:00:00,697.0,0.0,12.78,6.44 -2016-10-18 23:00:00,650.0,0.0,12.79,6.21 -2016-10-19 00:00:00,596.0,0.0,12.8,5.97 -2016-10-19 01:00:00,542.0,0.0,12.99,5.61 -2016-10-19 02:00:00,504.0,0.0,13.18,5.25 -2016-10-19 03:00:00,534.0,0.0,13.37,4.88 -2016-10-19 04:00:00,548.0,0.0,13.27,4.83 -2016-10-19 05:00:00,478.0,0.0,13.17,4.77 -2016-10-19 06:00:00,450.0,0.0,13.07,4.72 -2016-10-19 07:00:00,529.0,0.0,13.03,4.79 -2016-10-19 08:00:00,589.0,49.77,12.99,4.86 -2016-10-19 09:00:00,646.0,183.67,12.94,4.94 -2016-10-19 10:00:00,629.0,172.28,12.88,4.91 -2016-10-19 11:00:00,607.0,165.25,12.82,4.87 -2016-10-19 12:00:00,623.0,118.52,12.76,4.84 -2016-10-19 13:00:00,635.0,86.13,12.76,5.22 -2016-10-19 14:00:00,657.0,76.84,12.77,5.6 -2016-10-19 15:00:00,615.0,82.07,12.77,5.97 -2016-10-19 16:00:00,552.0,17.1,12.75,6.11 -2016-10-19 17:00:00,513.0,0.0,12.72,6.26 -2016-10-19 18:00:00,571.0,0.0,12.7,6.4 -2016-10-19 19:00:00,712.0,0.0,12.67,6.34 -2016-10-19 20:00:00,702.0,0.0,12.64,6.29 -2016-10-19 21:00:00,628.0,0.0,12.61,6.23 -2016-10-19 22:00:00,726.0,0.0,12.69,5.9 -2016-10-19 23:00:00,691.0,0.0,12.76,5.56 -2016-10-20 00:00:00,608.0,0.0,12.84,5.23 -2016-10-20 01:00:00,580.0,0.0,12.97,4.88 -2016-10-20 02:00:00,523.0,0.0,13.11,4.53 -2016-10-20 03:00:00,554.0,0.0,13.24,4.18 -2016-10-20 04:00:00,571.0,0.0,13.22,4.38 -2016-10-20 05:00:00,488.0,0.0,13.19,4.58 -2016-10-20 06:00:00,466.0,0.0,13.17,4.79 -2016-10-20 07:00:00,543.0,0.0,13.22,4.75 -2016-10-20 08:00:00,618.0,222.05,13.27,4.72 -2016-10-20 09:00:00,622.0,324.01,13.32,4.69 -2016-10-20 10:00:00,619.0,502.28,13.39,4.67 -2016-10-20 11:00:00,620.0,674.06,13.45,4.65 -2016-10-20 12:00:00,628.0,709.96,13.52,4.63 -2016-10-20 13:00:00,644.0,695.65,13.52,4.73 -2016-10-20 14:00:00,659.0,580.85,13.53,4.82 -2016-10-20 15:00:00,625.0,441.27,13.54,4.91 -2016-10-20 16:00:00,536.0,213.51,13.51,4.96 -2016-10-20 17:00:00,486.0,0.0,13.48,5.01 -2016-10-20 18:00:00,514.0,0.0,13.45,5.06 -2016-10-20 19:00:00,622.0,0.0,13.37,4.97 -2016-10-20 20:00:00,688.0,0.0,13.28,4.89 -2016-10-20 21:00:00,631.0,0.0,13.19,4.8 -2016-10-20 22:00:00,759.0,0.0,13.21,4.61 -2016-10-20 23:00:00,725.0,0.0,13.22,4.41 -2016-10-21 00:00:00,640.0,0.0,13.23,4.22 -2016-10-21 01:00:00,597.0,0.0,13.22,3.85 -2016-10-21 02:00:00,559.0,0.0,13.21,3.48 -2016-10-21 03:00:00,584.0,0.0,13.2,3.1 -2016-10-21 04:00:00,601.0,0.0,13.17,3.1 -2016-10-21 05:00:00,534.0,0.0,13.14,3.09 -2016-10-21 06:00:00,497.0,0.0,13.1,3.09 -2016-10-21 07:00:00,582.0,0.0,13.13,3.09 -2016-10-21 08:00:00,645.0,169.6,13.16,3.1 -2016-10-21 09:00:00,660.0,224.34,13.19,3.1 -2016-10-21 10:00:00,671.0,515.0,13.3,3.14 -2016-10-21 11:00:00,661.0,529.48,13.4,3.17 -2016-10-21 12:00:00,674.0,639.78,13.52,3.2 -2016-10-21 13:00:00,666.0,540.77,13.64,3.33 -2016-10-21 14:00:00,676.0,374.52,13.77,3.47 -2016-10-21 15:00:00,631.0,179.91,13.89,3.6 -2016-10-21 16:00:00,551.0,85.89,13.88,3.99 -2016-10-21 17:00:00,508.0,0.0,13.87,4.38 -2016-10-21 18:00:00,573.0,0.0,13.86,4.77 -2016-10-21 19:00:00,699.0,0.0,13.73,5.22 -2016-10-21 20:00:00,771.0,0.0,13.59,5.66 -2016-10-21 21:00:00,730.0,0.0,13.45,6.11 -2016-10-21 22:00:00,835.0,0.0,13.38,6.42 -2016-10-21 23:00:00,768.0,0.0,13.3,6.73 -2016-10-22 00:00:00,702.0,0.0,13.23,7.03 -2016-10-22 01:00:00,663.0,0.0,13.17,6.71 -2016-10-22 02:00:00,599.0,0.0,13.11,6.39 -2016-10-22 03:00:00,634.0,0.0,13.05,6.07 -2016-10-22 04:00:00,627.0,0.0,12.95,6.23 -2016-10-22 05:00:00,556.0,0.0,12.85,6.38 -2016-10-22 06:00:00,529.0,0.0,12.75,6.54 -2016-10-22 07:00:00,618.0,0.0,12.69,6.79 -2016-10-22 08:00:00,714.0,170.02,12.63,7.04 -2016-10-22 09:00:00,748.0,337.09,12.57,7.3 -2016-10-22 10:00:00,776.0,530.48,12.68,7.4 -2016-10-22 11:00:00,721.0,601.61,12.78,7.5 -2016-10-22 12:00:00,704.0,528.51,12.89,7.6 -2016-10-22 13:00:00,720.0,477.17,13.21,7.68 -2016-10-22 14:00:00,723.0,345.94,13.54,7.76 -2016-10-22 15:00:00,672.0,268.39,13.86,7.83 -2016-10-22 16:00:00,620.0,89.58,13.88,8.44 -2016-10-22 17:00:00,576.0,0.0,13.91,9.05 -2016-10-22 18:00:00,674.0,0.0,13.93,9.66 -2016-10-22 19:00:00,815.0,0.0,13.74,10.19 -2016-10-22 20:00:00,886.0,0.0,13.55,10.72 -2016-10-22 21:00:00,806.0,0.0,13.35,11.26 -2016-10-22 22:00:00,894.0,0.0,13.14,11.41 -2016-10-22 23:00:00,822.0,0.0,12.92,11.56 -2016-10-23 00:00:00,759.0,0.0,12.71,11.71 -2016-10-23 01:00:00,696.0,0.0,12.83,11.6 -2016-10-23 02:00:00,661.0,0.0,12.94,11.49 -2016-10-23 03:00:00,698.0,0.0,13.06,11.38 -2016-10-23 04:00:00,703.0,0.0,12.79,11.94 -2016-10-23 05:00:00,638.0,0.0,12.52,12.49 -2016-10-23 06:00:00,593.0,0.0,12.25,13.05 -2016-10-23 07:00:00,654.0,0.0,12.16,13.56 -2016-10-23 08:00:00,756.0,123.61,12.07,14.08 -2016-10-23 09:00:00,879.0,316.01,11.98,14.59 -2016-10-23 10:00:00,935.0,214.63,11.81,14.53 -2016-10-23 11:00:00,1007.0,67.43,11.65,14.46 -2016-10-23 12:00:00,1048.0,20.62,11.49,14.4 -2016-10-23 13:00:00,1084.0,30.37,12.34,13.64 -2016-10-23 14:00:00,1128.0,26.68,13.18,12.88 -2016-10-23 15:00:00,1129.0,10.6,14.02,12.12 -2016-10-23 16:00:00,1010.0,5.89,14.37,10.73 -2016-10-23 17:00:00,909.0,0.0,14.73,9.33 -2016-10-23 18:00:00,960.0,0.0,15.08,7.93 -2016-10-23 19:00:00,1036.0,0.0,15.17,6.8 -2016-10-23 20:00:00,1030.0,0.0,15.25,5.68 -2016-10-23 21:00:00,942.0,0.0,15.33,4.55 -2016-10-23 22:00:00,1009.0,0.0,15.39,4.78 -2016-10-23 23:00:00,915.0,0.0,15.44,5.0 -2016-10-24 00:00:00,840.0,0.0,15.49,5.23 -2016-10-24 01:00:00,787.0,0.0,15.31,5.49 -2016-10-24 02:00:00,729.0,0.0,15.12,5.75 -2016-10-24 03:00:00,747.0,0.0,14.94,6.01 -2016-10-24 04:00:00,737.0,0.0,14.78,5.93 -2016-10-24 05:00:00,672.0,0.0,14.61,5.85 -2016-10-24 06:00:00,621.0,0.0,14.44,5.77 -2016-10-24 07:00:00,684.0,0.0,14.37,5.5 -2016-10-24 08:00:00,779.0,89.06,14.3,5.24 -2016-10-24 09:00:00,850.0,131.67,14.23,4.98 -2016-10-24 10:00:00,880.0,228.98,14.19,4.86 -2016-10-24 11:00:00,857.0,504.83,14.15,4.73 -2016-10-24 12:00:00,845.0,651.03,14.1,4.61 -2016-10-24 13:00:00,827.0,638.02,14.21,4.52 -2016-10-24 14:00:00,862.0,561.43,14.31,4.44 -2016-10-24 15:00:00,846.0,389.24,14.41,4.36 -2016-10-24 16:00:00,682.0,197.79,14.43,4.41 -2016-10-24 17:00:00,609.0,0.0,14.46,4.46 -2016-10-24 18:00:00,698.0,0.0,14.48,4.51 -2016-10-24 19:00:00,855.0,0.0,14.49,4.28 -2016-10-24 20:00:00,882.0,0.0,14.5,4.04 -2016-10-24 21:00:00,822.0,0.0,14.5,3.81 -2016-10-24 22:00:00,895.0,0.0,14.52,3.68 -2016-10-24 23:00:00,806.0,0.0,14.53,3.56 -2016-10-25 00:00:00,749.0,0.0,14.54,3.43 -2016-10-25 01:00:00,693.0,0.0,14.35,3.39 -2016-10-25 02:00:00,635.0,0.0,14.15,3.34 -2016-10-25 03:00:00,670.0,0.0,13.96,3.3 -2016-10-25 04:00:00,665.0,0.0,13.75,3.37 -2016-10-25 05:00:00,591.0,0.0,13.54,3.44 -2016-10-25 06:00:00,561.0,0.0,13.33,3.52 -2016-10-25 07:00:00,632.0,0.0,13.12,3.63 -2016-10-25 08:00:00,733.0,174.85,12.91,3.75 -2016-10-25 09:00:00,827.0,386.92,12.7,3.86 -2016-10-25 10:00:00,862.0,548.99,12.66,3.2 -2016-10-25 11:00:00,847.0,646.05,12.62,2.54 -2016-10-25 12:00:00,802.0,634.67,12.59,1.88 -2016-10-25 13:00:00,751.0,475.39,12.55,1.63 -2016-10-25 14:00:00,763.0,153.83,12.52,1.39 -2016-10-25 15:00:00,739.0,148.32,12.49,1.14 -2016-10-25 16:00:00,693.0,66.51,12.51,1.42 -2016-10-25 17:00:00,613.0,0.0,12.53,1.69 -2016-10-25 18:00:00,759.0,0.0,12.55,1.96 -2016-10-25 19:00:00,879.0,0.0,12.52,2.35 -2016-10-25 20:00:00,906.0,0.0,12.48,2.74 -2016-10-25 21:00:00,790.0,0.0,12.44,3.13 -2016-10-25 22:00:00,885.0,0.0,12.49,2.95 -2016-10-25 23:00:00,819.0,0.0,12.53,2.76 -2016-10-26 00:00:00,745.0,0.0,12.57,2.58 -2016-10-26 01:00:00,700.0,0.0,12.58,2.3 -2016-10-26 02:00:00,632.0,0.0,12.59,2.02 -2016-10-26 03:00:00,646.0,0.0,12.6,1.74 -2016-10-26 04:00:00,650.0,0.0,12.56,1.74 -2016-10-26 05:00:00,571.0,0.0,12.52,1.74 -2016-10-26 06:00:00,541.0,0.0,12.47,1.74 -2016-10-26 07:00:00,608.0,0.0,12.51,1.96 -2016-10-26 08:00:00,714.0,10.05,12.55,2.18 -2016-10-26 09:00:00,815.0,65.65,12.6,2.4 -2016-10-26 10:00:00,879.0,141.41,12.59,2.38 -2016-10-26 11:00:00,870.0,209.87,12.57,2.35 -2016-10-26 12:00:00,909.0,379.29,12.56,2.33 -2016-10-26 13:00:00,937.0,507.35,12.99,2.24 -2016-10-26 14:00:00,966.0,129.25,13.41,2.16 -2016-10-26 15:00:00,893.0,84.58,13.83,2.07 -2016-10-26 16:00:00,812.0,47.91,13.89,1.76 -2016-10-26 17:00:00,753.0,0.0,13.94,1.44 -2016-10-26 18:00:00,873.0,0.0,14.0,1.13 -2016-10-26 19:00:00,1021.0,0.0,13.98,1.01 -2016-10-26 20:00:00,980.0,0.0,13.95,0.89 -2016-10-26 21:00:00,889.0,0.0,13.92,0.77 -2016-10-26 22:00:00,972.0,0.0,13.99,1.17 -2016-10-26 23:00:00,901.0,0.0,14.05,1.57 -2016-10-27 00:00:00,785.0,0.0,14.11,1.97 -2016-10-27 01:00:00,725.0,0.0,13.92,2.56 -2016-10-27 02:00:00,687.0,0.0,13.74,3.14 -2016-10-27 03:00:00,711.0,0.0,13.55,3.72 -2016-10-27 04:00:00,715.0,0.0,13.56,3.46 -2016-10-27 05:00:00,640.0,0.0,13.56,3.2 -2016-10-27 06:00:00,608.0,0.0,13.56,2.94 -2016-10-27 07:00:00,669.0,0.0,13.62,3.03 -2016-10-27 08:00:00,775.0,41.24,13.67,3.13 -2016-10-27 09:00:00,870.0,26.53,13.73,3.23 -2016-10-27 10:00:00,912.0,43.08,13.81,3.45 -2016-10-27 11:00:00,912.0,47.5,13.9,3.67 -2016-10-27 12:00:00,918.0,55.71,13.99,3.89 -2016-10-27 13:00:00,892.0,78.58,13.88,3.7 -2016-10-27 14:00:00,925.0,51.25,13.77,3.51 -2016-10-27 15:00:00,861.0,50.48,13.65,3.32 -2016-10-27 16:00:00,741.0,31.15,13.69,2.89 -2016-10-27 17:00:00,675.0,0.0,13.72,2.46 -2016-10-27 18:00:00,799.0,0.0,13.76,2.03 -2016-10-27 19:00:00,917.0,0.0,13.75,1.37 -2016-10-27 20:00:00,923.0,0.0,13.74,0.71 -2016-10-27 21:00:00,870.0,0.0,13.73,0.06 -2016-10-27 22:00:00,971.0,0.0,13.71,0.46 -2016-10-27 23:00:00,891.0,0.0,13.69,0.86 -2016-10-28 00:00:00,786.0,0.0,13.67,1.27 -2016-10-28 01:00:00,735.0,0.0,13.5,1.49 -2016-10-28 02:00:00,674.0,0.0,13.33,1.71 -2016-10-28 03:00:00,700.0,0.0,13.16,1.93 -2016-10-28 04:00:00,697.0,0.0,13.03,2.22 -2016-10-28 05:00:00,645.0,0.0,12.9,2.51 -2016-10-28 06:00:00,605.0,0.0,12.76,2.8 -2016-10-28 07:00:00,713.0,0.0,12.8,2.84 -2016-10-28 08:00:00,780.0,23.5,12.84,2.88 -2016-10-28 09:00:00,842.0,23.85,12.88,2.92 -2016-10-28 10:00:00,836.0,41.03,13.05,3.27 -2016-10-28 11:00:00,799.0,41.0,13.23,3.62 -2016-10-28 12:00:00,758.0,43.18,13.4,3.97 -2016-10-28 13:00:00,788.0,74.17,13.35,3.73 -2016-10-28 14:00:00,789.0,52.12,13.3,3.49 -2016-10-28 15:00:00,730.0,73.69,13.25,3.26 -2016-10-28 16:00:00,640.0,38.89,13.24,4.1 -2016-10-28 17:00:00,587.0,0.0,13.22,4.95 -2016-10-28 18:00:00,680.0,0.0,13.21,5.79 -2016-10-28 19:00:00,872.0,0.0,13.13,6.17 -2016-10-28 20:00:00,926.0,0.0,13.05,6.55 -2016-10-28 21:00:00,876.0,0.0,12.96,6.92 -2016-10-28 22:00:00,967.0,0.0,12.99,6.78 -2016-10-28 23:00:00,918.0,0.0,13.02,6.63 -2016-10-29 00:00:00,844.0,0.0,13.05,6.48 -2016-10-29 01:00:00,750.0,0.0,13.0,6.14 -2016-10-29 02:00:00,705.0,0.0,12.94,5.79 -2016-10-29 03:00:00,728.0,0.0,12.89,5.45 -2016-10-29 04:00:00,725.0,0.0,12.8,5.82 -2016-10-29 05:00:00,644.0,0.0,12.7,6.19 -2016-10-29 06:00:00,636.0,0.0,12.61,6.57 -2016-10-29 07:00:00,707.0,0.0,12.62,7.0 -2016-10-29 08:00:00,815.0,28.31,12.63,7.44 -2016-10-29 09:00:00,910.0,76.25,12.64,7.88 -2016-10-29 10:00:00,895.0,134.76,12.89,7.6 -2016-10-29 11:00:00,873.0,133.21,13.14,7.31 -2016-10-29 12:00:00,829.0,248.32,13.4,7.03 -2016-10-29 13:00:00,816.0,159.87,13.64,6.59 -2016-10-29 14:00:00,846.0,224.87,13.89,6.14 -2016-10-29 15:00:00,749.0,250.58,14.14,5.7 -2016-10-29 16:00:00,689.0,77.69,14.25,5.72 -2016-10-29 17:00:00,618.0,0.0,14.35,5.74 -2016-10-29 18:00:00,754.0,0.0,14.46,5.77 -2016-10-29 19:00:00,933.0,0.0,14.45,5.83 -2016-10-29 20:00:00,963.0,0.0,14.44,5.89 -2016-10-29 21:00:00,921.0,0.0,14.42,5.94 -2016-10-29 22:00:00,1010.0,0.0,14.41,5.92 -2016-10-29 23:00:00,945.0,0.0,14.4,5.9 -2016-10-30 00:00:00,854.0,0.0,14.39,5.88 -2016-10-30 01:00:00,803.0,0.0,14.0,5.47 -2016-10-30 02:00:00,751.0,0.0,13.6,5.06 -2016-10-30 03:00:00,719.0,0.0,13.21,4.65 -2016-10-30 04:00:00,726.0,0.0,13.11,5.1 -2016-10-30 05:00:00,657.0,0.0,13.01,5.56 -2016-10-30 06:00:00,651.0,0.0,12.91,6.01 -2016-10-30 07:00:00,710.0,0.0,12.87,6.29 -2016-10-30 08:00:00,831.0,30.34,12.83,6.57 -2016-10-30 09:00:00,935.0,21.83,12.78,6.84 -2016-10-30 10:00:00,1019.0,45.57,12.99,6.54 -2016-10-30 11:00:00,1044.0,36.67,13.19,6.23 -2016-10-30 12:00:00,1026.0,34.45,13.4,5.93 -2016-10-30 13:00:00,1022.0,40.99,13.54,5.83 -2016-10-30 14:00:00,1028.0,34.4,13.68,5.72 -2016-10-30 15:00:00,968.0,297.13,13.82,5.61 -2016-10-30 16:00:00,820.0,138.01,13.96,5.58 -2016-10-30 17:00:00,752.0,0.0,14.09,5.54 -2016-10-30 18:00:00,945.0,0.0,14.23,5.5 -2016-10-30 19:00:00,1061.0,0.0,14.27,5.49 -2016-10-30 20:00:00,972.0,0.0,14.31,5.48 -2016-10-30 21:00:00,929.0,0.0,14.35,5.46 -2016-10-30 22:00:00,1007.0,0.0,14.35,5.3 -2016-10-30 23:00:00,935.0,0.0,14.34,5.14 -2016-10-31 00:00:00,825.0,0.0,14.34,4.98 -2016-10-31 01:00:00,765.0,0.0,14.32,5.19 -2016-10-31 02:00:00,724.0,0.0,14.29,5.39 -2016-10-31 03:00:00,756.0,0.0,14.27,5.6 -2016-10-31 04:00:00,747.0,0.0,14.18,5.72 -2016-10-31 05:00:00,689.0,0.0,14.09,5.85 -2016-10-31 06:00:00,662.0,0.0,14.0,5.97 -2016-10-31 07:00:00,730.0,0.0,13.96,5.99 -2016-10-31 08:00:00,860.0,110.23,13.92,6.01 -2016-10-31 09:00:00,932.0,239.75,13.88,6.03 -2016-10-31 10:00:00,881.0,114.37,14.03,5.94 -2016-10-31 11:00:00,814.0,113.52,14.18,5.84 -2016-10-31 12:00:00,796.0,244.39,14.34,5.75 -2016-10-31 13:00:00,754.0,319.07,14.53,5.09 -2016-10-31 14:00:00,783.0,71.29,14.72,4.42 -2016-10-31 15:00:00,756.0,73.16,14.91,3.75 -2016-10-31 16:00:00,635.0,66.48,14.84,3.38 -2016-10-31 17:00:00,627.0,0.0,14.78,3.01 -2016-10-31 18:00:00,802.0,0.0,14.71,2.63 -2016-10-31 19:00:00,928.0,0.0,14.58,2.54 -2016-10-31 20:00:00,881.0,0.0,14.45,2.44 -2016-10-31 21:00:00,819.0,0.0,14.32,2.34 -2016-10-31 22:00:00,924.0,0.0,14.25,2.02 -2016-10-31 23:00:00,840.0,0.0,14.17,1.7 -2016-11-01 00:00:00,778.0,0.0,14.09,1.38 -2016-11-01 01:00:00,692.0,0.0,14.08,1.33 -2016-11-01 02:00:00,638.0,0.0,14.08,1.29 -2016-11-01 03:00:00,685.0,0.0,14.07,1.24 -2016-11-01 04:00:00,686.0,0.0,13.93,1.28 -2016-11-01 05:00:00,595.0,0.0,13.79,1.31 -2016-11-01 06:00:00,586.0,0.0,13.65,1.35 -2016-11-01 07:00:00,637.0,0.0,13.62,1.61 -2016-11-01 08:00:00,749.0,118.57,13.58,1.88 -2016-11-01 09:00:00,821.0,156.29,13.55,2.14 -2016-11-01 10:00:00,773.0,127.13,13.67,2.24 -2016-11-01 11:00:00,760.0,122.97,13.8,2.35 -2016-11-01 12:00:00,739.0,133.53,13.93,2.46 -2016-11-01 13:00:00,743.0,187.72,14.0,2.36 -2016-11-01 14:00:00,776.0,115.05,14.06,2.26 -2016-11-01 15:00:00,732.0,91.08,14.13,2.17 -2016-11-01 16:00:00,647.0,11.2,13.96,2.81 -2016-11-01 17:00:00,613.0,0.0,13.78,3.46 -2016-11-01 18:00:00,719.0,0.0,13.61,4.11 -2016-11-01 19:00:00,784.0,0.0,13.62,4.26 -2016-11-01 20:00:00,746.0,0.0,13.63,4.41 -2016-11-01 21:00:00,692.0,0.0,13.64,4.57 -2016-11-01 22:00:00,813.0,0.0,13.63,4.64 -2016-11-01 23:00:00,758.0,0.0,13.62,4.71 -2016-11-02 00:00:00,663.0,0.0,13.61,4.79 -2016-11-02 01:00:00,622.0,0.0,13.62,4.73 -2016-11-02 02:00:00,563.0,0.0,13.62,4.67 -2016-11-02 03:00:00,612.0,0.0,13.62,4.61 -2016-11-02 04:00:00,621.0,0.0,13.63,4.98 -2016-11-02 05:00:00,548.0,0.0,13.65,5.35 -2016-11-02 06:00:00,521.0,0.0,13.66,5.72 -2016-11-02 07:00:00,598.0,0.0,13.72,6.25 -2016-11-02 08:00:00,642.0,23.77,13.78,6.77 -2016-11-02 09:00:00,674.0,18.33,13.84,7.3 -2016-11-02 10:00:00,664.0,87.84,13.86,7.41 -2016-11-02 11:00:00,693.0,94.18,13.88,7.52 -2016-11-02 12:00:00,765.0,69.59,13.9,7.63 -2016-11-02 13:00:00,777.0,299.02,13.72,7.38 -2016-11-02 14:00:00,831.0,535.59,13.53,7.14 -2016-11-02 15:00:00,819.0,413.6,13.35,6.9 -2016-11-02 16:00:00,705.0,160.94,13.26,6.82 -2016-11-02 17:00:00,641.0,0.0,13.17,6.74 -2016-11-02 18:00:00,724.0,0.0,13.08,6.66 -2016-11-02 19:00:00,781.0,0.0,12.99,6.42 -2016-11-02 20:00:00,749.0,0.0,12.89,6.18 -2016-11-02 21:00:00,701.0,0.0,12.8,5.94 -2016-11-02 22:00:00,814.0,0.0,12.75,5.71 -2016-11-02 23:00:00,768.0,0.0,12.7,5.49 -2016-11-03 00:00:00,704.0,0.0,12.65,5.26 -2016-11-03 01:00:00,643.0,0.0,12.4,5.14 -2016-11-03 02:00:00,621.0,0.0,12.14,5.03 -2016-11-03 03:00:00,654.0,0.0,11.88,4.91 -2016-11-03 04:00:00,662.0,0.0,11.83,5.27 -2016-11-03 05:00:00,590.0,0.0,11.77,5.63 -2016-11-03 06:00:00,601.0,0.0,11.72,5.99 -2016-11-03 07:00:00,666.0,0.0,11.76,6.03 -2016-11-03 08:00:00,726.0,11.35,11.8,6.07 -2016-11-03 09:00:00,781.0,19.87,11.85,6.11 -2016-11-03 10:00:00,808.0,45.76,12.05,5.84 -2016-11-03 11:00:00,808.0,17.8,12.26,5.57 -2016-11-03 12:00:00,831.0,26.03,12.47,5.3 -2016-11-03 13:00:00,834.0,42.65,12.64,5.43 -2016-11-03 14:00:00,888.0,34.55,12.82,5.56 -2016-11-03 15:00:00,842.0,17.74,12.99,5.7 -2016-11-03 16:00:00,774.0,9.42,13.08,5.58 -2016-11-03 17:00:00,742.0,0.0,13.16,5.47 -2016-11-03 18:00:00,820.0,0.0,13.25,5.35 -2016-11-03 19:00:00,883.0,0.0,13.3,5.22 -2016-11-03 20:00:00,832.0,0.0,13.34,5.09 -2016-11-03 21:00:00,779.0,0.0,13.39,4.95 -2016-11-03 22:00:00,879.0,0.0,13.43,5.22 -2016-11-03 23:00:00,796.0,0.0,13.48,5.49 -2016-11-04 00:00:00,730.0,0.0,13.52,5.75 -2016-11-04 01:00:00,675.0,0.0,13.47,6.33 -2016-11-04 02:00:00,641.0,0.0,13.42,6.9 -2016-11-04 03:00:00,682.0,0.0,13.37,7.48 -2016-11-04 04:00:00,666.0,0.0,13.51,7.78 -2016-11-04 05:00:00,583.0,0.0,13.65,8.08 -2016-11-04 06:00:00,612.0,0.0,13.79,8.39 -2016-11-04 07:00:00,656.0,0.0,13.85,8.03 -2016-11-04 08:00:00,678.0,73.04,13.91,7.68 -2016-11-04 09:00:00,719.0,86.64,13.97,7.32 -2016-11-04 10:00:00,719.0,41.71,13.78,7.89 -2016-11-04 11:00:00,742.0,33.01,13.59,8.46 -2016-11-04 12:00:00,738.0,230.18,13.41,9.02 -2016-11-04 13:00:00,703.0,419.59,13.27,8.71 -2016-11-04 14:00:00,788.0,240.41,13.14,8.4 -2016-11-04 15:00:00,765.0,79.1,13.0,8.08 -2016-11-04 16:00:00,717.0,39.66,12.8,7.81 -2016-11-04 17:00:00,691.0,0.0,12.59,7.54 -2016-11-04 18:00:00,774.0,0.0,12.38,7.27 -2016-11-04 19:00:00,857.0,0.0,12.19,7.29 -2016-11-04 20:00:00,842.0,0.0,12.0,7.31 -2016-11-04 21:00:00,794.0,0.0,11.81,7.34 -2016-11-04 22:00:00,912.0,0.0,11.61,7.27 -2016-11-04 23:00:00,842.0,0.0,11.42,7.2 -2016-11-05 00:00:00,776.0,0.0,11.22,7.13 -2016-11-05 01:00:00,707.0,0.0,11.14,6.85 -2016-11-05 02:00:00,677.0,0.0,11.06,6.56 -2016-11-05 03:00:00,715.0,0.0,10.98,6.28 -2016-11-05 04:00:00,726.0,0.0,11.08,6.27 -2016-11-05 05:00:00,667.0,0.0,11.17,6.27 -2016-11-05 06:00:00,636.0,0.0,11.27,6.26 -2016-11-05 07:00:00,694.0,0.0,11.3,6.81 -2016-11-05 08:00:00,765.0,9.52,11.33,7.37 -2016-11-05 09:00:00,777.0,178.35,11.36,7.92 -2016-11-05 10:00:00,777.0,144.23,11.29,8.45 -2016-11-05 11:00:00,747.0,229.41,11.22,8.98 -2016-11-05 12:00:00,700.0,80.77,11.15,9.52 -2016-11-05 13:00:00,740.0,200.03,11.19,9.55 -2016-11-05 14:00:00,767.0,170.07,11.22,9.59 -2016-11-05 15:00:00,782.0,293.99,11.26,9.63 -2016-11-05 16:00:00,794.0,85.73,11.2,9.88 -2016-11-05 17:00:00,795.0,0.0,11.13,10.14 -2016-11-05 18:00:00,896.0,0.0,11.07,10.4 -2016-11-05 19:00:00,931.0,0.0,11.03,10.48 -2016-11-05 20:00:00,913.0,0.0,10.99,10.57 -2016-11-05 21:00:00,904.0,0.0,10.95,10.65 -2016-11-05 22:00:00,1003.0,0.0,10.78,10.57 -2016-11-05 23:00:00,967.0,0.0,10.62,10.49 -2016-11-06 00:00:00,872.0,0.0,10.45,10.41 -2016-11-06 01:00:00,825.0,0.0,10.22,10.25 -2016-11-06 02:00:00,787.0,0.0,9.99,10.09 -2016-11-06 03:00:00,803.0,0.0,9.75,9.93 -2016-11-06 04:00:00,817.0,0.0,9.64,10.03 -2016-11-06 05:00:00,769.0,0.0,9.52,10.12 -2016-11-06 06:00:00,756.0,0.0,9.41,10.22 -2016-11-06 07:00:00,809.0,0.0,9.32,10.17 -2016-11-06 08:00:00,886.0,135.95,9.23,10.11 -2016-11-06 09:00:00,942.0,163.86,9.14,10.06 -2016-11-06 10:00:00,985.0,464.76,9.27,9.99 -2016-11-06 11:00:00,1000.0,439.19,9.4,9.93 -2016-11-06 12:00:00,935.0,124.75,9.54,9.86 -2016-11-06 13:00:00,940.0,314.61,9.58,9.91 -2016-11-06 14:00:00,959.0,359.45,9.63,9.96 -2016-11-06 15:00:00,934.0,350.01,9.67,10.01 -2016-11-06 16:00:00,874.0,38.87,9.7,10.95 -2016-11-06 17:00:00,836.0,0.0,9.72,11.89 -2016-11-06 18:00:00,935.0,0.0,9.74,12.83 -2016-11-06 19:00:00,998.0,0.0,9.79,12.54 -2016-11-06 20:00:00,999.0,0.0,9.83,12.25 -2016-11-06 21:00:00,957.0,0.0,9.88,11.96 -2016-11-06 22:00:00,1055.0,0.0,9.85,11.66 -2016-11-06 23:00:00,1005.0,0.0,9.82,11.35 -2016-11-07 00:00:00,900.0,0.0,9.78,11.05 -2016-11-07 01:00:00,849.0,0.0,9.91,10.91 -2016-11-07 02:00:00,838.0,0.0,10.04,10.78 -2016-11-07 03:00:00,854.0,0.0,10.17,10.65 -2016-11-07 04:00:00,865.0,0.0,10.11,10.84 -2016-11-07 05:00:00,814.0,0.0,10.05,11.03 -2016-11-07 06:00:00,796.0,0.0,9.99,11.23 -2016-11-07 07:00:00,879.0,0.0,10.09,11.24 -2016-11-07 08:00:00,939.0,139.78,10.18,11.25 -2016-11-07 09:00:00,964.0,348.22,10.28,11.26 -2016-11-07 10:00:00,939.0,500.87,10.33,10.78 -2016-11-07 11:00:00,902.0,605.67,10.38,10.3 -2016-11-07 12:00:00,884.0,631.39,10.43,9.82 -2016-11-07 13:00:00,855.0,450.54,10.66,9.36 -2016-11-07 14:00:00,915.0,46.09,10.9,8.9 -2016-11-07 15:00:00,895.0,149.73,11.13,8.44 -2016-11-07 16:00:00,831.0,5.45,11.14,8.63 -2016-11-07 17:00:00,834.0,0.0,11.14,8.82 -2016-11-07 18:00:00,960.0,0.0,11.14,9.01 -2016-11-07 19:00:00,1049.0,0.0,10.97,8.97 -2016-11-07 20:00:00,993.0,0.0,10.79,8.94 -2016-11-07 21:00:00,934.0,0.0,10.62,8.91 -2016-11-07 22:00:00,1010.0,0.0,10.55,8.74 -2016-11-07 23:00:00,958.0,0.0,10.49,8.56 -2016-11-08 00:00:00,907.0,0.0,10.42,8.39 -2016-11-08 01:00:00,851.0,0.0,10.37,7.87 -2016-11-08 02:00:00,827.0,0.0,10.32,7.36 -2016-11-08 03:00:00,869.0,0.0,10.26,6.84 -2016-11-08 04:00:00,871.0,0.0,10.38,6.76 -2016-11-08 05:00:00,794.0,0.0,10.49,6.69 -2016-11-08 06:00:00,784.0,0.0,10.61,6.61 -2016-11-08 07:00:00,858.0,0.0,10.74,6.9 -2016-11-08 08:00:00,939.0,6.58,10.87,7.2 -2016-11-08 09:00:00,955.0,4.9,11.0,7.49 -2016-11-08 10:00:00,957.0,55.08,11.24,7.51 -2016-11-08 11:00:00,958.0,75.87,11.48,7.53 -2016-11-08 12:00:00,958.0,58.77,11.72,7.54 -2016-11-08 13:00:00,995.0,54.26,11.55,8.47 -2016-11-08 14:00:00,1037.0,76.17,11.39,9.39 -2016-11-08 15:00:00,1032.0,24.82,11.22,10.32 -2016-11-08 16:00:00,1032.0,6.56,11.38,11.62 -2016-11-08 17:00:00,972.0,0.0,11.53,12.92 -2016-11-08 18:00:00,1053.0,0.0,11.68,14.22 -2016-11-08 19:00:00,1080.0,0.0,12.12,13.55 -2016-11-08 20:00:00,1023.0,0.0,12.55,12.89 -2016-11-08 21:00:00,977.0,0.0,12.99,12.22 -2016-11-08 22:00:00,1065.0,0.0,12.86,13.54 -2016-11-08 23:00:00,977.0,0.0,12.73,14.86 -2016-11-09 00:00:00,921.0,0.0,12.59,16.18 -2016-11-09 01:00:00,867.0,0.0,12.35,16.13 -2016-11-09 02:00:00,858.0,0.0,12.1,16.08 -2016-11-09 03:00:00,920.0,0.0,11.85,16.03 -2016-11-09 04:00:00,904.0,0.0,12.06,15.99 -2016-11-09 05:00:00,847.0,0.0,12.26,15.94 -2016-11-09 06:00:00,846.0,0.0,12.47,15.9 -2016-11-09 07:00:00,906.0,0.0,12.49,15.75 -2016-11-09 08:00:00,992.0,22.44,12.5,15.59 -2016-11-09 09:00:00,1028.0,292.7,12.52,15.43 -2016-11-09 10:00:00,977.0,327.86,12.25,14.99 -2016-11-09 11:00:00,928.0,556.13,11.99,14.55 -2016-11-09 12:00:00,914.0,477.47,11.73,14.11 -2016-11-09 13:00:00,909.0,213.37,11.71,14.08 -2016-11-09 14:00:00,959.0,166.97,11.68,14.06 -2016-11-09 15:00:00,971.0,81.38,11.66,14.03 -2016-11-09 16:00:00,941.0,50.53,11.97,14.0 -2016-11-09 17:00:00,931.0,0.0,12.27,13.96 -2016-11-09 18:00:00,1025.0,0.0,12.58,13.93 -2016-11-09 19:00:00,1052.0,0.0,12.6,13.43 -2016-11-09 20:00:00,980.0,0.0,12.63,12.92 -2016-11-09 21:00:00,930.0,0.0,12.65,12.41 -2016-11-09 22:00:00,1054.0,0.0,12.72,12.2 -2016-11-09 23:00:00,976.0,0.0,12.8,11.98 -2016-11-10 00:00:00,899.0,0.0,12.87,11.77 -2016-11-10 01:00:00,890.0,0.0,12.82,11.96 -2016-11-10 02:00:00,856.0,0.0,12.77,12.16 -2016-11-10 03:00:00,863.0,0.0,12.72,12.36 -2016-11-10 04:00:00,876.0,0.0,12.68,12.41 -2016-11-10 05:00:00,808.0,0.0,12.64,12.47 -2016-11-10 06:00:00,794.0,0.0,12.6,12.52 -2016-11-10 07:00:00,870.0,0.0,12.55,12.43 -2016-11-10 08:00:00,906.0,30.88,12.49,12.33 -2016-11-10 09:00:00,917.0,179.89,12.44,12.23 -2016-11-10 10:00:00,897.0,109.55,12.22,12.07 -2016-11-10 11:00:00,894.0,346.4,12.01,11.9 -2016-11-10 12:00:00,893.0,226.33,11.8,11.74 -2016-11-10 13:00:00,893.0,290.1,12.13,11.69 -2016-11-10 14:00:00,954.0,267.45,12.45,11.64 -2016-11-10 15:00:00,967.0,253.05,12.78,11.59 -2016-11-10 16:00:00,907.0,26.96,12.71,11.26 -2016-11-10 17:00:00,873.0,0.0,12.63,10.92 -2016-11-10 18:00:00,954.0,0.0,12.55,10.59 -2016-11-10 19:00:00,1052.0,0.0,12.45,10.17 -2016-11-10 20:00:00,1024.0,0.0,12.36,9.75 -2016-11-10 21:00:00,980.0,0.0,12.26,9.32 -2016-11-10 22:00:00,1080.0,0.0,12.17,8.88 -2016-11-10 23:00:00,1022.0,0.0,12.09,8.43 -2016-11-11 00:00:00,949.0,0.0,12.0,7.99 -2016-11-11 01:00:00,890.0,0.0,12.25,7.41 -2016-11-11 02:00:00,847.0,0.0,12.5,6.84 -2016-11-11 03:00:00,881.0,0.0,12.74,6.26 -2016-11-11 04:00:00,881.0,0.0,12.67,5.49 -2016-11-11 05:00:00,806.0,0.0,12.61,4.73 -2016-11-11 06:00:00,782.0,0.0,12.54,3.96 -2016-11-11 07:00:00,822.0,0.0,12.53,3.21 -2016-11-11 08:00:00,891.0,21.73,12.52,2.46 -2016-11-11 09:00:00,927.0,208.46,12.52,1.71 -2016-11-11 10:00:00,891.0,128.69,12.57,2.08 -2016-11-11 11:00:00,840.0,104.58,12.63,2.46 -2016-11-11 12:00:00,841.0,81.96,12.69,2.83 -2016-11-11 13:00:00,898.0,108.95,12.71,4.07 -2016-11-11 14:00:00,970.0,104.77,12.72,5.32 -2016-11-11 15:00:00,1005.0,51.27,12.74,6.57 -2016-11-11 16:00:00,942.0,16.07,12.94,6.9 -2016-11-11 17:00:00,946.0,0.0,13.14,7.23 -2016-11-11 18:00:00,1066.0,0.0,13.34,7.56 -2016-11-11 19:00:00,1136.0,0.0,13.55,8.23 -2016-11-11 20:00:00,1082.0,0.0,13.75,8.89 -2016-11-11 21:00:00,1018.0,0.0,13.96,9.56 -2016-11-11 22:00:00,1121.0,0.0,14.05,10.03 -2016-11-11 23:00:00,1041.0,0.0,14.14,10.51 -2016-11-12 00:00:00,975.0,0.0,14.22,10.98 -2016-11-12 01:00:00,922.0,0.0,14.32,10.27 -2016-11-12 02:00:00,860.0,0.0,14.42,9.56 -2016-11-12 03:00:00,886.0,0.0,14.51,8.86 -2016-11-12 04:00:00,870.0,0.0,14.61,8.58 -2016-11-12 05:00:00,786.0,0.0,14.7,8.3 -2016-11-12 06:00:00,739.0,0.0,14.8,8.03 -2016-11-12 07:00:00,772.0,0.0,14.65,8.32 -2016-11-12 08:00:00,827.0,59.16,14.5,8.61 -2016-11-12 09:00:00,904.0,75.36,14.36,8.9 -2016-11-12 10:00:00,953.0,244.83,14.13,8.91 -2016-11-12 11:00:00,970.0,406.9,13.9,8.92 -2016-11-12 12:00:00,948.0,218.86,13.68,8.94 -2016-11-12 13:00:00,983.0,223.59,13.5,9.15 -2016-11-12 14:00:00,1014.0,163.67,13.31,9.37 -2016-11-12 15:00:00,1004.0,309.92,13.13,9.59 -2016-11-12 16:00:00,943.0,31.61,12.93,9.45 -2016-11-12 17:00:00,911.0,0.0,12.72,9.31 -2016-11-12 18:00:00,994.0,0.0,12.51,9.17 -2016-11-12 19:00:00,1043.0,0.0,12.34,9.1 -2016-11-12 20:00:00,990.0,0.0,12.18,9.03 -2016-11-12 21:00:00,928.0,0.0,12.01,8.97 -2016-11-12 22:00:00,1054.0,0.0,12.05,8.81 -2016-11-12 23:00:00,1031.0,0.0,12.09,8.66 -2016-11-13 00:00:00,963.0,0.0,12.13,8.51 -2016-11-13 01:00:00,880.0,0.0,12.55,8.5 -2016-11-13 02:00:00,835.0,0.0,12.97,8.49 -2016-11-13 03:00:00,853.0,0.0,13.38,8.48 -2016-11-13 04:00:00,841.0,0.0,13.37,8.34 -2016-11-13 05:00:00,771.0,0.0,13.36,8.2 -2016-11-13 06:00:00,735.0,0.0,13.35,8.06 -2016-11-13 07:00:00,776.0,0.0,13.27,7.74 -2016-11-13 08:00:00,846.0,20.13,13.19,7.43 -2016-11-13 09:00:00,925.0,30.92,13.12,7.12 -2016-11-13 10:00:00,906.0,128.07,12.94,6.88 -2016-11-13 11:00:00,832.0,79.24,12.76,6.64 -2016-11-13 12:00:00,780.0,382.44,12.59,6.4 -2016-11-13 13:00:00,723.0,421.07,12.54,6.28 -2016-11-13 14:00:00,765.0,242.86,12.48,6.15 -2016-11-13 15:00:00,753.0,350.6,12.43,6.03 -2016-11-13 16:00:00,674.0,31.94,12.35,5.92 -2016-11-13 17:00:00,670.0,0.0,12.26,5.82 -2016-11-13 18:00:00,737.0,0.0,12.18,5.71 -2016-11-13 19:00:00,783.0,0.0,12.12,5.35 -2016-11-13 20:00:00,760.0,0.0,12.07,4.99 -2016-11-13 21:00:00,742.0,0.0,12.01,4.63 -2016-11-13 22:00:00,867.0,0.0,11.93,4.03 -2016-11-13 23:00:00,840.0,0.0,11.86,3.43 -2016-11-14 00:00:00,756.0,0.0,11.78,2.83 -2016-11-14 01:00:00,681.0,0.0,11.82,2.8 -2016-11-14 02:00:00,666.0,0.0,11.85,2.76 -2016-11-14 03:00:00,700.0,0.0,11.88,2.73 -2016-11-14 04:00:00,695.0,0.0,11.95,2.71 -2016-11-14 05:00:00,635.0,0.0,12.02,2.69 -2016-11-14 06:00:00,628.0,0.0,12.09,2.66 -2016-11-14 07:00:00,696.0,0.0,12.15,2.89 -2016-11-14 08:00:00,777.0,3.81,12.21,3.11 -2016-11-14 09:00:00,782.0,189.44,12.28,3.34 -2016-11-14 10:00:00,749.0,279.73,12.38,3.76 -2016-11-14 11:00:00,716.0,191.7,12.49,4.18 -2016-11-14 12:00:00,682.0,246.73,12.6,4.61 -2016-11-14 13:00:00,659.0,117.12,12.37,4.86 -2016-11-14 14:00:00,726.0,69.83,12.14,5.11 -2016-11-14 15:00:00,707.0,66.84,11.91,5.37 -2016-11-14 16:00:00,656.0,10.11,11.91,5.76 -2016-11-14 17:00:00,636.0,0.0,11.9,6.15 -2016-11-14 18:00:00,734.0,0.0,11.89,6.54 -2016-11-14 19:00:00,777.0,0.0,12.04,6.34 -2016-11-14 20:00:00,734.0,0.0,12.2,6.15 -2016-11-14 21:00:00,701.0,0.0,12.35,5.96 -2016-11-14 22:00:00,820.0,0.0,12.51,6.05 -2016-11-14 23:00:00,759.0,0.0,12.67,6.13 -2016-11-15 00:00:00,690.0,0.0,12.83,6.22 -2016-11-15 01:00:00,673.0,0.0,12.95,6.51 -2016-11-15 02:00:00,635.0,0.0,13.06,6.79 -2016-11-15 03:00:00,658.0,0.0,13.17,7.08 -2016-11-15 04:00:00,663.0,0.0,13.22,7.61 -2016-11-15 05:00:00,586.0,0.0,13.28,8.14 -2016-11-15 06:00:00,570.0,0.0,13.33,8.68 -2016-11-15 07:00:00,633.0,0.0,13.37,8.14 -2016-11-15 08:00:00,700.0,27.75,13.41,7.6 -2016-11-15 09:00:00,715.0,133.05,13.46,7.06 -2016-11-15 10:00:00,733.0,87.56,13.39,6.68 -2016-11-15 11:00:00,701.0,189.98,13.33,6.3 -2016-11-15 12:00:00,715.0,200.08,13.27,5.92 -2016-11-15 13:00:00,721.0,150.61,13.33,6.09 -2016-11-15 14:00:00,794.0,67.21,13.38,6.27 -2016-11-15 15:00:00,794.0,110.4,13.44,6.44 -2016-11-15 16:00:00,737.0,14.18,13.47,6.72 -2016-11-15 17:00:00,691.0,0.0,13.49,6.99 -2016-11-15 18:00:00,752.0,0.0,13.52,7.27 -2016-11-15 19:00:00,803.0,0.0,13.53,7.59 -2016-11-15 20:00:00,768.0,0.0,13.54,7.91 -2016-11-15 21:00:00,732.0,0.0,13.55,8.23 -2016-11-15 22:00:00,825.0,0.0,13.53,8.68 -2016-11-15 23:00:00,765.0,0.0,13.51,9.13 -2016-11-16 00:00:00,704.0,0.0,13.48,9.57 -2016-11-16 01:00:00,672.0,0.0,13.48,9.83 -2016-11-16 02:00:00,647.0,0.0,13.48,10.1 -2016-11-16 03:00:00,680.0,0.0,13.47,10.36 -2016-11-16 04:00:00,677.0,0.0,13.47,10.15 -2016-11-16 05:00:00,607.0,0.0,13.46,9.94 -2016-11-16 06:00:00,591.0,0.0,13.46,9.72 -2016-11-16 07:00:00,658.0,0.0,13.49,9.23 -2016-11-16 08:00:00,707.0,0.0,13.51,8.73 -2016-11-16 09:00:00,727.0,199.66,13.54,8.23 -2016-11-16 10:00:00,745.0,252.94,13.49,8.12 -2016-11-16 11:00:00,725.0,133.89,13.45,8.01 -2016-11-16 12:00:00,748.0,597.12,13.41,7.9 -2016-11-16 13:00:00,758.0,583.97,13.4,8.29 -2016-11-16 14:00:00,813.0,71.55,13.38,8.68 -2016-11-16 15:00:00,793.0,88.06,13.37,9.08 -2016-11-16 16:00:00,699.0,60.99,13.41,8.82 -2016-11-16 17:00:00,672.0,0.0,13.44,8.57 -2016-11-16 18:00:00,776.0,0.0,13.47,8.32 -2016-11-16 19:00:00,825.0,0.0,13.43,8.29 -2016-11-16 20:00:00,779.0,0.0,13.39,8.26 -2016-11-16 21:00:00,739.0,0.0,13.35,8.23 -2016-11-16 22:00:00,843.0,0.0,13.22,8.43 -2016-11-16 23:00:00,766.0,0.0,13.09,8.63 -2016-11-17 00:00:00,710.0,0.0,12.95,8.83 -2016-11-17 01:00:00,678.0,0.0,12.86,9.34 -2016-11-17 02:00:00,664.0,0.0,12.76,9.86 -2016-11-17 03:00:00,715.0,0.0,12.66,10.37 -2016-11-17 04:00:00,737.0,0.0,12.66,11.0 -2016-11-17 05:00:00,648.0,0.0,12.66,11.62 -2016-11-17 06:00:00,632.0,0.0,12.66,12.25 -2016-11-17 07:00:00,711.0,0.0,12.71,12.6 -2016-11-17 08:00:00,747.0,50.63,12.76,12.96 -2016-11-17 09:00:00,803.0,7.66,12.82,13.31 -2016-11-17 10:00:00,788.0,41.25,12.77,13.86 -2016-11-17 11:00:00,783.0,31.03,12.72,14.4 -2016-11-17 12:00:00,739.0,138.65,12.68,14.95 -2016-11-17 13:00:00,755.0,110.12,12.63,14.57 -2016-11-17 14:00:00,822.0,29.61,12.58,14.2 -2016-11-17 15:00:00,842.0,124.96,12.53,13.82 -2016-11-17 16:00:00,790.0,45.76,12.52,13.62 -2016-11-17 17:00:00,773.0,0.0,12.51,13.43 -2016-11-17 18:00:00,834.0,0.0,12.5,13.23 -2016-11-17 19:00:00,860.0,0.0,12.32,13.51 -2016-11-17 20:00:00,836.0,0.0,12.13,13.8 -2016-11-17 21:00:00,787.0,0.0,11.95,14.08 -2016-11-17 22:00:00,908.0,0.0,11.54,14.48 -2016-11-17 23:00:00,861.0,0.0,11.14,14.88 -2016-11-18 00:00:00,787.0,0.0,10.73,15.28 -2016-11-18 01:00:00,796.0,0.0,10.33,15.2 -2016-11-18 02:00:00,764.0,0.0,9.93,15.13 -2016-11-18 03:00:00,814.0,0.0,9.52,15.05 -2016-11-18 04:00:00,829.0,0.0,9.36,15.18 -2016-11-18 05:00:00,752.0,0.0,9.21,15.31 -2016-11-18 06:00:00,753.0,0.0,9.05,15.45 -2016-11-18 07:00:00,851.0,0.0,9.18,15.17 -2016-11-18 08:00:00,892.0,4.3,9.31,14.89 -2016-11-18 09:00:00,891.0,2.86,9.45,14.61 -2016-11-18 10:00:00,914.0,122.37,9.25,14.15 -2016-11-18 11:00:00,871.0,316.35,9.06,13.69 -2016-11-18 12:00:00,834.0,31.64,8.87,13.23 -2016-11-18 13:00:00,865.0,460.87,8.82,13.08 -2016-11-18 14:00:00,907.0,81.51,8.77,12.93 -2016-11-18 15:00:00,926.0,38.67,8.72,12.79 -2016-11-18 16:00:00,866.0,13.94,8.86,12.87 -2016-11-18 17:00:00,820.0,0.0,9.0,12.96 -2016-11-18 18:00:00,906.0,0.0,9.14,13.05 -2016-11-18 19:00:00,974.0,0.0,8.97,13.01 -2016-11-18 20:00:00,955.0,0.0,8.8,12.97 -2016-11-18 21:00:00,917.0,0.0,8.63,12.94 -2016-11-18 22:00:00,1015.0,0.0,8.99,12.54 -2016-11-18 23:00:00,945.0,0.0,9.35,12.14 -2016-11-19 00:00:00,904.0,0.0,9.7,11.74 -2016-11-19 01:00:00,860.0,0.0,9.61,11.37 -2016-11-19 02:00:00,849.0,0.0,9.51,11.0 -2016-11-19 03:00:00,905.0,0.0,9.41,10.63 -2016-11-19 04:00:00,904.0,0.0,9.71,10.14 -2016-11-19 05:00:00,827.0,0.0,10.01,9.65 -2016-11-19 06:00:00,814.0,0.0,10.31,9.16 -2016-11-19 07:00:00,866.0,0.0,10.45,9.29 -2016-11-19 08:00:00,905.0,0.77,10.59,9.42 -2016-11-19 09:00:00,914.0,44.09,10.74,9.54 -2016-11-19 10:00:00,912.0,75.39,10.58,9.85 -2016-11-19 11:00:00,924.0,47.73,10.42,10.16 -2016-11-19 12:00:00,960.0,90.12,10.26,10.47 -2016-11-19 13:00:00,983.0,69.42,10.56,10.58 -2016-11-19 14:00:00,1063.0,62.96,10.85,10.7 -2016-11-19 15:00:00,1082.0,7.73,11.15,10.81 -2016-11-19 16:00:00,1041.0,6.95,11.22,12.06 -2016-11-19 17:00:00,1018.0,0.0,11.28,13.31 -2016-11-19 18:00:00,1077.0,0.0,11.35,14.57 -2016-11-19 19:00:00,1115.0,0.0,12.07,14.63 -2016-11-19 20:00:00,1057.0,0.0,12.79,14.7 -2016-11-19 21:00:00,970.0,0.0,13.51,14.77 -2016-11-19 22:00:00,1046.0,0.0,13.42,15.75 -2016-11-19 23:00:00,978.0,0.0,13.33,16.72 -2016-11-20 00:00:00,940.0,0.0,13.23,17.7 -2016-11-20 01:00:00,907.0,0.0,12.65,16.97 -2016-11-20 02:00:00,884.0,0.0,12.06,16.25 -2016-11-20 03:00:00,926.0,0.0,11.47,15.53 -2016-11-20 04:00:00,924.0,0.0,11.46,13.77 -2016-11-20 05:00:00,837.0,0.0,11.46,12.02 -2016-11-20 06:00:00,801.0,0.0,11.45,10.26 -2016-11-20 07:00:00,818.0,0.0,11.38,8.27 -2016-11-20 08:00:00,858.0,2.82,11.31,6.28 -2016-11-20 09:00:00,866.0,64.81,11.25,4.29 -2016-11-20 10:00:00,849.0,131.84,11.33,4.0 -2016-11-20 11:00:00,845.0,61.0,11.42,3.7 -2016-11-20 12:00:00,845.0,92.4,11.51,3.41 -2016-11-20 13:00:00,878.0,18.5,11.88,4.0 -2016-11-20 14:00:00,918.0,29.59,12.24,4.58 -2016-11-20 15:00:00,963.0,25.3,12.61,5.17 -2016-11-20 16:00:00,842.0,2.32,12.73,5.61 -2016-11-20 17:00:00,812.0,0.0,12.84,6.06 -2016-11-20 18:00:00,860.0,0.0,12.96,6.5 -2016-11-20 19:00:00,859.0,0.0,12.75,7.32 -2016-11-20 20:00:00,831.0,0.0,12.54,8.14 -2016-11-20 21:00:00,805.0,0.0,12.33,8.97 -2016-11-20 22:00:00,898.0,0.0,12.11,9.22 -2016-11-20 23:00:00,856.0,0.0,11.9,9.48 -2016-11-21 00:00:00,801.0,0.0,11.68,9.74 -2016-11-21 01:00:00,753.0,0.0,11.9,9.02 -2016-11-21 02:00:00,741.0,0.0,12.12,8.29 -2016-11-21 03:00:00,773.0,0.0,12.34,7.57 -2016-11-21 04:00:00,760.0,0.0,12.34,6.86 -2016-11-21 05:00:00,698.0,0.0,12.35,6.16 -2016-11-21 06:00:00,681.0,0.0,12.35,5.45 -2016-11-21 07:00:00,739.0,0.0,12.02,5.58 -2016-11-21 08:00:00,791.0,0.0,11.69,5.71 -2016-11-21 09:00:00,838.0,5.99,11.37,5.83 -2016-11-21 10:00:00,824.0,15.23,11.43,6.77 -2016-11-21 11:00:00,841.0,26.18,11.49,7.7 -2016-11-21 12:00:00,835.0,32.63,11.56,8.63 -2016-11-21 13:00:00,813.0,43.0,11.28,8.57 -2016-11-21 14:00:00,872.0,41.56,11.01,8.51 -2016-11-21 15:00:00,919.0,17.29,10.73,8.44 -2016-11-21 16:00:00,885.0,14.91,10.55,8.8 -2016-11-21 17:00:00,858.0,0.0,10.36,9.16 -2016-11-21 18:00:00,935.0,0.0,10.18,9.52 -2016-11-21 19:00:00,945.0,0.0,10.21,10.76 -2016-11-21 20:00:00,943.0,0.0,10.23,12.0 -2016-11-21 21:00:00,917.0,0.0,10.26,13.24 -2016-11-21 22:00:00,1032.0,0.0,10.12,14.46 -2016-11-21 23:00:00,972.0,0.0,9.98,15.67 -2016-11-22 00:00:00,939.0,0.0,9.83,16.88 -2016-11-22 01:00:00,916.0,0.0,9.98,15.87 -2016-11-22 02:00:00,878.0,0.0,10.13,14.86 -2016-11-22 03:00:00,914.0,0.0,10.28,13.85 -2016-11-22 04:00:00,905.0,0.0,10.32,13.22 -2016-11-22 05:00:00,826.0,0.0,10.37,12.59 -2016-11-22 06:00:00,819.0,0.0,10.41,11.96 -2016-11-22 07:00:00,872.0,0.0,10.39,11.31 -2016-11-22 08:00:00,947.0,1.54,10.36,10.65 -2016-11-22 09:00:00,973.0,41.64,10.34,10.0 -2016-11-22 10:00:00,930.0,8.37,10.23,9.14 -2016-11-22 11:00:00,923.0,32.86,10.13,8.27 -2016-11-22 12:00:00,915.0,51.58,10.03,7.41 -2016-11-22 13:00:00,892.0,50.08,9.96,7.69 -2016-11-22 14:00:00,945.0,50.52,9.89,7.98 -2016-11-22 15:00:00,930.0,115.5,9.82,8.26 -2016-11-22 16:00:00,887.0,0.0,9.84,8.13 -2016-11-22 17:00:00,876.0,0.0,9.86,8.0 -2016-11-22 18:00:00,920.0,0.0,9.88,7.86 -2016-11-22 19:00:00,960.0,0.0,9.86,7.67 -2016-11-22 20:00:00,926.0,0.0,9.84,7.49 -2016-11-22 21:00:00,899.0,0.0,9.82,7.3 -2016-11-22 22:00:00,992.0,0.0,9.78,6.99 -2016-11-22 23:00:00,951.0,0.0,9.74,6.69 -2016-11-23 00:00:00,890.0,0.0,9.7,6.39 -2016-11-23 01:00:00,862.0,0.0,9.7,6.13 -2016-11-23 02:00:00,836.0,0.0,9.69,5.87 -2016-11-23 03:00:00,899.0,0.0,9.68,5.61 -2016-11-23 04:00:00,877.0,0.0,9.77,5.68 -2016-11-23 05:00:00,788.0,0.0,9.85,5.74 -2016-11-23 06:00:00,781.0,0.0,9.94,5.81 -2016-11-23 07:00:00,851.0,0.0,10.04,6.34 -2016-11-23 08:00:00,915.0,0.0,10.14,6.86 -2016-11-23 09:00:00,929.0,284.18,10.25,7.39 -2016-11-23 10:00:00,940.0,159.49,10.38,7.66 -2016-11-23 11:00:00,918.0,467.07,10.51,7.92 -2016-11-23 12:00:00,952.0,138.23,10.65,8.18 -2016-11-23 13:00:00,966.0,57.5,10.9,8.53 -2016-11-23 14:00:00,1033.0,51.02,11.14,8.89 -2016-11-23 15:00:00,1051.0,167.37,11.39,9.24 -2016-11-23 16:00:00,989.0,1.44,11.44,10.03 -2016-11-23 17:00:00,953.0,0.0,11.48,10.81 -2016-11-23 18:00:00,1015.0,0.0,11.52,11.6 -2016-11-23 19:00:00,1009.0,0.0,11.53,12.06 -2016-11-23 20:00:00,992.0,0.0,11.53,12.52 -2016-11-23 21:00:00,936.0,0.0,11.54,12.98 -2016-11-23 22:00:00,1043.0,0.0,11.99,12.84 -2016-11-23 23:00:00,970.0,0.0,12.45,12.69 -2016-11-24 00:00:00,871.0,0.0,12.9,12.55 -2016-11-24 01:00:00,851.0,0.0,13.32,12.77 -2016-11-24 02:00:00,827.0,0.0,13.73,12.99 -2016-11-24 03:00:00,865.0,0.0,14.14,13.21 -2016-11-24 04:00:00,866.0,0.0,14.12,13.72 -2016-11-24 05:00:00,770.0,0.0,14.09,14.23 -2016-11-24 06:00:00,766.0,0.0,14.07,14.73 -2016-11-24 07:00:00,813.0,0.0,13.89,14.52 -2016-11-24 08:00:00,873.0,0.0,13.71,14.31 -2016-11-24 09:00:00,910.0,2.78,13.54,14.1 -2016-11-24 10:00:00,897.0,64.45,13.43,14.17 -2016-11-24 11:00:00,890.0,457.53,13.33,14.23 -2016-11-24 12:00:00,887.0,204.33,13.23,14.3 -2016-11-24 13:00:00,942.0,91.87,13.25,14.35 -2016-11-24 14:00:00,1014.0,184.45,13.26,14.4 -2016-11-24 15:00:00,1007.0,51.38,13.28,14.46 -2016-11-24 16:00:00,937.0,1.0,13.3,14.18 -2016-11-24 17:00:00,903.0,0.0,13.32,13.9 -2016-11-24 18:00:00,934.0,0.0,13.34,13.63 -2016-11-24 19:00:00,937.0,0.0,13.27,13.22 -2016-11-24 20:00:00,895.0,0.0,13.21,12.82 -2016-11-24 21:00:00,837.0,0.0,13.14,12.41 -2016-11-24 22:00:00,921.0,0.0,13.04,12.03 -2016-11-24 23:00:00,873.0,0.0,12.95,11.65 -2016-11-25 00:00:00,807.0,0.0,12.85,11.27 -2016-11-25 01:00:00,784.0,0.0,12.69,11.6 -2016-11-25 02:00:00,767.0,0.0,12.53,11.92 -2016-11-25 03:00:00,795.0,0.0,12.37,12.25 -2016-11-25 04:00:00,801.0,0.0,12.21,12.38 -2016-11-25 05:00:00,720.0,0.0,12.06,12.51 -2016-11-25 06:00:00,734.0,0.0,11.9,12.65 -2016-11-25 07:00:00,799.0,0.0,11.7,12.67 -2016-11-25 08:00:00,837.0,0.0,11.5,12.69 -2016-11-25 09:00:00,848.0,243.34,11.31,12.72 -2016-11-25 10:00:00,878.0,135.58,11.24,12.86 -2016-11-25 11:00:00,881.0,244.64,11.18,13.01 -2016-11-25 12:00:00,854.0,342.08,11.12,13.16 -2016-11-25 13:00:00,886.0,370.35,11.08,13.14 -2016-11-25 14:00:00,942.0,345.97,11.03,13.11 -2016-11-25 15:00:00,932.0,259.85,10.99,13.09 -2016-11-25 16:00:00,863.0,1.02,11.0,13.68 -2016-11-25 17:00:00,834.0,0.0,11.0,14.27 -2016-11-25 18:00:00,868.0,0.0,11.01,14.86 -2016-11-25 19:00:00,898.0,0.0,11.04,13.79 -2016-11-25 20:00:00,881.0,0.0,11.06,12.73 -2016-11-25 21:00:00,827.0,0.0,11.09,11.67 -2016-11-25 22:00:00,939.0,0.0,11.16,11.13 -2016-11-25 23:00:00,869.0,0.0,11.23,10.58 -2016-11-26 00:00:00,843.0,0.0,11.3,10.04 -2016-11-26 01:00:00,797.0,0.0,11.3,9.36 -2016-11-26 02:00:00,778.0,0.0,11.3,8.67 -2016-11-26 03:00:00,817.0,0.0,11.29,7.99 -2016-11-26 04:00:00,816.0,0.0,11.35,7.89 -2016-11-26 05:00:00,747.0,0.0,11.41,7.8 -2016-11-26 06:00:00,744.0,0.0,11.47,7.71 -2016-11-26 07:00:00,771.0,0.0,11.44,7.6 -2016-11-26 08:00:00,800.0,0.0,11.41,7.5 -2016-11-26 09:00:00,836.0,6.56,11.39,7.39 -2016-11-26 10:00:00,870.0,15.23,11.43,7.22 -2016-11-26 11:00:00,859.0,20.6,11.47,7.05 -2016-11-26 12:00:00,856.0,21.97,11.51,6.88 -2016-11-26 13:00:00,869.0,26.16,11.6,6.61 -2016-11-26 14:00:00,913.0,17.86,11.69,6.33 -2016-11-26 15:00:00,949.0,28.77,11.78,6.06 -2016-11-26 16:00:00,911.0,0.0,11.85,6.62 -2016-11-26 17:00:00,879.0,0.0,11.92,7.19 -2016-11-26 18:00:00,923.0,0.0,11.99,7.75 -2016-11-26 19:00:00,959.0,0.0,11.93,7.99 -2016-11-26 20:00:00,932.0,0.0,11.87,8.22 -2016-11-26 21:00:00,877.0,0.0,11.81,8.46 -2016-11-26 22:00:00,949.0,0.0,11.67,8.37 -2016-11-26 23:00:00,897.0,0.0,11.53,8.29 -2016-11-27 00:00:00,861.0,0.0,11.38,8.21 -2016-11-27 01:00:00,822.0,0.0,11.39,8.0 -2016-11-27 02:00:00,811.0,0.0,11.39,7.78 -2016-11-27 03:00:00,874.0,0.0,11.39,7.57 -2016-11-27 04:00:00,863.0,0.0,11.35,7.8 -2016-11-27 05:00:00,792.0,0.0,11.3,8.02 -2016-11-27 06:00:00,794.0,0.0,11.26,8.25 -2016-11-27 07:00:00,813.0,0.0,11.3,8.5 -2016-11-27 08:00:00,862.0,0.0,11.33,8.75 -2016-11-27 09:00:00,935.0,265.07,11.37,9.01 -2016-11-27 10:00:00,952.0,435.47,11.53,9.19 -2016-11-27 11:00:00,911.0,539.88,11.7,9.37 -2016-11-27 12:00:00,841.0,579.41,11.87,9.54 -2016-11-27 13:00:00,786.0,380.09,11.91,9.68 -2016-11-27 14:00:00,851.0,22.63,11.94,9.82 -2016-11-27 15:00:00,855.0,177.89,11.98,9.96 -2016-11-27 16:00:00,772.0,0.0,11.85,10.07 -2016-11-27 17:00:00,772.0,0.0,11.71,10.18 -2016-11-27 18:00:00,865.0,0.0,11.57,10.29 -2016-11-27 19:00:00,888.0,0.0,11.41,10.49 -2016-11-27 20:00:00,890.0,0.0,11.26,10.69 -2016-11-27 21:00:00,876.0,0.0,11.1,10.9 -2016-11-27 22:00:00,967.0,0.0,10.93,10.93 -2016-11-27 23:00:00,904.0,0.0,10.77,10.96 -2016-11-28 00:00:00,831.0,0.0,10.6,10.99 -2016-11-28 01:00:00,784.0,0.0,10.45,10.83 -2016-11-28 02:00:00,785.0,0.0,10.3,10.67 -2016-11-28 03:00:00,833.0,0.0,10.15,10.51 -2016-11-28 04:00:00,848.0,0.0,10.06,10.65 -2016-11-28 05:00:00,795.0,0.0,9.96,10.8 -2016-11-28 06:00:00,806.0,0.0,9.87,10.94 -2016-11-28 07:00:00,863.0,0.0,9.79,11.03 -2016-11-28 08:00:00,944.0,0.0,9.71,11.12 -2016-11-28 09:00:00,967.0,11.49,9.63,11.21 -2016-11-28 10:00:00,944.0,30.79,9.55,10.92 -2016-11-28 11:00:00,904.0,78.6,9.47,10.63 -2016-11-28 12:00:00,859.0,126.85,9.39,10.34 -2016-11-28 13:00:00,857.0,54.06,9.56,10.45 -2016-11-28 14:00:00,861.0,259.96,9.73,10.56 -2016-11-28 15:00:00,853.0,278.12,9.9,10.66 -2016-11-28 16:00:00,828.0,0.0,9.94,10.91 -2016-11-28 17:00:00,815.0,0.0,9.97,11.16 -2016-11-28 18:00:00,946.0,0.0,10.0,11.41 -2016-11-28 19:00:00,993.0,0.0,9.76,11.3 -2016-11-28 20:00:00,994.0,0.0,9.52,11.19 -2016-11-28 21:00:00,950.0,0.0,9.28,11.08 -2016-11-28 22:00:00,1054.0,0.0,8.98,10.8 -2016-11-28 23:00:00,1002.0,0.0,8.69,10.52 -2016-11-29 00:00:00,969.0,0.0,8.39,10.25 -2016-11-29 01:00:00,955.0,0.0,8.18,9.96 -2016-11-29 02:00:00,958.0,0.0,7.96,9.67 -2016-11-29 03:00:00,1021.0,0.0,7.74,9.38 -2016-11-29 04:00:00,1023.0,0.0,7.64,9.37 -2016-11-29 05:00:00,966.0,0.0,7.53,9.37 -2016-11-29 06:00:00,954.0,0.0,7.43,9.37 -2016-11-29 07:00:00,1016.0,0.0,7.3,9.35 -2016-11-29 08:00:00,1095.0,0.0,7.17,9.34 -2016-11-29 09:00:00,1122.0,267.83,7.04,9.32 -2016-11-29 10:00:00,1051.0,478.53,7.11,9.36 -2016-11-29 11:00:00,978.0,587.38,7.18,9.39 -2016-11-29 12:00:00,932.0,653.42,7.26,9.42 -2016-11-29 13:00:00,921.0,573.98,7.42,9.43 -2016-11-29 14:00:00,989.0,466.84,7.59,9.43 -2016-11-29 15:00:00,980.0,291.29,7.75,9.43 -2016-11-29 16:00:00,932.0,0.0,7.79,9.47 -2016-11-29 17:00:00,918.0,0.0,7.82,9.51 -2016-11-29 18:00:00,1029.0,0.0,7.85,9.54 -2016-11-29 19:00:00,1090.0,0.0,7.71,9.37 -2016-11-29 20:00:00,1086.0,0.0,7.57,9.2 -2016-11-29 21:00:00,1077.0,0.0,7.43,9.03 -2016-11-29 22:00:00,1180.0,0.0,7.39,8.79 -2016-11-29 23:00:00,1106.0,0.0,7.35,8.54 -2016-11-30 