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Mooi-Kickstart/Simulations/.ipynb_checkpoints/Test5-simulation-checkpoint...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from notepad import WaterStorage, Heatpump\n",
"# from pyrecoy.forecasts import Mipf\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# import cufflinks\n",
"# cufflinks.go_offline()\n",
"from numpy.polynomial import Polynomial\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ForeNeg</th>\n",
" <th>ForePos</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-11-01 00:00:00</th>\n",
" <td>91</td>\n",
" <td>34</td>\n",
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" <th>2018-11-01 00:15:00</th>\n",
" <td>39</td>\n",
" <td>31</td>\n",
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" <tr>\n",
" <th>2018-11-01 00:30:00</th>\n",
" <td>52</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:45:00</th>\n",
" <td>108</td>\n",
" <td>42</td>\n",
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" <td>49</td>\n",
" <td>36</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" ForeNeg ForePos\n",
"2018-11-01 00:00:00 91 34\n",
"2018-11-01 00:15:00 39 31\n",
"2018-11-01 00:30:00 52 29\n",
"2018-11-01 00:45:00 108 42\n",
"2018-11-01 01:00:00 49 36"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rng = pd.date_range('2018-11-01 00:00:00', end='2018-11-01 12:00:00', freq='15T' )\n",
"\n",
"price_data = pd.DataFrame(np.random.randint(30, 110, size=(len(rng))), \n",
" columns=['ForeNeg'], \n",
" index=rng)\n",
"\n",
"price_data['ForePos'] = np.random.randint(20, 50, size=(len(rng)))\n",
"\n",
"price_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"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 = price_data.resample('15T').mean()\n",
"# price_data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tsource (VDG)</th>\n",
" <th>Tsink (VDG)</th>\n",
" <th>MW (VDG)</th>\n",
" <th>Tsource (NDG)</th>\n",
" <th>Tsink (NDG)</th>\n",
" <th>MW (NDG)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-11-01 00:00:00</th>\n",
" <td>64.964783</td>\n",
" <td>142.003109</td>\n",
" <td>0.0</td>\n",
" <td>19.897433</td>\n",
" <td>147.731814</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>2018-11-01 00:15:00</th>\n",
" <td>54.578777</td>\n",
" <td>138.960493</td>\n",
" <td>0.0</td>\n",
" <td>17.950905</td>\n",
" <td>148.138964</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:30:00</th>\n",
" <td>65.166672</td>\n",
" <td>139.885329</td>\n",
" <td>0.0</td>\n",
" <td>33.500757</td>\n",
" <td>147.585426</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:45:00</th>\n",
" <td>65.358078</td>\n",
" <td>139.731901</td>\n",
" <td>0.0</td>\n",
" <td>42.203876</td>\n",
" <td>147.547612</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 01:00:00</th>\n",
" <td>64.947536</td>\n",
" <td>139.577871</td>\n",
" <td>0.0</td>\n",
" <td>18.702675</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n",
"2018-11-01 00:00:00 64.964783 142.003109 0.0 19.897433 \n",
"2018-11-01 00:15:00 54.578777 138.960493 0.0 17.950905 \n",
"2018-11-01 00:30:00 65.166672 139.885329 0.0 33.500757 \n",
"2018-11-01 00:45:00 65.358078 139.731901 0.0 42.203876 \n",
"2018-11-01 01:00:00 64.947536 139.577871 0.0 18.702675 \n",
"\n",
" Tsink (NDG) MW (NDG) \n",
"2018-11-01 00:00:00 147.731814 0.0 \n",
"2018-11-01 00:15:00 148.138964 0.0 \n",
"2018-11-01 00:30:00 147.585426 0.0 \n",
"2018-11-01 00:45:00 147.547612 0.0 \n",
"2018-11-01 01:00:00 148.260335 0.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_excel('Demand_Data_Smurfit_Preprocessed.xlsx', sheet_name='nov2018', index_col=0)\n",
"start, end = '2018-11-01 00:00:00', '2018-11-01 12:00:00'\n",
"df = data[start:end]\n",
"df = df.resample('15T', origin=start).mean()\n",
"df=df.drop(['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12'], axis=1)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# df[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 9,
"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": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"process demand 25\n"
