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Mooi-kickctart2
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Mooi-Kickstart/.ipynb_checkpoints/simulation_test (1)-checkpo...

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{
"cells": [
{
"cell_type": "code",
"execution_count": 73,
"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",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
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" Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n",
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"2018-11-01 16:30:00 64.940338 158.070484 20.799079 64.245148 \n",
"\n",
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"\n",
"[100 rows x 12 columns]"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_excel('Demand_Data_Smurfit_Preprocessed_minutely.xlsx', sheet_name='nov2018', index_col=0)\n",
"data[:100]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"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 01:30:00 65.007141 139.320026 0.0 21.213211 \n",
"\n",
" Tsink (NDG) MW (NDG) Unnamed: 7 Unnamed: 8 \\\n",
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]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"start, end = '2018-11-01 00:00:00', '2018-11-01 16:30:00'\n",
"df = data[start:end]\n",
"df[:10]\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"df = df.resample('15T', origin=start).mean()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
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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",
"</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": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=df.drop(['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12'], axis=1)\n",
"df[:5]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 97,
"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 * 10e-3,\n",
" volume = 500,\n",
" lifetime = 25,\n",
" temperature = 368, #K\n",
" min_storagelevel = 5,\n",
" max_storagelevel = 23\n",
" \n",
")\n",
"waterstorage.set_freq('15T')\n",
"waterstorage.set_storagelevel(15)\n",
"waterstorage.storagelevel"
]
},
{
"cell_type": "code",
"execution_count": 98,
"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": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'name': 'Heatpump',\n",
" 'max_th_power': 20,\n",
" 'min_th_power': 5,\n",
" 'cop_curve': <function __main__.cop_curve(Tsink, Tsource)>}"
]
},
"execution_count": 118,
"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, 13)\n",
"\n",
"heatpump = Heatpump(\n",
" name='Heatpump',\n",
" max_th_power=20,\n",
" min_th_power=5,\n",
" cop_curve=cop_curve\n",
")\n",
"\n",
"heatpump.__dict__\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"341.450974129671"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Tsink = 413 #K\n",
"Tsource = 333 #K\n",
"Tref = 273 #K\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",
"\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",
"\n",
"efficiency = 0.9\n",
"Tstorage = 95 + 273\n",
"discharged_heat = charged_heat * efficiency #MW\n",
"discharged_heat *= MW_to_J_per_s \n",
"discharge_mass_flow = discharged_heat /(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 + Tsource * process_mass_flow)/ (discharge_mass_flow + process_mass_flow)\n",
" \n",
"\n",
"Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n"
]
},
{
"cell_type": "code",
"execution_count": 170,
"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 \n",
" - \"New\" water storage level\n",
" \"\"\"\n",
" waterstorage.storage_level = waterstorage_level\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 = 320\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, 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"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-4.25531914893617"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eload = test_heatpump_and_waterstorage_system(\n",
" Tsink = 150+273, \n",
" Tsource = 60+273, \n",
" process_demand_MW = 25, \n",
" e_price = 130, \n",
" waterstorage_level = 23\n",
")\n",
"\n",
"eload[0]"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [],
"source": [
"for i in df.index:\n",
" df.loc[i, 'e_load'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 130, 23)[0]"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" 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",
" <th>hp_load</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>0.000000</td>\n",
" <td>19.897433</td>\n",
" <td>147.731814</td>\n",
" <td>0.000000</td>\n",
" <td>-0.928166</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>-1.024148</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>0.000000</td>\n",
" <td>33.500757</td>\n",
" <td>147.585426</td>\n",
" <td>0.000000</td>\n",
" <td>-0.904835</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>-0.900994</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>0.000000</td>\n",
" <td>18.702675</td>\n",
" <td>148.260335</td>\n",
" <td>0.000000</td>\n",
" <td>-0.904439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 15:30:00</th>\n",
" <td>65.016212</td>\n",
" <td>157.229508</td>\n",
" <td>20.716926</td>\n",
" <td>64.996845</td>\n",
" <td>167.135901</td>\n",
" <td>6.798455</td>\n",
" <td>-4.286702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 15:45:00</th>\n",
" <td>64.977669</td>\n",
" <td>157.649509</td>\n",
" <td>20.631462</td>\n",
" <td>64.929398</td>\n",
" <td>166.147289</td>\n",
" <td>6.785675</td>\n",
" <td>-4.303817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 16:00:00</th>\n",
" <td>64.946152</td>\n",
" <td>157.807772</td>\n",
" <td>20.608221</td>\n",
" <td>64.789982</td>\n",
" <td>164.705055</td>\n",
" <td>6.795423</td>\n",
" <td>-4.311047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 16:15:00</th>\n",
" <td>64.940338</td>\n",
" <td>157.965580</td>\n",
" <td>20.589894</td>\n",
" <td>64.489296</td>\n",
" <td>163.434915</td>\n",
" <td>6.804813</td>\n",
" <td>-4.317061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2018-11-01 16:30:00</th>\n",
" <td>64.940338</td>\n",
" <td>158.070484</td>\n",
" <td>20.799079</td>\n",
" <td>64.245148</td>\n",
" <td>163.312629</td>\n",
" <td>6.876395</td>\n",
" <td>-4.320878</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>67 rows × 7 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",
"... ... ... ... ... \n",
"2018-11-01 15:30:00 65.016212 157.229508 20.716926 64.996845 \n",
"2018-11-01 15:45:00 64.977669 157.649509 20.631462 64.929398 \n",
"2018-11-01 16:00:00 64.946152 157.807772 20.608221 64.789982 \n",
"2018-11-01 16:15:00 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) hp_load \n",
"2018-11-01 00:00:00 147.731814 0.000000 -0.928166 \n",
"2018-11-01 00:15:00 148.138964 0.000000 -1.024148 \n",
"2018-11-01 00:30:00 147.585426 0.000000 -0.904835 \n",
"2018-11-01 00:45:00 147.547612 0.000000 -0.900994 \n",
"2018-11-01 01:00:00 148.260335 0.000000 -0.904439 \n",
"... ... ... ... \n",
"2018-11-01 15:30:00 167.135901 6.798455 -4.286702 \n",
"2018-11-01 15:45:00 166.147289 6.785675 -4.303817 \n",
"2018-11-01 16:00:00 164.705055 6.795423 -4.311047 \n",
"2018-11-01 16:15:00 163.434915 6.804813 -4.317061 \n",
"2018-11-01 16:30:00 163.312629 6.876395 -4.320878 \n",
"\n",
"[67 rows x 7 columns]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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