00:00:00,1065.0,0.0,7.3,8.29 -2016-11-30 01:00:00,1034.0,0.0,7.42,8.16 -2016-11-30 02:00:00,1030.0,0.0,7.54,8.03 -2016-11-30 03:00:00,1081.0,0.0,7.65,7.9 -2016-11-30 04:00:00,1082.0,0.0,7.84,7.61 -2016-11-30 05:00:00,1014.0,0.0,8.03,7.31 -2016-11-30 06:00:00,1008.0,0.0,8.22,7.02 -2016-11-30 07:00:00,1085.0,0.0,8.46,6.61 -2016-11-30 08:00:00,1145.0,0.0,8.7,6.2 -2016-11-30 09:00:00,1141.0,155.42,8.94,5.79 -2016-11-30 10:00:00,1066.0,140.21,9.13,6.0 -2016-11-30 11:00:00,1024.0,553.02,9.32,6.2 -2016-11-30 12:00:00,958.0,596.0,9.52,6.4 -2016-11-30 13:00:00,976.0,363.36,9.84,6.45 -2016-11-30 14:00:00,1063.0,388.13,10.16,6.5 -2016-11-30 15:00:00,1073.0,115.8,10.48,6.55 -2016-11-30 16:00:00,997.0,0.0,10.57,6.66 -2016-11-30 17:00:00,965.0,0.0,10.66,6.77 -2016-11-30 18:00:00,1068.0,0.0,10.75,6.88 -2016-11-30 19:00:00,1119.0,0.0,10.78,6.88 -2016-11-30 20:00:00,1072.0,0.0,10.8,6.87 -2016-11-30 21:00:00,1039.0,0.0,10.83,6.87 -2016-11-30 22:00:00,1140.0,0.0,10.85,6.77 -2016-11-30 23:00:00,1062.0,0.0,10.87,6.68 -2016-12-01 00:00:00,1018.0,0.0,10.88,6.58 -2016-12-01 01:00:00,988.0,0.0,10.79,6.6 -2016-12-01 02:00:00,987.0,0.0,10.71,6.62 -2016-12-01 03:00:00,1019.0,0.0,10.62,6.63 -2016-12-01 04:00:00,1026.0,0.0,10.57,6.56 -2016-12-01 05:00:00,933.0,0.0,10.53,6.48 -2016-12-01 06:00:00,920.0,0.0,10.48,6.4 -2016-12-01 07:00:00,987.0,0.0,10.47,6.4 -2016-12-01 08:00:00,1039.0,0.0,10.46,6.4 -2016-12-01 09:00:00,1057.0,250.6,10.45,6.4 -2016-12-01 10:00:00,1008.0,432.41,10.58,6.43 -2016-12-01 11:00:00,919.0,546.4,10.71,6.46 -2016-12-01 12:00:00,880.0,594.36,10.84,6.48 -2016-12-01 13:00:00,880.0,564.52,10.93,6.17 -2016-12-01 14:00:00,904.0,458.48,11.01,5.85 -2016-12-01 15:00:00,882.0,292.21,11.1,5.53 -2016-12-01 16:00:00,843.0,0.0,11.0,5.46 -2016-12-01 17:00:00,808.0,0.0,10.9,5.38 -2016-12-01 18:00:00,924.0,0.0,10.8,5.31 -2016-12-01 19:00:00,964.0,0.0,10.62,5.52 -2016-12-01 20:00:00,968.0,0.0,10.44,5.72 -2016-12-01 21:00:00,962.0,0.0,10.26,5.93 -2016-12-01 22:00:00,1062.0,0.0,10.12,6.25 -2016-12-01 23:00:00,996.0,0.0,9.99,6.57 -2016-12-02 00:00:00,955.0,0.0,9.85,6.88 -2016-12-02 01:00:00,937.0,0.0,9.69,6.85 -2016-12-02 02:00:00,928.0,0.0,9.53,6.81 -2016-12-02 03:00:00,991.0,0.0,9.37,6.77 -2016-12-02 04:00:00,976.0,0.0,9.32,7.0 -2016-12-02 05:00:00,932.0,0.0,9.27,7.22 -2016-12-02 06:00:00,937.0,0.0,9.22,7.45 -2016-12-02 07:00:00,1007.0,0.0,9.23,7.63 -2016-12-02 08:00:00,1054.0,0.0,9.25,7.82 -2016-12-02 09:00:00,1036.0,71.65,9.27,8.0 -2016-12-02 10:00:00,993.0,131.75,9.39,7.94 -2016-12-02 11:00:00,946.0,116.21,9.52,7.87 -2016-12-02 12:00:00,877.0,99.63,9.65,7.81 -2016-12-02 13:00:00,888.0,323.0,9.8,7.81 -2016-12-02 14:00:00,919.0,214.17,9.94,7.82 -2016-12-02 15:00:00,931.0,138.64,10.08,7.82 -2016-12-02 16:00:00,865.0,0.0,10.03,8.05 -2016-12-02 17:00:00,872.0,0.0,9.99,8.27 -2016-12-02 18:00:00,989.0,0.0,9.94,8.5 -2016-12-02 19:00:00,1065.0,0.0,9.78,8.6 -2016-12-02 20:00:00,1043.0,0.0,9.62,8.71 -2016-12-02 21:00:00,1029.0,0.0,9.46,8.81 -2016-12-02 22:00:00,1133.0,0.0,9.23,8.86 -2016-12-02 23:00:00,1079.0,0.0,9.0,8.91 -2016-12-03 00:00:00,1058.0,0.0,8.77,8.95 -2016-12-03 01:00:00,1016.0,0.0,8.56,9.11 -2016-12-03 02:00:00,1017.0,0.0,8.35,9.27 -2016-12-03 03:00:00,1042.0,0.0,8.15,9.43 -2016-12-03 04:00:00,1060.0,0.0,8.16,9.66 -2016-12-03 05:00:00,978.0,0.0,8.17,9.88 -2016-12-03 06:00:00,967.0,0.0,8.18,10.1 -2016-12-03 07:00:00,1010.0,0.0,8.25,10.39 -2016-12-03 08:00:00,1048.0,0.0,8.32,10.68 -2016-12-03 09:00:00,1088.0,44.26,8.4,10.98 -2016-12-03 10:00:00,1105.0,53.48,8.51,11.29 -2016-12-03 11:00:00,1031.0,69.25,8.63,11.6 -2016-12-03 12:00:00,951.0,53.53,8.75,11.92 -2016-12-03 13:00:00,940.0,78.31,9.12,11.67 -2016-12-03 14:00:00,988.0,310.32,9.49,11.43 -2016-12-03 15:00:00,1012.0,59.59,9.86,11.19 -2016-12-03 16:00:00,980.0,0.0,9.93,11.21 -2016-12-03 17:00:00,972.0,0.0,9.99,11.24 -2016-12-03 18:00:00,1042.0,0.0,10.06,11.27 -2016-12-03 19:00:00,1096.0,0.0,9.94,11.39 -2016-12-03 20:00:00,1078.0,0.0,9.82,11.51 -2016-12-03 21:00:00,1074.0,0.0,9.69,11.63 -2016-12-03 22:00:00,1162.0,0.0,9.49,11.48 -2016-12-03 23:00:00,1121.0,0.0,9.28,11.34 -2016-12-04 00:00:00,1055.0,0.0,9.08,11.2 -2016-12-04 01:00:00,1057.0,0.0,8.87,10.74 -2016-12-04 02:00:00,1036.0,0.0,8.67,10.27 -2016-12-04 03:00:00,1059.0,0.0,8.47,9.81 -2016-12-04 04:00:00,1072.0,0.0,8.44,9.76 -2016-12-04 05:00:00,1012.0,0.0,8.41,9.71 -2016-12-04 06:00:00,1005.0,0.0,8.38,9.66 -2016-12-04 07:00:00,1043.0,0.0,8.47,9.77 -2016-12-04 08:00:00,1092.0,0.0,8.57,9.88 -2016-12-04 09:00:00,1142.0,32.76,8.67,9.99 -2016-12-04 10:00:00,1124.0,114.26,9.01,9.67 -2016-12-04 11:00:00,1124.0,51.03,9.35,9.36 -2016-12-04 12:00:00,1108.0,281.41,9.69,9.05 -2016-12-04 13:00:00,1128.0,534.6,10.35,8.68 -2016-12-04 14:00:00,1155.0,62.14,11.0,8.3 -2016-12-04 15:00:00,1116.0,130.3,11.66,7.93 -2016-12-04 16:00:00,1023.0,0.0,11.75,7.8 -2016-12-04 17:00:00,979.0,0.0,11.84,7.67 -2016-12-04 18:00:00,1037.0,0.0,11.93,7.54 -2016-12-04 19:00:00,1075.0,0.0,11.93,7.57 -2016-12-04 20:00:00,1065.0,0.0,11.93,7.59 -2016-12-04 21:00:00,1012.0,0.0,11.92,7.61 -2016-12-04 22:00:00,1115.0,0.0,12.0,7.52 -2016-12-04 23:00:00,1072.0,0.0,12.08,7.42 -2016-12-05 00:00:00,1009.0,0.0,12.16,7.32 -2016-12-05 01:00:00,971.0,0.0,12.39,7.12 -2016-12-05 02:00:00,974.0,0.0,12.62,6.91 -2016-12-05 03:00:00,999.0,0.0,12.86,6.7 -2016-12-05 04:00:00,1005.0,0.0,12.9,6.56 -2016-12-05 05:00:00,944.0,0.0,12.94,6.42 -2016-12-05 06:00:00,923.0,0.0,12.98,6.28 -2016-12-05 07:00:00,972.0,0.0,13.0,6.3 -2016-12-05 08:00:00,1018.0,0.0,13.02,6.32 -2016-12-05 09:00:00,1056.0,13.16,13.04,6.34 -2016-12-05 10:00:00,1032.0,19.75,13.07,6.28 -2016-12-05 11:00:00,969.0,41.82,13.11,6.21 -2016-12-05 12:00:00,930.0,27.35,13.15,6.14 -2016-12-05 13:00:00,940.0,28.04,13.2,5.57 -2016-12-05 14:00:00,1070.0,18.38,13.25,5.0 -2016-12-05 15:00:00,1079.0,23.24,13.3,4.43 -2016-12-05 16:00:00,997.0,0.0,13.18,3.97 -2016-12-05 17:00:00,968.0,0.0,13.07,3.52 -2016-12-05 18:00:00,1001.0,0.0,12.95,3.06 -2016-12-05 19:00:00,1009.0,0.0,12.86,3.3 -2016-12-05 20:00:00,982.0,0.0,12.77,3.54 -2016-12-05 21:00:00,952.0,0.0,12.67,3.78 -2016-12-05 22:00:00,1042.0,0.0,12.64,3.86 -2016-12-05 23:00:00,976.0,0.0,12.61,3.94 -2016-12-06 00:00:00,919.0,0.0,12.58,4.01 -2016-12-06 01:00:00,897.0,0.0,12.52,4.02 -2016-12-06 02:00:00,861.0,0.0,12.46,4.02 -2016-12-06 03:00:00,884.0,0.0,12.41,4.03 -2016-12-06 04:00:00,892.0,0.0,12.42,4.21 -2016-12-06 05:00:00,806.0,0.0,12.44,4.4 -2016-12-06 06:00:00,790.0,0.0,12.45,4.58 -2016-12-06 07:00:00,848.0,0.0,12.49,4.97 -2016-12-06 08:00:00,905.0,0.0,12.53,5.36 -2016-12-06 09:00:00,937.0,37.15,12.57,5.75 -2016-12-06 10:00:00,919.0,59.37,12.71,5.94 -2016-12-06 11:00:00,954.0,73.72,12.86,6.14 -2016-12-06 12:00:00,934.0,132.4,13.01,6.33 -2016-12-06 13:00:00,956.0,194.85,12.71,6.81 -2016-12-06 14:00:00,1020.0,69.68,12.41,7.29 -2016-12-06 15:00:00,1008.0,99.24,12.11,7.77 -2016-12-06 16:00:00,933.0,0.0,12.3,7.79 -2016-12-06 17:00:00,879.0,0.0,12.48,7.82 -2016-12-06 18:00:00,928.0,0.0,12.67,7.85 -2016-12-06 19:00:00,981.0,0.0,12.8,7.95 -2016-12-06 20:00:00,947.0,0.0,12.94,8.06 -2016-12-06 21:00:00,901.0,0.0,13.07,8.17 -2016-12-06 22:00:00,1009.0,0.0,13.17,8.33 -2016-12-06 23:00:00,923.0,0.0,13.26,8.5 -2016-12-07 00:00:00,852.0,0.0,13.36,8.66 -2016-12-07 01:00:00,817.0,0.0,13.13,9.06 -2016-12-07 02:00:00,792.0,0.0,12.91,9.45 -2016-12-07 03:00:00,827.0,0.0,12.69,9.85 -2016-12-07 04:00:00,838.0,0.0,12.91,9.99 -2016-12-07 05:00:00,777.0,0.0,13.14,10.13 -2016-12-07 06:00:00,764.0,0.0,13.36,10.28 -2016-12-07 07:00:00,814.0,0.0,13.54,10.26 -2016-12-07 08:00:00,892.0,0.0,13.71,10.25 -2016-12-07 09:00:00,884.0,183.77,13.89,10.23 -2016-12-07 10:00:00,872.0,159.18,13.88,10.06 -2016-12-07 11:00:00,796.0,302.9,13.88,9.88 -2016-12-07 12:00:00,769.0,470.48,13.87,9.71 -2016-12-07 13:00:00,830.0,251.61,13.86,9.55 -2016-12-07 14:00:00,886.0,92.02,13.84,9.39 -2016-12-07 15:00:00,880.0,107.85,13.83,9.23 -2016-12-07 16:00:00,835.0,0.0,13.8,8.98 -2016-12-07 17:00:00,809.0,0.0,13.76,8.74 -2016-12-07 18:00:00,866.0,0.0,13.73,8.5 -2016-12-07 19:00:00,905.0,0.0,13.69,8.43 -2016-12-07 20:00:00,864.0,0.0,13.66,8.37 -2016-12-07 21:00:00,833.0,0.0,13.62,8.3 -2016-12-07 22:00:00,931.0,0.0,13.63,8.59 -2016-12-07 23:00:00,871.0,0.0,13.63,8.88 -2016-12-08 00:00:00,789.0,0.0,13.64,9.17 -2016-12-08 01:00:00,794.0,0.0,13.79,9.84 -2016-12-08 02:00:00,764.0,0.0,13.94,10.51 -2016-12-08 03:00:00,795.0,0.0,14.1,11.19 -2016-12-08 04:00:00,793.0,0.0,14.14,10.98 -2016-12-08 05:00:00,708.0,0.0,14.18,10.77 -2016-12-08 06:00:00,698.0,0.0,14.23,10.57 -2016-12-08 07:00:00,747.0,0.0,14.19,9.94 -2016-12-08 08:00:00,829.0,0.0,14.16,9.31 -2016-12-08 09:00:00,816.0,90.13,14.13,8.69 -2016-12-08 10:00:00,813.0,53.07,14.01,8.25 -2016-12-08 11:00:00,812.0,34.39,13.9,7.81 -2016-12-08 12:00:00,802.0,71.25,13.78,7.37 -2016-12-08 13:00:00,868.0,19.0,13.86,7.3 -2016-12-08 14:00:00,913.0,7.03,13.95,7.24 -2016-12-08 15:00:00,902.0,151.11,14.03,7.17 -2016-12-08 16:00:00,870.0,0.0,14.05,7.28 -2016-12-08 17:00:00,815.0,0.0,14.07,7.38 -2016-12-08 18:00:00,849.0,0.0,14.09,7.49 -2016-12-08 19:00:00,889.0,0.0,14.08,7.82 -2016-12-08 20:00:00,839.0,0.0,14.08,8.14 -2016-12-08 21:00:00,799.0,0.0,14.07,8.47 -2016-12-08 22:00:00,903.0,0.0,14.03,8.64 -2016-12-08 23:00:00,845.0,0.0,13.98,8.82 -2016-12-09 00:00:00,767.0,0.0,13.94,8.99 -2016-12-09 01:00:00,742.0,0.0,13.9,9.44 -2016-12-09 02:00:00,730.0,0.0,13.86,9.89 -2016-12-09 03:00:00,784.0,0.0,13.83,10.34 -2016-12-09 04:00:00,782.0,0.0,13.77,10.12 -2016-12-09 05:00:00,714.0,0.0,13.72,9.9 -2016-12-09 06:00:00,708.0,0.0,13.66,9.68 -2016-12-09 07:00:00,773.0,0.0,13.65,9.57 -2016-12-09 08:00:00,818.0,0.0,13.63,9.46 -2016-12-09 09:00:00,818.0,90.52,13.62,9.35 -2016-12-09 10:00:00,783.0,126.68,13.64,9.34 -2016-12-09 11:00:00,760.0,392.98,13.67,9.32 -2016-12-09 12:00:00,721.0,347.59,13.69,9.31 -2016-12-09 13:00:00,738.0,237.24,13.68,9.37 -2016-12-09 14:00:00,830.0,177.3,13.66,9.43 -2016-12-09 15:00:00,874.0,4.29,13.64,9.49 -2016-12-09 16:00:00,801.0,0.0,13.71,9.57 -2016-12-09 17:00:00,764.0,0.0,13.77,9.66 -2016-12-09 18:00:00,830.0,0.0,13.84,9.74 -2016-12-09 19:00:00,863.0,0.0,13.86,9.76 -2016-12-09 20:00:00,837.0,0.0,13.89,9.78 -2016-12-09 21:00:00,825.0,0.0,13.91,9.81 -2016-12-09 22:00:00,922.0,0.0,13.91,9.71 -2016-12-09 23:00:00,840.0,0.0,13.9,9.62 -2016-12-10 00:00:00,781.0,0.0,13.9,9.53 -2016-12-10 01:00:00,747.0,0.0,13.86,9.01 -2016-12-10 02:00:00,717.0,0.0,13.83,8.48 -2016-12-10 03:00:00,768.0,0.0,13.8,7.96 -2016-12-10 04:00:00,776.0,0.0,13.83,8.16 -2016-12-10 05:00:00,691.0,0.0,13.86,8.36 -2016-12-10 06:00:00,675.0,0.0,13.89,8.57 -2016-12-10 07:00:00,710.0,0.0,13.94,8.86 -2016-12-10 08:00:00,764.0,0.0,13.99,9.16 -2016-12-10 09:00:00,818.0,18.68,14.05,9.46 -2016-12-10 10:00:00,832.0,57.97,14.0,9.55 -2016-12-10 11:00:00,845.0,64.59,13.96,9.65 -2016-12-10 12:00:00,850.0,38.04,13.92,9.74 -2016-12-10 13:00:00,897.0,15.04,13.91,9.81 -2016-12-10 14:00:00,933.0,64.03,13.89,9.88 -2016-12-10 15:00:00,918.0,61.0,13.88,9.94 -2016-12-10 16:00:00,873.0,0.0,13.75,8.9 -2016-12-10 17:00:00,845.0,0.0,13.61,7.86 -2016-12-10 18:00:00,888.0,0.0,13.48,6.81 -2016-12-10 19:00:00,899.0,0.0,13.27,6.04 -2016-12-10 20:00:00,858.0,0.0,13.07,5.26 -2016-12-10 21:00:00,824.0,0.0,12.86,4.48 -2016-12-10 22:00:00,925.0,0.0,12.71,4.84 -2016-12-10 23:00:00,860.0,0.0,12.55,5.2 -2016-12-11 00:00:00,819.0,0.0,12.4,5.56 -2016-12-11 01:00:00,769.0,0.0,12.37,4.76 -2016-12-11 02:00:00,735.0,0.0,12.34,3.97 -2016-12-11 03:00:00,791.0,0.0,12.32,3.17 -2016-12-11 04:00:00,794.0,0.0,12.31,3.45 -2016-12-11 05:00:00,714.0,0.0,12.3,3.72 -2016-12-11 06:00:00,693.0,0.0,12.28,4.0 -2016-12-11 07:00:00,720.0,0.0,12.29,4.33 -2016-12-11 08:00:00,790.0,0.0,12.3,4.66 -2016-12-11 09:00:00,841.0,192.25,12.31,4.99 -2016-12-11 10:00:00,854.0,377.69,12.29,5.24 -2016-12-11 11:00:00,851.0,513.39,12.28,5.49 -2016-12-11 12:00:00,867.0,71.0,12.27,5.74 -2016-12-11 13:00:00,872.0,68.04,12.41,5.79 -2016-12-11 14:00:00,946.0,28.14,12.55,5.85 -2016-12-11 15:00:00,973.0,76.23,12.69,5.9 -2016-12-11 16:00:00,870.0,0.0,12.85,6.0 -2016-12-11 17:00:00,826.0,0.0,13.01,6.09 -2016-12-11 18:00:00,852.0,0.0,13.17,6.18 -2016-12-11 19:00:00,866.0,0.0,13.24,6.35 -2016-12-11 20:00:00,851.0,0.0,13.32,6.52 -2016-12-11 21:00:00,819.0,0.0,13.39,6.69 -2016-12-11 22:00:00,929.0,0.0,13.36,7.13 -2016-12-11 23:00:00,867.0,0.0,13.33,7.56 -2016-12-12 00:00:00,773.0,0.0,13.3,8.0 -2016-12-12 01:00:00,717.0,0.0,13.2,8.4 -2016-12-12 02:00:00,720.0,0.0,13.1,8.79 -2016-12-12 03:00:00,736.0,0.0,13.0,9.19 -2016-12-12 04:00:00,741.0,0.0,12.92,8.91 -2016-12-12 05:00:00,670.0,0.0,12.85,8.63 -2016-12-12 06:00:00,665.0,0.0,12.77,8.36 -2016-12-12 07:00:00,716.0,0.0,12.74,7.88 -2016-12-12 08:00:00,769.0,0.0,12.71,7.4 -2016-12-12 09:00:00,814.0,17.54,12.68,6.92 -2016-12-12 10:00:00,831.0,39.83,12.63,6.78 -2016-12-12 11:00:00,833.0,52.18,12.58,6.64 -2016-12-12 12:00:00,844.0,140.46,12.53,6.5 -2016-12-12 13:00:00,835.0,201.3,12.64,6.53 -2016-12-12 14:00:00,894.0,107.46,12.74,6.57 -2016-12-12 15:00:00,842.0,145.72,12.85,6.61 -2016-12-12 16:00:00,792.0,0.0,12.92,6.25 -2016-12-12 17:00:00,767.0,0.0,13.0,5.9 -2016-12-12 18:00:00,830.0,0.0,13.07,5.54 -2016-12-12 19:00:00,850.0,0.0,13.07,5.88 -2016-12-12 20:00:00,800.0,0.0,13.07,6.21 -2016-12-12 21:00:00,780.0,0.0,13.07,6.54 -2016-12-12 22:00:00,899.0,0.0,13.05,6.91 -2016-12-12 23:00:00,830.0,0.0,13.02,7.28 -2016-12-13 00:00:00,768.0,0.0,13.0,7.66 -2016-12-13 01:00:00,727.0,0.0,13.24,8.06 -2016-12-13 02:00:00,696.0,0.0,13.48,8.47 -2016-12-13 03:00:00,738.0,0.0,13.72,8.88 -2016-12-13 04:00:00,733.0,0.0,13.62,8.91 -2016-12-13 05:00:00,655.0,0.0,13.52,8.94 -2016-12-13 06:00:00,649.0,0.0,13.43,8.97 -2016-12-13 07:00:00,692.0,0.0,13.36,9.07 -2016-12-13 08:00:00,755.0,0.0,13.3,9.17 -2016-12-13 09:00:00,781.0,81.62,13.24,9.27 -2016-12-13 10:00:00,789.0,67.45,13.18,9.39 -2016-12-13 11:00:00,782.0,143.14,13.13,9.51 -2016-12-13 12:00:00,814.0,42.62,13.08,9.63 -2016-12-13 13:00:00,827.0,73.23,13.12,9.78 -2016-12-13 14:00:00,893.0,82.38,13.17,9.93 -2016-12-13 15:00:00,876.0,188.04,13.21,10.08 -2016-12-13 16:00:00,790.0,0.0,13.22,10.04 -2016-12-13 17:00:00,770.0,0.0,13.23,10.0 -2016-12-13 18:00:00,826.0,0.0,13.24,9.96 -2016-12-13 19:00:00,873.0,0.0,13.19,10.17 -2016-12-13 20:00:00,858.0,0.0,13.14,10.38 -2016-12-13 21:00:00,834.0,0.0,13.08,10.59 -2016-12-13 22:00:00,926.0,0.0,13.06,10.64 -2016-12-13 23:00:00,845.0,0.0,13.05,10.69 -2016-12-14 00:00:00,805.0,0.0,13.03,10.74 -2016-12-14 01:00:00,798.0,0.0,12.99,10.48 -2016-12-14 02:00:00,780.0,0.0,12.95,10.21 -2016-12-14 03:00:00,814.0,0.0,12.91,9.94 -2016-12-14 04:00:00,822.0,0.0,12.95,9.98 -2016-12-14 05:00:00,748.0,0.0,13.0,10.01 -2016-12-14 06:00:00,737.0,0.0,13.05,10.04 -2016-12-14 07:00:00,806.0,0.0,13.13,10.08 -2016-12-14 08:00:00,866.0,0.0,13.21,10.12 -2016-12-14 09:00:00,874.0,5.93,13.3,10.17 -2016-12-14 10:00:00,856.0,12.52,13.16,9.28 -2016-12-14 11:00:00,801.0,13.81,13.02,8.39 -2016-12-14 12:00:00,824.0,26.69,12.88,7.5 -2016-12-14 13:00:00,880.0,79.38,12.8,7.25 -2016-12-14 14:00:00,936.0,26.71,12.71,7.0 -2016-12-14 15:00:00,923.0,140.91,12.63,6.74 -2016-12-14 16:00:00,842.0,0.0,12.6,5.98 -2016-12-14 17:00:00,815.0,0.0,12.58,5.21 -2016-12-14 18:00:00,851.0,0.0,12.55,4.44 -2016-12-14 19:00:00,885.0,0.0,12.44,4.18 -2016-12-14 20:00:00,861.0,0.0,12.33,3.92 -2016-12-14 21:00:00,820.0,0.0,12.22,3.66 -2016-12-14 22:00:00,913.0,0.0,12.16,4.34 -2016-12-14 23:00:00,846.0,0.0,12.11,5.03 -2016-12-15 00:00:00,800.0,0.0,12.05,5.72 -2016-12-15 01:00:00,775.0,0.0,12.11,6.01 -2016-12-15 02:00:00,746.0,0.0,12.17,6.3 -2016-12-15 03:00:00,819.0,0.0,12.23,6.59 -2016-12-15 04:00:00,797.0,0.0,12.23,6.51 -2016-12-15 05:00:00,724.0,0.0,12.23,6.43 -2016-12-15 06:00:00,722.0,0.0,12.23,6.34 -2016-12-15 07:00:00,772.0,0.0,12.23,6.78 -2016-12-15 08:00:00,841.0,0.0,12.22,7.22 -2016-12-15 09:00:00,863.0,187.69,12.22,7.66 -2016-12-15 10:00:00,864.0,385.56,12.18,7.97 -2016-12-15 11:00:00,812.0,72.72,12.15,8.29 -2016-12-15 12:00:00,821.0,70.47,12.11,8.61 -2016-12-15 13:00:00,865.0,39.1,12.17,8.85 -2016-12-15 14:00:00,933.0,100.62,12.22,9.08 -2016-12-15 15:00:00,948.0,72.7,12.28,9.32 -2016-12-15 16:00:00,884.0,0.0,12.34,9.83 -2016-12-15 17:00:00,847.0,0.0,12.4,10.34 -2016-12-15 18:00:00,906.0,0.0,12.46,10.84 -2016-12-15 19:00:00,942.0,0.0,12.49,11.13 -2016-12-15 20:00:00,920.0,0.0,12.52,11.41 -2016-12-15 21:00:00,874.0,0.0,12.55,11.7 -2016-12-15 22:00:00,978.0,0.0,12.62,11.48 -2016-12-15 23:00:00,898.0,0.0,12.68,11.27 -2016-12-16 00:00:00,821.0,0.0,12.75,11.06 -2016-12-16 01:00:00,819.0,0.0,12.79,10.7 -2016-12-16 02:00:00,787.0,0.0,12.83,10.34 -2016-12-16 03:00:00,808.0,0.0,12.87,9.97 -2016-12-16 04:00:00,824.0,0.0,12.79,9.1 -2016-12-16 05:00:00,747.0,0.0,12.71,8.23 -2016-12-16 06:00:00,734.0,0.0,12.64,7.37 -2016-12-16 07:00:00,763.0,0.0,12.6,6.94 -2016-12-16 08:00:00,827.0,0.0,12.56,6.52 -2016-12-16 09:00:00,841.0,1.42,12.53,6.1 -2016-12-16 10:00:00,874.0,23.23,12.53,5.75 -2016-12-16 11:00:00,858.0,67.19,12.54,5.4 -2016-12-16 12:00:00,850.0,205.69,12.54,5.05 -2016-12-16 13:00:00,855.0,331.16,12.47,4.38 -2016-12-16 14:00:00,859.0,203.84,12.4,3.71 -2016-12-16 15:00:00,830.0,211.44,12.33,3.03 -2016-12-16 16:00:00,784.0,0.0,12.3,2.64 -2016-12-16 17:00:00,798.0,0.0,12.27,2.24 -2016-12-16 18:00:00,854.0,0.0,12.24,1.85 -2016-12-16 19:00:00,866.0,0.0,12.15,1.67 -2016-12-16 20:00:00,833.0,0.0,12.07,1.49 -2016-12-16 21:00:00,800.0,0.0,11.98,1.31 -2016-12-16 22:00:00,917.0,0.0,11.9,1.48 -2016-12-16 23:00:00,855.0,0.0,11.83,1.65 -2016-12-17 00:00:00,822.0,0.0,11.75,1.82 -2016-12-17 01:00:00,791.0,0.0,11.67,2.19 -2016-12-17 02:00:00,751.0,0.0,11.59,2.57 -2016-12-17 03:00:00,793.0,0.0,11.52,2.94 -2016-12-17 04:00:00,793.0,0.0,11.49,2.72 -2016-12-17 05:00:00,737.0,0.0,11.47,2.5 -2016-12-17 06:00:00,711.0,0.0,11.44,2.28 -2016-12-17 07:00:00,757.0,0.0,11.4,2.08 -2016-12-17 08:00:00,805.0,0.0,11.35,1.88 -2016-12-17 09:00:00,847.0,167.87,11.31,1.68 -2016-12-17 10:00:00,866.0,111.04,11.33,1.53 -2016-12-17 11:00:00,874.0,127.58,11.36,1.37 -2016-12-17 12:00:00,906.0,250.67,11.39,1.21 -2016-12-17 13:00:00,942.0,217.73,11.36,1.56 -2016-12-17 14:00:00,985.0,99.48,11.33,1.9 -2016-12-17 15:00:00,989.0,56.89,11.3,2.25 -2016-12-17 16:00:00,955.0,0.0,11.36,2.32 -2016-12-17 17:00:00,935.0,0.0,11.41,2.4 -2016-12-17 18:00:00,966.0,0.0,11.47,2.47 -2016-12-17 19:00:00,986.0,0.0,11.41,2.3 -2016-12-17 20:00:00,957.0,0.0,11.36,2.14 -2016-12-17 21:00:00,928.0,0.0,11.3,1.97 -2016-12-17 22:00:00,1037.0,0.0,11.21,1.88 -2016-12-17 23:00:00,972.0,0.0,11.12,1.78 -2016-12-18 00:00:00,908.0,0.0,11.03,1.68 -2016-12-18 01:00:00,878.0,0.0,10.85,1.78 -2016-12-18 02:00:00,848.0,0.0,10.67,1.88 -2016-12-18 03:00:00,905.0,0.0,10.49,1.97 -2016-12-18 04:00:00,912.0,0.0,10.25,2.16 -2016-12-18 05:00:00,842.0,0.0,10.01,2.35 -2016-12-18 06:00:00,831.0,0.0,9.77,2.54 -2016-12-18 07:00:00,868.0,0.0,9.55,2.7 -2016-12-18 08:00:00,904.0,0.0,9.33,2.86 -2016-12-18 09:00:00,967.0,33.41,9.11,3.02 -2016-12-18 10:00:00,995.0,46.87,8.99,2.99 -2016-12-18 11:00:00,944.0,36.67,8.88,2.96 -2016-12-18 12:00:00,875.0,69.77,8.76,2.92 -2016-12-18 13:00:00,853.0,50.21,8.82,2.98 -2016-12-18 14:00:00,900.0,317.56,8.88,3.04 -2016-12-18 15:00:00,890.0,125.56,8.94,3.1 -2016-12-18 16:00:00,810.0,0.0,8.95,3.25 -2016-12-18 17:00:00,802.0,0.0,8.95,3.39 -2016-12-18 18:00:00,897.0,0.0,8.96,3.53 -2016-12-18 19:00:00,953.0,0.0,8.91,3.85 -2016-12-18 20:00:00,953.0,0.0,8.86,4.17 -2016-12-18 21:00:00,921.0,0.0,8.8,4.5 -2016-12-18 22:00:00,1014.0,0.0,8.71,3.93 -2016-12-18 23:00:00,956.0,0.0,8.61,3.36 -2016-12-19 00:00:00,910.0,0.0,8.52,2.79 -2016-12-19 01:00:00,906.0,0.0,8.54,2.97 -2016-12-19 02:00:00,876.0,0.0,8.57,3.15 -2016-12-19 03:00:00,915.0,0.0,8.6,3.34 -2016-12-19 04:00:00,918.0,0.0,8.74,3.48 -2016-12-19 05:00:00,846.0,0.0,8.88,3.61 -2016-12-19 06:00:00,829.0,0.0,9.02,3.75 -2016-12-19 07:00:00,889.0,0.0,9.36,4.15 -2016-12-19 08:00:00,948.0,0.0,9.71,4.54 -2016-12-19 09:00:00,980.0,52.98,10.06,4.94 -2016-12-19 10:00:00,1024.0,74.19,10.19,5.46 -2016-12-19 11:00:00,1062.0,63.47,10.33,5.99 -2016-12-19 12:00:00,1100.0,49.92,10.47,6.51 -2016-12-19 13:00:00,1097.0,34.24,10.52,6.91 -2016-12-19 14:00:00,1189.0,58.82,10.57,7.32 -2016-12-19 15:00:00,1204.0,29.88,10.62,7.72 -2016-12-19 16:00:00,1119.0,0.0,10.46,7.6 -2016-12-19 17:00:00,1091.0,0.0,10.29,7.49 -2016-12-19 18:00:00,1129.0,0.0,10.13,7.37 -2016-12-19 19:00:00,1161.0,0.0,9.93,6.9 -2016-12-19 20:00:00,1100.0,0.0,9.73,6.43 -2016-12-19 21:00:00,1059.0,0.0,9.53,5.96 -2016-12-19 22:00:00,1123.0,0.0,9.39,5.32 -2016-12-19 23:00:00,1040.0,0.0,9.24,4.69 -2016-12-20 00:00:00,969.0,0.0,9.09,4.06 -2016-12-20 01:00:00,973.0,0.0,9.25,4.12 -2016-12-20 02:00:00,923.0,0.0,9.41,4.18 -2016-12-20 03:00:00,944.0,0.0,9.58,4.25 -2016-12-20 04:00:00,950.0,0.0,9.44,3.72 -2016-12-20 05:00:00,878.0,0.0,9.3,3.19 -2016-12-20 06:00:00,878.0,0.0,9.16,2.66 -2016-12-20 07:00:00,934.0,0.0,9.19,2.55 -2016-12-20 08:00:00,983.0,0.0,9.21,2.44 -2016-12-20 09:00:00,1044.0,4.41,9.23,2.33 -2016-12-20 10:00:00,1100.0,20.11,9.37,2.64 -2016-12-20 11:00:00,1092.0,32.15,9.52,2.95 -2016-12-20 12:00:00,1115.0,24.26,9.66,3.26 -2016-12-20 13:00:00,1132.0,48.5,9.81,4.25 -2016-12-20 14:00:00,1210.0,63.77,9.96,5.25 -2016-12-20 15:00:00,1227.0,58.99,10.11,6.25 -2016-12-20 16:00:00,1150.0,0.0,10.16,6.52 -2016-12-20 17:00:00,1100.0,0.0,10.22,6.79 -2016-12-20 18:00:00,1168.0,0.0,10.27,7.06 -2016-12-20 19:00:00,1168.0,0.0,10.29,7.48 -2016-12-20 20:00:00,1133.0,0.0,10.31,7.9 -2016-12-20 21:00:00,1092.0,0.0,10.32,8.32 -2016-12-20 22:00:00,1149.0,0.0,10.4,9.47 -2016-12-20 23:00:00,1084.0,0.0,10.48,10.62 -2016-12-21 00:00:00,1030.0,0.0,10.56,11.77 -2016-12-21 01:00:00,1025.0,0.0,11.14,11.58 -2016-12-21 02:00:00,978.0,0.0,11.72,11.39 -2016-12-21 03:00:00,1000.0,0.0,12.3,11.2 -2016-12-21 04:00:00,1001.0,0.0,12.37,10.25 -2016-12-21 