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Mooi-kickctart2
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Mooi-Kickstart/test_simulation.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from notepad import WaterStorage, Heatpump\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" requirejs.config({\n",
" paths: {\n",
" 'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
" }\n",
" });\n",
" require(['plotly'], function(Plotly) {\n",
" window._Plotly = Plotly;\n",
" });\n",
" }\n",
" </script>\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import cufflinks\n",
"cufflinks.go_offline()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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" <th>2018-11-01 15:50:00</th>\n",
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" <th>2018-11-01 16:30:00</th>\n",
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"<p>100 rows × 12 columns</p>\n",
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" Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n",
"2018-11-01 00:00:00 64.986977 143.358798 0.000000 20.514811 \n",
"2018-11-01 00:10:00 64.942589 140.647419 0.000000 19.280056 \n",
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"... ... ... ... ... \n",
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"2018-11-01 16:20:00 64.940338 157.965580 20.589894 64.489296 \n",
"2018-11-01 16:30:00 64.940338 158.070484 20.799079 64.245148 \n",
"\n",
" Tsink (NDG) MW (NDG) Unnamed: 7 Unnamed: 8 \\\n",
"2018-11-01 00:00:00 147.621126 0.000000 NaN NaN \n",
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"2018-11-01 00:40:00 147.306191 0.000000 NaN NaN \n",
"... ... ... ... ... \n",
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"2018-11-01 16:00:00 165.059628 6.795409 NaN NaN \n",
"2018-11-01 16:10:00 164.350482 6.795437 NaN NaN \n",
"2018-11-01 16:20:00 163.434915 6.804813 NaN NaN \n",
"2018-11-01 16:30:00 163.312629 6.876395 NaN NaN \n",
"\n",
" Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 \n",
"2018-11-01 00:00:00 NaN NaN NaN NaN \n",
"2018-11-01 00:10:00 NaN NaN NaN NaN \n",
"2018-11-01 00:20:00 NaN NaN NaN NaN \n",
"2018-11-01 00:30:00 NaN NaN NaN NaN \n",
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"... ... ... ... ... \n",
"2018-11-01 15:50:00 NaN NaN NaN NaN \n",
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"2018-11-01 16:10:00 NaN NaN NaN NaN \n",
"2018-11-01 16:20:00 NaN NaN NaN NaN \n",
"2018-11-01 16:30:00 NaN NaN NaN NaN \n",
"\n",
"[100 rows x 12 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_excel('Demand_Data_Smurfit_Preprocessed.xlsx', sheet_name='nov2018', index_col=0)\n",
"data.head(100)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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" Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n",
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"2018-11-01 00:10:00 64.942589 140.647419 0.0 19.280056 \n",
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"2018-11-01 01:00:00 65.084549 139.629295 0.0 18.354761 \n",
"2018-11-01 01:10:00 64.810524 139.526447 0.0 19.050589 \n",
"2018-11-01 01:20:00 65.073433 139.423357 0.0 19.903652 \n",
"2018-11-01 01:30:00 65.007141 139.320026 0.0 21.213211 \n",
"\n",
" Tsink (NDG) MW (NDG) Unnamed: 7 Unnamed: 8 \\\n",
"2018-11-01 00:00:00 147.621126 0.0 NaN NaN \n",
"2018-11-01 00:10:00 147.842503 0.0 NaN NaN \n",
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"2018-11-01 01:10:00 148.183192 0.0 NaN NaN \n",
"2018-11-01 01:20:00 149.186865 0.0 NaN NaN \n",
"2018-11-01 01:30:00 147.764356 0.0 NaN NaN \n",
"\n",
" Unnamed: 9 Unnamed: 10 Unnamed: 11 Unnamed: 12 \n",
"2018-11-01 00:00:00 NaN NaN NaN NaN \n",
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"2018-11-01 01:20:00 NaN NaN NaN NaN \n",
"2018-11-01 01:30:00 NaN NaN NaN NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"start, end = '2018-11-01 00:00:00', '2018-11-01 12:00:00'\n",
"df = data[start:end]\n",
"df[:10]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = df.resample('15T', origin=start).mean()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
" <td>148.260335</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"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 "
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"metadata": {},
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],
"source": [
"df[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 8,
"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",
" energy_density = 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": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"50.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"waterstorage.max_storage_capacity"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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 (demand, Tsink, Tref, Cp):\n",
" return demand /(Cp*(Tsink - Tref)) \n",
"\n",
"def COP_calculation(Tsink, Tsource):\n",
" return Tsink / (Tsink - Tsource)\n",
"\n",
"from numpy.polynomial import Polynomial\n",
"\n",
"def cop_curve(Tsink, Tsource):\n",
" c0 = Tsink / (Tsink - Tsource) \n",
" return Polynomial([c0])\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 11,
"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": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.400000000000001"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Tsink = 140 #Celcius\n",
"Tsource = 60\n",
"Tref = 0\n",
"hp_capacity = 31 #MW\n",
"demand = 25 #MW\n",
"Cp = 4190 #J/kgK\n",
"MW_to_J_per_s = 1000_000\n",
"hp_capacity *= MW_to_J_per_s\n",
"demand *= MW_to_J_per_s\n",
"efficiency = 0.9\n",
"Tstorage = 95\n",
"\n",
"# charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (demand, Tsink, Tref, Cp)\n",
"# charged_heat = (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n",
"# charged_heat\n",
"charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (demand, Tsink, Tref, Cp)\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",
"\n",
"discharged_heat = charged_heat(charge_mass_flow, Cp, Tsink, Tref) * efficiency #MW\n",
"def discharge_mass_flow (discharged_heat, Cp, Tstorage, Tref):\n",
" return discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n",
"# discharge_mass_flow = discharged_heat * MW_to_J_per_s /(Cp*(Tstorage - Tref))\n",
"# discharge_mass_flow\n",
"# process_mass_flow\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)/ (discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref) + process_mass_flow)\n",
" \n",
"\n",
"Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n",
"discharged_heat\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def test_heatpump_and_waterstorage_system(Tsink, Tsource, process_demand_MW, e_price, waterstorage_level):\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",
" waterstorage.storage_level = waterstorage_level\n",
"\n",
" \n",
" if e_price < 50:\n",
" hp_load = heatpump.max_th_power #bunu yoxla heat pump-a birbasa set load demek olmur. Ve funksiyada heatpump obyekti var ama o evvel initialize olunmayib\n",
" energy_to_storage = hp_load - process_demand_MW\n",
" waterstorage.charge(energy_to_storage)\n",
" waterstorage.charged_energy = waterstorage.MW_to_MWh(energy_to_storage)\n",
" waterstorage_level = waterstorage.storage_level\n",
" new_cl = waterstorage.storage_level + waterstorage.charged_energy\n",
" if e_price > 100:\n",
" Tstorage = 95\n",
" energy_from_storage = discharged_heat\n",
" waterstorage_level = waterstorage.storage_level\n",
" waterstorage.discharged_energy = waterstorage.MW_to_MWh(energy_from_storage)\n",
" new_cl = waterstorage.storage_level - waterstorage.discharged_energy\n",
" def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n",
" return (\n",
" (Tstorage * discharge_mass_flow + Tsource * process_mass_flow)\n",
" / (discharge_mass_flow + process_mass_flow)\n",
" )\n",
" new_Tsource = Tsource_calculation(Tstorage, discharge_mass_flow(discharged_heat, Cp, Tstorage, Tref), Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n",
" new_COP = COP_calculation (Tsink, new_Tsource)\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": 14,
"metadata": {},
"outputs": [],
"source": [
"# hp_load, new_cl = test_heatpump_and_waterstorage_system(\n",
"# Tsink = 140+273, \n",
"# Tsource = 60+273, \n",
"# process_demand_MW = 25, \n",
"# e_price = 30, \n",
"# waterstorage_level = 15\n",
"# )\n",
"\n",
"# hp_load, new_cl"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bounded_energy 10.0\n",
"new_cl 25.0\n",
"bounded_energy 10.0\n",
"new_cl 35.0\n",
"bounded_energy 10.0\n",
"new_cl 45.0\n",
"bounded_energy 5.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
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"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
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"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
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"new_cl 50.0\n",
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"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n",
"bounded_energy 0.0\n",
"new_cl 50.0\n"
]
}
],
"source": [
"for i in df.index:\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, 15)[1]\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <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",
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" <th>2018-11-01 00:00:00</th>\n",
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" <td>42.203876</td>\n",
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" <th>2018-11-01 01:00:00</th>\n",
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" <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",
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" <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>25.000000</td>\n",
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" <th>2018-11-01 01:30:00</th>\n",
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" <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",
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" <td>65.929909</td>\n",
" <td>148.342325</td>\n",
" <td>19.585104</td>\n",
" <td>61.721718</td>\n",
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" 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) new_cl \n",
"2018-11-01 00:00:00 147.731814 0.000000 25.000000 \n",
"2018-11-01 00:15:00 148.138964 0.000000 25.000000 \n",
"2018-11-01 00:30:00 147.585426 0.000000 25.000000 \n",
"2018-11-01 00:45:00 147.547612 0.000000 25.000000 \n",
"2018-11-01 01:00:00 148.260335 0.000000 25.000000 \n",
"2018-11-01 01:15:00 149.186865 0.000000 25.000000 \n",
"2018-11-01 01:30:00 147.800016 0.000000 25.000000 \n",
"2018-11-01 01:45:00 147.906886 0.000000 25.000000 \n",
"2018-11-01 02:00:00 149.603741 3.333301 22.465103 \n",
"2018-11-01 02:15:00 155.887905 6.455359 20.103724 "
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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": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n",
"# return (\n",
"# th_load / cop * electricity_cost\n",
"# + (demand - th_load) * alt_heat_price\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# alt_heat_price and electricit_cost?"
]
}
],
"metadata": {
"interpreter": {
"hash": "dd1accba5c44bbc1a722925963d63420d7a225a16ee8ad40deae87a5c5fb7f29"
},
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}
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