{ "cells": [ { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "from notepad import WaterStorage, Heatpump\n", "from pyrecoy.forecasts import Mipf\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import cufflinks\n", "cufflinks.go_offline()\n", "from numpy.polynomial import Polynomial" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGForeNegForePos
datetime
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..................
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" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos\n", "datetime \n", "2018-11-01 00:00:00+01:00 44.90 46.39 46.39 80.19 48.13\n", "2018-11-01 00:01:00+01:00 44.90 46.39 46.39 61.21 28.76\n", "2018-11-01 00:02:00+01:00 44.90 46.39 46.39 37.47 21.35\n", "2018-11-01 00:03:00+01:00 44.90 46.39 46.39 65.78 50.15\n", "2018-11-01 00:04:00+01:00 44.90 46.39 46.39 58.43 37.43\n", "... ... ... ... ... ...\n", "2018-11-02 23:55:00+01:00 48.42 37.39 37.39 38.05 38.80\n", "2018-11-02 23:56:00+01:00 48.42 37.39 37.39 41.11 36.20\n", "2018-11-02 23:57:00+01:00 48.42 37.39 37.39 38.51 37.26\n", "2018-11-02 23:58:00+01:00 48.42 37.39 37.39 36.36 33.64\n", "2018-11-02 23:59:00+01:00 48.42 37.39 37.39 34.21 35.14\n", "\n", "[2880 rows x 5 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mipf = Mipf(\n", " start='2018-11-01', \n", " end='2018-11-02', \n", " tidy=True, \n", " include_nextQ=False,\n", " folder_path=r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\n", ").data\n", "# mipf.columns\n", "price_data = mipf[['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos']]\n", "price_data" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[['Tsource (VDG)', 'Tsink (VDG)']].iplot()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waterstorage = WaterStorage(\n", " name='MyStorage',\n", " max_power=10,\n", " min_power=-10,\n", " roundtrip_eff=0.90,\n", " capacity_per_volume = 50 * 1e-3,\n", " volume = 1000,\n", " lifetime = 25,\n", " temperature = 368, #K\n", " min_storagelevel = 5,\n", " # max_storagelevel = 50\n", " \n", ")\n", "waterstorage.set_freq('15T')\n", "waterstorage.set_storagelevel(15)\n", "waterstorage.storagelevel" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "Tsink = 140 #Celcius\n", "Tsource = 60\n", "Tref = 0\n", "hp_capacity = 31 #MW\n", "process_demand_MW = 25 #MW\n", "Cp = 4190 #J/kgK\n", "MW_to_J_per_s = 1000_000\n", "hp_capacity *= MW_to_J_per_s\n", "process_demand_MW *= MW_to_J_per_s\n", "efficiency = 0.9\n", "Tstorage = 95" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50.0" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waterstorage.max_storage_capacity" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def hp_mass_flow (hp_capacity, Tsink, Tref, Cp):\n", " return hp_capacity /(Cp*(Tsink - Tref)) \n", "\n", "def process_mass_flow (process_demand_MW, Tsink, Tref, Cp):\n", " return process_demand_MW /(Cp*(Tsink - Tref)) \n", "\n", "def cop_curve(Tsink, Tsource):\n", " c0 = Tsink / (Tsink - Tsource) \n", " return Polynomial([c0])\n", "\n", "charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (process_demand_MW, Tsink, Tref, Cp)\n", "\n", "def charged_heat (charge_mass_flow, Cp, Tsink, Tref):\n", " return (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n", "\n", "discharged_heat = charged_heat(charge_mass_flow, Cp, Tsink, Tref) #MW\n", "\n", "def discharge_mass_flow (discharged_heat, Cp, Tstorage, Tref):\n", " return discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n", "\n", "def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n", " return ((Tstorage * discharge_mass_flow + Tsource * process_mass_flow)\n", " / (discharge_mass_flow + process_mass_flow))\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'Heatpump',\n", " 'max_th_power': 40,\n", " 'min_th_power': 5,\n", " 'cop_curve': }" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# heatpump = Heatpump(\"heatpump1\", 50, cop_curve, 10)\n", "# heatpump.set_heat_output(50, Tsource=333, Tsink=413)\n", "cop_curve(140, 60)\n", "\n", "heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=40,\n", " min_th_power=5,\n", " cop_curve=cop_curve\n", ")\n", "\n", "heatpump.