05:00:00,919.0,0.0,12.44,9.31 -2016-12-21 06:00:00,913.0,0.0,12.52,8.36 -2016-12-21 07:00:00,937.0,0.0,12.62,8.49 -2016-12-21 08:00:00,990.0,0.0,12.72,8.63 -2016-12-21 09:00:00,1016.0,62.65,12.82,8.76 -2016-12-21 10:00:00,1052.0,30.99,12.77,8.92 -2016-12-21 11:00:00,1065.0,68.94,12.73,9.08 -2016-12-21 12:00:00,1064.0,40.48,12.68,9.24 -2016-12-21 13:00:00,1085.0,93.44,12.59,7.94 -2016-12-21 14:00:00,1111.0,63.02,12.49,6.65 -2016-12-21 15:00:00,1094.0,31.77,12.4,5.35 -2016-12-21 16:00:00,1090.0,0.0,12.36,5.54 -2016-12-21 17:00:00,1070.0,0.0,12.31,5.72 -2016-12-21 18:00:00,1116.0,0.0,12.27,5.9 -2016-12-21 19:00:00,1131.0,0.0,12.13,5.46 -2016-12-21 20:00:00,1080.0,0.0,11.99,5.02 -2016-12-21 21:00:00,1012.0,0.0,11.84,4.58 -2016-12-21 22:00:00,1106.0,0.0,11.79,4.07 -2016-12-21 23:00:00,1032.0,0.0,11.73,3.57 -2016-12-22 00:00:00,978.0,0.0,11.68,3.06 -2016-12-22 01:00:00,952.0,0.0,11.61,2.83 -2016-12-22 02:00:00,902.0,0.0,11.55,2.59 -2016-12-22 03:00:00,932.0,0.0,11.49,2.36 -2016-12-22 04:00:00,930.0,0.0,11.5,2.26 -2016-12-22 05:00:00,858.0,0.0,11.51,2.16 -2016-12-22 06:00:00,854.0,0.0,11.52,2.06 -2016-12-22 07:00:00,895.0,0.0,11.54,1.91 -2016-12-22 08:00:00,953.0,0.0,11.55,1.76 -2016-12-22 09:00:00,1009.0,160.2,11.56,1.61 -2016-12-22 10:00:00,1031.0,132.42,11.54,1.87 -2016-12-22 11:00:00,985.0,194.38,11.53,2.12 -2016-12-22 12:00:00,912.0,407.97,11.52,2.37 -2016-12-22 13:00:00,904.0,246.02,11.59,3.42 -2016-12-22 14:00:00,922.0,232.17,11.65,4.46 -2016-12-22 15:00:00,946.0,31.85,11.71,5.5 -2016-12-22 16:00:00,899.0,0.0,11.81,5.4 -2016-12-22 17:00:00,917.0,0.0,11.9,5.3 -2016-12-22 18:00:00,1004.0,0.0,12.0,5.2 -2016-12-22 19:00:00,1066.0,0.0,11.95,4.98 -2016-12-22 20:00:00,1047.0,0.0,11.9,4.77 -2016-12-22 21:00:00,983.0,0.0,11.84,4.55 -2016-12-22 22:00:00,1083.0,0.0,11.85,4.97 -2016-12-22 23:00:00,1006.0,0.0,11.85,5.38 -2016-12-23 00:00:00,949.0,0.0,11.85,5.79 -2016-12-23 01:00:00,937.0,0.0,11.83,6.88 -2016-12-23 02:00:00,890.0,0.0,11.81,7.97 -2016-12-23 03:00:00,915.0,0.0,11.8,9.06 -2016-12-23 04:00:00,935.0,0.0,11.84,9.83 -2016-12-23 05:00:00,869.0,0.0,11.89,10.6 -2016-12-23 06:00:00,853.0,0.0,11.94,11.37 -2016-12-23 07:00:00,921.0,0.0,12.01,11.7 -2016-12-23 08:00:00,969.0,0.0,12.09,12.03 -2016-12-23 09:00:00,996.0,41.4,12.17,12.36 -2016-12-23 10:00:00,1025.0,126.53,12.31,12.36 -2016-12-23 11:00:00,996.0,112.22,12.45,12.36 -2016-12-23 12:00:00,1032.0,37.58,12.59,12.36 -2016-12-23 13:00:00,1027.0,49.49,12.66,12.86 -2016-12-23 14:00:00,1071.0,36.1,12.74,13.35 -2016-12-23 15:00:00,1076.0,52.86,12.81,13.85 -2016-12-23 16:00:00,1027.0,0.0,12.9,13.25 -2016-12-23 17:00:00,978.0,0.0,12.98,12.64 -2016-12-23 18:00:00,1033.0,0.0,13.07,12.04 -2016-12-23 19:00:00,1054.0,0.0,13.02,10.95 -2016-12-23 20:00:00,1028.0,0.0,12.97,9.85 -2016-12-23 21:00:00,1013.0,0.0,12.91,8.76 -2016-12-23 22:00:00,1066.0,0.0,12.62,8.51 -2016-12-23 23:00:00,1001.0,0.0,12.34,8.27 -2016-12-24 00:00:00,947.0,0.0,12.05,8.03 -2016-12-24 01:00:00,905.0,0.0,11.9,8.03 -2016-12-24 02:00:00,913.0,0.0,11.75,8.03 -2016-12-24 03:00:00,930.0,0.0,11.6,8.03 -2016-12-24 04:00:00,960.0,0.0,11.44,8.05 -2016-12-24 05:00:00,897.0,0.0,11.29,8.07 -2016-12-24 06:00:00,867.0,0.0,11.14,8.1 -2016-12-24 07:00:00,912.0,0.0,11.07,7.99 -2016-12-24 08:00:00,969.0,0.0,11.0,7.88 -2016-12-24 09:00:00,1029.0,30.73,10.93,7.78 -2016-12-24 10:00:00,1045.0,126.08,11.0,7.91 -2016-12-24 11:00:00,1051.0,241.12,11.07,8.04 -2016-12-24 12:00:00,998.0,363.5,11.14,8.17 -2016-12-24 13:00:00,1008.0,288.41,11.28,8.76 -2016-12-24 14:00:00,1020.0,201.03,11.41,9.35 -2016-12-24 15:00:00,1017.0,185.71,11.55,9.94 -2016-12-24 16:00:00,998.0,0.0,11.65,10.04 -2016-12-24 17:00:00,992.0,0.0,11.74,10.13 -2016-12-24 18:00:00,1100.0,0.0,11.84,10.22 -2016-12-24 19:00:00,1119.0,0.0,12.01,10.11 -2016-12-24 20:00:00,1075.0,0.0,12.19,10.01 -2016-12-24 21:00:00,1024.0,0.0,12.36,9.9 -2016-12-24 22:00:00,1100.0,0.0,12.43,9.61 -2016-12-24 23:00:00,1059.0,0.0,12.49,9.31 -2016-12-25 00:00:00,999.0,0.0,12.55,9.02 -2016-12-25 01:00:00,921.0,0.0,12.48,8.93 -2016-12-25 02:00:00,899.0,0.0,12.41,8.84 -2016-12-25 03:00:00,907.0,0.0,12.35,8.74 -2016-12-25 04:00:00,901.0,0.0,12.43,8.88 -2016-12-25 05:00:00,840.0,0.0,12.52,9.02 -2016-12-25 06:00:00,807.0,0.0,12.6,9.16 -2016-12-25 07:00:00,825.0,0.0,12.68,9.1 -2016-12-25 08:00:00,843.0,0.0,12.75,9.05 -2016-12-25 09:00:00,895.0,13.95,12.82,8.99 -2016-12-25 10:00:00,951.0,63.59,12.74,9.03 -2016-12-25 11:00:00,973.0,142.34,12.67,9.07 -2016-12-25 12:00:00,961.0,222.88,12.59,9.1 -2016-12-25 13:00:00,984.0,88.83,12.46,9.18 -2016-12-25 14:00:00,988.0,129.53,12.33,9.26 -2016-12-25 15:00:00,980.0,97.03,12.2,9.34 -2016-12-25 16:00:00,910.0,0.0,12.29,9.42 -2016-12-25 17:00:00,872.0,0.0,12.38,9.49 -2016-12-25 18:00:00,917.0,0.0,12.47,9.57 -2016-12-25 19:00:00,929.0,0.0,12.5,9.8 -2016-12-25 20:00:00,930.0,0.0,12.53,10.02 -2016-12-25 21:00:00,898.0,0.0,12.55,10.25 -2016-12-25 22:00:00,981.0,0.0,12.59,10.51 -2016-12-25 23:00:00,928.0,0.0,12.63,10.76 -2016-12-26 00:00:00,868.0,0.0,12.67,11.02 -2016-12-26 01:00:00,865.0,0.0,12.5,10.08 -2016-12-26 02:00:00,828.0,0.0,12.33,9.14 -2016-12-26 03:00:00,860.0,0.0,12.16,8.21 -2016-12-26 04:00:00,874.0,0.0,11.96,8.4 -2016-12-26 05:00:00,815.0,0.0,11.76,8.58 -2016-12-26 06:00:00,804.0,0.0,11.56,8.77 -2016-12-26 07:00:00,840.0,0.0,11.48,8.36 -2016-12-26 08:00:00,888.0,0.0,11.4,7.94 -2016-12-26 09:00:00,954.0,126.86,11.33,7.53 -2016-12-26 10:00:00,954.0,289.66,11.23,7.6 -2016-12-26 11:00:00,933.0,438.63,11.13,7.67 -2016-12-26 12:00:00,982.0,189.16,11.03,7.74 -2016-12-26 13:00:00,959.0,216.46,10.99,6.89 -2016-12-26 14:00:00,1024.0,294.84,10.96,6.05 -2016-12-26 15:00:00,1051.0,244.24,10.92,5.2 -2016-12-26 16:00:00,975.0,0.0,10.86,4.96 -2016-12-26 17:00:00,975.0,0.0,10.79,4.72 -2016-12-26 18:00:00,1087.0,0.0,10.73,4.48 -2016-12-26 19:00:00,1165.0,0.0,10.61,4.44 -2016-12-26 20:00:00,1125.0,0.0,10.5,4.4 -2016-12-26 21:00:00,1085.0,0.0,10.38,4.36 -2016-12-26 22:00:00,1159.0,0.0,10.26,4.89 -2016-12-26 23:00:00,1088.0,0.0,10.14,5.43 -2016-12-27 00:00:00,1074.0,0.0,10.02,5.96 -2016-12-27 01:00:00,1024.0,0.0,9.73,6.4 -2016-12-27 02:00:00,1010.0,0.0,9.45,6.84 -2016-12-27 03:00:00,1049.0,0.0,9.17,7.28 -2016-12-27 04:00:00,1076.0,0.0,9.1,7.31 -2016-12-27 05:00:00,1022.0,0.0,9.03,7.34 -2016-12-27 06:00:00,1009.0,0.0,8.97,7.37 -2016-12-27 07:00:00,1072.0,0.0,8.94,7.51 -2016-12-27 08:00:00,1144.0,0.0,8.91,7.65 -2016-12-27 09:00:00,1245.0,53.09,8.89,7.79 -2016-12-27 10:00:00,1281.0,54.65,8.87,7.94 -2016-12-27 11:00:00,1235.0,179.74,8.86,8.1 -2016-12-27 12:00:00,1279.0,232.33,8.85,8.25 -2016-12-27 13:00:00,1287.0,179.6,8.9,8.13 -2016-12-27 14:00:00,1334.0,89.61,8.95,8.01 -2016-12-27 15:00:00,1332.0,103.82,9.0,7.89 -2016-12-27 16:00:00,1238.0,0.0,9.14,7.95 -2016-12-27 17:00:00,1198.0,0.0,9.29,8.02 -2016-12-27 18:00:00,1297.0,0.0,9.43,8.08 -2016-12-27 19:00:00,1421.0,0.0,9.44,8.29 -2016-12-27 20:00:00,1410.0,0.0,9.45,8.51 -2016-12-27 21:00:00,1352.0,0.0,9.45,8.72 -2016-12-27 22:00:00,1431.0,0.0,9.4,8.77 -2016-12-27 23:00:00,1338.0,0.0,9.34,8.83 -2016-12-28 00:00:00,1282.0,0.0,9.29,8.88 -2016-12-28 01:00:00,1273.0,0.0,9.28,8.49 -2016-12-28 02:00:00,1256.0,0.0,9.27,8.09 -2016-12-28 03:00:00,1296.0,0.0,9.27,7.7 -2016-12-28 04:00:00,1308.0,0.0,9.22,7.56 -2016-12-28 05:00:00,1233.0,0.0,9.17,7.42 -2016-12-28 06:00:00,1219.0,0.0,9.12,7.28 -2016-12-28 07:00:00,1254.0,0.0,9.14,7.29 -2016-12-28 08:00:00,1339.0,0.0,9.15,7.3 -2016-12-28 09:00:00,1467.0,103.79,9.17,7.31 -2016-12-28 10:00:00,1422.0,320.97,9.33,7.08 -2016-12-28 11:00:00,1319.0,497.33,9.5,6.85 -2016-12-28 12:00:00,1326.0,542.54,9.67,6.62 -2016-12-28 13:00:00,1295.0,258.75,10.1,5.9 -2016-12-28 14:00:00,1321.0,137.96,10.52,5.18 -2016-12-28 15:00:00,1339.0,68.99,10.95,4.46 -2016-12-28 16:00:00,1294.0,0.0,10.95,4.21 -2016-12-28 17:00:00,1277.0,0.0,10.94,3.96 -2016-12-28 18:00:00,1451.0,0.0,10.94,3.71 -2016-12-28 19:00:00,1566.0,0.0,10.88,3.69 -2016-12-28 20:00:00,1554.0,0.0,10.82,3.67 -2016-12-28 21:00:00,1456.0,0.0,10.75,3.66 -2016-12-28 22:00:00,1529.0,0.0,10.73,3.37 -2016-12-28 23:00:00,1442.0,0.0,10.72,3.09 -2016-12-29 00:00:00,1394.0,0.0,10.7,2.8 -2016-12-29 01:00:00,1358.0,0.0,10.22,2.23 -2016-12-29 02:00:00,1314.0,0.0,9.74,1.67 -2016-12-29 03:00:00,1326.0,0.0,9.26,1.1 -2016-12-29 04:00:00,1333.0,0.0,9.28,1.43 -2016-12-29 05:00:00,1259.0,0.0,9.3,1.75 -2016-12-29 06:00:00,1231.0,0.0,9.31,2.07 -2016-12-29 07:00:00,1261.0,0.0,9.37,2.08 -2016-12-29 08:00:00,1376.0,0.0,9.43,2.1 -2016-12-29 09:00:00,1506.0,25.66,9.49,2.11 -2016-12-29 10:00:00,1492.0,65.0,9.53,2.24 -2016-12-29 11:00:00,1423.0,469.57,9.57,2.38 -2016-12-29 12:00:00,1499.0,535.56,9.61,2.51 -2016-12-29 13:00:00,1492.0,519.5,9.51,2.98 -2016-12-29 14:00:00,1490.0,450.26,9.4,3.45 -2016-12-29 15:00:00,1480.0,300.83,9.29,3.92 -2016-12-29 16:00:00,1429.0,0.0,9.33,3.81 -2016-12-29 17:00:00,1370.0,0.0,9.38,3.7 -2016-12-29 18:00:00,1469.0,0.0,9.42,3.59 -2016-12-29 19:00:00,1540.0,0.0,9.42,3.45 -2016-12-29 20:00:00,1529.0,0.0,9.42,3.32 -2016-12-29 21:00:00,1464.0,0.0,9.41,3.19 -2016-12-29 22:00:00,1515.0,0.0,9.39,3.15 -2016-12-29 23:00:00,1453.0,0.0,9.37,3.12 -2016-12-30 00:00:00,1379.0,0.0,9.35,3.09 -2016-12-30 01:00:00,1304.0,0.0,9.49,2.9 -2016-12-30 02:00:00,1274.0,0.0,9.63,2.7 -2016-12-30 03:00:00,1309.0,0.0,9.78,2.51 -2016-12-30 04:00:00,1308.0,0.0,9.8,2.44 -2016-12-30 05:00:00,1246.0,0.0,9.82,2.37 -2016-12-30 06:00:00,1218.0,0.0,9.85,2.3 -2016-12-30 07:00:00,1315.0,0.0,9.78,2.24 -2016-12-30 08:00:00,1398.0,0.0,9.72,2.17 -2016-12-30 09:00:00,1479.0,149.17,9.66,2.11 -2016-12-30 10:00:00,1472.0,340.16,9.63,2.26 -2016-12-30 11:00:00,1448.0,259.51,9.6,2.41 -2016-12-30 12:00:00,1348.0,492.09,9.57,2.57 -2016-12-30 13:00:00,1450.0,537.16,9.55,2.83 -2016-12-30 14:00:00,1479.0,451.31,9.53,3.1 -2016-12-30 15:00:00,1508.0,306.78,9.51,3.37 -2016-12-30 16:00:00,1410.0,0.0,9.41,3.54 -2016-12-30 17:00:00,1370.0,0.0,9.3,3.72 -2016-12-30 18:00:00,1467.0,0.0,9.2,3.9 -2016-12-30 19:00:00,1587.0,0.0,9.11,3.92 -2016-12-30 20:00:00,1561.0,0.0,9.02,3.93 -2016-12-30 21:00:00,1472.0,0.0,8.92,3.94 -2016-12-30 22:00:00,1533.0,0.0,8.85,3.93 -2016-12-30 23:00:00,1483.0,0.0,8.78,3.91 +Day;Month;Year;Hour;Load;Ppv1k;Temp;Wind +1;1;2016;0;1453.0;0;10.23;3.78 +1;1;2016;1;1331.0;0;10.69;5.28 +1;1;2016;2;1214.0;0;11.14;6.78 +1;1;2016;3;1146.0;0;11.6;8.28 +1;1;2016;4;1078.0;0;11.39;9.6 +1;1;2016;5;1035.0;0;11.18;10.91 +1;1;2016;6;1032.0;0;10.97;12.23 +1;1;2016;7;1072.0;0;11.14;12.61 +1;1;2016;8;1142.0;0;11.31;12.98 +1;1;2016;9;1208.0;0.07;11.49;13.35 +1;1;2016;10;1299.0;0;11.65;14.18 +1;1;2016;11;1352.0;0;11.82;15.02 +1;1;2016;12;1363.0;0;11.98;15.85 +1;1;2016;13;1366.0;0;12.09;15.83 +1;1;2016;14;1336.0;0;12.19;15.8 +1;1;2016;15;1336.0;0;12.3;15.78 +1;1;2016;16;1307.0;0;12.31;15.99 +1;1;2016;17;1315.0;0;12.32;16.19 +1;1;2016;18;1396.0;0;12.34;16.4 +1;1;2016;19;1432.0;0;12.26;16.53 +1;1;2016;20;1452.0;0;12.19;16.66 +1;1;2016;21;1580.0;0;12.11;16.79 +1;1;2016;22;1692.0;0;12.08;16.32 +1;1;2016;23;1521.0;0;12.06;15.85 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+30;12;2016;22;1533.0;0;8.85;3.93 +30;12;2016;23;1483.0;0;8.78;3.91 diff --git a/src/Microgrids.jl b/src/Microgrids.jl index f24b75b..ef9badd 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -71,12 +71,24 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca capex[8], om_price_wind, lifetime_wind, replacement_price_ratio, salvage_price_ratio) - - mg = Microgrid(project, load,dispatchables, - [elyz,],tanks,batt, [ + hb=ProductionUnit(sizing.Hb,cons_intercept_hb, cons_rate_hb,cons_price_hb,capex[3], om_price_hour_hb, om_price_hb,lifetime_hb_y,lifetime_hb_h,lifetime_hb_starts, + load_min_ratio_hb,replacement_price_ratio, salvage_price_ratio,input_unit_hb,output_unit_hb) + + if sizing.Hb==0 + mg = Microgrid(project, load,dispatchables, + [elyz,],tanks,batt, [ pv, windgen ]) + else + + mg = Microgrid_nh3(project, load,dispatchables, +[elyz,],hb,tanks,batt, [ + pv, + windgen + ]) + end + return mg end @@ -112,16 +124,15 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca return oper_traj, oper_stats, mg_costs end - - +# to update function save_mg(mg::Microgrid , stats::OperationStats, traj::OperationTraj, path::String,mg_name::String) mkdir(mg_name) proj_path=path*mg_name*"/" nsteps=Int(length(mg.load)/mg.project.timestep) size_frame=DataFrame(Cgen=Float64[], - Cbatt=Float64[],Cpv=Float64[],Cwind=Float64[],Cfc=Float64[],Cel=Float64[],Htank=Float64[],Ftank=Float64[]) + Cbatt=Float64[],Cpv=Float64[],Cwind=Float64[],Cfc=Float64[],Cel=Float64[],Htank=Float64[],Ftank=Float64[],Hb=Float64[]) push!(size_frame,(mg.dispatchables.generator[1].power_rated,mg.storage.energy_rated,mg.nondispatchables[1].power_rated,mg.nondispatchables[2].power_rated, - mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity)) + mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity,0.0)) CSV.write(proj_path*"sizing.csv",size_frame) op_frame=DataFrame([name => [] for name in propertynames(stats)]) @@ -135,7 +146,7 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca function load_mg(project_path :: String) sizing_df = DataFrame(CSV.File(project_path*"/sizing.csv")) - sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8]) + sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8],0.0) traj_df = DataFrame(CSV.File(project_path*"/traj.csv")) traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],) diff --git a/src/components.jl b/src/components.jl index ef9d41b..5d87dbe 100644 --- a/src/components.jl +++ b/src/components.jl @@ -24,6 +24,7 @@ struct Sizing Cel::Float64 Htank::Float64 Ftank::Float64 + Hb:: Float64 end """ @@ -327,6 +328,24 @@ struct Microgrid{Topt<:Real} nondispatchables end - +"""Microgrid system description""" +struct Microgrid_nh3{Topt<:Real} + "microgrid project information" + project::Project + "desired load time series (kW)" + load::Vector{Float64} + " Dispatchable Compound" + dispatchables:: DispatchableCompound{Topt} + "electrolyzer" + electrolyzer :: Vector{ProductionUnit{Topt}} + "process Haber-Bosch" + haber_bosch :: ProductionUnit{Topt} + "Tanks compound" + tanks :: TankCompound{Topt} + "energy storage (e.g. battery)" + storage::Battery{Topt} + "non-dispatchable sources (e.g. renewables like wind and solar)" + nondispatchables +end From 3322bc1cabe382f52d0bff57605b9600a8d3830a Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 21 Mar 2025 14:15:16 +0100 Subject: [PATCH 14/19] aggregation, and economics updated to take account HB and now return hb data; Default data updated with hb infos; --- .../Microgrid-H2_sizing_optimization1.ipynb | 2 +- examples/data/Microgrid_Wind-Solar-H2_data.jl | 17 +++-- src/Microgrids.jl | 12 +-- src/components.jl | 18 ----- src/economics.jl | 9 ++- src/operation.jl | 75 ++++++++++++++++++- 6 files changed, 91 insertions(+), 42 deletions(-) diff --git a/examples/Microgrid-H2_sizing_optimization1.ipynb b/examples/Microgrid-H2_sizing_optimization1.ipynb index a62c49a..e3bddb1 100644 --- a/examples/Microgrid-H2_sizing_optimization1.ipynb +++ b/examples/Microgrid-H2_sizing_optimization1.ipynb @@ -1077,7 +1077,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.10.0_threads", + "display_name": "Julia 1.10.0", "language": "julia", "name": "julia-1.10" }, diff --git a/examples/data/Microgrid_Wind-Solar-H2_data.jl b/examples/data/Microgrid_Wind-Solar-H2_data.jl index babb4e8..d0425a2 100644 --- a/examples/data/Microgrid_Wind-Solar-H2_data.jl +++ b/examples/data/Microgrid_Wind-Solar-H2_data.jl @@ -102,19 +102,22 @@ output_unit_fc="KWh" #Haber Bosch numbers to update!!! they have been taken arbitrary power_rated_hb = 4000. # rated power capacity (KW) cons_intercept_hb=0. # -cons_rate_hb = 0.0625 # consumption rate (KWhé/KgH2) +cons_rate_hb = 0.344 # consumption rate (KWhé/KgH2) cons_price_hb = 0. # -investment_price_hb = 1600. # initial investment price ($/KW) -om_price_hb = 45. # operation and maintenance price ($/kW/y) +investment_price_hb_only = 0.418*580 #energy consumption of HB process for 1 kW of HB+ASU multiplied by CAPEX of HB ($/kW) [Achour et al., 2025] +investment_price_asu_only = 0.582*224 #energy consumption of ASU for 1 kW of HB+ASU multiplied by CAPEX of ASU ($/kW) [Achour et al., 2025] +investment_price_hb = investment_price_asu_only+investment_price_hb_only # initial investment price ($/KW) +om_price_hb = 0.017*1000 # operation and maintenance price ($/kW/y)[Achour et al., 2025] om_price_hour_hb = 0. # operation and maintenance price ($/kW/h) -lifetime_hb_y = 15. -lifetime_hb_h = 45000. # Fuel Cell lifetime (h) +lifetime_hb_y = 25. +lifetime_hb_h = 45000. # FOR NOW NOT USED. THIS IS THE VALUE FOR FUELL CELL. lifetime_hb_starts = 5000. # fuel cell maximum starts on -load_min_ratio_hb= 0.05 # minimum load ratio ∈ [0,1] +load_min_ratio_hb= 0.3 # minimum load ratio ∈ [0,1] input_unit_hb= "Kwh" output_unit_hb="Kg" + # Battery energy storage energy_rated_sto = 5000. # rated energy capacity (kWh) investment_price_sto = 350. # initial investiment price ($/kWh) @@ -160,6 +163,6 @@ investment_price_wind = 3500. # initial investiment price ($/kW) om_price_wind = 100.# operation and maintenance price ($/kW/y) lifetime_wind = 25. # lifetime (y) -default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0,4000.0) +default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0,0.0) capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] ini_filling_state=[0.0,0.0,0.0]; \ No newline at end of file diff --git a/src/Microgrids.jl b/src/Microgrids.jl index ef9badd..884419e 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -71,23 +71,17 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca capex[8], om_price_wind, lifetime_wind, replacement_price_ratio, salvage_price_ratio) + hb=ProductionUnit(sizing.Hb,cons_intercept_hb, cons_rate_hb,cons_price_hb,capex[3], om_price_hour_hb, om_price_hb,lifetime_hb_y,lifetime_hb_h,lifetime_hb_starts, load_min_ratio_hb,replacement_price_ratio, salvage_price_ratio,input_unit_hb,output_unit_hb) - if sizing.Hb==0 - mg = Microgrid(project, load,dispatchables, - [elyz,],tanks,batt, [ - pv, - windgen - ]) - else - mg = Microgrid_nh3(project, load,dispatchables, + mg = Microgrid(project, load,dispatchables, [elyz,],hb,tanks,batt, [ pv, windgen ]) - end + return mg end diff --git a/src/components.jl b/src/components.jl index 5d87dbe..11d186f 100644 --- a/src/components.jl +++ b/src/components.jl @@ -312,24 +312,6 @@ end """Microgrid system description""" struct Microgrid{Topt<:Real} - "microgrid project information" - project::Project - "desired load time series (kW)" - load::Vector{Float64} - " Dispatchable Compound" - dispatchables:: DispatchableCompound{Topt} - "electrolyzer" - electrolyzer :: Vector{ProductionUnit{Topt}} - "Tanks compound" - tanks :: TankCompound{Topt} - "energy storage (e.g. battery)" - storage::Battery{Topt} - "non-dispatchable sources (e.g. renewables like wind and solar)" - nondispatchables -end - -"""Microgrid system description""" -struct Microgrid_nh3{Topt<:Real} "microgrid project information" project::Project "desired load time series (kW)" diff --git a/src/economics.jl b/src/economics.jl index 3a86f67..0c32828 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -112,6 +112,7 @@ end h2_tank :: Vector{Float64} pv :: Vector{Float64} wind :: Vector{Float64} + hb :: Vector{Float64} end @@ -143,6 +144,7 @@ also includes `system` cost (all components) and two key economic data: fuel_cell:: CostFactors fuel_tank:: CostFactors h2_tank:: CostFactors + hb:: CostFactors cashflow :: MicrogridCashflow end @@ -255,7 +257,7 @@ function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_un fuel_annual = prod_unit.combustible_price * prod_unit_cons # effective production unit lifetime (in years) - if prod_unit_hours > 0.0 || prod_unit_hours >0.0 + if prod_unit_hours > 0.0 || prod_unit_starts >0.0 lifetime = min(prod_unit.lifetime_hours / prod_unit_hours,prod_unit.lifetime_on_off / prod_unit_starts, prod_unit.lifetime_calendar) else lifetime = prod_unit.lifetime_calendar @@ -400,6 +402,7 @@ function economics(mg::Microgrid, oper_stats::OperationStats) fc_costs,fc_cashflow = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.fc_starts,oper_stats.h2_consumed) fuel_tank_cost,fuel_tank_cashflow = component_costs(mg.tanks.fuelTank, mg.project) h2_tank_cost ,h2_tank_cashflow = component_costs(mg.tanks.h2Tank, mg.project) + hb_costs,hb_cashflow = component_costs(mg.haber_bosch, mg.project, oper_stats.hb_hours,oper_stats.hb_starts,oper_stats.hb_cons) # Energy storage sto_costs, sto_cashflow = component_costs(mg.storage, mg.project, oper_stats) @@ -420,7 +423,7 @@ function economics(mg::Microgrid, oper_stats::OperationStats) for i = 1:mg.project.lifetime] crf = 1/sum(discount_factors) # Cost of all components and NPC of the project - system_costs = gen_costs + sto_costs + sum(nd_costs) + elyz_costs + fc_costs + fuel_tank_cost + h2_tank_cost + system_costs = gen_costs + sto_costs + sum(nd_costs) + elyz_costs + fc_costs + fuel_tank_cost + h2_tank_cost + hb_costs npc = system_costs.total # levelized cost of energy @@ -428,7 +431,7 @@ function economics(mg::Microgrid, oper_stats::OperationStats) lcoe = annualized_cost / oper_stats.served_energy # ($/y) / (kWh/y) → $/kWh costs = MicrogridCosts(lcoe, npc, - system_costs, gen_costs, sto_costs, nd_costs, elyz_costs,fc_costs,fuel_tank_cost,h2_tank_cost,MicrogridCashflow(gen_cashflow,fuel_tank_cashflow,sto_cashflow,fc_cashflow,elyz_cashflow,h2_tank_cashflow,pv_cashflow,wind_cashflow) + system_costs, gen_costs, sto_costs, nd_costs, elyz_costs,fc_costs,fuel_tank_cost,h2_tank_cost,hb_costs,MicrogridCashflow(gen_cashflow,fuel_tank_cashflow,sto_cashflow,fc_cashflow,elyz_cashflow,h2_tank_cashflow,pv_cashflow,wind_cashflow,hb_cashflow) ) return costs diff --git a/src/operation.jl b/src/operation.jl index 