diff --git a/Kickstart/V2/README.md b/Kickstart/V2/README.md new file mode 100644 index 0000000..a979c04 --- /dev/null +++ b/Kickstart/V2/README.md @@ -0,0 +1 @@ +jupyter nbconvert simulations.ipynb --TagRemovePreprocessor.enabled=True --TagRemovePreprocessor.remove_cell_tags="['exclude']" --no-input --to html \ No newline at end of file diff --git a/Kickstart/V2/simulations-storageEncore_final.ipynb b/Kickstart/V2/simulations-storageEncore_final.ipynb new file mode 100644 index 0000000..82fa901 --- /dev/null +++ b/Kickstart/V2/simulations-storageEncore_final.ipynb @@ -0,0 +1,4509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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 pyrecoy.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\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": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Development backlog" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "* aFRR (can improve the optimisation case)\n", + "* Report\n", + "\n", + "--\n", + "* Create strategy on imbalance POS (buy 100% day-ahead, and respond to high prices)\n", + "* Graphs\n", + " * EBITDA vs. baseline (earnings vs baseline)\n", + "* Show COP curves in different cases, just for illustration\n", + "* Energy report --> Check + add gas\n", + "* Fix comparison reports\n", + "* Model verification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Meeting Notes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "##### Meeting 25-11-2020" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "* aFRR can help optimisation case\n", + "* SDE++ should be included\n", + "* Tsource sensitivity really gives interesting insights\n", + "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n", + "* AP TNO: Update/verify COP curve\n", + "* AP TNO: Update CAPEX\n", + "* AP Mark: \n", + " * Create graphs on COP curve with different Tsource, Tsink\n", + " * Generate table and output in .csv (send it to Andrew)\n", + "* Investigate opportunity to lower the COP and negative electricity prices\n", + " * Technically feasible, but not really needed/possible to do it in this project\n", + "* Could be interesting to run this model on a usecase with higher delta T\n", + "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "# ENCORE : Heatpump Optimisation Framework\n", + "\n", + "***\n", + "© Mark Kremer \n", + "July 2020\n", + "##### Cases\n", + "In this model, 3 cases are compared:\n", + "* **Baseline** : All heat is supplied by steam turbine\n", + "* **Heatpump case** : All heat is supplied by heatpump\n", + "* **Hybrid case** : Steam turbine and heatpump run in hybrid mode, and are optimised on costs in real-time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Loading config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "class Config():\n", + " start = '2019-01-01'\n", + " end = '2019-12-31'\n", + " \n", + " hp_vdg_e_power = 23.3 # MW\n", + " hp_ndg_e_power = 7.7 # MW\n", + " hp_min_load = 0\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", + " storage_power = 25\n", + " storage_volume = 1000\n", + " storage_cap_per_volume = 50 * 1e-3\n", + " storage_lifetime = 25\n", + " storage_temperature = 95\n", + " storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n", + " storage_capex_per_MW = 7_000\n", + " storage_capex_per_MWh = 5_000\n", + " storage_opex_perc_of_capex = 0.02\n", + " # storage_initial_level = 5\n", + " threshold = 20\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": 3, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "class Store():\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Model set-up" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "s = Store()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n", + "\n", + "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", + "\n" + ] + }, + { + "ename": "TypeError", + "evalue": "Forecast.__init__() got an unexpected keyword argument 'tidy'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[6], line 25\u001b[0m\n\u001b[0;32m 22\u001b[0m s\u001b[38;5;241m.\u001b[39mdemand \u001b[38;5;241m=\u001b[39m load_demand_data(c, s)\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m s\n\u001b[1;32m---> 25\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[43msetup_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[1;32mIn[6], line 5\u001b[0m, in \u001b[0;36msetup_model\u001b[1;34m(c)\u001b[0m\n\u001b[0;32m 2\u001b[0m s \u001b[38;5;241m=\u001b[39m Store()\n\u001b[0;32m 4\u001b[0m s\u001b[38;5;241m.\u001b[39mtime_fw \u001b[38;5;241m=\u001b[39m TimeFramework(start\u001b[38;5;241m=\u001b[39mc\u001b[38;5;241m.\u001b[39mstart, end\u001b[38;5;241m=\u001b[39mc\u001b[38;5;241m.\u001b[39mend)\n\u001b[1;32m----> 5\u001b[0m mipf \u001b[38;5;241m=\u001b[39m \u001b[43mMipf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime_fw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtime_fw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtidy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude_nextQ\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m#folder_path = r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\"\u001b[39;49;00m\n\u001b[0;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdata\n\u001b[0;32m 9\u001b[0m s\u001b[38;5;241m.\u001b[39mbaseline \u001b[38;5;241m=\u001b[39m CaseStudy(time_fw\u001b[38;5;241m=\u001b[39ms\u001b[38;5;241m.\u001b[39mtime_fw, freq\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1T\u001b[39m\u001b[38;5;124m'\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBaseline\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 10\u001b[0m s\u001b[38;5;241m.\u001b[39mhpcase \u001b[38;5;241m=\u001b[39m CaseStudy(time_fw\u001b[38;5;241m=\u001b[39ms\u001b[38;5;241m.\u001b[39mtime_fw, freq\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1T\u001b[39m\u001b[38;5;124m'\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHeatpump only\u001b[39m\u001b[38;5;124m'\u001b[39m, data\u001b[38;5;241m=\u001b[39mmipf)\n", + "File \u001b[1;32mc:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\forecasts.py:98\u001b[0m, in \u001b[0;36mMipf.__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 97\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 99\u001b[0m forecasts \u001b[38;5;241m=\u001b[39m get_imbalance_forecasts_from_database_on_quarter_start_time(kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart\u001b[39m\u001b[38;5;124m'\u001b[39m], kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNLD\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 100\u001b[0m forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPublicationTime\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(forecasts[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPublicationTime\u001b[39m\u001b[38;5;124m'\u001b[39m], utc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[1;31mTypeError\u001b[0m: Forecast.__init__() got an unexpected keyword argument 'tidy'" + ] + } + ], + "source": [ + "def setup_model(c):\n", + " s = Store()\n", + " \n", + " s.time_fw = TimeFramework(start=c.start, end=c.end)\n", + " mipf = Mipf(start=s.time_fw.start, end=s.time_fw.end, tidy=True, 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.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n", + " s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n", + " #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n", + " #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n", + " #s.afrr_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation + aFRR', data=mipf)\n", + " s.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.hpcase_sde, s.optcase1, s.afrr_case]\n", + " s.optcases = [s.hpcase, s.storage_case]\n", + " #s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n", + " s.sde_cases = []\n", + " \n", + " s.demand = load_demand_data(c, s)\n", + " return s\n", + "\n", + "s = setup_model(c)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.demand.