__dict__\n", "\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.1625" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "heatpump.get_cop(50, Tsource=333, Tsink=413)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# # if price_data < price_data['POS']:\n", "# if price_data['ForePos'].empty< 50:\n", "# hp_load = heatpump.max_th_power\n", "# energy_to_storage = hp_load - process_demand_MW\n", "# waterstorage.charge(energy_to_storage)\n", "# new_cl = waterstorage.storagelevel\n", "# # def charged_heat (charge_mass_flow, Cp, Tsink, Tref):\n", "# # return (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n", "# if price_data['ForeNeg'].empty > 100:\n", "# energy_from_storage = discharged_heat\n", "# waterstorage.discharge(energy_from_storage)\n", "# new_cl = waterstorage.storagelevel\n", "# discharged_heat = charged_heat(charge_mass_flow, Cp, Tsink, Tref)\n", "# def discharge_mass_flow (discharged_heat, Cp, Tstorage, Tref):\n", "# return discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n", "# def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n", "# return ((Tstorage * discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + Tsource * process_mass_flow(process_demand_MW, Tsink, Tref, Cp))\n", "# / (discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + process_mass_flow(process_demand_MW, Tsink, Tref, Cp)))\n", "# new_COP = cop_curve (Tsink, Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow))\n", "# hp_load = heatpump.set_heat_output(process_demand_MW, Tsink, Tsource) #bu da hemcinin set load assetin funksiyasidir, \n", "# #heatpump da overwrite edilib. men evezinde yazdim ki set_heat_output\n", "# #sen gor hansi funksiya sene lazimdir.\n", "# # iki dene funksiyadan cixardim cunki eliye bilmedim onu indi bir funksiya eledim, foreNeg ve ForePosu da elave eledim, indi charged heat ve discharged heati de o logice elave elemeliyem ki eyni vaxtda charge discharge elemesin ondan sonra tsource ve new copni hesablayiram. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def test_heatpump_and_waterstorage_system(Tsink, Tsource, process_demand_MW, e_price):\n", " \"\"\"\n", " 1. Follow a certain logic based on given price:\n", " - If price is low --> Heatpump at full power, and charge the heatbuffer\n", " - If price is high --> Discharge the heat buffer, and increase Tsource, which will increase COP\n", " 2. Above logic should adhere to a couple of constraints:\n", " - Storage levels\n", " - Capacity of the heat pump \n", " - Process demand\n", " - ....\n", " 3. This function should contain: \n", " - Heat pump \n", " - Water storage\n", " - Interactions / logic between them\n", " 4. Output of the function:\n", " - Power of the heatpump (MWe)\n", " - \"New\" water storage level\n", " - (optional) Thermal output of the heatpump\n", " - (optional) In/outflow from the storage\n", " \"\"\"\n", " \n", " if e_price < 50:\n", " hp_load = heatpump.max_th_power\n", " energy_to_storage = hp_load - process_demand_MW\n", " waterstorage.charge(energy_to_storage)\n", " new_cl = waterstorage.storagelevel\n", " if e_price > 100:\n", " energy_from_storage = discharged_heat\n", " waterstorage.discharge(energy_from_storage)\n", " new_cl = waterstorage.storagelevel\n", " \n", " def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n", " return ((Tstorage * discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + Tsource * process_mass_flow(process_demand_MW, Tsink, Tref, Cp))\n", " / (discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + process_mass_flow(process_demand_MW, Tsink, Tref, Cp)))\n", " new_COP = cop_curve (Tsink, Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow))\n", " hp_load = heatpump.set_heat_output(process_demand_MW, Tsink, Tsource) #bu da hemcinin set load assetin funksiyasidir, \n", " #heatpump da overwrite edilib. men evezinde yazdim ki set_heat_output\n", " #sen gor hansi funksiya sene lazimdir.\n", "\n", " return hp_load, new_cl" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# waterstorage.get_soc (30, 50)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)hp_massprocess_massCOPcharge_masscharged_heatdischarged_heatdischarge_massTsource_newnew_COP
2018-11-01 00:00:0064.964783142.0031090.00000019.897433147.7318140.00000052.10145142.017299(5.386969459992516)10.0841526.06.015.073483345.894878(6.005118422067262)
2018-11-01 00:15:0054.578777138.9604930.00000017.950905148.1389640.00000053.24224142.937291(4.882106135030027)10.3049506.06.015.073483338.081803(5.576174793886459)
2018-11-01 00:30:0065.166672139.8853290.00000033.500757147.5854260.00000052.89023642.653416(5.5258665771869335)10.2368206.06.015.073483345.956665(6.1690359297000565)
2018-11-01 00:45:0065.358078139.7319010.00000042.203876147.5476120.00000052.94831042.700250(5.549424302045392)10.2480606.06.015.073483346.091818(6.1934481513445885)
2018-11-01 01:00:0064.947536139.5778710.00000018.702675148.2603350.00000053.00674142.747372(5.528286493353138)10.2593696.06.015.073483345.781999(6.176697097419298)
2018-11-01 01:15:0065.073433139.4233570.00000019.903652149.1868650.00000053.06548542.794746(5.547058190973266)10.2707396.06.015.073483345.868688(6.196760671334125)
2018-11-01 01:30:0047.711559140.3287300.00000019.574467147.8000160.00000052.72311742.518642(4.462765657815752)10.2044746.06.015.073483333.088278(5.151126598798878)
2018-11-01 01:45:0029.525829140.2989020.00000017.065464147.9068860.00000052.73432642.527682(3.731041234226957)10.2066446.06.015.073483319.659577(4.4137321606230016)
2018-11-01 02:00:0065.715569139.99165010.13958749.339708149.6037413.33330152.85006742.621021(5.560223997670469)10.2290456.06.015.073483346.366529(6.198737740428015)
2018-11-01 02:15:0065.929909148.34232519.58510461.721718155.8879056.45535949.87496340.221745(5.112607341877656)9.6532196.06.015.073483346.854418(5.6565198313793505)
\n", "
" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n", "2018-11-01 00:00:00 64.964783 142.003109 0.000000 19.897433 \n", "2018-11-01 00:15:00 54.578777 138.960493 0.000000 17.950905 \n", "2018-11-01 00:30:00 65.166672 139.885329 0.000000 33.500757 \n", "2018-11-01 00:45:00 65.358078 139.731901 0.000000 42.203876 \n", "2018-11-01 01:00:00 64.947536 139.577871 0.000000 18.702675 \n", "2018-11-01 01:15:00 65.073433 139.423357 0.000000 19.903652 \n", "2018-11-01 01:30:00 47.711559 140.328730 0.000000 19.574467 \n", "2018-11-01 01:45:00 29.525829 140.298902 0.000000 17.065464 \n", "2018-11-01 02:00:00 65.715569 139.991650 10.139587 49.339708 \n", "2018-11-01 02:15:00 65.929909 148.342325 19.585104 61.721718 \n", "\n", " Tsink (NDG) MW (NDG) hp_mass process_mass \\\n", "2018-11-01 00:00:00 147.731814 0.000000 52.101451 42.017299 \n", "2018-11-01 00:15:00 148.138964 0.000000 53.242241 42.937291 \n", "2018-11-01 00:30:00 147.585426 0.000000 52.890236 42.653416 \n", "2018-11-01 00:45:00 147.547612 0.000000 52.948310 42.700250 \n", "2018-11-01 01:00:00 148.260335 0.000000 53.006741 42.747372 \n", "2018-11-01 01:15:00 149.186865 0.000000 53.065485 42.794746 \n", "2018-11-01 01:30:00 