3807d23..69628c3 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -22,6 +22,8 @@ struct OperationTraj{T<:Real} # electrolyzer "Electrolyzer power at each time instant (kW)" Pelyz:: Vector{T} + "haber_bosch power at each time instant (kW)" + Phb:: Vector{T} # battery "Battery energy at each time instant (kWh)" Ebatt::Vector{T} @@ -147,6 +149,34 @@ struct OperationStats storage_dis_energy "energy lossed in the energy storage (kWh/y)" storage_loss_energy + + # haber-bosch statistics + "energy supplied to the hb (kWh/y)" + hb_cons_el + "hydrogen supplied to the hb (kg/y)" + hb_cons_h2 + "cumulated operating hours of the dispatchable generator (h/y)" + hb_hours + "cumulated starts on of the dispatchable generator " + hb_starts + "fuel consumption (L/y)" + hb_prod + "running time at nominal power" + hb_rt_at_nom + "minimum running power" + hb_min_rp + "average running power" + hb_avg_rp + "average running time by per start" + hb_avg_rt + "maximum running time per start" + hb_max_rt + "minimum running time per start" + hb_min_rt + "number of runs that last 2h or less" + hb_ru2 + "number of runs that last more than 2h" + hb_ro2 # Non-dispatchable (typ. renewables) sources statistics "spilled energy (typ. from excess of renewables) (kWh/y)" @@ -213,6 +243,19 @@ const oper_stats_units = Dict( :storage_char_energy => "kWh", :storage_dis_energy => "kWh", :storage_loss_energy => "kWh", + :hb_cons_el => "kWh", + :hb_cons_h2 => "kWh", + :hb_hours => "h", + :hb_starts => "", + :hb_prod => "kg", + :hb_rt_at_nom => "h", + :hb_min_rp => "h", + :hb_avg_rp => "kWh", + :hb_avg_rt => "h", + :hb_max_rt => "h", + :hb_min_rt => "h", + :hb_ru2 => "", + :hb_ro2 => "", :spilled_energy => "kWh", :spilled_max => "kW", :spilled_rate => "in [0,1]", @@ -366,6 +409,7 @@ function operation(mg::Microgrid, dispatch::Function) Pfc_emax = zeros(Topt, n_fc) Pfc_pmax = zeros(Topt, n_fc) + Phb = zeros(Topt,K) LoH = zeros(Topt,K+1) LoF = zeros(Topt,K+1) @@ -422,7 +466,7 @@ function operation(mg::Microgrid, dispatch::Function) end - oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1], Esto, Psto, LoH, LoF, Pspill,Pdump) + oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1],Phb[:,1], Esto, Psto, LoH, LoF, Pspill,Pdump) return oper_traj end @@ -479,7 +523,11 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) LoHfin = oper_traj.LoH[end] LoHini = oper_traj.LoH[1] chain_loss = elyz_consumed_energy - fc_produced_energy -(LoHfin-LoHini)*mg.dispatchables.fuel_cell[1].consumption_slope - + # Haber-bosch statistics + hb_cons_el = sum(oper_traj.Phb) * dt + hb_prod = hb_cons_el / mg.haber_bosch.consumption_slope + hb_cons_h2= hb_prod*0.176 + # Non-dispatchable (typ. renewables) sources statistics spilled_energy = sum(oper_traj.power_curtailment) * dt # kWh/y spilled_max = maximum(oper_traj.power_curtailment) # kW @@ -528,11 +576,21 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) fc_min_rt=K*dt; fc_ru2=0; fc_ro2=0; + hb_hours=0.; + hb_starts=0; + hb_rt_at_nom=0.; + hb_min_rp=mg.haber_bosch.power_rated; + hb_avg_rp=0.; + hb_avg_rt=0.; + hb_max_rt=0.; + hb_min_rt=0.; + hb_ru2=0; + hb_ro2 =0; # auxilliary counter: shed_duration = 0.0; # duration of current load shedding event (h) Pload_avg = load_energy/(K*dt) # kW - cr=zeros(Float64,3) + cr=zeros(Float64,4) for k = 1:K gen_starts,gen_min_rp,gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_hours,gen_fuel=usage!(oper_traj.Pgen[k],mg.dispatchables.generator[1],gen_hours,gen_fuel,gen_min_rp,dt, gen_rt_at_nom,gen_max_rt,gen_min_rt,gen_ru2,gen_ro2,cr[1],gen_starts) @@ -541,6 +599,9 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) fc_rt_at_nom,fc_max_rt,fc_min_rt,fc_ru2,fc_ro2,cr[2],fc_starts) elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage!(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_starts) + + hb_starts,hb_min_rp,hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_hours,hb_cons=usage!(oper_traj.Phb[k],mg.haber_bosch,hb_hours,hb_cons,hb_min_rp,dt, + hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_starts) # Load shedding: shedding duration and maximum shedding duration Pshed = oper_traj.power_shedding[k] if Pshed > 0.0 @@ -567,9 +628,11 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) fc_avg_rt=fc_hours/fc_starts; elyz_avg_rp= sum(oper_traj.Pelyz)/elyz_hours; elyz_avg_rt=elyz_hours/elyz_starts; + hb_avg_rp= sum(oper_traj.Phb)/hb_hours; + hb_avg_rt= hb_hours/hb_starts; # Outputs - oper_stats = OperationStats( +oper_stats = OperationStats( # Load statistics served_energy*co, shed_energy*co, shed_max, shed_hours*co, shed_duration_max, shed_rate, # Dispatchable generator statistics @@ -581,6 +644,10 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) # Energy storage (e.g. battery) statistics storage_cycles*co, storage_char_energy*co, storage_dis_energy*co, storage_loss_energy*co, + # Energy storage (e.g. battery) statistics + hb_cons_el, hb_cons_h2,hb_hours,hb_starts,hb_prod,hb_rt_at_nom,hb_min_rp,hb_avg_rp,hb_avg_rt, + hb_max_rt,hb_min_rt,hb_ru2,hb_ro2, + # Non-dispatchable (typ. renewables) sources statistics spilled_energy*co, spilled_max, spilled_rate,dumped_energy*co,dumped_max, renew_potential_energy*co, renew_energy*co, renew_rate From ff17b413c2188d85c975ba97c6f7cf59ed549d98 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 21 Mar 2025 15:10:51 +0100 Subject: [PATCH 15/19] =?UTF-8?q?bug=20corrig=C3=A9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/operation.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/operation.jl b/src/operation.jl index 69628c3..322dd17 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -600,7 +600,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage!(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_starts) - hb_starts,hb_min_rp,hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_hours,hb_cons=usage!(oper_traj.Phb[k],mg.haber_bosch,hb_hours,hb_cons,hb_min_rp,dt, + hb_starts,hb_min_rp,hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_hours,hb_cons_el=usage!(oper_traj.Phb[k],mg.haber_bosch,hb_hours,hb_cons,hb_min_rp,dt, hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_starts) # Load shedding: shedding duration and maximum shedding duration Pshed = oper_traj.power_shedding[k] From dd65f9ea9da82af67ec9f94497511c36d98ba4cf Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 21 Mar 2025 15:20:10 +0100 Subject: [PATCH 16/19] =?UTF-8?q?bug=20corrig=C3=A9=202?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/operation.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/operation.jl b/src/operation.jl index 322dd17..19bee00 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -600,7 +600,7 @@ function aggregation(mg::Microgrid, oper_traj::OperationTraj, ε::Real=0.0) elyz_starts,elyz_min_rp,elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_hours,elyz_cons=usage!(oper_traj.Pelyz[k],mg.electrolyzer[1],elyz_hours,elyz_cons,elyz_min_rp,dt, elyz_rt_at_nom,elyz_max_rt,elyz_min_rt,elyz_ru2,elyz_ro2,cr[3],elyz_starts) - hb_starts,hb_min_rp,hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_hours,hb_cons_el=usage!(oper_traj.Phb[k],mg.haber_bosch,hb_hours,hb_cons,hb_min_rp,dt, + hb_starts,hb_min_rp,hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_hours,hb_cons_el=usage!(oper_traj.Phb[k],mg.haber_bosch,hb_hours,hb_cons_el,hb_min_rp,dt, hb_rt_at_nom,hb_max_rt,hb_min_rt,hb_ru2,hb_ro2,cr[4],hb_starts) # Load shedding: shedding duration and maximum shedding duration Pshed = oper_traj.power_shedding[k] From e374e0c9efd44ad7a455000ad6103fd18aac7c38 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 21 Mar 2025 15:33:15 +0100 Subject: [PATCH 17/19] =?UTF-8?q?bug=20r=C3=A9gl=C3=A9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/economics.jl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/economics.jl b/src/economics.jl index 0c32828..faba3ad 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -402,7 +402,7 @@ function economics(mg::Microgrid, oper_stats::OperationStats) fc_costs,fc_cashflow = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.fc_starts,oper_stats.h2_consumed) fuel_tank_cost,fuel_tank_cashflow = component_costs(mg.tanks.fuelTank, mg.project) h2_tank_cost ,h2_tank_cashflow = component_costs(mg.tanks.h2Tank, mg.project) - hb_costs,hb_cashflow = component_costs(mg.haber_bosch, mg.project, oper_stats.hb_hours,oper_stats.hb_starts,oper_stats.hb_cons) + hb_costs,hb_cashflow = component_costs(mg.haber_bosch, mg.project, oper_stats.hb_hours,oper_stats.hb_starts,oper_stats.hb_cons_el) # Energy storage sto_costs, sto_cashflow = component_costs(mg.storage, mg.project, oper_stats) From 87463ea23a18713e697a7c60e5de285dea331dbd Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Fri, 12 Sep 2025 10:50:20 +0200 Subject: [PATCH 18/19] too much updates --- examples/Useful_functions.jl | 6 +- examples/data/Microgrid_Wind-Solar-H2_data.jl | 7 +- src/Microgrids.jl | 19 ++- src/economics.jl | 7 + src/operation.jl | 127 ++++++++++++++++++ 5 files changed, 158 insertions(+), 8 deletions(-) diff --git a/examples/Useful_functions.jl b/examples/Useful_functions.jl index 5c9eb26..00e86f9 100644 --- a/examples/Useful_functions.jl +++ b/examples/Useful_functions.jl @@ -10,8 +10,8 @@ This file must be included after the instructions: Simulate the performance of a Microgrid project Returns mg, traj, stats, costs """ - function simulate_microgrid(x=X,capex=capex_def,dispatch=dispatch_1) - mg=new_microgrid(x) + function simulate_microgrid(x=X,capex=capex_def,dispatch=dispatch_1,load=Pload,cf_pv=Ppv1k,wind=cf_wind) + mg=new_microgrid(Sizing(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9]),capex,ini_filling_state,load,wind,cf_pv) # Split decision variables (converted MW → kW): oper_traj = operation(mg, dispatch) if mg.tanks.h2Tank.capacity>0.0 @@ -27,7 +27,7 @@ Returns mg, traj, stats, costs end ini=[0.0,a,b] - mg=new_microgrid(x,capex,ini) + mg=new_microgrid(Sizing(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9]),capex,ini,load,wind,cf_pv) # Launch simulation: traj, stats, costs = simulate(mg,dispatch) diff --git a/examples/data/Microgrid_Wind-Solar-H2_data.jl b/examples/data/Microgrid_Wind-Solar-H2_data.jl index d0425a2..52dcd39 100644 --- a/examples/data/Microgrid_Wind-Solar-H2_data.jl +++ b/examples/data/Microgrid_Wind-Solar-H2_data.jl @@ -121,7 +121,7 @@ output_unit_hb="Kg" # Battery energy storage energy_rated_sto = 5000. # rated energy capacity (kWh) investment_price_sto = 350. # initial investiment price ($/kWh) -om_price_sto = 10. # operation and maintenance price ($/kWh/y) +om_price_sto =10. # operation and maintenance price ($/kWh/y) lifetime_sto = 15. # calendar lifetime (y) lifetime_cycles = 3000. # maximum number of cycles over life (1) # Parameters with default values @@ -165,4 +165,7 @@ lifetime_wind = 25. # lifetime (y) default_sizing= Sizing( 0.0, 5000., 5000., 1800., 2000., 1800., 12000., 0.0,0.0) capex_def=[400., 0.0, 1600., 500., 1600., 350., 1200., 3500.] -ini_filling_state=[0.0,0.0,0.0]; \ No newline at end of file +ini_filling_state=[0.0,0.0,0.0]; +Eol=0.9 +deg_ratio_rt=1.5e-6/0.7 # 10 μV*/h same ratio as Morin +deg_ratio_st=14e-6/0.7 # 106 μV*/h same ratio as Morin \ No newline at end of file diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 884419e..4de2624 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -23,7 +23,7 @@ module Microgrids import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions - export simulate,save_mg,load_mg,new_microgrid, + export simulate,save_mg,load_mg,new_microgrid,simulate_pnl, NonDispatchableSource, ProductionUnit, Tank, TankCompound, Project, Sizing,DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, capacity_from_wind, @@ -40,7 +40,9 @@ module Microgrids """ -function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state,load::Vector{Float64}=Pload) +function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state,load::Vector{Float64}=Pload, + wind_speed::Vector{Float64}=wind_speed,irradiance::Vector{Float64}=irradiance) + cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52) project = Project(lifetime, discount_rate, timestep, "€") gen = ProductionUnit(sizing.Cgen, fuel_intercept, fuel_slope, fuel_price, @@ -117,6 +119,17 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca return oper_traj, oper_stats, mg_costs end + function simulate_pnl(mg::Microgrid, dispatch::Function,Pnl_request::Vector{Float64},ε::Real=0.0) + # Run the microgrid operation + oper_traj = operation_pnl(mg, dispatch,Pnl_request) + # Aggregate the operation variables + oper_stats = aggregation(mg, oper_traj, ε) + + # Eval the microgrid costs + mg_costs = economics(mg, oper_stats) + + return oper_traj, oper_stats, mg_costs + end # to update function save_mg(mg::Microgrid , stats::OperationStats, traj::OperationTraj, path::String,mg_name::String) @@ -143,7 +156,7 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8],0.0) traj_df = DataFrame(CSV.File(project_path*"/traj.csv")) - traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],) + traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],zeros(8760),traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],) if sizing.Ftank>0.0 a = traj.LoF[1]/sizing.Ftank diff --git a/src/economics.jl b/src/economics.jl index faba3ad..748c035 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -257,11 +257,18 @@ function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_un fuel_annual = prod_unit.combustible_price * prod_unit_cons # effective production unit lifetime (in years) + """ if prod_unit_hours > 0.0 || prod_unit_starts >0.0 lifetime = min(prod_unit.lifetime_hours / prod_unit_hours,prod_unit.lifetime_on_off / prod_unit_starts, prod_unit.lifetime_calendar) else lifetime = prod_unit.lifetime_calendar end + """ + if prod_unit_hours > 0.0 + lifetime = min((1-Eol) /( prod_unit_hours*deg_ratio_rt + prod_unit_starts*deg_ratio_st), prod_unit.lifetime_calendar) + else + lifetime = prod_unit.lifetime_calendar + end c,cashflow = component_costs( mg_project, lifetime, diff --git a/src/operation.jl b/src/operation.jl index 19bee00..9d7e2f3 100644 --- a/src/operation.jl +++ b/src/operation.jl @@ -427,6 +427,133 @@ function operation(mg::Microgrid, dispatch::Function) + for k=1:K + # Storage energy and power limits + Psto_emin = - (Esto_max - Esto[k]) / ((1 - sto_loss) * dt) + Psto_emax = (Esto[k] - Esto_min) / ((1 + sto_loss) * dt) + Psto_dmax = min(Psto_emax, Psto_pmax) + Psto_cmax = max(Psto_emin, Psto_pmin) + + # Electrolyzers power limits + Pelyz_emax .= prod_rate .* ((LoH_max - LoH[k]) * dt) + Pelyz_cmax .= min.(Pelyz_emax,Pelyz_max) + + # fuel_cells power limits + Pfc_emax .= max.(dt * (LoH[k] - LoH_min) ./ cons_rate , 0.0 ) + Pfc_pmax .= min.(Pfc_emax,Pfc_max) + + # Diesel generators power limits + Pgen_emax .= ((LoF[K] - LoF_min)/dt .- fuel_intercept.* Pgen_max )./ (fuel_slope) + Pgen_dmax .= min.(Pgen_emax,Pgen_max) + + # dispatch + Pnl[k], Pgen[k,1], Psto[k], Pspill[k], Pshed[k], Pdump[k], Pelyz[k,1], Pfc[k,1] = dispatch(Pnl_request[k], Psto_cmax, Psto_dmax, Pgen_min[1], Pgen_dmax[1], Pelyz_min[1],Pelyz_cmax[1], Pfc_min[1],Pfc_pmax[1]) + + # Battery storage dynamics + Esto[k+1] = Esto[k] - (Psto[k] + sto_loss * abs(Psto[k])) * dt + + # Hydrogen storage dynamics + LoH[k+1] = LoH[k] + ( + sum(Pelyz[k,i]/prod_rate[i] for i in eachindex(prod_rate)) - + sum(Pfc[k,i]*cons_rate[i] for i in eachindex(cons_rate)) + ) * dt + + # fuel storage dynamics + LoF[k+1] = LoF[k] - sum( + Pgen[k,i] / fuel_slope[i] + fuel_intercept[i]* Pgen_max[i] + for i in eachindex(fuel_slope) + ) * dt + + end + + oper_traj = OperationTraj(Pnl, Pshed, renew_potential, Pgen[:,1], Pfc[:,1], Pelyz[:,1],Phb[:,1], Esto, Psto, LoH, LoF, Pspill,Pdump) + + return oper_traj +end + +function operation_pnl(mg::Microgrid, dispatch::Function,Pnl_request::Vector{Float64}) + # Type of all variables: Float64 or ForwardDiff.Dual{...} + Topt = typeof(mg).parameters[1] + + # Renewable power generation + # (remark on naming convention: all non-dispatchable sources are assumed renewable!) + + + # Desired net load + + # Fixed parameters and short aliases + K = length(mg.load) + renew_potential = zeros(Topt,K) + dt = mg.project.timestep + + n_gen = length(mg.dispatchables.generator) + Pgen_max = collect(gen.power_rated for gen in mg.dispatchables.generator) + Pgen_min = collect(mg.dispatchables.generator[i].minimum_load_ratio * Pgen_max[i] for i in eachindex(Pgen_max) ) + fuel_slope = collect(gen.consumption_slope for gen in mg.dispatchables.generator) + fuel_intercept = collect(gen.consumption_intercept for gen in mg.dispatchables.generator) + + LoF_max = mg.tanks.fuelTank.max_filling_ratio * mg.tanks.fuelTank.capacity + LoF_min = mg.tanks.fuelTank.min_filling_ratio * mg.tanks.fuelTank.capacity + + Esto_max = mg.storage.energy_rated + Esto_min = mg.storage.SoC_min * Esto_max + Psto_pmax = mg.storage.discharge_rate * Esto_max + Psto_pmin = -mg.storage.charge_rate * Esto_max # <0 in line with the generator convention for Psto + sto_loss = mg.storage.loss_factor + + n_elyz = length(mg.electrolyzer) + Pelyz_max = collect(el.power_rated for el in mg.electrolyzer) + Pelyz_min = collect(mg.electrolyzer[i].minimum_load_ratio * Pelyz_max[i] for i in eachindex(Pelyz_max) ) + prod_rate = collect(el.consumption_slope for el in mg.electrolyzer) + + n_fc = length(mg.dispatchables.fuel_cell) + Pfc_max = collect(fc.power_rated for fc in mg.dispatchables.fuel_cell) + Pfc_min = collect(mg.dispatchables.fuel_cell[i].minimum_load_ratio * Pfc_max[i] for i in eachindex(Pfc_max) ) + cons_rate = collect(fc.consumption_slope for fc in mg.dispatchables.fuel_cell) + + LoH_max = mg.tanks.h2Tank.max_filling_ratio * mg.tanks.h2Tank.capacity + LoH_min = mg.tanks.h2Tank.min_filling_ratio * mg.tanks.h2Tank.capacity + + # Initialization of loop variables + Pnl = zeros(Topt,K) + + Pgen = zeros(Topt,K,n_elyz) + Pgen_dmax = zeros(Topt, n_gen) + Pgen_emax = zeros(Topt, n_gen) + + Esto = zeros(Topt,K+1) + Psto = zeros(Topt,K) + + Pspill = zeros(Topt,K) + Pshed = zeros(Topt,K) + Pdump = zeros(Topt,K) + + Pelyz = zeros(Topt,K, n_elyz) + Pelyz_emax = zeros(Topt, n_elyz) + Pelyz_cmax = zeros(Topt, n_elyz) + + Pfc = zeros(Topt,K, n_fc) + Pfc_emax = zeros(Topt, n_fc) + Pfc_pmax = zeros(Topt, n_fc) + + Phb = zeros(Topt,K) + LoH = zeros(Topt,K+1) + LoF = zeros(Topt,K+1) + + # Initial battery storage state + Esto_ini = mg.storage.SoC_ini * mg.storage.energy_rated + Esto[1] = Esto_ini + + # Initial H2 storage state + LoH_ini = mg.tanks.h2Tank.ini_filling_ratio * mg.tanks.h2Tank.capacity + LoH[1] = LoH_ini + + # Initial fuel tank state + LoF_ini = mg.tanks.fuelTank.ini_filling_ratio * mg.tanks.fuelTank.capacity + LoF[1] = LoF_ini + + + for k=1:K # Storage energy and power limits Psto_emin = - (Esto_max - Esto[k]) / ((1 - sto_loss) * dt) From d8199ddd90565cb4d42e11604ab45ea2ed6f4b95 Mon Sep 17 00:00:00 2001 From: jean NIKIEMA Date: Wed, 5 Nov 2025 12:58:49 +0100 Subject: [PATCH 19/19] new version with optimization toolboxes --- Optimization/BB_toolbox.jl | 57 + Optimization/SDP_h2_toolbox.jl | 1341 +++++++++++++++++ Optimization/SDP_h2_toolbox_v2.jl | 1420 +++++++++++++++++++ Project.toml | 2 + examples/Microgrid_Wind-Solar-H2.ipynb | 4 +- examples/Useful_functions.jl => graphics.jl | 54 +- src/Microgrids.jl | 57 +- src/economics.jl | 16 +- 8 files changed, 2893 insertions(+), 58 deletions(-) create mode 100644 Optimization/BB_toolbox.jl create mode 100644 Optimization/SDP_h2_toolbox.jl create mode 100644 Optimization/SDP_h2_toolbox_v2.jl rename examples/Useful_functions.jl => graphics.jl (81%) diff --git a/Optimization/BB_toolbox.jl b/Optimization/BB_toolbox.jl new file mode 100644 index 0000000..bfcd79a --- /dev/null +++ b/Optimization/BB_toolbox.jl @@ -0,0 +1,57 @@ +using Microgrids +using NLopt + + +"Multi-objective criterion for microgrid performance: lcoe, shedding rate" +function obj_multi(x= X,capex=capex_def,dispatch=dispatch_1,load=Pload,cf_pv=Ppv1k,cf_wind=cf_wind) + mg=new_microgrid(Sizing((1000*x)...),capex,ini_filling_state,load,cf_wind,cf_pv) + mg,traj,stats, costs = simulate_double(mg,capex,dispatch) + # Extract KPIs of interest + lcoe = costs.lcoe # $/kWh + shed_rate = stats.shed_rate; # in [0,1] + return lcoe, shed_rate +end + +"""Mono-objective criterion: LCOE + penalty if shedding rate > `shed_max` + +with signature adapted to NLopt with `grad` as 2nd argument + +load shedding penalty threshold `shed_max` should be in [0,1[ +""" +function obj(x,grad, shed_max,capex=capex_def,dispatch=dispatch_1, w_shed_max=1e5,load=Pload,cf_pv=Ppv1k,cf_wind=cf_wind) + lcoe, shed_rate = obj_multi(x,capex,dispatch,load,cf_pv,cf_wind) + over_shed = shed_rate - shed_max + if over_shed > 0.0 + penalty = w_shed_max*over_shed + else + penalty = 0.0 + end + J = lcoe + penalty +end + +"""Optimize sizing of microgrid based on the `obj` function + +Parameters: +- `x0`: initial sizing (for the algorithms which need them) +- `shed_max`: load shedding penalty threshold (same as in `obj`) +- `algo` could be one of LN_SBPLX, GN_DIRECT, GN_ESCH... +- `maxeval`: maximum allowed number of calls to the objective function, + that is to the microgrid simulation +- `xtol_rel`: termination condition based on relative change of sizing, see NLopt doc. +- `srand`: random number generation seed (for algorithms which use some stochastic search) + +""" +function optim_mg(x0, shed_max, xmin,xmax, algo=:GN_CRS2_LM, maxeval=1000, xtol_rel=1e-8, srand=1,load=Pload,cf_pv=Ppv1k,cf_wind=cf_wind) + nx = length(x0) # number of optim variables + opt = Opt(algo, nx) + NLopt.srand(srand) + + opt.lower_bounds = xmin + opt.upper_bounds = xmax + opt.min_objective = (x, grad) -> obj(x, grad, shed_max,capex_def,Microgrids.dispatch_1,1e5,load,cf_pv,cf_wind) + opt.xtol_rel = xtol_rel + opt.maxeval = maxeval + + (fopt, xopt, ret) = optimize(opt, x0) + return xopt, ret, opt.numevals +end \ No newline at end of file diff --git a/Optimization/SDP_h2_toolbox.jl b/Optimization/SDP_h2_toolbox.jl new file mode 100644 index 0000000..2bc8b86 --- /dev/null +++ b/Optimization/SDP_h2_toolbox.jl @@ -0,0 +1,1341 @@ +# Load Microgrid prior using this file +using Interpolations, Base.Threads, LinearAlgebra, Base.Iterators, Distributions,FreqTables,StatsBase +using Microgrids + +# Structure describing the state of the Microgrid +struct State + Pnl::Float64 + LoH::Float64 + Ebatt::Float64 + cycles::Float64 + RT_fc::Float64 + RT_el::Float64 +end +""" + State(a::NTuple{6, Float64}) + +TBW +""" +function State(a::NTuple{6,Float64}) + State(a[1], a[2], a[3], a[4], a[5], a[6]) +end + +struct Time + hi::Float64 + di::Int64 + mi::Int64 +end + +""" + time_dyn_forward(time::Array{Float64, 1},dt::Float64,mi_l::Vector{Int64}) + + +""" +function time_dyn_forward(time::Array{Float64,1}, dt::Float64, mi_l::Vector{Int64}) + + if time[1] < 23.0 + next_hi = time[1] + dt + next_di = time[2] + next_mi = time[3] + else + next_hi = 0.0 + if time[2] < mi_l[Int(time[3])] + next_di = time[2] + 1 + next_mi = time[3] + else + next_di = 1 + next_mi = time[3] + 1 + end + end + + time[1] = next_hi + time[2] = next_di + time[3] = next_mi +end + + + + +""" + time_dyn_backward!