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Mooi-kickctart2
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Mooi-Kickstart/pilot_run.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 89,
"id": "3cd3ada2-e064-412b-aaf3-09af3279f411",
"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"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"from datetime import timedelta\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"\n",
"import cufflinks\n",
"cufflinks.go_offline()\n",
"import numpy as np\n",
"from numpy.polynomial import Polynomial\n",
"import pandas as pd\n",
"from tqdm.notebook import tqdm\n",
"\n",
"from notepad import WaterStorage, Heatpump\n",
"from pyrecoy.colors import *\n",
"from pyrecoy.converters import *\n",
"from pyrecoy.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
"from pyrecoy.framework import TimeFramework\n",
"from pyrecoy.casestudy import CaseStudy\n",
"from pyrecoy.plotting import ebitda_bar_chart, npv_bar_chart\n",
"from pyrecoy.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
"from pyrecoy.sensitivity import SensitivityAnalysis\n",
"from pyrecoy.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
"from pyrecoy.forecasts import Mipf\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 90,
"id": "4213d9a9-8a4e-465d-b982-540fad36dde0",
"metadata": {},
"outputs": [],
"source": [
"class Config():\n",
" start = '2019-01-01'\n",
" end = '2019-12-31'\n",
" \n",
" hp_vdg_e_power = 23.3 # MW before\n",
" hp_ndg_e_power = 7.7 # MW after\n",
" hp_min_load = 0.3\n",
" hp_lifetime = 25\n",
" hp_capex = 200_000 # EUR/MWth\n",
" hp_opex = 0.01 # in % of CAPEX\n",
" hp_devex = 0.005 # in % of CAPEX\n",
" \n",
" gb_power = 35 # MW\n",
" gb_efficiency = 0.9\n",
" \n",
" tax_bracket_g = 4 \n",
" tax_bracket_e = 4\n",
" \n",
" include_transport_costs = False\n",
" grid_operator = 'Liander'\n",
" connection_type = 'TS/MS'\n",
" \n",
" discount_rate = 0.1\n",
" project_duration = 12\n",
"\n",
" forecast = 'ForeNeg'\n",
" gas_price_multiplier = 1\n",
" e_price_multiplier = 1\n",
" e_price_volatility_multiplier = 1\n",
" co2_price_multiplier = 1\n",
" tsource_delta = 0\n",
" tsink_delta = 0\n",
" energy_tax_multiplier = 1\n",
" \n",
" # Review the SDE implementation\n",
" sde_base_amount = 81\n",
" longterm_gas_price = 24.00\n",
" longterm_co2_price = 37.90\n",
" sde_switch_price_correction = 40\n",
" \n",
" day_ahead_buying_perc = 0.3\n",
" \n",
" afrr_capacity_fee = 25_000\n",
"\n",
"c = Config()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"id": "55d320d4-0590-4b4b-b361-9b3742134254",
"metadata": {},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 104,
"id": "e976f5b5-ab1c-48af-be8c-589f0d62e68b",
"metadata": {},
"outputs": [],
"source": [
"def load_demand_data(c, s):\n",
" demand = pd.read_csv('data/smurfit_demand_preprocessed.csv', delimiter=';', decimal=',')\n",
" dt_index = pd.date_range(\n",
" start=s.time_fw.start,\n",
" end=s.time_fw.start + timedelta(days=365), freq='1T',\n",
" closed='left',\n",
" tz='Europe/Amsterdam')\n",
" demand.index = dt_index\n",
" demand['Total demand'] = demand['MW (VDG)'] + demand['MW (NDG)']\n",
" demand = demand[c.start:c.end]\n",
" return demand\n"
]
},
{
"cell_type": "code",
"execution_count": 94,
"id": "98285112-9b23-4cb9-9b66-0f62e23360c4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_27972/4233922362.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"[<pyrecoy.casestudy.CaseStudy at 0x20329051910>,\n",
" <pyrecoy.casestudy.CaseStudy at 0x20329051a30>]"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def setup_model(c):\n",
" s = Store()\n",
" \n",
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
" mipf = Mipf(\n",
" start=s.time_fw.start, \n",
" end=s.time_fw.end, \n",
" tidy=True, \n",
" include_nextQ=True, \n",
" folder_path=r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\n",
" ).data\n",
"\n",
" s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
" s.hp_storage_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage', data=mipf)\n",
" \n",
" s.cases = list(CaseStudy.instances.values())\n",