]
}
],
"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",
"MWtoJs = 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\n",
"\n",
"print('process demand', process_demand_MW)\n",
"# hp_capacity vs hp_load?"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50.0"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"waterstorage.max_storage_capacity"
]
},
{
"cell_type": "code",
"execution_count": 112,
"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",
"\n",
"def energy_to_storage(hp_capacity, process_demand_MW):\n",
" return hp_capacity - process_demand_MW\n",
"\n",
"\n",
"discharged_heat = energy_to_storage(hp_capacity, process_demand_MW) #MW\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*MWtoJs/(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",
"\n",
"\n",
"# charged_heat can be also defined as hp_capacity-process_demand_MW"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.414895999999999\n",
"19.459420388680527\n"
]
}
],
"source": [
"discharged_heat = energy_to_storage(hp_capacity,19.585104)\n",
"\n",
"print(discharged_heat)\n",
"\n",
"discharge_mass = discharge_mass_flow(discharged_heat, Cp, 140+273, Tref+273)\n",
"\n",
"print(discharge_mass)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'Heatpump',\n",
" 'max_th_power': 40,\n",
" 'min_th_power': 5,\n",
" 'cop_curve': <function __main__.cop_curve(Tsink, Tsource)>}"
]
},
"execution_count": 114,
"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": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.1625"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heatpump.get_cop(50, Tsource=333, Tsink=413)\n"
]
},
{
"cell_type": "code",
"execution_count": 116,
"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": 117,
"metadata": {},
"outputs": [],
"source": [
"# waterstorage.get_soc (30, 50)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Tsink (NDG)</th>\n",
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" <th>2018-11-01 00:00:00</th>\n",
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" <td>64.947536</td>\n",
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" <td>65.073433</td>\n",
" <td>139.423357</td>\n",
" <td>0.000000</td>\n",
" <td>19.903652</td>\n",
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" <td>27</td>\n",
" <td>0.000053</td>\n",
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" <td>0.000053</td>\n",
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" <th>2018-11-01 01:30:00</th>\n",
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" <td>29.525829</td>\n",
" <td>140.298902</td>\n",
" <td>0.000000</td>\n",
" <td>17.065464</td>\n",
" <td>147.906886</td>\n",
" <td>0.000000</td>\n",
" <td>54</td>\n",
" <td>45</td>\n",
" <td>0.000053</td>\n",
" <td>0.000000</td>\n",
" <td>(3.731041234226957)</td>\n",
" <td>0.000053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:00:00</th>\n",
" <td>65.715569</td>\n",
" <td>139.991650</td>\n",
" <td>10.139587</td>\n",
" <td>49.339708</td>\n",
" <td>149.603741</td>\n",
" <td>3.333301</td>\n",
" <td>100</td>\n",
" <td>42</td>\n",
" <td>0.000053</td>\n",
" <td>0.000017</td>\n",
" <td>(5.560223997670469)</td>\n",
" <td>0.000036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:15:00</th>\n",
" <td>65.929909</td>\n",
" <td>148.342325</td>\n",
" <td>19.585104</td>\n",
" <td>61.721718</td>\n",
" <td>155.887905</td>\n",
" <td>6.455359</td>\n",
" <td>85</td>\n",
" <td>36</td>\n",
" <td>0.000050</td>\n",
" <td>0.000032</td>\n",
" <td>(5.112607341877656)</td>\n",
" <td>0.000018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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) ForeNeg ForePos hp_mass \\\n",
"2018-11-01 00:00:00 147.731814 0.000000 91 34 0.000052 \n",
"2018-11-01 00:15:00 148.138964 0.000000 39 31 0.000053 \n",
"2018-11-01 00:30:00 147.585426 0.000000 52 29 0.000053 \n",
"2018-11-01 00:45:00 147.547612 0.000000 108 42 0.000053 \n",
"2018-11-01 01:00:00 148.260335 0.000000 49 36 0.000053 \n",
"2018-11-01 01:15:00 149.186865 0.000000 75 27 0.000053 \n",