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Load in data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def load_data(c, s):\n", + " if hasattr(s, 'afrr_case'):\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", + " if hasattr(s, 'afrr_case'):\n", + " for case in [s.afrr_case]:\n", + " case.data['Hoogste_prijs_opregelen'] = multiply_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_multiplier)\n", + " case.data['Hoogste_prijs_opregelen'] = increase_volatility_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_volatility_multiplier)\n", + " case.data['aFRR_up'] = multiply_by_factor(case.data['aFRR_up'], c.e_price_multiplier)\n", + " case.data['aFRR_up'] = increase_volatility_by_factor(case.data['aFRR_up'], 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": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.hpcase.data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Assets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### COP curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def cop_curve(Tsink, Tsource):\n", + " Tsink += 273\n", + " Tsource += 273\n", + "\n", + " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", + " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", + " \n", + " return Polynomial([c2, c1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cop_curve(140, 13)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cop_curve(140, 73)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "heatpump = Heatpump(\n", + " name='Heatpump',\n", + " max_th_power=1,\n", + " min_th_power=0,\n", + " cop_curve=cop_curve\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=13)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=99)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "source_Ts = np.arange(25, 75) + 273\n", + "sink_Ts = np.arange(80, 170) + 273\n", + "\n", + "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n", + "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n", + " df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n", + " \n", + "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "loads = np.arange(0, 1, 0.01)\n", + "\n", + "cop_series = pd.Series(index=loads, dtype='float')\n", + "load_series = pd.Series(index=loads, dtype='float')\n", + "for load in loads:\n", + " cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n", + " cop_series[load] = cop\n", + " load_series[load] = load / cop " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "fig_cop_curve = cop_series.iplot(title=f'Heatpump COP curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='COP', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n", + "fig_cop_curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n", + "fig_load_curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def cop_curve_new(Tsink, Tsource):\n", + " Tsink += 273\n", + " Tsource += 273\n", + " Tlift = Tsink - Tsource\n", + "\n", + " c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n", + " c1 = 6.7058 \n", + " c2 = -1.79\n", + " \n", + " return Polynomial([c0, c1, c2])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "cop_curve_new(sourceT, sinkT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "heatpump = Heatpump(\n", + " name='Heatpump',\n", + " max_th_power=1,\n", + " min_th_power=0,\n", + " cop_curve=cop_curve_new\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "source_Ts = np.arange(25, 75) + 273\n", + "sink_Ts = np.arange(80, 170) + 273\n", + "\n", + "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n", + "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n", + " df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n", + " \n", + "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "loads = np.arange(0, 1, 0.01)\n", + "\n", + "cop_series = pd.Series(index=loads, dtype='float')\n", + "load_series = pd.Series(index=loads, dtype='float')\n", + "for load in loads:\n", + " cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n", + " cop_series[load] = cop\n", + " load_series[load] = load / cop " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceTs = np.arange(50, 100)\n", + "sinkT = 140 + 273\n", + "load = 1 \n", + "cop_series = pd.Series(index=sourceTs, dtype='float')\n", + "\n", + "for sourceT in sourceTs:\n", + " cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT + 273)\n", + " cop_series[sourceT] = cop\n", + " \n", + "cop_series.iplot(yrange=[0, 8], xTitle='Source Temperature', yTitle='COP', dimensions=(800, 400))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "fig_cop_curve = cop_series.iplot(title=f'Heatpump COP curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='COP', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n", + "fig_cop_curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n", + "fig_load_curve" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Create and assign assets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "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_curve_new\n", + " )\n", + "\n", + " heatpump_ndg = Heatpump(\n", + " name='Heatpump NDG',\n", + " max_th_power=c.hp_ndg_e_power,\n", + " min_th_power=c.hp_ndg_e_power * c.hp_min_load,\n", + " cop_curve=cop_curve_new\n", + " )\n", + "\n", + " capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n", + " capex_ndg = c.hp_capex*(heatpump_ndg.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", + " heatpump_ndg.set_financials(capex=capex_ndg, opex=c.hp_opex*capex_ndg, devex=c.hp_devex*capex_ndg, lifetime=25)\n", + "\n", + " gasboiler = GasBoiler(\n", + " name='Gasboiler',\n", + " max_th_output=c.gb_power,\n", + " efficiency=c.gb_efficiency\n", + " )\n", + " gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n", + " \n", + " waterstorage = HotWaterStorage(\n", + " name='HotWaterStorage',\n", + " rated_power=c.storage_power, \n", + " capacity_per_volume=c.storage_cap_per_volume,\n", + " volume=c.storage_volume, \n", + " temperature=c.storage_temperature,\n", + " min_storagelevel=c.storage_min_level,\n", + " # initial_storagelevel=c.storage_initial_level\n", + " )\n", + " capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n", + " opex_ws = c.storage_opex_perc_of_capex * capex_ws\n", + " waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n", + " \n", + " s.baseline.add_asset(gasboiler)\n", + " s.hpcase.add_asset(heatpump_vdg)\n", + " s.hpcase.add_asset(heatpump_ndg)\n", + " s.storage_case.add_asset(heatpump_vdg)\n", + " s.storage_case.add_asset(heatpump_ndg)\n", + " s.storage_case.add_asset(waterstorage)\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, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.assets['HotWaterStorage']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Strategies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def baseline_sim(case):\n", + " gasboiler = list(case.assets.values())[0]\n", + " data = case.data\n", + " demand = (data['MW (VDG)'] + data['MW (NDG)']).to_list()\n", + "\n", + " minutes = iter(range(len(case.data)))\n", + " th_output = [0] * len(case.data)\n", + " gas_input = [0] * len(case.data)\n", + "\n", + " for m in minutes:\n", + " th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n", + "\n", + " data['output_MW_th'] = np.array(th_output)\n", + " data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n", + " data['gb_input_MW'] = np.array(gas_input)\n", + " data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n", + " case.data = data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hponly(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].to_list()\n", + " demand_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + "\n", + " hp_vdg_input = [0] * len(case.data)\n", + " hp_ndg_input = [0] * len(case.data)\n", + " hp_vdg_output = [0] * len(case.data)\n", + " hp_ndg_output = [0] * len(case.data)\n", + "\n", + " minutes = iter(range(len(case.data)))\n", + " for m in minutes:\n", + " demand = demand_vdg[m]\n", + " if demand != 0:\n", + " hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n", + " heat_output=demand,\n", + " Tsink=Tsink_vdg[m],\n", + " Tsource=Tsource_vdg[m]\n", + " )\n", + "\n", + " demand = demand_ndg[m]\n", + " if demand != 0:\n", + " hp_ndg_input[m], hp_ndg_output[m] = hp_ndg.set_heat_output(\n", + " heat_output=demand_ndg[m],\n", + " Tsink=Tsink_ndg[m],\n", + " Tsource=Tsource_ndg[m]\n", + " )\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n", + " case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n", + " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", + " \n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(3)\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Tref = 0\n", + "Cp = 4190 #J/kgK\n", + "MWtoJs = 1000_000\n", + "\n", + "def power_to_mass_flow(power_MW, Tsink, Tref, Cp):\n", + " return power_MW * MWtoJs /(Cp*(Tsink - Tref))\n", + "def energy_to_storage(hp_heat_output_MW, process_demand_MW):\n", + " return hp_heat_output_MW - process_demand_MW #MW\n", + "\n", + "def Tsource_calculation(Tstorage, discharge_power, Tsource, process_mass_flow): \n", + " discharge_mass_flow = power_to_mass_flow(discharge_power, Tstorage, Tref, Cp)\n", + " \n", + " combined_mass_flow = (discharge_mass_flow + process_mass_flow)\n", + " if combined_mass_flow == 0:\n", + " return Tsource\n", + " else: \n", + " return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow) / combined_mass_flow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg):\n", + " \n", + " from_storage_vdg_MW = 0\n", + " to_storage_vdg_MW = 0 \n", + " from_storage_ndg_MW = 0\n", + " to_storage_ndg_MW = 0 \n", + " \n", + " if neg < dam - c.threshold:\n", + " # overproduce\n", + " if demand_vdg != 0:\n", + " desired_hp_load_vdg = min(demand_vdg + ws.charging_power_limit, hp_vdg.max_th_power)\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + " to_storage_vdg_MW = th_load_vdg - demand_vdg\n", + " to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n", + "\n", + " extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n", + " else:\n", + " e_load_vdg, th_load_vdg = (0,0)\n", + " \n", + " if demand_ndg != 0:\n", + " desired_hp_load_ndg = min(\n", + " demand_ndg + min(ws.charging_power_limit, extra_charging_power_constraint), \n", + " hp_ndg.max_th_power\n", + " )\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(desired_hp_load_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + " to_storage_ndg_MW = th_load_ndg - demand_ndg\n", + " to_storage_ndg_MW = -ws.charge(round(to_storage_ndg_MW, 3))\n", + " else:\n", + " e_load_ndg, th_load_ndg = (0,0)\n", + " \n", + "\n", + " elif pos > dam + c.threshold:\n", + " # take from storage\n", + " if demand_vdg != 0:\n", + " from_process_massflow_vdg = power_to_mass_flow(demand_vdg, tsink_vdg, Tref, Cp)\n", + " try:\n", + " from_storage_vdg_MW = ws.discharging_power_limit\n", + " tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + " except: \n", + " from_storage_vdg_MW = 0\n", + " tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + " from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\n", + " else:\n", + " e_load_vdg, th_load_vdg = (0,0)\n", + " \n", + " #print({f'from_storage_vdg_MW='})\n", + " if demand_ndg != 0:\n", + " from_process_massflow_ndg = power_to_mass_flow(demand_ndg, tsink_ndg, Tref, Cp)\n", + " \n", + " try:\n", + " from_storage_ndg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n", + " tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + " except: \n", + " from_storage_vdg_MW = 0\n", + " tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + " from_storage_ndg_MW = ws.discharge(round(from_storage_ndg_MW))\n", + " else:\n", + " e_load_ndg, th_load_ndg = (0,0)\n", + " else:\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n", + " heat_output=demand_vdg,\n", + " Tsink=tsink_vdg,\n", + " Tsource=tsource_vdg\n", + " )\n", + "\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(\n", + " heat_output=demand_ndg,\n", + " Tsink=tsink_ndg,\n", + " Tsource=tsource_ndg\n", + " )\n", + " return e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg\n", + "# Neg-take price, Pos-feed price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hponly_with_storage(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " ws = case.assets['HotWaterStorage']\n", + " demands_vdg = case.data['MW (VDG)'].to_list()\n", + " demands_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + " dam_prices = case.data['DAM'].to_list()\n", + " pos_prices = case.data['POS'].to_list()\n", + " neg_prices = case.data['NEG'].to_list()\n", + "\n", + " hp_vdg_input = [0] * len(case.data)\n", + " hp_ndg_input = [0] * len(case.data)\n", + " hp_vdg_output = [0] * len(case.data)\n", + " hp_ndg_output = [0] * len(case.data)\n", + "\n", + " minutes = len(case.data)\n", + " storage_levels = [None] * minutes\n", + " to_storage_list = [0] * minutes\n", + " from_storage_vdg_list = [0] * minutes\n", + " to_storage_vdg_list = [0] * minutes\n", + " from_storage_ndg_list = [0] * minutes\n", + " to_storage_ndg_list = [0] * minutes\n", + " \n", + " new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n", + " new_tsources_ndg = case.data['Tsource (NDG)'].to_list()\n", + " \n", + " ws.set_chargelevel(ws.min_chargelevel)\n", + " \n", + " for m in range(minutes):\n", + " tsource_vdg = Tsource_vdg[m]\n", + " tsink_vdg = Tsink_vdg[m]\n", + " tsource_ndg = Tsource_ndg[m]\n", + " tsink_ndg = Tsink_ndg[m]\n", + " demand_vdg = demands_vdg[m]\n", + " demand_ndg = demands_ndg[m]\n", + " \n", + " e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg = hp_storage_opt_enginge(\n", + " c, ws, hp_vdg, hp_ndg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, demand_ndg, \n", + " tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg\n", + " )\n", + " \n", + " hp_vdg_input[m] = e_load_vdg\n", + " hp_vdg_output[m] = th_load_vdg\n", + " hp_ndg_input[m] = e_load_ndg\n", + " hp_ndg_output[m] = th_load_ndg\n", + " \n", + " storage_levels[m] = ws.chargelevel\n", + " new_tsources_vdg[m] = tsource_vdg\n", + " new_tsources_ndg[m] = tsource_ndg\n", + " to_storage_vdg_list[m] = to_storage_vdg_MW\n", + " from_storage_vdg_list[m] = from_storage_vdg_MW\n", + " to_storage_ndg_list[m] = to_storage_ndg_MW\n", + " from_storage_ndg_list[m] = from_storage_ndg_MW\n", + " \n", + " case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n", + " case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n", + " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", + " case.data['tsource_vdg'] = new_tsources_vdg\n", + " case.data['tsource_ndg'] = new_tsources_ndg\n", + " case.data['to_storage_ndg_MW'] = to_storage_ndg_list\n", + " case.data['from_storage_ndg_MW'] = from_storage_ndg_list\n", + " case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n", + " case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n", + " case.data['to_storage_MW'] = case.data['to_storage_vdg_MW'] + case.data['to_storage_ndg_MW']\n", + " case.data['from_storage_MW'] = case.data['from_storage_vdg_MW'] + case.data['from_storage_ndg_MW']\n", + " case.data['storage_level_MWh'] = storage_levels\n", + " \n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(3)\n", + " return case\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# charge_times = print(sum(s.storage_case.data['to_storage_MW']!= 0))\n", + "# discharge_times = print(sum(s.storage_case.data['from_storage_MW']!= 0))\n", + "# # 154476 times out of 525600, the storage is used to charge/dischage\n", + "# # from 39 000 to 110 000 discharge time increases\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case = hponly_with_storage(s.storage_case)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.sample(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.hpcase.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n", + "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n", + "storage_selection = s.storage_case.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n", + "\n", + "selection = pd.concat([hp_selection, storage_selection], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selection.sample(20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "selection.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "########################\n", + "\n", + "# This is where we left\n", + "\n", + "########################" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hybrid_imb_optimisation(case, decimals, s):\n", + " gb = case.assets['Gasboiler']\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", + " demand_ndg = case.data['MW (NDG)'].round(decimals).to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].round(decimals).to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].round(decimals).to_list()\n", + " fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n", + " gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n", + " co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n", + " eb_ode_g = s.eb_ode_g\n", + " eb_ode_e = s.eb_ode_e\n", + " \n", + " gb_input = [0] * len(case.data)\n", + " gb_output = [0] * len(case.data)\n", + "\n", + " minutes = range(len(case.data))\n", + " hp_output = [0] * len(case.data)\n", + " hp_input = [0] * len(case.data)\n", + "\n", + " for m in tqdm(minutes):\n", + " dem_vgd = demand_vdg[m]\n", + " if dem_vgd != 0:\n", + " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", + " min_load = hp_vdg.min_th_power\n", + " Tsink = Tsink_vdg[m]\n", + " Tsource = Tsource_vdg[m]\n", + " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load < cost_min_load:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = (0, 0)\n", + "\n", + " dem_ngd = demand_ndg[m]\n", + " if dem_ngd != 0:\n", + " max_load = min(hp_ndg.max_th_power, dem_ngd)\n", + " min_load = hp_ndg.min_th_power\n", + " Tsink = Tsink_ndg[m]\n", + " Tsource = Tsource_ndg[m]\n", + " cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_ngd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load <= cost_min_load:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = (0, 0)\n", + "\n", + " hp_out = hp_vdg_output + hp_ndg_output\n", + " hp_output[m] = hp_out\n", + " hp_input[m] = hp_vdg_input + hp_ndg_input\n", + " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", + " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_output)\n", + " case.data['hp_input_MW'] = np.array(hp_input)\n", + " case.data['gb_output_MW'] = np.array(gb_output)\n", + " case.data['gb_input_MW'] = np.array(gb_input)\n", + "\n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "aFRR\n", + "* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n", + "* Remaining demand is filed in by gasboiler\n", + "* Bid price at switch price?