" s.optcases = [s.hpcase, s.hp_storage_case]\n",
" # s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n",
" \n",
" s.demand = load_demand_data(c, s)\n",
" return s\n",
"\n",
"s = setup_model(c)\n",
"s.optcases"
]
},
{
"cell_type": "code",
"execution_count": 95,
"id": "59d72c05-698f-47d6-96e1-2dba6ebf7dfb",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_27972/4233922362.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
},
{
"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>Total demand</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2019-01-01 00:00:00+01:00</th>\n",
" <td>64.990</td>\n",
" <td>143.360</td>\n",
" <td>0.00</td>\n",
" <td>20.510</td>\n",
" <td>147.620</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-01 00:01:00+01:00</th>\n",
" <td>64.985</td>\n",
" <td>143.089</td>\n",
" <td>0.00</td>\n",
" <td>20.387</td>\n",
" <td>147.642</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-01 00:02:00+01:00</th>\n",
" <td>64.980</td>\n",
" <td>142.818</td>\n",
" <td>0.00</td>\n",
" <td>20.264</td>\n",
" <td>147.664</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-01 00:03:00+01:00</th>\n",
" <td>64.975</td>\n",
" <td>142.547</td>\n",
" <td>0.00</td>\n",
" <td>20.141</td>\n",
" <td>147.686</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-01-01 00:04:00+01:00</th>\n",
" <td>64.970</td>\n",
" <td>142.276</td>\n",
" <td>0.00</td>\n",
" <td>20.018</td>\n",
" <td>147.708</td>\n",
" <td>0.00</td>\n",
" <td>0.00</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>2019-12-31 23:55:00+01:00</th>\n",
" <td>65.050</td>\n",
" <td>129.970</td>\n",
" <td>17.04</td>\n",
" <td>67.070</td>\n",
" <td>129.350</td>\n",
" <td>5.69</td>\n",
" <td>22.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31 23:56:00+01:00</th>\n",
" <td>65.050</td>\n",
" <td>129.970</td>\n",
" <td>17.04</td>\n",
" <td>67.070</td>\n",
" <td>129.350</td>\n",
" <td>5.69</td>\n",
" <td>22.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31 23:57:00+01:00</th>\n",
" <td>65.050</td>\n",
" <td>129.970</td>\n",
" <td>17.04</td>\n",
" <td>67.070</td>\n",
" <td>129.350</td>\n",
" <td>5.69</td>\n",
" <td>22.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31 23:58:00+01:00</th>\n",
" <td>65.050</td>\n",
" <td>129.970</td>\n",
" <td>17.04</td>\n",
" <td>67.070</td>\n",
" <td>129.350</td>\n",
" <td>5.69</td>\n",
" <td>22.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-12-31 23:59:00+01:00</th>\n",
" <td>65.050</td>\n",
" <td>129.970</td>\n",
" <td>17.04</td>\n",
" <td>67.070</td>\n",
" <td>129.350</td>\n",
" <td>5.69</td>\n",
" <td>22.73</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>525600 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Tsource (VDG) Tsink (VDG) MW (VDG) \\\n",
"2019-01-01 00:00:00+01:00 64.990 143.360 0.00 \n",
"2019-01-01 00:01:00+01:00 64.985 143.089 0.00 \n",
"2019-01-01 00:02:00+01:00 64.980 142.818 0.00 \n",
"2019-01-01 00:03:00+01:00 64.975 142.547 0.00 \n",
"2019-01-01 00:04:00+01:00 64.970 142.276 0.00 \n",
"... ... ... ... \n",
"2019-12-31 23:55:00+01:00 65.050 129.970 17.04 \n",
"2019-12-31 23:56:00+01:00 65.050 129.970 17.04 \n",
"2019-12-31 23:57:00+01:00 65.050 129.970 17.04 \n",
"2019-12-31 23:58:00+01:00 65.050 129.970 17.04 \n",
"2019-12-31 23:59:00+01:00 65.050 129.970 17.04 \n",
"\n",
" Tsource (NDG) Tsink (NDG) MW (NDG) Total demand \n",
"2019-01-01 00:00:00+01:00 20.510 147.620 0.00 0.00 \n",
"2019-01-01 00:01:00+01:00 20.387 147.642 0.00 0.00 \n",
"2019-01-01 00:02:00+01:00 20.264 147.664 0.00 0.00 \n",
"2019-01-01 00:03:00+01:00 20.141 147.686 0.00 0.00 \n",
"2019-01-01 00:04:00+01:00 20.018 147.708 0.00 0.00 \n",
"... ... ... ... ... \n",
"2019-12-31 23:55:00+01:00 67.070 129.350 5.69 22.73 \n",
"2019-12-31 23:56:00+01:00 67.070 129.350 5.69 22.73 \n",
"2019-12-31 23:57:00+01:00 67.070 129.350 5.69 22.73 \n",
"2019-12-31 23:58:00+01:00 67.070 129.350 5.69 22.73 \n",
"2019-12-31 23:59:00+01:00 67.070 129.350 5.69 22.73 \n",
"\n",
"[525600 rows x 7 columns]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demand = load_demand_data(c, s)\n",
"demand\n",
"# how this dataframe is created in the class??"