"2018-11-01 01:30:00 147.800016 0.000000 100 22 0.000053 \n",
"2018-11-01 01:45:00 147.906886 0.000000 54 45 0.000053 \n",
"2018-11-01 02:00:00 149.603741 3.333301 100 42 0.000053 \n",
"2018-11-01 02:15:00 155.887905 6.455359 85 36 0.000050 \n",
"\n",
" process_mass COP charge_mass \n",
"2018-11-01 00:00:00 0.000000 (5.386969459992516) 0.000052 \n",
"2018-11-01 00:15:00 0.000000 (4.882106135030027) 0.000053 \n",
"2018-11-01 00:30:00 0.000000 (5.5258665771869335) 0.000053 \n",
"2018-11-01 00:45:00 0.000000 (5.549424302045392) 0.000053 \n",
"2018-11-01 01:00:00 0.000000 (5.528286493353138) 0.000053 \n",
"2018-11-01 01:15:00 0.000000 (5.547058190973266) 0.000053 \n",
"2018-11-01 01:30:00 0.000000 (4.462765657815752) 0.000053 \n",
"2018-11-01 01:45:00 0.000000 (3.731041234226957) 0.000053 \n",
"2018-11-01 02:00:00 0.000017 (5.560223997670469) 0.000036 \n",
"2018-11-01 02:15:00 0.000032 (5.112607341877656) 0.000018 "
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for col in price_data.columns:\n",
" df[col] = price_data[col]\n",
"df.head(10)\n",
"# iki dataframeni birlesdirdik burda"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"# for i in df.index:\n",
"# df.loc[i, 'hp_mass'] = hp_mass_flow(hp_capacity, df.loc[i, 'Tsink (VDG)'], Tref, Cp)\n",
"# df.loc[i, 'process_mass'] = process_mass_flow(df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)'],Tref, 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.\n",
"\n",
"# process mass results are wrong"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"52.94831003899137\n",
"17.232571785757365\n",
"15.690906523897725\n",
"15.833641124244465\n",
"15.766858500063137\n"
]
}
],
"source": [
"# df.index = df.index.tz_localize('Europe/Amsterdam')\n",
"for i in df.index:\n",
" # if df.loc[i, 'ForePos'] < 50:\n",
" # hp_load = heatpump.max_th_power\n",
" # energy_to_storage = hp_load - df.loc[i, 'MW (VDG)']\n",
" # waterstorage.charge(energy_to_storage)\n",
" # new_cl = waterstorage.storagelevel\n",
" # print(new_cl)\n",
" if price_data.loc[i,'ForeNeg'] > 100:\n",
" # discharged_heat = energy_to_storage(hp_capacity, df.loc[i, 'MW (VDG)'])\n",
" energy_from_storage = energy_to_storage(hp_capacity, df.loc[i, 'MW (VDG)'])\n",
" waterstorage.discharge(energy_from_storage)\n",
" new_cl = waterstorage.storagelevel\n",
" discharge_mass = discharge_mass_flow(energy_from_storage, Cp, df.loc[i, 'Tsink (VDG)']+273, Tref + 273)\n",
" print(discharge_mass)\n",
" Tsource_new = Tsource_calculation(Tstorage, discharge_mass, Tsource, process_mass_flow(df.loc[i, 'MW (VDG)'], df.loc[i, 'Tsink (VDG)'], Tref, Cp))\n",
" new_COP = cop_curve (df.loc[i, 'Tsink (VDG)']+273, Tsource_new+273)\n",
" # # print(new_COP) "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'numpy.float64' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_20628/3532083246.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'charged_heat'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menergy_to_storage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhp_capacity\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'MW (VDG)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'discharged_heat'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'charged_heat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'discharge_mass'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdischarge_mass_flow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'discharged_heat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mCp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTstorage\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m273\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTref\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m273\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Tsource_new'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTsource_calculation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTstorage\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m273\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'discharge_mass'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Tsource (VDG)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m273\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'process_mass'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: 'numpy.float64' object is not callable"