(time::Array{Float64, 1},dt::Float64,mi_l::Vector{Int64}) + +TBW +""" +function time_dyn_backward!(time::Array{Float64,1}, dt::Float64, mi_l::Vector{Int64}) + + if time[1] > 0 + next_hi = time[1] - dt + next_di = time[2] + next_mi = time[3] + else + next_hi = 23.0 + if time[2] > 1 + next_di = time[2] - 1 + next_mi = time[3] + else + next_di = mi_l[Int(time[3])-1] + next_mi = time[3] - 1 + end + end + + time[1] = next_hi + time[2] = next_di + time[3] = next_mi +end + + + +""" + bt_bounds( x::State,mg::Microgrid) + +TBW +""" +function bt_bounds(x::State, mg::Microgrid) + bt = mg.storage + dt = mg.project.timestep + if x.cycles >= mg.storage.lifetime_cycles / 5.0 + Pdis_max = 0. + Pcha_max = 0. + else + Pdis_max = min((x.Ebatt - bt.energy_rated * bt.SoC_min) / ((1 + bt.loss_factor) * dt), bt.energy_rated * bt.discharge_rate) + Pcha_max = max((x.Ebatt - bt.energy_rated * bt.SoC_max) / ((1 - bt.loss_factor) * dt), -bt.energy_rated * bt.charge_rate) + end + return Pcha_max, Pdis_max +end + + + +""" + h2_bounds( x::State,mg::Microgrid) + +TBW +""" +function h2_bounds(x::State, mg::Microgrid) + + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + Pel_min, Pfc_min = -el.power_rated * el.minimum_load_ratio, fc.power_rated * fc.minimum_load_ratio + + if x.RT_el == el.lifetime_hours / 5.0 + + Pel_max = 0. + else + Pel_max = ifelse((x.LoH - hytank.capacity * hytank.max_filling_ratio) * el.consumption_slope / dt > Pel_min, 0.0, max((x.LoH - hytank.capacity * hytank.max_filling_ratio) * el.consumption_slope / dt, -el.power_rated)) + end + + if x.RT_fc == fc.lifetime_hours / 5.0 + Pfc_max = 0. + else + Pfc_max = ifelse((x.LoH - hytank.capacity * hytank.min_filling_ratio) / (fc.consumption_slope * dt) < Pfc_min, 0.0, min((x.LoH - hytank.capacity * hytank.min_filling_ratio) / (fc.consumption_slope * dt), fc.power_rated)) + end + + return Pel_max, Pfc_max, Pel_min, Pfc_min +end + +""" + u_bounds( x::State,mg) + +TBW +""" +function u_bounds(x::State, mg) + bt = mg.storage + dt = mg.project.timestep + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(x, mg) + Pcha_max, Pdis_max = bt_bounds(x, mg) + + if x.Pnl >= 0. + u_max = min(Pdis_max, x.Pnl) + u_min = ifelse(Pfc_max >= x.Pnl, max(Pcha_max, x.Pnl - Pfc_max), min(Pdis_max, x.Pnl - Pfc_max)) + else + u_max = max(min(x.Pnl - Pel_max, 0.0), Pcha_max) + u_min = max(Pcha_max, x.Pnl - Pfc_max) + end + + return u_min, u_max, Pel_min, Pfc_min +end + + +""" + u_range!(u_range,x::State,mg,step=100,db=10,da=10) + +TBW +""" +function u_range!(u_range, x::State, mg, step=100, db=10, da=10) + + u_min, u_max, Pel_min, Pfc_min = u_bounds(x, mg) + + na = da + nb = db + p1 = x.Pnl - Pfc_min + p2 = x.Pnl - Pel_min + nu = 0 + # u_range=zeros(25) + + if x.Pnl >= 0. + if u_min != u_max + + if p1 <= u_max && p1 >= u_min + if p1 >= 0. + + if 0. <= u_max && 0. >= u_min + na = min(Int(div(-u_min, step) + 2), da) + nb = min(Int(div(p1, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (-u_min)) / (na - 1) + end + for i = na:na+nb-2 + u_range[i+1] = ((i - na + 1) * (p1)) / (nb - 1) + end + nu = na + nb - 1 + else + nb = min(Int(div(p1 - u_min, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (nb - 1) + end + nu = nb + end + else + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + nu = na + end + else + if p1 > u_max + if 0. <= u_max && 0. >= u_min + na = min(Int(div(-u_min, step) + 2), da) + nb = min(Int(div(u_max, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (-u_min)) / (na - 1) + end + for i = na:na+nb-2 + u_range[i+1] = ((i - na + 1) * (u_max)) / (nb - 1) + end + nu = na + nb - 1 + else + nb = min(Int(div(u_max - u_min, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = u_min + (i * (u_max - u_min)) / (nb - 1) + end + nu = nb + + end + end + + end + if u_max == x.Pnl + u_range[nu+1] = x.Pnl + nu += 1 + end + else + u_range[1] = u_max + nu = 1 + end + + else + if u_min == u_max + u_range[1] = u_max + nu = 1 + else + if p2 <= u_max && p2 >= u_min + if x.Pnl >= u_min + if p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + nb = min(Int(div(u_max - p2, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + u_range[na+1] = x.Pnl + for i = na+1:na+nb + u_range[i+1] = p2 + ((i - na - 1) * (u_max - p2)) / (nb - 1) + end + nu = na + nb + 1 + + else + nb = min(Int(div(u_max - p2, step) + 2), db) + u_range[1] = x.Pnl + for i = 0:nb-1 + u_range[i+2] = p2 + (i * (u_max - p2)) / (nb - 1) + end + nu = nb + 1 + + end + else + nb = min(Int(div(u_max - p2, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = p2 + (i * (u_max - p2)) / (nb - 1) + end + nu = nb + end + else + if x.Pnl <= u_max && x.Pnl >= u_min + if p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + u_range[na+1] = x.Pnl + nu = na + 1 + else + u_range[1] = x.Pnl + nu = 1 + end + else + if p1 <= u_max && p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + nu = na + else + na = min(Int(div(u_max - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (u_max - u_min)) / (na - 1) + end + nu = na + + end + end + + end + end + end + + return nu +end + + +""" + dynamic(x::State,Pbt::Float64,w::Float64,time_next::Vector{Float64},rep_days::Array{Float64, 2},rep_days_dev::Array{Float64, 2},mg) + +TBW +""" +function dynamic(x::State, Pbt::Float64,Pnl_next::Float64, mg) + + bt = mg.storage + + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + + dt = mg.project.timestep + # next net load submit to the uncertainty + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(x, mg) + #level of hydrogen update + Pnl = x.Pnl + + next_RT_el = x.RT_el + next_RT_fc = x.RT_fc + + if (Pnl - Pbt > 1e-12) + PH2 = min(Pfc_max, max(Pfc_min, Pnl - Pbt)) + LoH_next = x.LoH - PH2 * fc.consumption_slope * dt + if (PH2>1e-12) + next_RT_fc = x.RT_fc + dt + end + + elseif (Pnl - Pbt <= Pel_min + 1e-12) + PH2 = max(Pel_max, Pnl - Pbt) + LoH_next = x.LoH - PH2 / el.consumption_slope * dt + + next_RT_el = x.RT_el + dt + + else + PH2 = 0. + LoH_next = x.LoH + end + + #battery dynamic + E_bt_next = max(0.0, min(x.Ebatt - (Pbt + bt.loss_factor * abs(Pbt)) * dt, bt.energy_rated)) + cycles_next = x.cycles + (abs(Pbt) * dt) / (2 * bt.lifetime_cycles) + + x_next = State(Pnl_next, LoH_next, E_bt_next, cycles_next, next_RT_fc, next_RT_el) + Pshed = Pnl - Pbt - PH2 + + return x_next, Pshed,PH2 + +end + +function m_pnl() + + t = [1.0, 1, 1] + + dt = mg.project.timestep + # next time indexes + Pnl_gen = zeros(Float64, 8760) + w_law = Normal(0, 1) + for i in eachindex(Pnl_gen) + Pnl_gen[i] = rep_days[Int(t[3]), Int(t[1])+1] + rand(w_law) * rep_days_dev[Int(t[3]), Int(t[1])+1] + if i != length(Pnl_gen) + time_dyn_forward(t, dt, mi_l) + end + end + + return Pnl_gen + +end + +""" + gen_mark_c(probs,cats,ini) + +TBW +""" +function gen_mark_c(probs,cats,ini) + + Pnl=zeros(Float64,8760) + Pnl[1]=ini + for i=2:8760 + h=data.Hour[i] + m=data.Month[i] + h_prev=data.Hour[i-1] + m_prev=data.Month[i-1] + cat = Int(abs(div(Pnl[i-1] - cats[m_prev, h_prev+1, 1], cats[m_prev, h_prev+1, 2] - cats[m_prev, h_prev+1, 1])) + 1) + + if cat > 16 + cat = 16 + end + prob_val=zeros(Float64,count(x->(x>0.),probs[m_prev,h_prev+1,:,cat])) + indexes=zeros(Int64,length(prob_val)) + z=0 + + + for j=1:length(probs[m_prev,h_prev+1,:,cat]) + + if probs[m_prev,h_prev+1,j,cat] > 0. + z=z+1 + indexes[z]=j + prob_val[z]=probs[m_prev,h_prev+1,j,cat] + end + end + Pnl[i]=cats[m,h+1,indexes[sample(Weights(prob_val))]+1] + end + return Pnl +end + +""" + simple_dyn(x::State,Pbt::Float64,mg) + +TBW +""" +function simple_dyn(x::State, Pbt::Float64, mg) + + bt = mg.storage + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + + dt = mg.project.timestep + + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(x, mg) + + Pnl = x.Pnl + + next_RT_el = x.RT_el + next_RT_fc = x.RT_fc + + if (Pnl - Pbt > 0.) + PH2 = min(Pfc_max, max(Pfc_min, Pnl - Pbt)) + LoH_next = x.LoH - PH2 * fc.consumption_slope * dt + + next_RT_fc = x.RT_fc + dt + + elseif (Pnl - Pbt <= Pel_min + 1e-12) + PH2 = max(Pel_max, Pnl - Pbt) + LoH_next = x.LoH - PH2 / el.consumption_slope * dt + + next_RT_el = x.RT_el + dt + + else + PH2 = 0. + LoH_next = x.LoH + end + + #battery dynamic + E_bt_next = max(0.0, min(x.Ebatt - (Pbt + bt.loss_factor * abs(Pbt)) * dt, bt.energy_rated)) + cycles_next = x.cycles + (abs(Pbt) * dt) / (2 * bt.lifetime_cycles) + + x_next = State(0.0, LoH_next, E_bt_next, cycles_next, next_RT_fc, next_RT_el) + Ps = Pnl - Pbt - PH2 + + return x_next, Ps, PH2 + +end + +""" + CRF(i::Float64,T) + +TBW +""" +function CRF(i::Float64, T) + if i != 0.0 + a = (1 + i)^T + return i * a / (a - 1) + else + return 1 / T + end +end +CRF(0.05, 25.0) +CRFproj(T::Float64) = CRF(mg.project.discount_rate, T) + +""" + term(x::State,T,mg::Microgrid) + +TBW +""" +function term(x::State, T, mg::Microgrid) + + bt = mg.storage + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + τ = mg.project.discount_rate + hprice=mg.tanks.h2Tank.combustible_price + co = T / 8760 + Lt_bt = min(bt.lifetime_calendar, co * (bt.lifetime_cycles / x.cycles)) + Lt_fc = min(fc.lifetime_calendar, co * (fc.lifetime_hours / x.RT_fc)) + Lt_el = min(el.lifetime_calendar, co * (el.lifetime_hours / x.RT_el)) + + c = bt.investment_price * bt.energy_rated * CRF(τ, Lt_bt) + + fc.investment_price * fc.power_rated * CRF(τ, Lt_fc) + + el.investment_price * el.power_rated * CRF(τ, Lt_el) #+ hprice * x.LoH + + return c + + +end + + +function dispatch_1(s::State, mg::Microgrid) + + Pbatt_cmax, Pbatt_dmax = bt_bounds(s, mg) + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(s, mg) + Pnl, Pgen, Pbatt, Pspill, Pshed, Pdump, Pelyz, Pfc = Microgrids.dispatch_1(s.Pnl, Pbatt_cmax, Pbatt_dmax, 0., 0., Pel_min, Pel_max, Pfc_min, Pfc_max) + + return Pbatt, Pfc - Pelyz +end + + +""" + DPrecursion(K::Int64,pen::Float64,time_init::Vector{Float64},max_pnl::Float64,min_pnl::Float64,mi_l::Vector{Int64},rep_days,rep_days_dev,mg) + +TBW +""" +function DPrecursion(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64, ref,σ,ϕ, mg) + + bt = mg.storage + # el=mg.electrolyzer[1] + #fc= mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + nRT = 10 + ncycles = 10 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + RT_grid = range(0, 9000, nRT) + + + + # dbt=0.05*bt.energy_rated + # dh2=0.05*fc.power_rated + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt, ncycles, nRT, nRT, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + policy = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, K) + polh2 = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, K) + u_rb = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, 2) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] = term(xk, K, mg) #arranger ça + u_rb[pnl, loh, ebt, cy, rt1, rt2, :] .= dispatch_1(xk, mg)[:] + end + end + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + err=(xk.Pnl-ref[k]) + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + ph_opt = 0.0 + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + PH2 = 0.0 + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += pen *max(0.0, Pshed) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = PH2 + end + + end #each control + + + Jopt[pnl, loh, ebt, cy, rt1, rt2, k] = Jk_xu_min + policy[pnl, loh, ebt, cy, rt1, rt2, k] = u_opt + polh2[pnl, loh, ebt, cy, rt1, rt2, k] = ph_opt + + end # each state + end + # + end #each instant + + + return Jopt, policy, polh2, u_rb + +end + + +function DPrecursion_jopt(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64,ref::Vector{Float64},σ::Float64,ϕ::Float64, mg) + + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + nRT = 10 + ncycles = 10 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + RT_grid = range(0, 9000, nRT) + + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt, ncycles, nRT, nRT, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] = term(xk, K, mg) #arranger ça + end + end + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + err=(xk.Pnl-ref[k]) + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]), x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += pen * max(0.0, Pshed) + + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + + Jopt[pnl, loh, ebt, cy, rt1, rt2, k] = Jk_xu_min + end # each state + end + # + + end #each instant + + + return Jopt + +end + +function DPrecursion_jopt_mc(K::Int64, pen::Float64, time_init::Vector{Float64}, mi_l::Vector{Int64}, Pnl_cate, Pnl_prob, mg) + + bt = mg.storage + # el=mg.electrolyzer[1] + #fc= mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + nRT = 10 + ncycles = 10 + + #Pnl_grid=range(min_pnl,max_pnl,nPnl) + n_cat = length(Pnl_cate[1, 1, :]) - 1 + + Pnl_grid = zeros(Float64, 12, 24, nPnl) + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + RT_grid = range(0, 9000, nRT) + + + + # dbt=0.05*bt.energy_rated + # dh2=0.05*fc.power_rated + + time = time_init + + IBS = BSpline(Linear()) + + + + Jopt = zeros(Float32, nPnl, nloh, nEbt, ncycles, nRT, nRT, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + xk = State(Pnl_grid[12, 24, pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] = term(xk, K, mg) #arranger ça + + end + end + + + @inbounds for k = K:-1:1 + time_next = time + time_dyn_backward!(time, dt, mi_l) + + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid) + + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + + xk = State(Pnl_grid[m, h+1, pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + cat = Int(div(xk.Pnl - Pnl_cate[m, h+1, 1], Pnl_cate[m, h+1, 2] - Pnl_cate[m, h+1, 1]) + 1) + if cat > 16 + cat = 16 + end + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + + #CREATE UGRID + Jk_xu_min = Inf32 + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw = 1:n_cat # for each uncertainty value + if Pnl_prob[m, h+1, iw, cat] > 1e-10 + Jk_xu += Jopt_next(Pnl_cate[m_next, h_next+1, iw+1] , x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * Pnl_prob[m, h+1, iw, cat] + @assert Jk_xu >= 0. " problem, $m, $(h+1), $cat, $Jk_xu, $(Pnl_prob[m, h+1, iw, cat])" + end + end # each uncertainty + + Jk_xu += pen * max(0.0, Pshed) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + @assert Jk_xu_min >= Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] - 0.1 " problem, $m, $(h+1), $cat,$xk, $(dynamic(xk, u_opt, 0.0, mg)), $u_opt,$( Jk_xu_min), $(Jopt[pnl,loh,ebt,cy,rt1,rt2,K+1])" + + Jopt[pnl, loh, ebt, cy, rt1, rt2, k] = Jk_xu_min + + + end # each state + end + # + + + end #each instant + + + return Jopt + +end + +function DPrecursion_uopt(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64, ref,σ,ϕ, mg) + + bt = mg.storage + # el=mg.electrolyzer[1] + #fc= mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + nRT = 10 + ncycles = 10 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + RT_grid = range(0, 9000, nRT) + + + + # dbt=0.05*bt.energy_rated + # dh2=0.05*fc.power_rated + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt, ncycles, nRT, nRT, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + policy = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, K) + + u_rb = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, 2) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] = term(xk, K, mg) #arranger ça + u_rb[pnl, loh, ebt, cy, rt1, rt2, :] .= dispatch_1(xk, mg)[:] + end + end + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + err=(xk.Pnl-ref[k]) + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += pen * max(0.0, Pshed) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + + Jopt[pnl, loh, ebt, cy, rt1, rt2, k] = Jk_xu_min + policy[pnl, loh, ebt, cy, rt1, rt2, k] = u_opt + + end # each state + end + # + + + + end #each instant + + + return policy, u_rb + +end + + +function DPrecursion_rb_cost(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64,ref,σ,ϕ, mg) + + bt = mg.storage + + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + nRT = 10 + ncycles = 10 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + RT_grid = range(0, 9000, nRT) + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt, ncycles, nRT, nRT, K + 1) + # Policy over time + + + + + u_rb = zeros(Float16, nPnl, nloh, nEbt, ncycles, nRT, nRT, 2) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + Jopt[pnl, loh, ebt, cy, rt1, rt2, K+1] = term(xk, K, mg) #arranger ça + u_rb[pnl, loh, ebt, cy, rt1, rt2, :] .= dispatch_1(xk, mg)[:] + end + end + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, cy = 1:ncycles, rt1 = 1:nRT, rt2 = 1:nRT + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], cycles_grid[cy], RT_grid[rt1], RT_grid[rt2]) + err=(xk.Pnl-ref[k]) + + + + + Jk_xu = 0.0 + uk = u_rb[pnl, loh, ebt, cy, rt1, rt2, 1] + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * prob_demand[iw] + end # each uncertainty + + Jk_xu += pen * max(0.0, Pshed) + + + + Jopt[pnl, loh, ebt, cy, rt1, rt2, k] = Jk_xu + + + end # each state + end + # + + end #each instant + + + return Jopt[:, :, :, :, :, :, 1] + +end +function sdp_simu_jopt(Jopt, n, pen,ϕ,σ, mg1,ref::Vector{Float64},max_pnl::Float64, min_pnl::Float64,Pnl::Vector{Float64}) + Pren = zeros(n) + Pren = production(mg1.nondispatchables[1]) .+ production(mg1.nondispatchables[2]) + Pbt = zeros(n) + Pel = zeros(n) + Pfc = zeros(n) + Ebt = zeros(n + 1) + cycles = 0.0 + Loh = zeros(n + 1) + Lof = zeros(n + 1) + Pgen = zeros(n) + Phb = zeros(n) + Pshed = zeros(n) + Pdump = zeros(n) + Pspill = zeros(n) + + IBS = BSpline(Linear()) + + Ebt[1] = 0. + Loh[1] = mg1.tanks.h2Tank.ini_filling_ratio * mg1.tanks.h2Tank.capacity + RT_fc, RT_el = 0, 0 + nEbt = size(Jopt)[3] + nPnl = size(Jopt)[1] + nloh = size(Jopt)[2] + nRT = size(Jopt)[5] + ncycles = size(Jopt)[4] + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + Pnl_grid = range(min_pnl, max_pnl, nPnl) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + RT_grid = range(0, 9000, nRT) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + Pbt_grid = zeros(50) + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + for k = 1:n + J_interp = Interpolations.extrapolate(Interpolations.scale(Interpolations.interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid), Line()) + + xk = State(Pnl[k], Loh[k], Ebt[k], cycles, RT_fc, RT_el) + err=(xk.Pnl-ref[k]) + nu = u_range!(Pbt_grid, xk, mg1, 20, 20, 10) + + + Jk_xu_min = Inf + u_opt = 0.0 + ph_opt = 0.0 + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[u_i] + Jk_xu = 0.0 + + x_next, Ps, Ph2 = dynamic(xk, uk, 0.0, mg1) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += J_interp(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * prob_demand[iw] + end # each uncertainty + + + + Jk_xu += pen * max(0.0, Ps) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = Ph2 + end + + end #each control + x_next, Ps, Ph2 = dynamic(xk, u_opt, 0.0, mg1) + Pbt[k] = u_opt + Pel[k] = ifelse(Ph2 > 0.0, 0.0, -Ph2) + Pfc[k] = ifelse(Ph2 > 0.0, Ph2, 0.0) + Ebt[k+1] = x_next.Ebatt + Loh[k+1] = x_next.LoH + cycles = x_next.cycles + Pspill[k] = ifelse(Ps > 0.0, 0.0, -Ps) + Pshed[k] = ifelse(Ps > 0.0, Ps, 0.0) + RT_el=x_next.RT_el + RT_fc=x_next.RT_fc + + end + traj = OperationTraj(Pnl, Pshed, Pren, Pgen, Pfc, Pel, Phb, Ebt, Pbt, Loh, Lof, Pspill, Pdump) + stats = aggregation(mg1, traj) + # Eval the microgrid costs + costs = economics(mg1, stats) + + return traj, stats, costs + +end + + +function sdp_simu_jopt_mc(Jopt, n, pen, mg1, Pnl_cate, Pnl_prob,Pnl) + Pren = zeros(n) + Pren = production(mg1.nondispatchables[1]) .+ production(mg1.nondispatchables[2]) + + Pbt = zeros(n) + Pel = zeros(n) + Pfc = zeros(n) + Ebt = zeros(n + 1) + cycles = 0.0 + Loh = zeros(n + 1) + Lof = zeros(n + 1) + Pgen = zeros(n) + Phb = zeros(n) + Pshed = zeros(n) + Pdump = zeros(n) + Pspill = zeros(n) + + IBS = BSpline(Linear()) + + Ebt[1] = 0. + Loh[1] = mg1.tanks.h2Tank.ini_filling_ratio * mg1.tanks.h2Tank.capacity + RT_fc, RT_el = 0, 0 + nEbt = size(Jopt)[3] + nPnl = size(Jopt)[1] + nloh = size(Jopt)[2] + nRT = size(Jopt)[5] + ncycles = size(Jopt)[4] + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + Pnl_grid = zeros(Float64, 12, 24, nPnl) + n_cat = length(Pnl_cate[1, 1, :]) - 1 + + + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + RT_grid = range(0, 9000, nRT) + cycles_grid = range(0, bt.lifetime_cycles / 5, ncycles) + Pbt_grid = zeros(100) + + for k = 1:n + if k < n + time_next = [Float64(data.Hour[k+1]), Float64(data.Day[k+1]), Float64(data.Month[k+1])] + + else + time_next = [0., 1, 1] + end + + time = [Float64(data.Hour[k]), Float64(data.Day[k]), Float64(data.Month[k])] + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + + + J_interp = Interpolations.extrapolate(Interpolations.scale(Interpolations.interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), + LoH_grid, Ebt_grid, cycles_grid, RT_grid, RT_grid), Line()) + + xk = State(Pnl[k], Loh[k], Ebt[k], cycles, RT_fc, RT_el) + cat = Int(div(xk.Pnl - Pnl_cate[m, h+1, 1], Pnl_cate[m, h+1, 2] - Pnl_cate[m, h+1, 1]) + 1) + + if cat > 16 + cat = 16 + end + + if cat < 1 + cat = 1 + end + + @assert cat >= 1 && cat <= 16 "error $cat, $(xk.Pnl), $(h+1), $m, $(Pnl_cate[m,h+1,1]), $(xk.Pnl-Pnl_cate[m,h+1,1]), $(Pnl_cate[m,h+1,2]-Pnl_cate[m,h+1,1]))" + nu = u_range!