]
},
{
"cell_type": "code",
"execution_count": 114,
"id": "35d436bc-6846-4888-982d-1a501fc2654d",
"metadata": {},
"outputs": [],
"source": [
"def add_afrr_prices(c, case):\n",
" try:\n",
" aFRR_signal = pd.read_csv(f'data/aFRR_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
" aFRR_signal.index = case.data.index\n",
" except:\n",
" data = get_tennet_data('balansdelta2017', pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
" data.index = data[[\"datum\", \"tijd\"]].apply(lambda x: \" \".join(x), axis=1)\n",
" data.index = pd.to_datetime(data.index, format=\"%d-%m-%Y %H:%M\").tz_localize(\n",
" \"Europe/Amsterdam\", ambiguous=True\n",
" )\n",
" data = data[~data.index.duplicated(keep=\"first\")]\n",
" date_ix = pd.date_range(\n",
" data.index[0], data.index[-1], freq=\"1T\", tz=\"Europe/Amsterdam\"\n",
" )\n",
" data = data.reindex(date_ix)\n",
" aFRR_signal = data[['Hoogste_prijs_opregelen', 'Mid_prijs_opregelen', 'Laagste_prijs_afregelen']]\n",
" aFRR_signal.to_csv(f'data/aFRR_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
"\n",
" try:\n",
" aFRR_prices = pd.read_csv(f'data/aFRR_prices_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
" aFRR_prices.index = case.data.index\n",
" except:\n",
" data = get_aFRR_prices_nl(pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
" data.index = pd.date_range(\n",
" start=c.start,\n",
" end=pd.to_datetime(c.end) + timedelta(days=1),\n",
" tz='Europe/Amsterdam', \n",
" freq='15T', \n",
" closed='left'\n",
" )\n",
" aFRR_prices = data.reindex(case.data.index, method='ffill')\n",
" aFRR_prices.to_csv(f'data/aFRR_prices_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
" \n",
" case.data = pd.concat([case.data, aFRR_signal, aFRR_prices], axis=1)\n",
" return case"
]
},
{
"cell_type": "code",
"execution_count": 97,
"id": "066c1955-a448-443a-ac2c-6ec7930b02fb",
"metadata": {},
"outputs": [],
"source": [
"def increase_volatility_by_factor(col, factor):\n",
" mean = col.mean()\n",
" diff_to_mean = col - mean\n",
" new_diff = diff_to_mean * factor\n",
" return mean + new_diff\n",
"\n",
"def multiply_by_factor(col, factor):\n",
" mean = col.mean()\n",
" diff_to_mean = col - mean\n",
" \n",
" cond = diff_to_mean > 0\n",
" diff_to_mean[cond] *= factor\n",
" diff_to_mean[~cond] /= factor\n",
" return mean + diff_to_mean"
]
},
{
"cell_type": "code",
"execution_count": 98,
"id": "3f2710a5-2744-4aa8-9c9d-2a5770389f52",
"metadata": {},
"outputs": [],
"source": [
"def load_data(c, s):\n",
" # s.afrr_case = add_afrr_prices(c, s.afrr_case)\n",
" \n",
" for case in s.cases:\n",
" case.add_gasprices()\n",
" case.add_co2prices(perMWh=True)\n",
" \n",
" case.data['Gas prices (€/MWh)'] *= c.gas_price_multiplier\n",
" case.data['CO2 prices (€/MWh)'] *= c.co2_price_multiplier\n",
" case.data['CO2 prices (€/ton)'] *= c.co2_price_multiplier\n",
" \n",
" for case in s.optcases:\n",
" case.data['NEG'] = multiply_by_factor(case.data['NEG'], c.e_price_multiplier)\n",
" case.data['ForeNeg'] = multiply_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n",
" case.data['DAM'] = multiply_by_factor(case.data['DAM'], c.e_price_multiplier)\n",
" \n",
" case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n",
" case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n",
" \n",
" s.demand[['Tsource (VDG)', 'Tsource (NDG)']] += c.tsource_delta\n",
" s.demand[['Tsink (VDG)', 'Tsink (NDG)']] += c.tsink_delta\n",
" for case in s.cases:\n",
" case.data = pd.concat([case.data, s.demand], axis=1) \n",
"\n",
" s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
" s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
" s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n",
" s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n",
" return s\n",
"\n",
"s = load_data(c, s)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"id": "5df8557b-1d2e-4c2b-bfce-4a45c898dcc9",
"metadata": {},
"outputs": [],
"source": [
"def cop_value(Tsink, Tsource):\n",
" Tsink += 273\n",
" Tsource += 273\n",
" \n",
" return Tsink / (Tsink - Tsource)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"id": "fd0e191c-0c3d-4c93-9be4-19a59a9e4856",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.591304347826087"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cop_value(140, 25)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"id": "8db4c66e-4675-4277-bcd3-596b4afc9b4e",
"metadata": {},
"outputs": [],
"source": [
"heatpump = Heatpump(\n",
" name='Heatpump',\n",
" max_th_power=1,\n",
" min_th_power=0,\n",
" cop_curve=cop_value\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 118,
"id": "f503259c-a00a-4348-bda7-3d32e76788b7",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "_set_cop_curve() takes 1 positional argument but 2 were given",