147.800016 0.000000 52.723117 42.518642 \n", "2018-11-01 01:45:00 147.906886 0.000000 52.734326 42.527682 \n", "2018-11-01 02:00:00 149.603741 3.333301 52.850067 42.621021 \n", "2018-11-01 02:15:00 155.887905 6.455359 49.874963 40.221745 \n", "\n", " COP charge_mass charged_heat \\\n", "2018-11-01 00:00:00 (5.386969459992516) 10.084152 6.0 \n", "2018-11-01 00:15:00 (4.882106135030027) 10.304950 6.0 \n", "2018-11-01 00:30:00 (5.5258665771869335) 10.236820 6.0 \n", "2018-11-01 00:45:00 (5.549424302045392) 10.248060 6.0 \n", "2018-11-01 01:00:00 (5.528286493353138) 10.259369 6.0 \n", "2018-11-01 01:15:00 (5.547058190973266) 10.270739 6.0 \n", "2018-11-01 01:30:00 (4.462765657815752) 10.204474 6.0 \n", "2018-11-01 01:45:00 (3.731041234226957) 10.206644 6.0 \n", "2018-11-01 02:00:00 (5.560223997670469) 10.229045 6.0 \n", "2018-11-01 02:15:00 (5.112607341877656) 9.653219 6.0 \n", "\n", " discharged_heat discharge_mass Tsource_new \\\n", "2018-11-01 00:00:00 6.0 15.073483 345.894878 \n", "2018-11-01 00:15:00 6.0 15.073483 338.081803 \n", "2018-11-01 00:30:00 6.0 15.073483 345.956665 \n", "2018-11-01 00:45:00 6.0 15.073483 346.091818 \n", "2018-11-01 01:00:00 6.0 15.073483 345.781999 \n", "2018-11-01 01:15:00 6.0 15.073483 345.868688 \n", "2018-11-01 01:30:00 6.0 15.073483 333.088278 \n", "2018-11-01 01:45:00 6.0 15.073483 319.659577 \n", "2018-11-01 02:00:00 6.0 15.073483 346.366529 \n", "2018-11-01 02:15:00 6.0 15.073483 346.854418 \n", "\n", " new_COP \n", "2018-11-01 00:00:00 (6.005118422067262) \n", "2018-11-01 00:15:00 (5.576174793886459) \n", "2018-11-01 00:30:00 (6.1690359297000565) \n", "2018-11-01 00:45:00 (6.1934481513445885) \n", "2018-11-01 01:00:00 (6.176697097419298) \n", "2018-11-01 01:15:00 (6.196760671334125) \n", "2018-11-01 01:30:00 (5.151126598798878) \n", "2018-11-01 01:45:00 (4.4137321606230016) \n", "2018-11-01 02:00:00 (6.198737740428015) \n", "2018-11-01 02:15:00 (5.6565198313793505) " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in df.index:\n", " df.loc[i, 'hp_mass'] = hp_mass_flow(hp_capacity, df.loc[i, 'Tsink (VDG)']+273, Tref + 273, Cp)\n", " df.loc[i, 'process_mass'] = process_mass_flow(process_demand_MW, df.loc[i, 'Tsink (VDG)']+273,Tref + 273, Cp)\n", " df.loc[i, 'COP'] = cop_curve(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273)\n", " df.loc[i, 'charge_mass'] = df.loc[i, 'hp_mass'] - df.loc[i, 'process_mass']\n", " df.loc[i, 'charged_heat'] = charged_heat(df.loc[i, 'charge_mass'], Cp, df.loc[i, 'Tsink (VDG)']+273, Tref + 273)\n", " df.loc[i, 'discharged_heat'] = charged_heat(df.loc[i, 'charge_mass'], Cp, df.loc[i, 'Tsink (VDG)']+273, Tref + 273)\n", " df.loc[i, 'discharge_mass'] = discharge_mass_flow(df.loc[i, 'discharged_heat'], Cp, Tstorage+273, Tref+273)\n", " df.loc[i, 'Tsource_new'] = Tsource_calculation(Tstorage + 273, df.loc[i, 'discharge_mass'], df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'process_mass'])\n", " df.loc[i, 'new_COP'] = cop_curve(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource_new'])\n", " \n", "df.head(10)\n", "# Tsource_new should be in the nested function but now it is calculated separately, need to be checked again\n", "# discharge_mass were checked manually,there is very slight change in the last decimals that's why seems constant here, but it calculates correctly." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "for i in df.index:\n", " # df.loc[i, 'MWe'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 130)[0][0]\n", " df.loc[i, 'new_cl'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 30)[1]\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)hp_massprocess_massCOPcharge_masscharged_heatdischarged_heatdischarge_massTsource_newnew_COPnew_cl