(Pbt_grid, xk, mg1, 20, 50, 50) + # nu_bt= Int(ceil((umax.P_bt-umin.P_bt)/dbt)) + 1 + # nu_h2= Int(ceil((umax.P_H2-umin.P_H2)/dh2)) + 1 + + + + + #CREATE UGRID + Jk_xu_min = Inf + u_opt = 0.0 + ph_opt = 0.0 + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[u_i] + Jk_xu = 0.0 + x_next, Ps, Ph2 = dynamic(xk, uk, 0.0, mg1) + + @fastmath for iw = 1:n_cat # for each uncertainty value + if Pnl_prob[m, h+1, iw, cat] != 0.0 + + Jk_xu += J_interp(Pnl_cate[m_next, h_next+1, iw+1], x_next.LoH, x_next.Ebatt, x_next.cycles, x_next.RT_fc, x_next.RT_el) * Pnl_prob[m, h+1, iw, cat] + + end + end # each uncertainty + + # @assert Jk_xu=0.0!=0 " cout nul" + Jk_xu += pen * max(0.0, Ps) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = Ph2 + end + + end #each control + x_next, Ps, Ph2 = dynamic(xk, u_opt, 0.0, mg1) + Pbt[k] = u_opt + Pel[k] = ifelse(Ph2 > 0.0, 0.0, -Ph2) + Pfc[k] = ifelse(Ph2 > 0.0, Ph2, 0.0) + Ebt[k+1] = x_next.Ebatt + Loh[k+1] = x_next.LoH + cycles = x_next.cycles + RT_el=x_next.RT_el + RT_fc=x_next.RT_fc + Pspill[k] = ifelse(Ps > 0.0, 0.0, -Ps) + Pshed[k] = ifelse(Ps > 0.0, Ps, 0.0) + + + end + traj = OperationTraj(Pnl, Pshed, Pren, Pgen, Pfc, Pel, Phb, Ebt, Pbt, Loh, Lof, Pspill, Pdump) + stats = aggregation(mg1, traj) + # Eval the microgrid costs + costs = economics(mg1, stats) + + return traj, stats, costs +end + + +function moving_average(vs,q;even=true) + + + n=length(vs) + mav=zeros(n) + vs_im=zeros(n+2q) + vs_im[1:q].=vs[end-q+1:end] + vs_im[n+q+1:n+2q].=vs[1:q] + vs_im[q+1:n+q].=vs + coef=ones(2q+1) + if even + coef[1]=0.5 + coef[end]=0.5 + end + for i=1:n + mav[i]=sum( vs_im[i:i+2q] .* coef) /sum(coef) + end + + return mav +end + +function sp_tr_remove(signal,per,d) + n=length(signal) + tre=zeros(length(signal)) + sig=zeros(n+2d*per) + sig[1:d*per].=signal[end-d*per+1:end] + sig[d*per+1:n+d*per] .= signal[:] + sig[n+d*per+1:end]=signal[1:d*per] + + stv=zeros(n) + vec=zeros(2d+1) + for i=1:n + tot=signal[i] + for j=1:d + tot+=sig[i-j*per+d*per] + sig[i+j*per+d*per] + vec[2(j-1)+1]=sig[i-j*per+d*per] + vec[2j]=sig[i+j*per+d*per] + end + vec[end]=signal[i] + tre[i]=tot/(2d+1) + stv[i]=std(vec) + end + return tre,stv + +end + +function hma_spe(signal,n) + m=Int64((n-1)/2) + new_sig=zeros(length(signal)+2m) + new_sig[1:m]=signal[end-m+1:end] + new_sig[m+1:end-m]=signal[:] + new_sig[end-m+1:end]=signal[1:m] + + return hma(new_sig,n)[m+1:end-m] + +end +function plot_periodigram(signal) + n=length(signal) + w=range(1,n/2,n)./n + i=zeros(Float64,n) + t=range(1,n) + for k=1:n + i[k]=(n^-1)*abs(sum(signal .* exp.(-2 .*pi .* im .*(t.-1) .*w[k])))^2 + + end + + plot(w,i) + # xscale("log") + return i +end +function cal_w(a,j) + return (1-a^(2j))*(1+a^2)*((1-a^2)^-1)-2j*a^(2j) +end +function season_estim(signal,d,q) + + n=length(signal) + w=zeros(d) + w_final=zeros(d) + nj=Int64(n/d) + for i=1:d + if i<=q + j=range(1,nj-1) + else + j=range(0,nj-2) + end + + sum_int=0 + for z in j + sum_int+=signal[i+z*d] + end + w[i]=sum_int/length(j) + + end + for i in eachindex(w) + w_final[i]=w[i]-mean(w) + end + return w_final +end \ No newline at end of file diff --git a/Optimization/SDP_h2_toolbox_v2.jl b/Optimization/SDP_h2_toolbox_v2.jl new file mode 100644 index 0000000..763495d --- /dev/null +++ b/Optimization/SDP_h2_toolbox_v2.jl @@ -0,0 +1,1420 @@ +# Load Microgrid prior using this file +using Interpolations, Base.Threads, LinearAlgebra, Base.Iterators, Distributions,FreqTables,StatsBase +using Microgrids + +# Structure describing the state of the Microgrid +struct State + Pnl::Float64 + LoH::Float64 + Ebatt::Float64 + δ::Int64 +end +""" + State(a::NTuple{6, Float64}) + +TBW +""" +function State(a::NTuple{6,Float64}) + State(a[1], a[2], a[3], a[4]) +end + +struct Time + hi::Float64 + di::Int64 + mi::Int64 +end + +""" + time_dyn_forward(time::Array{Float64, 1},dt::Float64,mi_l::Vector{Int64}) + + +""" +function time_dyn_forward(time::Array{Float64,1}, dt::Float64, mi_l::Vector{Int64}) + + if time[1] < 23.0 + next_hi = time[1] + dt + next_di = time[2] + next_mi = time[3] + else + next_hi = 0.0 + if time[2] < mi_l[Int(time[3])] + next_di = time[2] + 1 + next_mi = time[3] + else + next_di = 1 + next_mi = time[3] + 1 + end + end + + time[1] = next_hi + time[2] = next_di + time[3] = next_mi +end + + + + +""" + time_dyn_backward!(time::Array{Float64, 1},dt::Float64,mi_l::Vector{Int64}) + +TBW +""" +function time_dyn_backward!(time::Array{Float64,1}, dt::Float64, mi_l::Vector{Int64}) + + if time[1] > 0 + next_hi = time[1] - dt + next_di = time[2] + next_mi = time[3] + else + next_hi = 23.0 + if time[2] > 1 + next_di = time[2] - 1 + next_mi = time[3] + else + next_di = mi_l[Int(time[3])-1] + next_mi = time[3] - 1 + end + end + + time[1] = next_hi + time[2] = next_di + time[3] = next_mi +end + + + +""" + bt_bounds( x::State,mg::Microgrid) + +TBW +""" +function bt_bounds(x::State, mg::Microgrid) + bt = mg.storage + dt = mg.project.timestep + + Pdis_max = min((x.Ebatt - bt.energy_rated * bt.SoC_min) / ((1 + bt.loss_factor) * dt), bt.energy_rated * bt.discharge_rate) + Pcha_max = max((x.Ebatt - bt.energy_rated * bt.SoC_max) / ((1 - bt.loss_factor) * dt), -bt.energy_rated * bt.charge_rate) + + return Pcha_max, Pdis_max +end + + + +""" + h2_bounds( x::State,mg::Microgrid) + +TBW +""" +function h2_bounds(x::State, mg::Microgrid) + + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + Pel_min, Pfc_min = -el.power_rated * el.minimum_load_ratio, fc.power_rated * fc.minimum_load_ratio + + + Pel_max = ifelse((x.LoH - hytank.capacity * hytank.max_filling_ratio) * el.consumption_slope / dt > Pel_min, 0.0, max((x.LoH - hytank.capacity * hytank.max_filling_ratio) * el.consumption_slope / dt, -el.power_rated)) + + + + Pfc_max = ifelse((x.LoH - hytank.capacity * hytank.min_filling_ratio) / (fc.consumption_slope * dt) < Pfc_min, 0.0, min((x.LoH - hytank.capacity * hytank.min_filling_ratio) / (fc.consumption_slope * dt), fc.power_rated)) + + + return Pel_max, Pfc_max, Pel_min, Pfc_min +end + +""" + u_bounds( x::State,mg) + +TBW +""" +function u_bounds(x::State, mg) + bt = mg.storage + dt = mg.project.timestep + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(x, mg) + Pcha_max, Pdis_max = bt_bounds(x, mg) + + if x.Pnl >= 0. + u_max = min(Pdis_max, x.Pnl) + u_min = ifelse(Pfc_max >= x.Pnl, max(Pcha_max, x.Pnl - Pfc_max), min(Pdis_max, x.Pnl - Pfc_max)) + else + u_max = max(min(x.Pnl - Pel_max, 0.0), Pcha_max) + u_min = max(Pcha_max, x.Pnl - Pfc_max) + end + + return u_min, u_max, Pel_min, Pfc_min +end + + +""" + u_range!(u_range,x::State,mg,step=100,db=10,da=10) + +TBW +""" +function u_range!(u_range, x::State, mg, step=100, db=10, da=10) + + u_min, u_max, Pel_min, Pfc_min = u_bounds(x, mg) + + na = da + nb = db + p1 = x.Pnl - Pfc_min + p2 = x.Pnl - Pel_min + nu = 0 + # u_range=zeros(25) + + if x.Pnl >= 0. + if u_min != u_max + + if p1 <= u_max && p1 >= u_min + if p1 >= 0. + + if 0. <= u_max && 0. >= u_min + na = min(Int(div(-u_min, step) + 2), da) + nb = min(Int(div(p1, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (-u_min)) / (na - 1) + end + for i = na:na+nb-2 + u_range[i+1] = ((i - na + 1) * (p1)) / (nb - 1) + end + nu = na + nb - 1 + else + nb = min(Int(div(p1 - u_min, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (nb - 1) + end + nu = nb + end + else + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + nu = na + end + else + if p1 > u_max + if 0. <= u_max && 0. >= u_min + na = min(Int(div(-u_min, step) + 2), da) + nb = min(Int(div(u_max, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (-u_min)) / (na - 1) + end + for i = na:na+nb-2 + u_range[i+1] = ((i - na + 1) * (u_max)) / (nb - 1) + end + nu = na + nb - 1 + else + nb = min(Int(div(u_max - u_min, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = u_min + (i * (u_max - u_min)) / (nb - 1) + end + nu = nb + + end + end + + end + if u_max == x.Pnl + u_range[nu+1] = x.Pnl + nu += 1 + end + else + u_range[1] = u_max + nu = 1 + end + + else + if u_min == u_max + u_range[1] = u_max + nu = 1 + else + if p2 <= u_max && p2 >= u_min + if x.Pnl >= u_min + if p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + nb = min(Int(div(u_max - p2, step) + 2), db) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + u_range[na+1] = x.Pnl + for i = na+1:na+nb + u_range[i+1] = p2 + ((i - na - 1) * (u_max - p2)) / (nb - 1) + end + nu = na + nb + 1 + + else + nb = min(Int(div(u_max - p2, step) + 2), db) + u_range[1] = x.Pnl + for i = 0:nb-1 + u_range[i+2] = p2 + (i * (u_max - p2)) / (nb - 1) + end + nu = nb + 1 + + end + else + nb = min(Int(div(u_max - p2, step) + 2), db) + for i = 0:nb-1 + u_range[i+1] = p2 + (i * (u_max - p2)) / (nb - 1) + end + nu = nb + end + else + if x.Pnl <= u_max && x.Pnl >= u_min + if p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + u_range[na+1] = x.Pnl + nu = na + 1 + else + u_range[1] = x.Pnl + nu = 1 + end + else + if p1 <= u_max && p1 >= u_min + na = min(Int(div(p1 - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (p1 - u_min)) / (na - 1) + end + nu = na + else + na = min(Int(div(u_max - u_min, step) + 2), da) + for i = 0:na-1 + u_range[i+1] = u_min + (i * (u_max - u_min)) / (na - 1) + end + nu = na + + end + end + + end + end + end + + return nu +end + + +""" + dynamic(x::State,Pbt::Float64,w::Float64,time_next::Vector{Float64},rep_days::Array{Float64, 2},rep_days_dev::Array{Float64, 2},mg) + +TBW +""" +function dynamic(x::State, Pbt::Float64,Pnl_next::Float64, mg) + + bt = mg.storage + + el = mg.electrolyzer[1] + fc = mg.dispatchables.fuel_cell[1] + + dt = mg.project.timestep + # next net load submit to the uncertainty + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(x, mg) + #level of hydrogen update + Pnl = x.Pnl + + δ_next = 0 + LoH_next = x.LoH + PH2=0. + + if (Pnl - Pbt > 1e-12) + PH2 = min(Pfc_max, max(Pfc_min, Pnl - Pbt)) + LoH_next = x.LoH - PH2 * fc.consumption_slope * dt + if (PH2>1e-12) + δ_next = 1 + end + + elseif (Pnl - Pbt <= Pel_min + 1e-12) + PH2 = max(Pel_max, Pnl - Pbt) + LoH_next = x.LoH - PH2 / el.consumption_slope * dt + + if (PH2<1e-12) + δ_next = -1 + end + + end + + #battery dynamic + E_bt_next = max(0.0, min(x.Ebatt - (Pbt + bt.loss_factor * abs(Pbt)) * dt, bt.energy_rated)) + x_next = State(Pnl_next, LoH_next, E_bt_next, δ_next) + Pshed = Pnl - Pbt - PH2 + + return x_next, Pshed,PH2 + +end + +function m_pnl() + + t = [1.0, 1, 1] + + dt = mg.project.timestep + # next time indexes + Pnl_gen = zeros(Float64, 8760) + w_law = Normal(0, 1) + for i in eachindex(Pnl_gen) + Pnl_gen[i] = rep_days[Int(t[3]), Int(t[1])+1] + rand(w_law) * rep_days_dev[Int(t[3]), Int(t[1])+1] + if i != length(Pnl_gen) + time_dyn_forward(t, dt, mi_l) + end + end + + return Pnl_gen + +end + +""" + gen_mark_c(probs,cats,ini) + +TBW +""" +function gen_mark_c(probs,cats,ini) + + Pnl=zeros(Float64,8760) + Pnl[1]=ini + for i=2:8760 + h=data.Hour[i] + m=data.Month[i] + h_prev=data.Hour[i-1] + m_prev=data.Month[i-1] + cat = Int(abs(div(Pnl[i-1] - cats[m_prev, h_prev+1, 1], cats[m_prev, h_prev+1, 2] - cats[m_prev, h_prev+1, 1])) + 1) + + if cat > 16 + cat = 16 + end + prob_val=zeros(Float64,count(x->(x>0.),probs[m_prev,h_prev+1,:,cat])) + indexes=zeros(Int64,length(prob_val)) + z=0 + + + for j=1:length(probs[m_prev,h_prev+1,:,cat]) + + if probs[m_prev,h_prev+1,j,cat] > 0. + z=z+1 + indexes[z]=j + prob_val[z]=probs[m_prev,h_prev+1,j,cat] + end + end + Pnl[i]=cats[m,h+1,indexes[sample(Weights(prob_val))]+1] + end + return Pnl +end + +""" + CRF(i::Float64,T) + +TBW +""" +function CRF(i::Float64, T) + if i != 0.0 + a = (1 + i)^T + return i * a / (a - 1) + else + return 1 / T + end +end +CRF(0.05, 25.0) +CRFproj(T::Float64) = CRF(mg.project.discount_rate, T) + + +function dispatch_1(s::State, mg::Microgrid) + + Pbatt_cmax, Pbatt_dmax = bt_bounds(s, mg) + Pel_max, Pfc_max, Pel_min, Pfc_min = h2_bounds(s, mg) + Pnl, Pgen, Pbatt, Pspill, Pshed, Pdump, Pelyz, Pfc = Microgrids.dispatch_1(s.Pnl, Pbatt_cmax, Pbatt_dmax, 0., 0., Pel_min, Pel_max, Pfc_min, Pfc_max) + + return Pbatt, Pfc - Pelyz +end + +function cost_to_go(pen::Float64,Pshed::Float64,Ph2::Float64,Pbt::Float64,δ::Int64,fc::ProductionUnit,el::ProductionUnit,bt::Battery,d_rt::Float64,d_st::Float64) + c=0 + c+= pen * max(0.0, Pshed) + if Ph2> ( 10e-12) + c += (d_rt/0.1)*(fc.power_rated*fc.investment_price) + if δ !=1 + c += (d_st/0.1)*(fc.power_rated*fc.investment_price) + end + elseif (Ph2< -10e-12) + c += (d_rt/0.1)*(el.power_rated*el.investment_price) + if δ !=-1 + c += (d_st/0.1)*(el.power_rated*el.investment_price) + end + end + if Pbt<=-10e-12 || Pbt>=10e-12 + c+=(abs(Pbt)*bt.investment_price)/(2*bt.lifetime_cycles) + end + return c +end + + + +""" + DPrecursion(K::Int64,pen::Float64,time_init::Vector{Float64},max_pnl::Float64,min_pnl::Float64,mi_l::Vector{Int64},rep_days,rep_days_dev,mg) + +TBW +""" +function DPrecursion(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64, ref::Vector{Float64},σ::Float64,ϕ::Float64, mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st) + + bt = mg.storage + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt,3, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + policy = zeros(Float16, nPnl, nloh, nEbt, 3, K) + polh2 = zeros(Float16, nPnl, nloh, nEbt, 3, K) + u_rb = zeros(Float16, nPnl, nloh, nEbt,3, 2) + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, δ_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt,δ =1:3 + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], δ_grid[δ]) + + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + err=(xk.Pnl-ref[k]) + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + ph_opt = 0.0 + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + PH2 = 0.0 + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.δ) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) #+ max(x_next.δ,0)*(d_rt/1-Eol)*(fc.investment_price) + + + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = PH2 + end + + end #each control + + + Jopt[pnl, loh, ebt, δ, k] = Jk_xu_min + policy[pnl, loh, ebt, δ, k] = u_opt + polh2[pnl, loh, ebt, δ, k] = ph_opt + + end # each state + end + # + end #each instant + + + return Jopt, policy, polh2, u_rb + +end + + +function DPrecursion_jopt(K::Int64, pen::Float64,ref::Vector{Float64},σ::Float64,ϕ::Float64, Pnl_cate,mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st) + + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 40 + nEbt =40 + nloh = 40 + Pnl_grid = zeros(Float64, 12, 24, nPnl) + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + time=[23.0,31,12] + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt,3, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 50) + + + @inbounds for k = K:-1:1 + time_next = time + time_dyn_backward!(time, dt, mi_l) + + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS),range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), LoH_grid, Ebt_grid, δ_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt,δ =1:3 + + xk = State(Pnl_grid[m, h+1, pnl], LoH_grid[loh], Ebt_grid[ebt], δ_grid[δ]) + + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 5, 24, 24) #peut changer de taille à régler + err=(xk.Pnl-ref[k]) + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]), x_next.LoH, x_next.Ebatt, x_next.δ) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) + + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + + Jopt[pnl, loh, ebt, δ, k] = Jk_xu_min + end # each state + end + # + + end #each instant + + + return Jopt + +end +function DP_jopt(K::Int64, pen::Float64,Pnl::Vector{Float64},mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st) + + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + + nEbt =20 + nloh = 20 + + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + + + + IBS = BSpline(Linear()) + + + Jopt = zeros(Float32,nloh, nEbt,3, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 50) + + + @inbounds for k = K:-1:1 + + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), LoH_grid, Ebt_grid, δ_grid) + + @inbounds @threads for loh = 1:nloh + + @inbounds for ebt = 1:nEbt,δ =1:3 + + xk = State(Pnl[k], LoH_grid[loh], Ebt_grid[ebt], δ_grid[δ]) + + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 10, 12, 6) #peut changer de taille à régler + + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + + Jk_xu = Jopt_next( x_next.LoH, x_next.Ebatt, x_next.δ) + cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) + + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + + Jopt[loh, ebt, δ, k] = Jk_xu_min + end # each state + end + # + + end #each instant + + + return Jopt + +end + +function DPrecursion_jopt_mc(K::Int64, pen::Float64, time_init::Vector{Float64}, mi_l::Vector{Int64}, Pnl_cate, Pnl_prob, mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st;mode::Int64=1) + + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 40 + nEbt = 40 + nloh = 40 + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + + + n_cat = length(Pnl_cate[1, 1, :]) - 1 + Pnl_grid = zeros(Float64, 12, 24, nPnl) + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + time = time_init + + IBS = BSpline(Linear()) + + + + Jopt = zeros(Float32, nPnl, nloh, nEbt, 3, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 50) + + + @inbounds for k = K:-1:1 + time_next = time + time_dyn_backward!(time, dt, mi_l) + + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), LoH_grid, Ebt_grid, δ_grid) + + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt,δ=1:3 + + xk = State(Pnl_grid[m, h+1, pnl], LoH_grid[loh], Ebt_grid[ebt],δ_grid[δ]) + cat = Int(div(xk.Pnl - Pnl_cate[m, h+1, 1], Pnl_cate[m, h+1, 2] - Pnl_cate[m, h+1, 1]) + 1) + if cat > 16 + cat = 16 + end + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 5, 24, 24) #peut changer de taille à régler + + #CREATE UGRID + Jk_xu_min = Inf32 + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw = 1:n_cat # for each uncertainty value + if Pnl_prob[m, h+1, iw, cat] > 1e-10 + if mode==1 + Jk_xu += Jopt_next(Pnl_cate[m_next, h_next+1, iw] , x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==2 + Jk_xu += Jopt_next((Pnl_cate[m_next, h_next+1, iw] + Pnl_cate[m_next, h_next+1, iw+1])/2, x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==3 + Jk_xu += Jopt_next(Pnl_cate[m_next, h_next+1, iw+1] , x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==4 + Jk_xu += (Jopt_next(Pnl_cate[m_next, h_next+1, iw+1] , x_next.LoH, x_next.Ebatt, x_next.δ) + Jopt_next(Pnl_cate[m_next, h_next+1, iw] , x_next.LoH, x_next.Ebatt, x_next.δ)) * Pnl_prob[m, h+1, iw, cat]/2 + end + # @assert Jk_xu >= 0. " problem, $m, $(h+1), $cat, $Jk_xu, $(Pnl_prob[m, h+1, iw, cat])" + end + end # each uncertainty + + Jk_xu += cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + # @assert Jk_xu_min >= Jopt[pnl, loh, ebt, δ, K+1] - 0.1 " problem, $m, $(h+1), $cat,$xk, $(dynamic(xk, u_opt, 0.0, mg)), $u_opt,$( Jk_xu_min), $(Jopt[pnl,loh,ebt,cy,rt1,rt2,K+1])" + + Jopt[pnl, loh, ebt, δ, k] = Jk_xu_min + + + end # each state + end + # + + + end #each instant + + + return Jopt + +end + +function DPrecursion_uopt(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64, ref,σ,ϕ, mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st) + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + + + + # dbt=0.05*bt.energy_rated + # dh2=0.05*fc.power_rated + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt,3, K + 1) + # Policy over time + Pbt_grid = zeros(nthreads(), 25) + + policy = zeros(Float16, nPnl, nloh, nEbt,3, K) + + u_rb = zeros(Float16, nPnl, nloh, nEbt,3, 2) + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, δ_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, δ=1:3 + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], δ_grid[δ]) + err=(xk.Pnl-ref[k]) + nu = u_range!(selectdim(Pbt_grid, 1, threadid()), xk, mg, 25, 12, 12) #peut changer de taille à régler + + #CREATE UGRID + Jk_xu_min = Inf + u_opt = Pbt_grid[threadid(), 1] + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[threadid(), u_i] + Jk_xu = 0.0 + + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.δ) * prob_demand[iw] + + end # each uncertainty + + Jk_xu += cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + + end + + end #each control + + + Jopt[pnl, loh, ebt, δ, k] = Jk_xu_min + policy[pnl, loh, ebt, δ, k] = u_opt + + end # each state + end + # + + + + end #each instant + + + return policy, u_rb + +end + + +function DPrecursion_rb_cost(K::Int64, pen::Float64, max_pnl::Float64, min_pnl::Float64,ref,σ,ϕ, mg,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st) + el=mg.electrolyzer[1] + fc= mg.dispatchables.fuel_cell[1] + bt = mg.storage + hytank = mg.tanks.h2Tank + dt = mg.project.timestep + nPnl = 16 + nEbt = 8 + nloh = 8 + + Pnl_grid = range(min_pnl, max_pnl, nPnl) + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + + + IBS = BSpline(Linear()) + + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + + Jopt = zeros(Float32, nPnl, nloh, nEbt, 3, K + 1) + # Policy over time + + + + + u_rb = zeros(Float16, nPnl, nloh, nEbt, 3, 2) + + + + @inbounds for k = K:-1:1 + + Jopt_next = Interpolations.scale(interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), Pnl_grid, LoH_grid, Ebt_grid, δ_grid) + + @inbounds @threads for pnl = 1:nPnl + + @inbounds for loh = 1:nloh, ebt = 1:nEbt, δ=1:3 + + xk = State(Pnl_grid[pnl], LoH_grid[loh], Ebt_grid[ebt], δ_grid[δ]) + err=(xk.Pnl-ref[k]) + + Jk_xu = 0.0 + uk = u_rb[pnl, loh, ebt, δ, 1] + + x_next, Pshed, PH2 = dynamic(xk, uk, 0.0, mg) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += Jopt_next(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.