"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_27972/1526814104.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_and_assign_assets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_27972/1526814104.py\u001b[0m in \u001b[0;36mcreate_and_assign_assets\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mcreate_and_assign_assets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\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;32m----> 2\u001b[1;33m heatpump_vdg = Heatpump(\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Heatpump VDG'\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[0mmax_th_power\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhp_vdg_e_power\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[0mmin_th_power\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhp_vdg_e_power\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhp_min_load\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\python\\Encore\\notepad.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, max_th_power, cop_curve, min_th_power)\u001b[0m\n\u001b[0;32m 206\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax_th_power\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax_th_power\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_th_power\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin_th_power\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcop_curve\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_cop_curve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcop_curve\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 209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 210\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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: _set_cop_curve() takes 1 positional argument but 2 were given"
]
}
],
"source": [
"def create_and_assign_assets(c, s):\n",
" heatpump_vdg = Heatpump(\n",
" name='Heatpump VDG',\n",
" max_th_power=c.hp_vdg_e_power,\n",
" min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
" cop_curve=cop_value\n",
" )\n",
"\n",
" capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n",
" heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n",
"\n",
" vessel = WaterStorage(\n",
" name='MyStorage',\n",
" max_power=10,\n",
" min_power=-10,\n",
" roundtrip_eff=0.90,\n",
" specific_heat_capacity =1.16*1e-3,\n",
" volume = 1000,\n",
" lifetime = 25,\n",
" min_temperature = 55 + 273,\n",
" max_temperature = 95 + 273\n",
" )\n",
" capex_per_MW = 3_000\n",
" capex_per_MWh = 10_000\n",
"\n",
" vessel.set_financials(\n",
" capex_per_MW,\n",
" capex_per_MWh,\n",
" 2_000,\n",
" 0,\n",
" lifetime=30\n",
" )\n",
" \n",
"# s.hp_storage_case.add_asset(vessel)\n",
" s.baseline.add_asset(gasboiler)\n",
" s.hpcase.add_asset(heatpump_vdg)\n",
" s.hpcase.add_asset(heatpump_ndg)\n",
" s.hpcase_sde.add_asset(heatpump_vdg)\n",
" s.hpcase_sde.add_asset(heatpump_ndg)\n",
" s.optcase1.add_asset(heatpump_vdg)\n",
" s.optcase1.add_asset(heatpump_ndg)\n",
" s.optcase1.add_asset(gasboiler)\n",
" s.afrr_case.add_asset(heatpump_vdg)\n",
" s.afrr_case.add_asset(heatpump_ndg)\n",
" s.afrr_case.add_asset(gasboiler)\n",
" return s\n",
"\n",
"s = create_and_assign_assets(c, s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c097f427-755e-418d-ba1d-fc0de1aa729c",
"metadata": {},
"outputs": [],
"source": [
"def vessel_charge(self, temperature):\n",
" if case.data is NEG and load_perc =! 1\n",
" return Tsink\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb2d60bf-eb09-400e-810a-9d78e062d3f3",
"metadata": {},
"outputs": [],
"source": [
"def vessel_discharge(self):\n",
" if case.data is DAM and Tsource<Tsource (VDG).mean()\n",
" return max_temperature\n",
" # if case.data is DAM and load_perc\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 110,
"id": "0238165a-5e41-41ea-9a33-9931afbe8ce4",
"metadata": {},
"outputs": [],
"source": [
"def cop_value_new (Tsink, Tsource_new):\n",
" Tsink += 273\n",
" Tsource_new = (vessel.max_temperature + Tsource).mean()\n",
" Tsource_new +=273\n",
" return Tsink / (Tsink - Tsource_new)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da8d25e5-6880-4e0a-b75a-219ecebbcad7",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "dcfd7f2e-fe07-4c8b-85b3-e907045e0240",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd22c93e-7a1b-46b2-84ae-4ac76e3968cf",
"metadata": {},
"outputs": [],
"source": [
"Tsink = 140 + 273\n",
"Tsource = 60 + 273\n",
"HP_power = 31 "
]
}
],
"metadata": {
"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": 5
}
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