]
}
],
"source": [
"for i in df.index:\n",
" df.loc[i, 'charged_heat'] = energy_to_storage(hp_capacity, df.loc[i, 'MW (VDG)'])\n",
" df.loc[i, 'discharged_heat'] = df.loc[i, 'charged_heat']\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)"
]
},
{
"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": 19,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tsource (VDG)</th>\n",
" <th>Tsink (VDG)</th>\n",
" <th>MW (VDG)</th>\n",
" <th>Tsource (NDG)</th>\n",
" <th>Tsink (NDG)</th>\n",
" <th>MW (NDG)</th>\n",
" <th>ForeNeg</th>\n",
" <th>ForePos</th>\n",
" <th>hp_mass</th>\n",
" <th>process_mass</th>\n",
" <th>COP</th>\n",
" <th>charge_mass</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-11-01 00:00:00</th>\n",
" <td>64.964783</td>\n",
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" <td>19.897433</td>\n",
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" <td>52.101451</td>\n",
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" <td>52.101451</td>\n",
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" <tr>\n",
" <th>2018-11-01 00:15:00</th>\n",
" <td>54.578777</td>\n",
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" <td>0.000000</td>\n",
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" <td>53.242241</td>\n",
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" <tr>\n",
" <th>2018-11-01 00:30:00</th>\n",
" <td>65.166672</td>\n",
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" <td>52.890236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:45:00</th>\n",
" <td>65.358078</td>\n",
" <td>139.731901</td>\n",
" <td>0.000000</td>\n",
" <td>42.203876</td>\n",
" <td>147.547612</td>\n",
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" <td>18.702675</td>\n",
" <td>148.260335</td>\n",
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" <td>102</td>\n",
" <td>24</td>\n",
" <td>53.006741</td>\n",
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" <tr>\n",
" <th>2018-11-01 01:15:00</th>\n",
" <td>65.073433</td>\n",
" <td>139.423357</td>\n",
" <td>0.000000</td>\n",
" <td>19.903652</td>\n",
" <td>149.186865</td>\n",
" <td>0.000000</td>\n",
" <td>69</td>\n",
" <td>40</td>\n",
" <td>53.065485</td>\n",
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" <td>52.734326</td>\n",
" <td>0.000000</td>\n",
" <td>(3.731041234226957)</td>\n",
" <td>52.734326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:00:00</th>\n",
" <td>65.715569</td>\n",
" <td>139.991650</td>\n",
" <td>10.139587</td>\n",
" <td>49.339708</td>\n",
" <td>149.603741</td>\n",
" <td>3.333301</td>\n",
" <td>97</td>\n",
" <td>24</td>\n",
" <td>52.850067</td>\n",
" <td>0.000017</td>\n",
" <td>(5.560223997670469)</td>\n",
" <td>52.850049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:15:00</th>\n",
" <td>65.929909</td>\n",
" <td>148.342325</td>\n",
" <td>19.585104</td>\n",
" <td>61.721718</td>\n",
" <td>155.887905</td>\n",
" <td>6.455359</td>\n",
" <td>105</td>\n",
" <td>44</td>\n",
" <td>49.874963</td>\n",
" <td>0.000032</td>\n",
" <td>(5.112607341877656)</td>\n",
" <td>49.874932</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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) ForeNeg ForePos hp_mass \\\n",
"2018-11-01 00:00:00 147.731814 0.000000 109 43 52.101451 \n",
"2018-11-01 00:15:00 148.138964 0.000000 34 46 53.242241 \n",
"2018-11-01 00:30:00 147.585426 0.000000 45 48 52.890236 \n",
"2018-11-01 00:45:00 147.547612 0.000000 94 38 52.948310 \n",
"2018-11-01 01:00:00 148.260335 0.000000 102 24 53.006741 \n",
"2018-11-01 01:15:00 149.186865 0.000000 69 40 53.065485 \n",
"2018-11-01 01:30:00 147.800016 0.000000 78 46 52.723117 \n",
"2018-11-01 01:45:00 147.906886 0.000000 94 23 52.734326 \n",
"2018-11-01 02:00:00 149.603741 3.333301 97 24 52.850067 \n",
"2018-11-01 02:15:00 155.887905 6.455359 105 44 49.874963 \n",