\n", + "* Assume direct response to 0% for now?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def calc_afrr_capacity(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " \n", + " capacity = 0\n", + " for hp in [hp_vdg, hp_ndg]:\n", + " max_th_output = hp.max_th_power\n", + " cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n", + " e_power = max_th_output / cop\n", + " capacity += e_power\n", + " return capacity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def aFRR_optimisation(case, s):\n", + " s.afrr_case.afrr_capacity = calc_afrr_capacity(s.afrr_case)\n", + " gb = case.assets['Gasboiler']\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].to_list()\n", + " demand_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + " afrr_up = case.data['Hoogste_prijs_opregelen'].fillna(-999).to_list()\n", + " gas_prices = case.data['Gas prices (€/MWh)'].to_list()\n", + " co2_prices = case.data['CO2 prices (€/MWh)'].to_list()\n", + " eb_ode_g = s.eb_ode_g\n", + " eb_ode_e = s.eb_ode_e\n", + " \n", + " gb_input = [0] * len(case.data)\n", + " gb_output = [0] * len(case.data)\n", + "\n", + " minutes = range(len(case.data))\n", + " hp_output = [0] * len(case.data)\n", + " hp_input = [0] * len(case.data)\n", + "\n", + " for m in tqdm(minutes):\n", + " dem_vgd = demand_vdg[m]\n", + " if dem_vgd != 0:\n", + " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", + " min_load = hp_vdg.min_th_power\n", + " Tsink = Tsink_vdg[m]\n", + " Tsource = Tsource_vdg[m]\n", + " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e - c.sde_switch_price_correction,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load < cost_min_load:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = (0, 0)\n", + "\n", + " dem_ngd = demand_ndg[m]\n", + " if dem_ngd != 0:\n", + " max_load = min(hp_ndg.max_th_power, dem_ngd)\n", + " min_load = hp_ndg.min_th_power\n", + " Tsink = Tsink_ndg[m]\n", + " Tsource = Tsource_ndg[m]\n", + " cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_ngd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load <= cost_min_load:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = (0, 0)\n", + "\n", + " hp_out = hp_vdg_output + hp_ndg_output\n", + " hp_output[m] = hp_out\n", + " hp_input[m] = hp_vdg_input + hp_ndg_input\n", + " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", + " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_output)\n", + " case.data['hp_input_MW'] = np.array(hp_input)\n", + " case.data['gb_output_MW'] = np.array(gb_output)\n", + " case.data['gb_input_MW'] = np.array(gb_input)\n", + "\n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Run optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def run_optimisation(c, s):\n", + " s.baseline = baseline_sim(s.baseline)\n", + " s.hpcase = hponly(s.hpcase)\n", + " s.storage_case = hponly_with_storage(s.storage_case)\n", + "# s.hpcase_sde.assign_algorithm(hponly)\n", + "# s.hpcase_sde.run()\n", + " \n", + "# s.optcase1.assign_algorithm(hybrid_imb_optimisation)\n", + "# s.optcase1.run(decimals=2, s=s)\n", + " \n", + "# s.afrr_case.assign_algorithm(aFRR_optimisation)\n", + "# s.afrr_case.run(s=s)\n", + " \n", + " for case in [s.hpcase, s.storage_case]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n", + " \n", + " return s\n", + "\n", + "s = run_optimisation(c, s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data['hp_output_MW'].sum() /s.storage_case.data['hp_input_MW'].abs().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.hpcase.data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vis_data = s.storage_case.data[[\n", + " 'storage_level_MWh', \n", + " #'to_storage_ndg_MW', \n", + " #'to_storage_vdg_MW', \n", + " 'to_storage_MW', \n", + " #'from_storage_ndg_MW', \n", + " #'from_storage_vdg_MW',\n", + " 'from_storage_MW'\n", + "]]\n", + "\n", + "cops = pd.concat([s.hpcase.data['cop'], s.storage_case.data['cop']], axis=1)\n", + "vis_data = pd.concat([vis_data, cops], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vis_data.loc['2019-01-01', :].iplot(subplots=True, shape=(5,1), dimensions=(1000, 800))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "total_to_storage = s.storage_case.data['to_storage_MWh'].sum()\n", + "total_from_storage = s.storage_case.data['from_storage_MWh'].sum()\n", + "total_to_storage, total_from_storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hp_output = s.storage_case.data['hp_output_MW'].round(2)\n", + "demand_and_to_storage = (s.storage_case.data['Total demand'] + s.storage_case.data['to_storage_MW']).round(2)\n", + "hp_output.equals(demand_and_to_storage)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Financials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def calculate_sde_subsidy(c, s, case):\n", + " hp_capacity = abs(case.assets['Heatpump VDG'].max_th_power) + abs(case.assets['Heatpump NDG'].max_th_power)\n", + " case.full_load_hours = abs(case.data['hp_output_MWh'].sum() / hp_capacity)\n", + "\n", + " subsidized_hours = min(case.full_load_hours, 8000)\n", + " subsidized_MWh = subsidized_hours * hp_capacity\n", + "\n", + " base_amount_per_MWh_th = round(c.sde_base_amount*0.173 + c.longterm_gas_price, 2)\n", + " base_subsidy_amount = round(base_amount_per_MWh_th * subsidized_MWh, 2)\n", + " long_term_gas_price_LHV = round(EURperHHV_to_EURperLHV(c.longterm_gas_price), 2)\n", + " base_amount_gas = (2/3)*long_term_gas_price_LHV*0.9\n", + " mean_ttf_price_LHV = round(EURperHHV_to_EURperLHV(case.data['Gas prices (€/MWh)'].mean()), 2)\n", + " correction_amount_gas = max(mean_ttf_price_LHV, base_amount_gas)\n", + " avoided_gas_consumption = subsidized_MWh/0.9\n", + " correction_gas = round(correction_amount_gas * avoided_gas_consumption, 2)\n", + "\n", + " base_amount_co2 = (2/3)*c.longterm_co2_price\n", + " avoided_co2_emission = avoided_gas_consumption * 0.226\n", + " mean_ets_price = round(EURperHHV_to_EURperLHV(case.data['CO2 prices (€/ton)'].mean()), 2)\n", + " correction_amount_co2 = max(base_amount_co2, mean_ets_price)\n", + " correction_co2 = round(correction_amount_co2 * avoided_co2_emission, 2)\n", + "\n", + " sde_subsidy_corrected = max(base_subsidy_amount - (correction_gas + correction_co2), 0)\n", + " sde_per_MWh_th = sde_subsidy_corrected / subsidized_MWh\n", + " sde_per_MWh_e = sde_subsidy_corrected / case.data['hp_input_MWh'].abs().sum()\n", + "\n", + " case.sde_results = {\n", + " 'base_amount': base_subsidy_amount,\n", + " 'correction_gas': correction_gas,\n", + " 'correction_co2': correction_co2,\n", + " 'corrected_amount': sde_subsidy_corrected,\n", + " 'sde_per_MWh_th': sde_per_MWh_th,\n", + " 'sde_per_MWh_e': sde_per_MWh_e\n", + " }\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def collect_cashflows(c, s):\n", + " s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", + " s.storage_case.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n", + " s.storage_case.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n", + "\n", + "# s.hpcase_sde.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", + " \n", + "# s.optcase1.data['DA Nom'] = s.hpcase.data['hp_input_MWh'] * c.day_ahead_buying_perc\n", + "# s.optcase1.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n", + " \n", + "# s.afrr_case.data['POS'] = s.afrr_case.data['aFRR_up'].fillna(value=s.afrr_case.data['POS'])\n", + "# s.afrr_case.data['DA Nom'] = s.hpcase.data['hp_input_MWh']\n", + "# s.afrr_case.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n", + "# s.afrr_case.add_cashflow('aFRR capacity fee (€)', c.afrr_capacity_fee * s.afrr_case.afrr_capacity)\n", + " \n", + " for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n", + " case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n", + " case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n", + " case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n", + " \n", + " for case in [s.hpcase, s.storage_case]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " \n", + " case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n", + " case.add_grid_costs(\n", + " power_MW_col='hp_input_MW',\n", + " grid_operator=c.grid_operator,\n", + " year=2020, \n", + " connection_type=c.connection_type\n", + " )\n", + " \n", + " for case in s.sde_cases:\n", + " case = calculate_sde_subsidy(c=c, s=s, case=case)\n", + " case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n", + " return s\n", + "\n", + "s = collect_cashflows(c, s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def calculate_financials(c, s):\n", + " for case in s.cases:\n", + " case.calculate_ebitda()\n", + "\n", + " for case in [s.hpcase, s.storage_case]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " case.calculate_business_case(\n", + " project_duration=c.project_duration, \n", + " discount_rate=c.discount_rate, \n", + " baseline=s.baseline\n", + " )\n", + " \n", + " return s\n", + "\n", + "s = calculate_financials(c, s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Visualisations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "fig_demands_over_time = s.demand['Total demand'].resample('H').mean().iplot(\n", + " title='Smurfit Kappa: Heat demand by Hour in MW', \n", + " yTitle='MW', \n", + " colors=recoygreen,\n", + " asFigure=True,\n", + " dimensions=(800, 400)\n", + ")\n", + "fig_demands_over_time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].resample('H').mean().iplot(\n", + " kind='box',\n", + " title='Smurfit Kappa: Source and Sink temperatures',\n", + " color=recoygreen,\n", + " yTitle='Temperature in degrees C',\n", + " legend=False,\n", + " asFigure=True,\n", + " dimensions=(800, 400)\n", + ")\n", + "demands_fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_comparison = pd.concat([s.storage_case.data[['tsource_ndg', 'tsource_vdg']], s.storage_case.