δ) * prob_demand[iw] + end # each uncertainty + + Jk_xu += cost_to_go(pen,Pshed,PH2,uk,xk.δ,fc,el,bt,d_rt,d_st) + + + Jopt[pnl, loh, ebt, δ, k] = Jk_xu + + + end # each state + end + # + + end #each instant + + + return Jopt[:, :, :, :, 1] + +end +function sdp_simu_jopt(Jopt, n, pen,ϕ,σ,Pnl_cate, mg1,ref::Vector{Float64},Pnl::Vector{Float64},d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st,data=data) + + el=mg1.electrolyzer[1] + fc= mg1.dispatchables.fuel_cell[1] + bt = mg1.storage + hytank = mg1.tanks.h2Tank + Pren = zeros(n) + Pren = production(mg1.nondispatchables[1]) .+ production(mg1.nondispatchables[2]) + Pbt = zeros(n) + Pel = zeros(n) + Pfc = zeros(n) + Ebt = zeros(n + 1) + Loh = zeros(n + 1) + Lof = zeros(n + 1) + Pgen = zeros(n) + Phb = zeros(n) + Pshed = zeros(n) + Pdump = zeros(n) + Pspill = zeros(n) + + IBS = BSpline(Linear()) + δ=0 + Ebt[1] = 0. + Loh[1] = hytank.ini_filling_ratio * hytank.capacity + nEbt = size(Jopt)[3] + nPnl = size(Jopt)[1] + nloh = size(Jopt)[2] + + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + Pnl_grid = zeros(Float64, 12, 24, nPnl) + + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + Pbt_grid = zeros(100) + nw = 11 + w_law = Normal(0, σ) + demand = range(-3σ, 3σ, nw) + prob_demand = pdf.(w_law, demand) / (sum(pdf(w_law, demand))) + for k = 1:n + if k < n + time_next = [Float64(data.Hour[k+1]), Float64(data.Day[k+1]), Float64(data.Month[k+1])] + + else + time_next = [0., 1, 1] + end + + time = [Float64(data.Hour[k]), Float64(data.Day[k]), Float64(data.Month[k])] + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + J_interp = Interpolations.extrapolate(Interpolations.scale(Interpolations.interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), LoH_grid, Ebt_grid, δ_grid), Line()) + + xk = State(Pnl[k], Loh[k], Ebt[k], δ) + err=(xk.Pnl-ref[k]) + nu = u_range!(Pbt_grid, xk, mg1, 5, 49, 49) + + + Jk_xu_min = Inf + u_opt = 0.0 + ph_opt = 0.0 + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[u_i] + Jk_xu = 0.0 + + x_next, Ps, Ph2 = dynamic(xk, uk, 0.0, mg1) + + @inbounds @fastmath for iw in eachindex(demand) # for each uncertainty value + Jk_xu += J_interp(ref[k+1] + (err * ϕ + demand[iw]) , x_next.LoH, x_next.Ebatt, x_next.δ) * prob_demand[iw] + end # each uncertainty + + + + Jk_xu += cost_to_go(pen,Ps,Ph2,uk,xk.δ,fc,el,bt,d_rt,d_st) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = Ph2 + end + + end #each control + x_next, Ps, Ph2 = dynamic(xk, u_opt, 0.0, mg1) + Pbt[k] = u_opt + Pel[k] = ifelse(Ph2 > 0.0, 0.0, -Ph2) + Pfc[k] = ifelse(Ph2 > 0.0, Ph2, 0.0) + Ebt[k+1] = x_next.Ebatt + Loh[k+1] = x_next.LoH + Pspill[k] = ifelse(Ps > 0.0, 0.0, -Ps) + Pshed[k] = ifelse(Ps > 0.0, Ps, 0.0) + δ=x_next.δ + + end + traj = OperationTraj(Pnl, Pshed, Pren, Pgen, Pfc, Pel, Phb, Ebt, Pbt, Loh, Lof, Pspill, Pdump) + stats = aggregation(mg1, traj) + # Eval the microgrid costs + costs = economics(mg1, stats) + + return traj, stats, costs + +end + +function Dp_simu(Jopt, n, pen, mg1,Pnl::Vector{Float64},d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st,data=data) + + el=mg1.electrolyzer[1] + fc= mg1.dispatchables.fuel_cell[1] + bt = mg1.storage + hytank = mg1.tanks.h2Tank + Pren = zeros(n) + Pren = production(mg1.nondispatchables[1]) .+ production(mg1.nondispatchables[2]) + Pbt = zeros(n) + Pel = zeros(n) + Pfc = zeros(n) + Ebt = zeros(n + 1) + Loh = zeros(n + 1) + Lof = zeros(n + 1) + Pgen = zeros(n) + Phb = zeros(n) + Pshed = zeros(n) + Pdump = zeros(n) + Pspill = zeros(n) + + IBS = BSpline(Linear()) + δ=0 + Ebt[1] = 0. + Loh[1] = hytank.ini_filling_ratio * hytank.capacity + nEbt = size(Jopt)[2] + + nloh = size(Jopt)[1] + + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + + + + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + + Pbt_grid = zeros(100) + + for k = 1:n + if k < n + time_next = [Float64(data.Hour[k+1]), Float64(data.Day[k+1]), Float64(data.Month[k+1])] + + else + time_next = [0., 1, 1] + end + + time = [Float64(data.Hour[k]), Float64(data.Day[k]), Float64(data.Month[k])] + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + J_interp = Interpolations.extrapolate(Interpolations.scale(Interpolations.interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), LoH_grid, Ebt_grid, δ_grid), Line()) + + xk = State(Pnl[k], Loh[k], Ebt[k], δ) + + nu = u_range!(Pbt_grid, xk, mg1, 5, 49, 49) + + + Jk_xu_min = Inf + u_opt = 0.0 + ph_opt = 0.0 + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[u_i] + Jk_xu = 0.0 + + x_next, Ps, Ph2 = dynamic(xk, uk, 0.0, mg1) + + + Jk_xu = J_interp( x_next.LoH, x_next.Ebatt, x_next.δ) + cost_to_go(pen,Ps,Ph2,uk,xk.δ,fc,el,bt,d_rt,d_st) + + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = Ph2 + end + + end #each control + x_next, Ps, Ph2 = dynamic(xk, u_opt, 0.0, mg1) + Pbt[k] = u_opt + Pel[k] = ifelse(Ph2 > 0.0, 0.0, -Ph2) + Pfc[k] = ifelse(Ph2 > 0.0, Ph2, 0.0) + Ebt[k+1] = x_next.Ebatt + Loh[k+1] = x_next.LoH + Pspill[k] = ifelse(Ps > 0.0, 0.0, -Ps) + Pshed[k] = ifelse(Ps > 0.0, Ps, 0.0) + δ=x_next.δ + + end + traj = OperationTraj(Pnl, Pshed, Pren, Pgen, Pfc, Pel, Phb, Ebt, Pbt, Loh, Lof, Pspill, Pdump) + stats = aggregation(mg1, traj) + # Eval the microgrid costs + costs = economics(mg1, stats) + + return traj, stats, costs + +end + + +function sdp_simu_jopt_mc(Jopt, n, pen, mg1, Pnl_cate, Pnl_prob,Pnl,d_rt::Float64=deg_ratio_rt,d_st::Float64=deg_ratio_st;mode::Int64=1,data=data) + + + el=mg1.electrolyzer[1] + fc= mg1.dispatchables.fuel_cell[1] + bt = mg1.storage + hytank = mg1.tanks.h2Tank + Pren = zeros(n) + Pren = production(mg1.nondispatchables[1]) .+ production(mg1.nondispatchables[2]) + + Pbt = zeros(n) + Pel = zeros(n) + Pfc = zeros(n) + Ebt = zeros(n + 1) + + Loh = zeros(n + 1) + Lof = zeros(n + 1) + Pgen = zeros(n) + Phb = zeros(n) + Pshed = zeros(n) + Pdump = zeros(n) + Pspill = zeros(n) + + IBS = BSpline(Linear()) + δ=0 + Ebt[1] = 0. + Loh[1] = hytank.ini_filling_ratio * hytank.capacity + nEbt = size(Jopt)[3] + nPnl = size(Jopt)[1] + nloh = size(Jopt)[2] + + Ebt_grid = range(bt.energy_rated * bt.SoC_min, bt.energy_rated * bt.SoC_max, nEbt) + Pnl_grid = zeros(Float64, 12, 24, nPnl) + n_cat = length(Pnl_cate[1, 1, :]) - 1 + + + for i = 1:12, j = 1:24 + Pnl_grid[i, j, :] = range(Pnl_cate[i, j, 1], Pnl_cate[i, j, end], nPnl) + end + + LoH_grid = range(hytank.capacity * hytank.min_filling_ratio, hytank.capacity * hytank.max_filling_ratio, nloh) + δ_grid = range(-1,1,3) + Pbt_grid = zeros(100) + + for k = 1:n + if k < n + time_next = [Float64(data.Hour[k+1]), Float64(data.Day[k+1]), Float64(data.Month[k+1])] + + else + time_next = [0., 1, 1] + end + + time = [Float64(data.Hour[k]), Float64(data.Day[k]), Float64(data.Month[k])] + m = Int(time[3]) + h = Int(time[1]) + m_next = Int(time_next[3]) + h_next = Int(time_next[1]) + + + J_interp = Interpolations.extrapolate(Interpolations.scale(Interpolations.interpolate!(selectdim(Jopt, ndims(Jopt), k + 1), IBS), range(Pnl_cate[m_next, h_next+1, 1], Pnl_cate[m_next, h_next+1, end], nPnl), + LoH_grid, Ebt_grid, δ_grid), Line()) + + xk = State(Pnl[k], Loh[k], Ebt[k],δ) + cat = Int(div(xk.Pnl - Pnl_cate[m, h+1, 1], Pnl_cate[m, h+1, 2] - Pnl_cate[m, h+1, 1]) + 1) + + if cat > 16 + cat = 16 + end + + if cat < 1 + cat = 1 + end + + # @assert cat >= 1 && cat <= 16 "error $cat, $(xk.Pnl), $(h+1), $m, $(Pnl_cate[m,h+1,1]), $(xk.Pnl-Pnl_cate[m,h+1,1]), $(Pnl_cate[m,h+1,2]-Pnl_cate[m,h+1,1]))" + nu = u_range!(Pbt_grid, xk, mg1, 5, 49, 49) + # nu_bt= Int(ceil((umax.P_bt-umin.P_bt)/dbt)) + 1 + # nu_h2= Int(ceil((umax.P_H2-umin.P_H2)/dh2)) + 1 + + + + + #CREATE UGRID + Jk_xu_min = Inf + u_opt = 0.0 + ph_opt = 0.0 + + + @inbounds for u_i = 1:nu #gérer les situations où il n'y pas de u + uk = Pbt_grid[u_i] + Jk_xu = 0.0 + x_next, Ps, Ph2 = dynamic(xk, uk, 0.0, mg1) + + @fastmath for iw = 1:n_cat # for each uncertainty value + if Pnl_prob[m, h+1, iw, cat] != 0.0 + if mode==1 + Jk_xu += J_interp(Pnl_cate[m_next, h_next+1, iw], x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==2 + Jk_xu += J_interp((Pnl_cate[m_next, h_next+1, iw]+Pnl_cate[m_next, h_next+1, iw+1])/2, x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==3 + Jk_xu += J_interp(Pnl_cate[m_next, h_next+1, iw+1], x_next.LoH, x_next.Ebatt, x_next.δ) * Pnl_prob[m, h+1, iw, cat] + elseif mode==4 + Jk_xu += (J_interp(Pnl_cate[m_next, h_next+1, iw], x_next.LoH, x_next.Ebatt, x_next.δ)+J_interp(Pnl_cate[m_next, h_next+1, iw+1], x_next.LoH, x_next.Ebatt, x_next.δ)) * Pnl_prob[m, h+1, iw, cat]/2 + end + end + end # each uncertainty + + # @assert Jk_xu=0.0!=0 " cout nul" + Jk_xu += cost_to_go(pen,Ps,Ph2,uk,xk.δ,fc,el,bt,d_rt,d_st) + if (Jk_xu < Jk_xu_min) + u_opt = uk + Jk_xu_min = Jk_xu + ph_opt = Ph2 + end + + end #each control + x_next, Ps, Ph2 = dynamic(xk, u_opt, 0.0, mg1) + Pbt[k] = u_opt + Pel[k] = ifelse(Ph2 > 0.0, 0.0, -Ph2) + Pfc[k] = ifelse(Ph2 > 0.0, Ph2, 0.0) + Ebt[k+1] = x_next.Ebatt + Loh[k+1] = x_next.LoH + Pspill[k] = ifelse(Ps > 0.0, 0.0, -Ps) + Pshed[k] = ifelse(Ps > 0.0, Ps, 0.0) + δ=x_next.δ + + + end + traj = OperationTraj(Pnl, Pshed, Pren, Pgen, Pfc, Pel, Phb, Ebt, Pbt, Loh, Lof, Pspill, Pdump) + stats = aggregation(mg1, traj) + # Eval the microgrid costs + costs = economics(mg1, stats) + + return traj, stats, costs +end + + +function moving_average(vs,q;even=true) + + + n=length(vs) + mav=zeros(n) + vs_im=zeros(n+2q) + vs_im[1:q].=vs[end-q+1:end] + vs_im[n+q+1:n+2q].=vs[1:q] + vs_im[q+1:n+q].=vs + coef=ones(2q+1) + if even + coef[1]=0.5 + coef[end]=0.5 + end + for i=1:n + mav[i]=sum( vs_im[i:i+2q] .* coef) /sum(coef) + end + + return mav +end + +function sp_tr_remove(signal,per,d) + n=length(signal) + tre=zeros(length(signal)) + sig=zeros(n+2d*per) + sig[1:d*per].=signal[end-d*per+1:end] + sig[d*per+1:n+d*per] .= signal[:] + sig[n+d*per+1:end]=signal[1:d*per] + + stv=zeros(n) + vec=zeros(2d+1) + for i=1:n + tot=signal[i] + for j=1:d + tot+=sig[i-j*per+d*per] + sig[i+j*per+d*per] + vec[2(j-1)+1]=sig[i-j*per+d*per] + vec[2j]=sig[i+j*per+d*per] + end + vec[end]=signal[i] + tre[i]=tot/(2d+1) + stv[i]=std(vec) + end + return tre,stv + +end + +function hma_spe(signal,n) + m=Int64((n-1)/2) + new_sig=zeros(length(signal)+2m) + new_sig[1:m]=signal[end-m+1:end] + new_sig[m+1:end-m]=signal[:] + new_sig[end-m+1:end]=signal[1:m] + + return hma(new_sig,n)[m+1:end-m] + +end +function plot_periodigram(signal) + n=length(signal) + w=range(1,n/2,n)./n + i=zeros(Float64,n) + t=range(1,n) + for k=1:n + i[k]=(n^-1)*abs(sum(signal .* exp.(-2 .*pi .* im .*(t.-1) .*w[k])))^2 + + end + + plot(w,i) + # xscale("log") + return i +end +function cal_w(a,j) + return (1-a^(2j))*(1+a^2)*((1-a^2)^-1)-2j*a^(2j) +end +function season_estim(signal,d,q) + + n=length(signal) + w=zeros(d) + w_final=zeros(d) + nj=Int64(n/d) + for i=1:d + if i<=q + j=range(1,nj-1) + else + j=range(0,nj-2) + end + + sum_int=0 + for z in j + sum_int+=signal[i+z*d] + end + w[i]=sum_int/length(j) + + end + for i in eachindex(w) + w_final[i]=w[i]-mean(w) + end + return w_final +end \ No newline at end of file diff --git a/Project.toml b/Project.toml index 56a59b7..d9a948c 100644 --- a/Project.toml +++ b/Project.toml @@ -9,6 +9,8 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b" IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a" +NumPyArrays = "359eecd2-3036-4797-89e9-498f868f96c0" [compat] +NumPyArrays = "0.1.1" julia = "1.6" diff --git a/examples/Microgrid_Wind-Solar-H2.ipynb b/examples/Microgrid_Wind-Solar-H2.ipynb index ddd7769..6ad37b8 100644 --- a/examples/Microgrid_Wind-Solar-H2.ipynb +++ b/examples/Microgrid_Wind-Solar-H2.ipynb @@ -5,7 +5,7 @@ "id": "6edd7377-ce3a-468e-82f0-d3a1f5292a1d", "metadata": {}, "source": [ - "# Microgrid performance simulation\n", + "# +Microgrid performance simulation\n", "\n", "Case of Microgrid project with *wind* and *solar* sources,\n", "plus a *battery* and a *dispatchable generator*:\n", @@ -127,7 +127,7 @@ "Pload = data.Load # kW\n", "Ppv1k = data.Ppv1k ./ 1000; # convert to kW/kWp\n", "wind_speed = data.Wind; # m/s\n", - "\n", + "!\n", "# Calibrate wind speed data against a mast measurement \n", "ws_gain = 1.059 # ratio of Mast's mean /PVGIS' mean\n", "wind_speed = ws_gain*wind_speed\n", diff --git a/examples/Useful_functions.jl b/graphics.jl similarity index 81% rename from examples/Useful_functions.jl rename to graphics.jl index 00e86f9..c56ac5c 100644 --- a/examples/Useful_functions.jl +++ b/graphics.jl @@ -1,4 +1,4 @@ -td = collect((0:nsteps-1)/24); + td = collect((0:nsteps-1)/24); """ This file must be included after the instructions: @@ -6,50 +6,16 @@ This file must be included after the instructions: - Include ("../examples/data/Microgrid_Wind-Solar-H2_data.jl") """ -""" -Simulate the performance of a Microgrid project -Returns mg, traj, stats, costs -""" - function simulate_microgrid(x=X,capex=capex_def,dispatch=dispatch_1,load=Pload,cf_pv=Ppv1k,wind=cf_wind) - mg=new_microgrid(Sizing(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9]),capex,ini_filling_state,load,wind,cf_pv) - # Split decision variables (converted MW → kW): - oper_traj = operation(mg, dispatch) - if mg.tanks.h2Tank.capacity>0.0 - a = oper_traj.LoH[end]/mg.tanks.h2Tank.capacity - else - a=0.0 - end - - if mg.storage.energy_rated >0.0 - b = oper_traj.Ebatt[end]/mg.storage.energy_rated - else - b=0.0 - end - - ini=[0.0,a,b] - mg=new_microgrid(Sizing(x[1],x[2],x[3],x[4],x[5],x[6],x[7],x[8],x[9]),capex,ini,load,wind,cf_pv) - # Launch simulation: - traj, stats, costs = simulate(mg,dispatch) - - return mg, traj ,stats, costs -end - """ Plot the instantaneous power sharing of a microgrid furing one year plot_oper_traj(mg, oper_traj) """ function plot_oper_traj(mg, oper_traj) - """ - ns=Int64(nsteps/730) - z=zeros(Float64,ns) -tm=zeros(Float64,ns +1) - tm[end]=nsteps/24 - for i=1:ns - z[i]= oper_traj.LoH[i*730+1] - tm[i]=(i-1)*730/24 - end - """ - fig,( ax1, ax2) = plt.subplots(2,1) + + + + fig,( ax1, ax2) = plt.subplots(2,1,sharex=true) + y1 = min.(oper_traj.Prenew_pot,mg.load) y2 = max.( oper_traj.Pbatt,0.0) y3 = oper_traj.Pfc @@ -59,8 +25,8 @@ tm=zeros(Float64,ns +1) y7 = oper_traj.power_curtailment - y=np.vstack([y1,y2,y3,y4,y5,y6,y7]) - fieldNames =(["Pren used by the load","battery_discharge","fuel cell","battery_charge", "elyz","shedding","spillage"]) + y=np.vstack([y1,y2,y3,y4,y5,y6,y7]) + fieldNames =(["Pren used by the load","battery_discharge","fuel cell","battery_charge", "elyz","shedding","spillage"]) fieldColors = (["orange","mediumseagreen","hotpink","seagreen","orchid","black","red"]) @@ -95,6 +61,8 @@ tm=zeros(Float64,ns +1) ax2.grid(true) ax2.set(ylabel="kg") + + fig.tight_layout() pygui(true) plt.show() @@ -102,7 +70,7 @@ tm=zeros(Float64,ns +1) end -function comp_oper_traj(mg1,mg2, oper_traj1, oper_traj2, costs1,costs2) +function comp_oper_traj(mg1,mg2, oper_traj1, oper_traj2) ns=Int64(nsteps/730) z=zeros(Float64,ns) diff --git a/src/Microgrids.jl b/src/Microgrids.jl index 4de2624..86a35ba 100644 --- a/src/Microgrids.jl +++ b/src/Microgrids.jl @@ -18,12 +18,14 @@ module Microgrids include("production.jl") include("operation.jl") include("economics.jl") + include("../examples/data/Microgrid_Wind-Solar-H2_data.jl") + import Base.@kwdef # backport Julia 1.9 syntax to 1.6-1.8 versions - export simulate,save_mg,load_mg,new_microgrid,simulate_pnl, + export simulate,save_mg,load_mg,new_microgrid,simulate_pnl,simulate_double, NonDispatchableSource, ProductionUnit, Tank, TankCompound, Project, Sizing,DispatchableCompound,Battery, Photovoltaic, PVInverter, WindPower, Microgrid, capacity_from_wind, @@ -41,8 +43,8 @@ module Microgrids """ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=capex_def,initial_fill_rate::Vector{Float64}=ini_filling_state,load::Vector{Float64}=Pload, - wind_speed::Vector{Float64}=wind_speed,irradiance::Vector{Float64}=irradiance) - cf_wind = capacity_from_wind.(wind_speed; TSP=TSP_D52, Cp=Cp_D52, v_out=v_out, α=α_D52) + cf_wind::Vector{Float64}=cf_wind,irradiance::Vector{Float64}=irradiance) + project = Project(lifetime, discount_rate, timestep, "€") gen = ProductionUnit(sizing.Cgen, fuel_intercept, fuel_slope, fuel_price, @@ -131,7 +133,45 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca return oper_traj, oper_stats, mg_costs end -# to update + +""" +Simulate the performance of a Microgrid project +Returns mg, traj, stats, costs +""" + function simulate_double(mg=mg,capex=capex_def,dispatch=dispatch_1) + + # Split decision variables (converted MW → kW): + oper_traj = operation(mg, dispatch) + if mg.tanks.h2Tank.capacity>0.0 + a = oper_traj.LoH[end]/mg.tanks.h2Tank.capacity + else + a=0.0 + end + + if mg.storage.energy_rated >0.0 + b = oper_traj.Ebatt[end]/mg.storage.energy_rated + else + b=0.0 + end + + ini=[0.0,a,b] + sizes=Sizing(mg.dispatchables.generator[1].power_rated,mg.storage.energy_rated,mg.nondispatchables[1].power_rated,mg.nondispatchables[2].power_rated, + mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity,mg.haber_bosch.power_rated) + + + load= mg.load + wind= mg.nondispatchables[2].capacity_factor + cf_pv= mg.nondispatchables[1].irradiance + + mg=new_microgrid(sizes,capex,ini,load,wind,cf_pv) + # Launch simulation: + traj, stats, costs = simulate(mg,dispatch) + + return mg, traj ,stats, costs +end + + +# to be updated function save_mg(mg::Microgrid , stats::OperationStats, traj::OperationTraj, path::String,mg_name::String) mkdir(mg_name) proj_path=path*mg_name*"/" @@ -139,7 +179,7 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca size_frame=DataFrame(Cgen=Float64[], Cbatt=Float64[],Cpv=Float64[],Cwind=Float64[],Cfc=Float64[],Cel=Float64[],Htank=Float64[],Ftank=Float64[],Hb=Float64[]) push!(size_frame,(mg.dispatchables.generator[1].power_rated,mg.storage.energy_rated,mg.nondispatchables[1].power_rated,mg.nondispatchables[2].power_rated, - mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity,0.0)) + mg.dispatchables.fuel_cell[1].power_rated,mg.electrolyzer[1].power_rated,mg.tanks.h2Tank.capacity,mg.tanks.fuelTank.capacity,mg.haber_bosch.power_rated)) CSV.write(proj_path*"sizing.csv",size_frame) op_frame=DataFrame([name => [] for name in propertynames(stats)]) @@ -153,11 +193,12 @@ function new_microgrid(sizing::Sizing = default_sizing,capex::Vector{Float64}=ca function load_mg(project_path :: String) sizing_df = DataFrame(CSV.File(project_path*"/sizing.csv")) - sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8],0.0) + #sizing= Sizing(sizing_df[1,1],sizing_df[1,2],sizing_df[1,3],sizing_df[1,4],sizing_df[1,5],sizing_df[1,6],sizing_df[1,7],sizing_df[1,8],0.0) + sizing= Sizing(sizing_df[1,:]...) traj_df = DataFrame(CSV.File(project_path*"/traj.csv")) - traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],zeros(8760),traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],) - + # traj= OperationTraj(traj_df[:,1],traj_df[:,2],traj_df[:,3],traj_df[:,4],traj_df[:,5],traj_df[:,6],traj_df[:,7],traj_df[:,8],traj_df[:,9],traj_df[:,10],traj_df[:,11],traj_df[:,12],traj_df[:,13]) + traj=OperationTraj(eachcol(traj_df)...) if sizing.Ftank>0.0 a = traj.LoF[1]/sizing.Ftank else diff --git a/src/economics.jl b/src/economics.jl index 748c035..5b65d0b 100644 --- a/src/economics.jl +++ b/src/economics.jl @@ -269,7 +269,13 @@ function component_costs(prod_unit::ProductionUnit, mg_project::Project, prod_un else lifetime = prod_unit.lifetime_calendar end - + """ + if prod_unit_hours > 0.0 + lifetime = min(prod_unit.lifetime_hours / prod_unit_hours, prod_unit.lifetime_calendar) + else + lifetime = prod_unit.lifetime_calendar + end + """ c,cashflow = component_costs( mg_project, lifetime, investment, replacement, salvage, @@ -283,13 +289,13 @@ end Compute net present cost factors for a `Tank`. """ -function component_costs(tank::Tank, mg_project::Project) +function component_costs(tank::Tank, mg_project::Project,dif::Float64) rating = tank.capacity - investment = tank.investment_price * rating + (tank.ini_filling_ratio * tank.combustible_price * rating ) + investment = tank.investment_price * rating + (tank.ini_filling_ratio * tank.combustible_price * rating ) replacement = investment * tank.replacement_price_ratio salvage = investment * tank.salvage_price_ratio om_annual = tank.om_price * rating - fuel_annual = 0.0 + fuel_annual = dif * tank.combustible_price c, cashflow = component_costs( mg_project, tank.lifetime, @@ -408,7 +414,7 @@ function economics(mg::Microgrid, oper_stats::OperationStats) elyz_costs, elyz_cashflow = component_costs(mg.electrolyzer[1], mg.project, oper_stats.elyz_hours,oper_stats.elyz_starts,oper_stats.elyz_consumed_energy) fc_costs,fc_cashflow = component_costs(mg.dispatchables.fuel_cell[1], mg.project, oper_stats.fc_hours,oper_stats.fc_starts,oper_stats.h2_consumed) fuel_tank_cost,fuel_tank_cashflow = component_costs(mg.tanks.fuelTank, mg.project) - h2_tank_cost ,h2_tank_cashflow = component_costs(mg.tanks.h2Tank, mg.project) + h2_tank_cost ,h2_tank_cashflow = component_costs(mg.tanks.h2Tank, mg.project,oper_stats.fc_consumed_h2-oper_stats.elyz_produced_h2) hb_costs,hb_cashflow = component_costs(mg.haber_bosch, mg.project, oper_stats.hb_hours,oper_stats.hb_starts,oper_stats.hb_cons_el) # Energy storage sto_costs, sto_cashflow = component_costs(mg.storage, mg.project, oper_stats)