"\n",
" process_mass COP charge_mass \n",
"2018-11-01 00:00:00 0.000000 (5.386969459992516) 52.101451 \n",
"2018-11-01 00:15:00 0.000000 (4.882106135030027) 53.242241 \n",
"2018-11-01 00:30:00 0.000000 (5.5258665771869335) 52.890236 \n",
"2018-11-01 00:45:00 0.000000 (5.549424302045392) 52.948310 \n",
"2018-11-01 01:00:00 0.000000 (5.528286493353138) 53.006741 \n",
"2018-11-01 01:15:00 0.000000 (5.547058190973266) 53.065485 \n",
"2018-11-01 01:30:00 0.000000 (4.462765657815752) 52.723117 \n",
"2018-11-01 01:45:00 0.000000 (3.731041234226957) 52.734326 \n",
"2018-11-01 02:00:00 0.000017 (5.560223997670469) 52.850049 \n",
"2018-11-01 02:15:00 0.000032 (5.112607341877656) 49.874932 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:10]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# data['Total demand'] = data['MW (VDG)'] + data['MW (NDG)']\n",
"# data = data[start:end]\n",
"# fig_demands_nov2018 = data['Total demand'].resample('1H').mean().iplot(\n",
"# title='Smurfit Kappa: Heat demand in MW', \n",
"# yTitle='MW', \n",
"# asFigure=True,\n",
"# dimensions=(800, 400)\n",
"# )\n",
"# fig_demands_nov2018"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# import plotly.express as px\n",
"\n",
"# fig = px.line(df['new_cl'])\n",
"# fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"df.index = df.index.tz_localize('Europe/Amsterdam')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Tsource (VDG)</th>\n",
" <th>Tsink (VDG)</th>\n",
" <th>MW (VDG)</th>\n",
" <th>Tsource (NDG)</th>\n",
" <th>Tsink (NDG)</th>\n",
" <th>MW (NDG)</th>\n",
" <th>hp_mass</th>\n",
" <th>process_mass</th>\n",
" <th>COP</th>\n",
" <th>charge_mass</th>\n",
" <th>...</th>\n",
" <th>ForeNeg</th>\n",
" <th>ForePos</th>\n",
" <th>charged_heat</th>\n",
" <th>discharged_heat</th>\n",
" <th>nomination_MWh</th>\n",
" <th>heatpump_cons_MWh</th>\n",
" <th>imbalance_MWh</th>\n",
" <th>day-ahead costs</th>\n",
" <th>imbalance costs</th>\n",
" <th>total cost</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2018-11-01 00:00:00</th>\n",
" <td>64.964783</td>\n",
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" <td>0.000000</td>\n",
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" <td>147.731814</td>\n",
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" <td>NaN</td>\n",
" <td>30.999975</td>\n",
" <td>30.999975</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:15:00</th>\n",
" <td>54.578777</td>\n",
" <td>138.960493</td>\n",
" <td>0.000000</td>\n",
" <td>17.950905</td>\n",
" <td>148.138964</td>\n",
" <td>0.000000</td>\n",
" <td>53.242241</td>\n",
" <td>0.000043</td>\n",
" <td>(4.882106135030027)</td>\n",
" <td>53.242198</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2018-11-01 00:30:00</th>\n",
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" <td>0.000000</td>\n",
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" <td>0.000043</td>\n",
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" <td>NaN</td>\n",
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" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 00:45:00</th>\n",
" <td>65.358078</td>\n",
" <td>139.731901</td>\n",
" <td>0.000000</td>\n",
" <td>42.203876</td>\n",
" <td>147.547612</td>\n",
" <td>0.000000</td>\n",
" <td>52.948310</td>\n",
" <td>0.000043</td>\n",
" <td>(5.549424302045392)</td>\n",
" <td>52.948267</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2018-11-01 01:00:00</th>\n",
" <td>64.947536</td>\n",
" <td>139.577871</td>\n",
" <td>0.000000</td>\n",
" <td>18.702675</td>\n",
" <td>148.260335</td>\n",
" <td>0.000000</td>\n",
" <td>53.006741</td>\n",
" <td>0.000043</td>\n",
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" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2018-11-01 01:15:00</th>\n",
" <td>65.073433</td>\n",
" <td>139.423357</td>\n",
" <td>0.000000</td>\n",
" <td>19.903652</td>\n",
" <td>149.186865</td>\n",
" <td>0.000000</td>\n",
" <td>53.065485</td>\n",
" <td>0.000043</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2018-11-01 01:30:00</th>\n",