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n", + "temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n", + "temp_comparison.resample('15T').mean().iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data[['tsource_ndg', 'tsource_vdg']].idxmin()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case.data.loc['2019-01-04 07:45:00']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "case = s.storage_case\n", + "hp_vdg = case.assets['Heatpump VDG']\n", + "hp_ndg = case.assets['Heatpump NDG']\n", + "ws = case.assets['HotWaterStorage']\n", + "ws.storage_level = 47.083 + 25\n", + "pos = 35.650\n", + "neg = 43.850\n", + "dam = 68.400\n", + "demand_vdg = 0\n", + "demand_ndg = 0\n", + "tsource_vdg = 29.635\n", + "tsink_vdg = 134.840\n", + "tsource_ndg = 21.420\n", + "tsink_ndg = 118.755\n", + "\n", + "hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n", + "Tsource_VDG_after = s.storage_case.data['Tsource (VDG)'].round(2)\n", + "Tsource_VDG_before.equals(Tsource_VDG_after)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Tsource_NDG_before = s.hpcase.data['Tsource (NDG)'].round(2)\n", + "Tsource_NDG_after = s.storage_case.data['Tsource (NDG)'].round(2)\n", + "Tsource_NDG_before.equals(Tsource_NDG_after)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "test_hp = Heatpump(\n", + " name='Heatpump for Testing',\n", + " max_th_power=1,\n", + " min_th_power=0.3,\n", + " cop_curve=cop_curve\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# Tsrc_vdg, Tsnk_vdg, Tsrc_ndg, Tsnk_ndg = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].mean().to_list()\n", + "# mean_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].mean() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].mean()\n", + "# )\n", + "\n", + "# max_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].max() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].max()\n", + "# )\n", + "\n", + "# min_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].min() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].min()\n", + "# )\n", + "\n", + "# Tsrc_vdg_min, Tsnk_vdg_min, Tsrc_ndg_min, Tsnk_ndg_min = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].min().to_list()\n", + "# Tsrc_vdg_max, Tsnk_vdg_max, Tsrc_ndg_max, Tsnk_ndg_max = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].max().to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import plotly.graph_objs as go\n", + "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n", + " loads = []\n", + " eprices = list(range(1000))\n", + " for eprice in eprices:\n", + " _, load = test_hp.set_opt_load(\n", + " electricity_cost=eprice + s.eb_ode_e,\n", + " alt_heat_price=gas_price / 0.9,\n", + " demand=1, \n", + " Tsink=Tsnk,\n", + " Tsource=Tsrc\n", + " )\n", + " loads.append(load)\n", + " trace = go.Scatter(x=eprices, y=loads, name=name)\n", + " return trace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# import plotly.graph_objects as go\n", + "# import numpy as np\n", + "\n", + "# fig = go.Figure()\n", + "\n", + "# configs = {\n", + "# 'mean': [mean_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'unfav_gas': [min_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'fav_gas': [max_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'unfav_all': [min_gas_price, Tsnk_vdg_max, Tsrc_vdg_min],\n", + "# 'fav_all': [max_gas_price, Tsnk_vdg_min, Tsrc_vdg_max],\n", + "# }\n", + "\n", + "# for name, config in configs.items():\n", + "# trace = create_load_trace(*config, name)\n", + "# fig.add_trace(trace)\n", + "\n", + "# fig.update_layout(title='Switch prices for different configurations')\n", + "# fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "date = s.baseline.data.index[0].strftime('%Y-%m-%d')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n", + " subplots=True, \n", + " title=f'Steamboiler only case on {date}',\n", + " subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n", + " legend=False,\n", + " dimensions=(800, 400),\n", + " colors=[recoydarkblue, recoygreen], \n", + " asFigure=True,\n", + " shape = (2, 1)\n", + ")\n", + "fig_steamboiler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n", + " subplots=True, \n", + " title=f'Heatpump only case on {date}', \n", + " subplot_titles=['Total demand', 'Heatpump output (MW)'],\n", + " legend=False,\n", + " dimensions=(800, 400),\n", + " colors=[recoydarkblue, recoygreen], \n", + " asFigure=True,\n", + " shape = (2, 1)\n", + ")\n", + "fig_heatpump" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# fig_optcase = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n", + "# subplots=True, \n", + "# title=f'Hybrid case on {date}',\n", + "# subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n", + "# legend=False,\n", + "# dimensions=(800, 600),\n", + "# colors=[recoydarkblue, recoygreen, recoyred],\n", + "# asFigure=True,\n", + "# shape=(3,1)\n", + "# )\n", + "# fig_optcase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# date = '2019-09-04'\n", + "\n", + "# fig_optcase2 = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n", + "# subplots=True, \n", + "# title=f'Hybrid case on {date}',\n", + "# subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n", + "# legend=False,\n", + "# dimensions=(800, 600),\n", + "# colors=[recoydarkblue, recoygreen, recoyred],\n", + "# asFigure=True,\n", + "# shape=(3,1)\n", + "# )\n", + "# fig_optcase2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "report = ComparisonReport(\n", + " cases=s.optcases, \n", + " kind='electr_market_results',\n", + ")\n", + "report.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n", + "casereport.show(presentation_format=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "BusinessCaseReport(s.hpcase).show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "#price chart\n", + "from copy import deepcopy\n", + "\n", + "data = deepcopy(s.baseline.data)\n", + "data = data[data.columns[:2]]\n", + "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n", + "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n", + "\n", + "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# #price chart\n", + "# from copy import deepcopy\n", + "\n", + "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n", + "\n", + "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n", + "# _total_output = _source_output.sum(axis=1)\n", + "# _data = _source_output.divide(_total_output, axis=0).rename(\n", + "# columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n", + "# ) * 100\n", + "\n", + "# production_fig = _data.iplot(\n", + "# kind='bar',\n", + "# barmode='stack',\n", + "# colors=[recoydarkblue, recoygreen],\n", + "# title='Hybrid case: Heat production per Month by Source in % share',\n", + "# yTitle='Share of production in %',\n", + "# dimensions=(600, 400),\n", + "# asFigure=True\n", + "# )\n", + "\n", + "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n", + "# production_fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "report = ComparisonReport(s.cases, kind='capex').report\n", + "report = report[report.index.str.contains('CAPEX')].T\n", + "capex_fig = report.iplot(\n", + " kind='bar', \n", + " barmode='relative',\n", + " colors=recoycolors,\n", + " title='CAPEX by Casestudy',\n", + " yTitle='CAPEX in €',\n", + " dimensions=(600, 400),\n", + " asFigure=True,\n", + ")\n", + "\n", + "capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n", + "capex_fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n", + "\n", + "fig = cashflow_report.report.T.iplot(\n", + " kind='bar',\n", + " barmode='relative',\n", + " colors=recoycolors,\n", + " title='OPEX breakdown',\n", + " asFigure=True,\n", + " yTitle='€',\n", + " dimensions=(800, 600)\n", + ")\n", + "\n", + "ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n", + "scat = go.Scatter(\n", + " mode='markers',\n", + " y=ebitda_report.report.loc['EBITDA (€)', :].values, \n", + " x=ebitda_report.report.columns, \n", + " line=go.scatter.Line(color=recoydarkgrey),\n", + " marker=dict(\n", + " color=recoydarkgrey,\n", + " size=20,\n", + " line=dict(\n", + " color=recoydarkgrey,\n", + " width=3\n", + " ),\n", + " symbol='line-ew'\n", + " ),\n", + " name='EBITDA (€)'\n", + ")\n", + "fig.add_trace(scat)\n", + "fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ebitda_report.show(comparison='relative')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n", + "ebitda_graph = _series.iplot(\n", + " kind='bar',\n", + " title='Yearly EBITDA by Casestudy in €',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400), \n", + " yTitle='EBITDA in €',\n", + " asFigure=True,\n", + " yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n", + ")\n", + "\n", + "ebitda_graph.update_traces(\n", + " text=_series.values/1000_000, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.1f}M €\", \n", + ")\n", + "\n", + "ebitda_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n", + "\n", + "npv_graph = _series.iplot(\n", + " kind='bar',\n", + " title='NPV by Casestudy in €',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400), \n", + " yTitle='NPV in €',\n", + " asFigure=True,\n", + " yrange=[0, _series.max() * 1.1]\n", + ")\n", + "\n", + "npv_graph.update_traces(\n", + " text=_series.values/1000_000, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.1f}M €\", \n", + ")\n", + "\n", + "npv_graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n", + "irr_fig = irr_report.iplot(\n", + " kind='bar',\n", + " title='IRR by Casestudy in %',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400),\n", + " yTitle='IRR in %',\n", + " asFigure=True,\n", + " yrange=[0, irr_report.max() * 1.2]\n", + ")\n", + "\n", + "irr_fig.update_traces(\n", + " text=irr_report.values, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.0f} %\", \n", + ")\n", + "\n", + "irr_fig" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n", + "spt_fig = _series.iplot(\n", + " kind='bar',\n", + " title='Simple Payback Time by Casestudy in Years',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400),\n", + " yTitle='Years',\n", + " asFigure=True,\n", + " yrange=[0, _series.max() * 1.2]\n", + ")\n", + "\n", + "spt_fig.update_traces(\n", + " text=_series.values, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.1f} years\", \n", + ")\n", + "\n", + "spt_fig " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n", + "cop_fig = mean_cops.iplot(\n", + " kind='bar',\n", + " title='Mean COP by Casestudy',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400),\n", + " yTitle='COP',\n", + " asFigure=True,\n", + " yrange=[0, mean_cops.max() * 1.2]\n", + ")\n", + "\n", + "cop_fig.update_traces(\n", + " text=mean_cops.values, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.1f}\", \n", + ")\n", + "\n", + "cop_fig " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Sensitivity analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n", + "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n", + "2. aFRR prices +/- 30%\n", + "3. CAPEX\n", + "4. CO2 price\n", + "5. Tsource +/- 10 degrees Celsius\n", + "6. Tsink\n", + " * Roam off the peak / lower pressure\n", + " * Stabalize / running average per hour/ 2 hours\n", + "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n", + "8. Energy Tax and ODE +/- 30%" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def setup():\n", + " _c = Config()\n", + " s = setup_model(c=_c)\n", + " return s\n", + "\n", + "def routine(c, s):\n", + " s = load_data(c=c, s=s)\n", + " s = create_and_assign_assets(c=c, s=s)\n", + " #s = preprocessing(c=c, s=s)\n", + " s = run_optimisation(c=c, s=s)\n", + " #s = postprocessing(c=c, s=s)\n", + " s = collect_cashflows(c=c, s=s)\n", + " s = calculate_financials(c=c, s=s)\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%time _s = setup()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%time result = routine(c, _s)\n", + "npv = result.hpcase.npv\n", + "npv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sensitivity:Storage temperature\n", + "values = range(80, 120, 10)\n", + "param = 'storage_temperature'\n", + "kpis = ['npv','irr','spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: Storage volume',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: Water storage volume" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%time _s = setup()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(100, 500, 100)\n", + "param = 'storage_volume'\n", + "kpis = ['npv','irr','spp']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[case.name for case in s.cases][1:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: Storage volume',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='irr in %',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: IRR',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='COP',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: COP',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: CAPEX per MW storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(4_000, 10_000, 2_000)\n", + "param = 'storage_capex_per_MW'\n", + "kpis = ['npv', 'irr', 'spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX',\n", + " yTitle='NPV in Euro',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: NPV in euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MW',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: CAPEX per MWh storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(1_000, 5_000, 1000)\n", + "param = 'storage_capex_per_MWh'\n", + "kpis = ['npv', 'spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MWh',\n", + " yTitle='NPV',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: NPV in Euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MWh',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: Payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: threshold price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(10, 60, 10)\n", + "param = 'threshold'\n", + "kpis = ['npv', 'spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='NPV',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: NPV in Euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: SPP in years',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='COP',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: SOP',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Heat Pump CAPEX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3, 2]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.hp_capex *= value\n", + " configs[value*100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "\n", + "sens_capex = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX factor (%)',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Heat Pump CAPEX (€)',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_capex" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: CO2 prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "mean = 24.86\n", + "co2_prices = [10, 25, 50, 100]\n", + "\n", + "for price in co2_prices:\n", + " _c = Config()\n", + " multiplier = price / mean\n", + " _c.co2_price_multiplier = multiplier\n", + " configs[price] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='EBITDA in €',\n", + " yrange=[output.min().min() * 1.1, 0],\n", + " title='Sensitivity: CO2 prices > EBITDA',\n", + " colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_co2_spp = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='Payback Period in Years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: CO2 prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_co2_spp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min() * 10, output.max().max()*1.1],\n", + " title='Sensitivity: CO2 prices > NPV',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Gas prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.gas_price_multiplier = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Gas price factor (%)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.5, output.max().max()*1.1],\n", + " title='Sensitivity: Gas prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Day Ahead buying amount" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0, 0.5, 1]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.day_ahead_buying_perc = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Volume in %',\n", + " yTitle='NPV in €',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Volume bought on Day-Ahead market in %',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Electricity prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.e_price_multiplier = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_eprices = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Electricity price factor in %',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Electricity prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_eprices" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Electricity price volatility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.e_price_volatility_multiplier = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_evol = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Volatility factor',\n", + " yTitle='Payback period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: E-price volatility',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_evol" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: aFRR capacity fee" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [10_000, 25_000, 50_000]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.afrr_capacity_fee = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_affr_fee = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='aFRR capacity fee in €/MW',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: aFRR capacity fee',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_affr_fee" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Energy tax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.energy_tax_multiplier = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Factor',\n", + " yTitle='NPV in €',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Energy taxes',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Tsource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [-25, -10, 0, 10, 25]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.tsource_delta = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.loc[-25, 'Heatpump only'] = np.nan\n", + "sens_tsource = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsource delta',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsource',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsource delta',\n", + " yTitle='COP',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsource > Mean COP',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [-25, -10, 0, 10, 25]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.tsink_delta = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_tsink = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsink delta',\n", + " yTitle='Payback Period in Years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsink',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_tsink_cop = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsink delta',\n", + " yTitle='COP',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsink > Mean COP',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsink_cop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Time period" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def routine2(c, s):\n", + " s = setup_model(c=c)\n", + " s = load_data(c=c, s=s)\n", + " s = create_and_assign_assets(c=c, s=s)\n", + " s = run_optimisation(c=c, s=s)\n", + " s = collect_cashflows(c=c, s=s)\n", + " s = calculate_financials(c=c, s=s)\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "configs = {}\n", + "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n", + "\n", + "for value in start_values:\n", + " _c = Config()\n", + " _c.start = value\n", + " _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine2, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "result.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Start date',\n", + " yTitle='NPV in €',\n", + " yrange=[0, result.max().max()*1.1],\n", + " title='Sensitivity: Modelled time period',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "renderer = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# __Presentation Flexible Heatpumps__\n", + "\n", + "ENCORE meeting: 17-12-2020\n", + "Mark Kremer\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## __Central question:__\n", + "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Flexible operations on imbalance market__\n", + "\n", + "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n", + " title='Day-Ahead prices on arbitrary day in €/MWh',\n", + " yrange=[-100, 200],\n", + " colors=recoygreen, \n", + " yTitle='Price in €/MWh',\n", + " xTitle='Time of Day',\n", + " dimensions=(800, 400),\n", + " asFigure=True\n", + ")\n", + "\n", + "fig_example_1.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n", + " title='Imbalance Prices on arbitrary day in €/MWh',\n", + " colors=[recoygreen, recoydarkblue], \n", + " yTitle='Price in €/MWh',\n", + " xTitle='Time of Day',\n", + " dimensions=(800, 400),\n", + " asFigure=True\n", + ")\n", + "\n", + "fig_example_2.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to benefit from these fluctiations, if you have __flexibility__\n", + "* Storage options\n", + "* Hybrid installations (e.g. with gas-powered assets)\n", + "\n", + "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Simulations & mathematical modelling__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* To answer the central question, we have build a simulation model\n", + "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n", + "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n", + "* We are taking into account all major investment and operating costs, including:\n", + " * Asset CAPEX\n", + " * Commodity costs for gas and electricity\n", + " * Energy taxes\n", + " * Grid transport costs\n", + " * SDE++ subsidies\n", + " * Maintenance costs\n", + " * CO2 allowances\n", + "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Casestudies__\n", + "5 main scenario's" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "1. Steamboiler only (baseline, baseload)\n", + "2. Heatpump only (stand-alone, baseload, without SDE++)\n", + "3. Heatpump + SDE\n", + "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n", + "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n", + "\n", + "Besides that, we modelled 11 sensitivities:\n", + "* Heatpump CAPEX\n", + "* Gas & CO2 prices\n", + "* Electricity prices & volatility\n", + "* Energy taxes\n", + "* Bidding strategies\n", + "* Source temperatures (affecting COP)\n", + "* Sink temperatures (affecting COP)\n", + "* Time period (2018, 2019, 2020)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Smurfit Kappa case__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is based on the context of Smurfit Kappa (paper factory)\n", + "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n", + "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n", + "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n", + "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_demands_over_time.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Source temperatures of around 65 degrees C\n", + "* Sink temperatures of 125-170 degrees C \n", + "* Average Temperature lift of about 85 degrees C" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demands_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Heat pump__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cop_curve(Tsink, Tsource):\n", + " Tsink += 273\n", + " Tsource += 273\n", + "\n", + " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", + " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", + " \n", + " return Polynomial([c2, c1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sourceT = 63\n", + "sinkT = 140\n", + "cop_curve(sourceT, sinkT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_cop_curve.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Optimisation__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* At each moment in time, we calculate the cheapest option to produce the required heat. \n", + "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n", + "* Taking into account fluctuating market prices (electricity, gas, CO2)\n", + "* We are predicting real-time electricity prices using our forecasting models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Some example days:__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Steamboiler only is following demand pattern" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_steamboiler.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar pattern for heatpump only case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_heatpump.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_optcase.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_optcase2.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Business case__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "capex_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n", + "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ebitda_graph.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spt_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "production_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The heatpump is filling in 95-98 % of the demand. \n", + "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n", + "* Switch price is on average around 90€/MWh (excluding subsidies)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Sensitivities__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If CAPEX is 200%, subsidy is needed to keep a good Payback Time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_capex.