2018-11-01 00:00:0064.964783142.0031090.00000019.897433147.7318140.00000052.10145142.017299(5.386969459992516)10.0841526.06.015.073483345.894878(6.005118422067262)25.0
2018-11-01 00:15:0054.578777138.9604930.00000017.950905148.1389640.00000053.24224142.937291(4.882106135030027)10.3049506.06.015.073483338.081803(5.576174793886459)35.0
2018-11-01 00:30:0065.166672139.8853290.00000033.500757147.5854260.00000052.89023642.653416(5.5258665771869335)10.2368206.06.015.073483345.956665(6.1690359297000565)45.0
2018-11-01 00:45:0065.358078139.7319010.00000042.203876147.5476120.00000052.94831042.700250(5.549424302045392)10.2480606.06.015.073483346.091818(6.1934481513445885)47.5
2018-11-01 01:00:0064.947536139.5778710.00000018.702675148.2603350.00000053.00674142.747372(5.528286493353138)10.2593696.06.015.073483345.781999(6.176697097419298)47.5
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"2018-11-01 00:45:00 147.547612 0.000000 52.948310 42.700250 \n", "2018-11-01 01:00:00 148.260335 0.000000 53.006741 42.747372 \n", "2018-11-01 01:15:00 149.186865 0.000000 53.065485 42.794746 \n", "2018-11-01 01:30:00 147.800016 0.000000 52.723117 42.518642 \n", "2018-11-01 01:45:00 147.906886 0.000000 52.734326 42.527682 \n", "2018-11-01 02:00:00 149.603741 3.333301 52.850067 42.621021 \n", "2018-11-01 02:15:00 155.887905 6.455359 49.874963 40.221745 \n", "\n", " COP charge_mass charged_heat \\\n", "2018-11-01 00:00:00 (5.386969459992516) 10.084152 6.0 \n", "2018-11-01 00:15:00 (4.882106135030027) 10.304950 6.0 \n", "2018-11-01 00:30:00 (5.5258665771869335) 10.236820 6.0 \n", "2018-11-01 00:45:00 (5.549424302045392) 10.248060 6.0 \n", "2018-11-01 01:00:00 (5.528286493353138) 10.259369 6.0 \n", "2018-11-01 01:15:00 (5.547058190973266) 10.270739 6.0 \n", "2018-11-01 01:30:00 (4.462765657815752) 10.204474 6.0 \n", "2018-11-01 01:45:00 (3.731041234226957) 10.206644 6.0 \n", "2018-11-01 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "\n", "fig = px.line(df['new_cl'])\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "df.index = df.index.tz_localize('Europe/Amsterdam')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "for col in price_data.columns:\n", " df[col] = price_data[col]\n", "\n", "df['nomination_MWh'] = df['DAM'] * df['MW (VDG)']\n", "df['heatpump_cons_MWh'] = 9\n", "df['imbalance_MWh'] = df['nomination_MWh'] - df['heatpump_cons_MWh']\n", "df['day-ahead costs'] = df['nomination_MWh'] * df['DAM'] \n", "\n", "is_pos = df['imbalance_MWh'] > 0\n", "df.loc[is_pos, 'imbalance costs'] = -df.loc[is_pos, 'imbalance_MWh'] * df['POS'] \n", "\n", "is_neg = df['imbalance_MWh'] < 0\n", "df.loc[is_neg, 'imbalance costs'] = -df.loc[is_neg, 'imbalance_MWh'] * df['NEG'] \n", "\n", "df['total cost'] = df['day-ahead costs'] + df['imbalance costs']" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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10 rows × 27 columns

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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) \\\n", "2018-11-01 00:00:00+01:00 64.964783 142.003109 0.000000 \n", "2018-11-01 00:15:00+01:00 54.578777 138.960493 0.000000 \n", "2018-11-01 00:30:00+01:00 65.166672 139.885329 0.000000 \n", "2018-11-01 00:45:00+01:00 65.358078 139.731901 0.000000 \n", "2018-11-01 01:00:00+01:00 64.947536 139.577871 0.000000 \n", "2018-11-01 01:15:00+01:00 65.073433 139.423357 0.000000 \n", "2018-11-01 01:30:00+01:00 47.711559 140.328730 0.000000 \n", "2018-11-01 01:45:00+01:00 29.525829 140.298902 0.000000 \n", "2018-11-01 02:00:00+01:00 65.715569 139.991650 10.139587 \n", "2018-11-01 02:15:00+01:00 65.929909 148.342325 19.585104 \n", "\n", " Tsource (NDG) Tsink (NDG) MW (NDG) hp_mass \\\n", "2018-11-01 00:00:00+01:00 19.897433 