" <td>47.711559</td>\n",
" <td>140.328730</td>\n",
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" <td>0.000043</td>\n",
" <td>(4.462765657815752)</td>\n",
" <td>52.723074</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2018-11-01 01:45:00</th>\n",
" <td>29.525829</td>\n",
" <td>140.298902</td>\n",
" <td>0.000000</td>\n",
" <td>17.065464</td>\n",
" <td>147.906886</td>\n",
" <td>0.000000</td>\n",
" <td>52.734326</td>\n",
" <td>0.000043</td>\n",
" <td>(3.731041234226957)</td>\n",
" <td>52.734283</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:00:00</th>\n",
" <td>65.715569</td>\n",
" <td>139.991650</td>\n",
" <td>10.139587</td>\n",
" <td>49.339708</td>\n",
" <td>149.603741</td>\n",
" <td>3.333301</td>\n",
" <td>52.850067</td>\n",
" <td>0.000043</td>\n",
" <td>(5.560223997670469)</td>\n",
" <td>52.850024</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 02:15:00</th>\n",
" <td>65.929909</td>\n",
" <td>148.342325</td>\n",
" <td>19.585104</td>\n",
" <td>61.721718</td>\n",
" <td>155.887905</td>\n",
" <td>6.455359</td>\n",
" <td>49.874963</td>\n",
" <td>0.000040</td>\n",
" <td>(5.112607341877656)</td>\n",
" <td>49.874923</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 23 columns</p>\n",
"</div>"
],
"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 0.000042 \n",
"2018-11-01 00:15:00 148.138964 0.000000 53.242241 0.000043 \n",
"2018-11-01 00:30:00 147.585426 0.000000 52.890236 0.000043 \n",
"2018-11-01 00:45:00 147.547612 0.000000 52.948310 0.000043 \n",
"2018-11-01 01:00:00 148.260335 0.000000 53.006741 0.000043 \n",
"2018-11-01 01:15:00 149.186865 0.000000 53.065485 0.000043 \n",
"2018-11-01 01:30:00 147.800016 0.000000 52.723117 0.000043 \n",
"2018-11-01 01:45:00 147.906886 0.000000 52.734326 0.000043 \n",
"2018-11-01 02:00:00 149.603741 3.333301 52.850067 0.000043 \n",
"2018-11-01 02:15:00 155.887905 6.455359 49.874963 0.000040 \n",
"\n",
" COP charge_mass ... ForeNeg ForePos \\\n",
"2018-11-01 00:00:00 (5.386969459992516) 52.101409 ... NaN NaN \n",
"2018-11-01 00:15:00 (4.882106135030027) 53.242198 ... NaN NaN \n",
"2018-11-01 00:30:00 (5.5258665771869335) 52.890193 ... NaN NaN \n",
"2018-11-01 00:45:00 (5.549424302045392) 52.948267 ... NaN NaN \n",
"2018-11-01 01:00:00 (5.528286493353138) 53.006698 ... NaN NaN \n",
"2018-11-01 01:15:00 (5.547058190973266) 53.065442 ... NaN NaN \n",
"2018-11-01 01:30:00 (4.462765657815752) 52.723074 ... NaN NaN \n",
"2018-11-01 01:45:00 (3.731041234226957) 52.734283 ... NaN NaN \n",
"2018-11-01 02:00:00 (5.560223997670469) 52.850024 ... NaN NaN \n",
"2018-11-01 02:15:00 (5.112607341877656) 49.874923 ... NaN NaN \n",
"\n",
" charged_heat discharged_heat nomination_MWh \\\n",
"2018-11-01 00:00:00 30.999975 30.999975 NaN \n",
"2018-11-01 00:15:00 NaN NaN NaN \n",
"2018-11-01 00:30:00 NaN NaN NaN \n",
"2018-11-01 00:45:00 NaN NaN NaN \n",
"2018-11-01 01:00:00 NaN NaN NaN \n",
"2018-11-01 01:15:00 NaN NaN NaN \n",
"2018-11-01 01:30:00 NaN NaN NaN \n",
"2018-11-01 01:45:00 NaN NaN NaN \n",
"2018-11-01 02:00:00 NaN NaN NaN \n",
"2018-11-01 02:15:00 NaN NaN NaN \n",
"\n",
" heatpump_cons_MWh imbalance_MWh day-ahead costs \\\n",
"2018-11-01 00:00:00 9 NaN NaN \n",
"2018-11-01 00:15:00 9 NaN NaN \n",
"2018-11-01 00:30:00 9 NaN NaN \n",
"2018-11-01 00:45:00 9 NaN NaN \n",
"2018-11-01 01:00:00 9 NaN NaN \n",
"2018-11-01 01:15:00 9 NaN NaN \n",
"2018-11-01 01:30:00 9 NaN NaN \n",
"2018-11-01 01:45:00 9 NaN NaN \n",
"2018-11-01 02:00:00 9 NaN NaN \n",
"2018-11-01 02:15:00 9 NaN NaN \n",
"\n",
" imbalance costs total cost \n",
"2018-11-01 00:00:00 NaN NaN \n",
"2018-11-01 00:15:00 NaN NaN \n",
"2018-11-01 00:30:00 NaN NaN \n",
"2018-11-01 00:45:00 NaN NaN \n",
"2018-11-01 01:00:00 NaN NaN \n",
"2018-11-01 01:15:00 NaN NaN \n",
"2018-11-01 01:30:00 NaN NaN \n",
"2018-11-01 01:45:00 NaN NaN \n",
"2018-11-01 02:00:00 NaN NaN \n",
"2018-11-01 02:15:00 NaN NaN \n",
"\n",
"[10 rows x 23 columns]"
]
},
"execution_count": 37,
"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": []
}
],
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