147.731814 0.000000 52.101451 \n", "2018-11-01 00:15:00+01:00 17.950905 148.138964 0.000000 53.242241 \n", "2018-11-01 00:30:00+01:00 33.500757 147.585426 0.000000 52.890236 \n", "2018-11-01 00:45:00+01:00 42.203876 147.547612 0.000000 52.948310 \n", "2018-11-01 01:00:00+01:00 18.702675 148.260335 0.000000 53.006741 \n", "2018-11-01 01:15:00+01:00 19.903652 149.186865 0.000000 53.065485 \n", "2018-11-01 01:30:00+01:00 19.574467 147.800016 0.000000 52.723117 \n", "2018-11-01 01:45:00+01:00 17.065464 147.906886 0.000000 52.734326 \n", "2018-11-01 02:00:00+01:00 49.339708 149.603741 3.333301 52.850067 \n", "2018-11-01 02:15:00+01:00 61.721718 155.887905 6.455359 49.874963 \n", "\n", " process_mass COP charge_mass \\\n", "2018-11-01 00:00:00+01:00 42.017299 (5.386969459992516) 10.084152 \n", "2018-11-01 00:15:00+01:00 42.937291 (4.882106135030027) 10.304950 \n", "2018-11-01 00:30:00+01:00 42.653416 (5.5258665771869335) 10.236820 \n", "2018-11-01 00:45:00+01:00 42.700250 (5.549424302045392) 10.248060 \n", "2018-11-01 01:00:00+01:00 42.747372 (5.528286493353138) 10.259369 \n", "2018-11-01 01:15:00+01:00 42.794746 (5.547058190973266) 10.270739 \n", "2018-11-01 01:30:00+01:00 42.518642 (4.462765657815752) 10.204474 \n", "2018-11-01 01:45:00+01:00 42.527682 (3.731041234226957) 10.206644 \n", "2018-11-01 02:00:00+01:00 42.621021 (5.560223997670469) 10.229045 \n", "2018-11-01 02:15:00+01:00 40.221745 (5.112607341877656) 9.653219 \n", "\n", " ... POS NEG ForeNeg ForePos nomination_MWh \\\n", "2018-11-01 00:00:00+01:00 ... 46.39 46.39 80.19 48.13 0.000000 \n", "2018-11-01 00:15:00+01:00 ... 43.08 43.08 83.91 83.82 0.000000 \n", "2018-11-01 00:30:00+01:00 ... 43.13 43.13 60.04 70.77 0.000000 \n", "2018-11-01 00:45:00+01:00 ... 46.29 46.29 56.14 54.47 0.000000 \n", "2018-11-01 01:00:00+01:00 ... 32.03 32.03 51.11 43.04 0.000000 \n", "2018-11-01 01:15:00+01:00 ... 32.03 32.03 36.80 32.58 0.000000 \n", "2018-11-01 01:30:00+01:00 ... 34.48 34.48 47.60 41.91 0.000000 \n", "2018-11-01 01:45:00+01:00 ... 32.07 32.07 35.57 29.42 0.000000 \n", "2018-11-01 02:00:00+01:00 ... 40.66 40.66 37.61 34.40 446.141846 \n", "2018-11-01 02:15:00+01:00 ... 46.04 46.04 32.69 34.20 861.744591 \n", "\n", " heatpump_cons_MWh imbalance_MWh day-ahead costs \\\n", "2018-11-01 00:00:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 00:15:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 00:30:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 00:45:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 01:00:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 01:15:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 01:30:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 01:45:00+01:00 9 -9.000000 0.000000 \n", "2018-11-01 02:00:00+01:00 9 437.141846 19630.241213 \n", "2018-11-01 02:15:00+01:00 9 852.744591 37916.762021 \n", "\n", " imbalance costs total cost \n", "2018-11-01 00:00:00+01:00 417.510000 417.510000 \n", "2018-11-01 00:15:00+01:00 387.720000 387.720000 \n", "2018-11-01 00:30:00+01:00 388.170000 388.170000 \n", "2018-11-01 00:45:00+01:00 416.610000 416.610000 \n", "2018-11-01 01:00:00+01:00 288.270000 288.270000 \n", "2018-11-01 01:15:00+01:00 288.270000 288.270000 \n", "2018-11-01 01:30:00+01:00 310.320000 310.320000 \n", "2018-11-01 01:45:00+01:00 288.630000 288.630000 \n", "2018-11-01 02:00:00+01:00 -17774.187448 1856.053765 \n", "2018-11-01 02:15:00+01:00 -39260.360987 -1343.598966 \n", "\n", "[10 rows x 27 columns]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "raw", "metadata": {}, "source": [] } ], "metadata": { "interpreter": { "hash": "dd1accba5c44bbc1a722925963d63420d7a225a16ee8ad40deae87a5c5fb7f29" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 4 }