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Subsidies are protecting the business case againsts low CO2 prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_co2_spp.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n", + "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n", + "* When the temperature lift is higher, the COP will decrease and the business case will degrade" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsource.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsink.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsink_cop.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_eprices.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Conclusions__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The business case for a Heat Pump seems favourable\n", + "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n", + "* However, this only results in a marginal improvement of the business case\n", + "* SDE++ has a very favourable effect on the business case, but is not critical\n", + "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "fea6e7ab4c0ed1d184e153838ac4b2d24a0985a5f17e99f4f437a114b66796b8" + }, + "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": "3.10.9" + }, + "toc-autonumbering": false, + "toc-showmarkdowntxt": false, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Kickstart/V2/simulations-storageMark1 - Copy.ipynb b/Kickstart/V2/simulations-storageMark1 - Copy.ipynb new file mode 100644 index 0000000..aa523cf --- /dev/null +++ b/Kickstart/V2/simulations-storageMark1 - Copy.ipynb @@ -0,0 +1,1078102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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 pyrecoy.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\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": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Development backlog" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "* aFRR (can improve the optimisation case)\n", + "* Report\n", + "\n", + "--\n", + "* Create strategy on imbalance POS (buy 100% day-ahead, and respond to high prices)\n", + "* Graphs\n", + " * EBITDA vs. baseline (earnings vs baseline)\n", + "* Show COP curves in different cases, just for illustration\n", + "* Energy report --> Check + add gas\n", + "* Fix comparison reports\n", + "* Model verification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Meeting Notes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "##### Meeting 25-11-2020" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "* aFRR can help optimisation case\n", + "* SDE++ should be included\n", + "* Tsource sensitivity really gives interesting insights\n", + "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n", + "* AP TNO: Update/verify COP curve\n", + "* AP TNO: Update CAPEX\n", + "* AP Mark: \n", + " * Create graphs on COP curve with different Tsource, Tsink\n", + " * Generate table and output in .csv (send it to Andrew)\n", + "* Investigate opportunity to lower the COP and negative electricity prices\n", + " * Technically feasible, but not really needed/possible to do it in this project\n", + "* Could be interesting to run this model on a usecase with higher delta T\n", + "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "# ENCORE : Heatpump Optimisation Framework\n", + "\n", + "***\n", + "© Mark Kremer \n", + "July 2020\n", + "##### Cases\n", + "In this model, 3 cases are compared:\n", + "* **Baseline** : All heat is supplied by steam turbine\n", + "* **Heatpump case** : All heat is supplied by heatpump\n", + "* **Hybrid case** : Steam turbine and heatpump run in hybrid mode, and are optimised on costs in real-time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "#### Loading config" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "class Config():\n", + " start = '2019-01-01'\n", + " end = '2019-12-31'\n", + " \n", + " hp_vdg_e_power = 23.3 # MW\n", + " hp_ndg_e_power = 7.7 # MW\n", + " hp_min_load = 0\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", + " storage_power = 25\n", + " storage_volume = 1000\n", + " storage_cap_per_volume = 50 * 1e-3\n", + " storage_lifetime = 25\n", + " storage_temperature = 95\n", + " storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n", + " storage_capex_per_MW = 7_000\n", + " storage_capex_per_MWh = 5_000\n", + " storage_opex_perc_of_capex = 0.02\n", + " # storage_initial_level = 5\n", + " threshold = 20\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": 3, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "class Store():\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Model set-up" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "s = Store()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n", + "\n", + "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Entries missing from dataset 'imbalanceRT_nl.csv'.\n", + "Issue solved: Dataset was reindexed automatically.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_42308\\332434720.py:3: FutureWarning:\n", + "\n", + "Argument `closed` is deprecated in favor of `inclusive`.\n", + "\n" + ] + } + ], + "source": [ + "def setup_model(c):\n", + " s = Store()\n", + " \n", + " s.time_fw = TimeFramework(start=c.start, end=c.end)\n", + " s.mipf = Mipf(start=s.time_fw.start, end=s.time_fw.end, tidy=True, 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.demand = load_demand_data(c, s)\n", + " return s\n", + "\n", + "s = setup_model(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": true + }, + "data": [ + { + "line": { + "color": "rgba(255, 153, 51, 1.0)", + "dash": "solid", + "shape": "linear", + "width": 1.3 + }, + "mode": "lines", + "name": "Tsource (VDG)", + "text": "", + "type": "scatter", + "x": [ + "2019-01-01 00:00:00+01:00", + "2019-01-01 00:15:00+01:00", + "2019-01-01 00:30:00+01:00", + "2019-01-01 00:45:00+01:00", + "2019-01-01 01:00:00+01:00", + "2019-01-01 01:15:00+01:00", + "2019-01-01 01:30:00+01:00", + "2019-01-01 01:45:00+01:00", + "2019-01-01 02:00:00+01:00", + "2019-01-01 02:15:00+01:00", + "2019-01-01 02:30:00+01:00", + "2019-01-01 02:45:00+01:00", + "2019-01-01 03:00:00+01:00", + "2019-01-01 03:15:00+01:00", + "2019-01-01 03:30:00+01:00", + "2019-01-01 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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)Total demand
count525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000
mean63.658798138.63767417.63671262.176305145.3516455.82382423.460537
std6.42277314.3285805.12325210.25255514.8960561.6916976.814125
min25.31000082.4800000.00000013.540000106.3300000.0000000.000000
25%64.892000126.07400017.01400063.516000131.5515005.61200022.676000
50%64.991000142.45000019.89200064.313000148.4900006.54000026.441000
75%65.076000151.73000020.81900065.546000158.1000006.88000027.690000
max70.070000166.02000023.25000073.470000172.0500007.65000030.900000
\n", + "
" + ], + "text/plain": [ + " Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n", + "count 525600.000000 525600.000000 525600.000000 525600.000000 \n", + "mean 63.658798 138.637674 17.636712 62.176305 \n", + "std 6.422773 14.328580 5.123252 10.252555 \n", + "min 25.310000 82.480000 0.000000 13.540000 \n", + "25% 64.892000 126.074000 17.014000 63.516000 \n", + "50% 64.991000 142.450000 19.892000 64.313000 \n", + "75% 65.076000 151.730000 20.819000 65.546000 \n", + "max 70.070000 166.020000 23.250000 73.470000 \n", + "\n", + " Tsink (NDG) MW (NDG) Total demand \n", + "count 525600.000000 525600.000000 525600.000000 \n", + "mean 145.351645 5.823824 23.460537 \n", + "std 14.896056 1.691697 6.814125 \n", + "min 106.330000 0.000000 0.000000 \n", + "25% 131.551500 5.612000 22.676000 \n", + "50% 148.490000 6.540000 26.441000 \n", + "75% 158.100000 6.880000 27.690000 \n", + "max 172.050000 7.650000 30.900000 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.demand.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Load in data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": 11, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": 12, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def load_data(c, s):\n", + " s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n", + " s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=s.mipf)\n", + " #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n", + " #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n", + " #s.afrr_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation + aFRR', data=mipf)\n", + " s.storage_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage', data=s.mipf)\n", + " \n", + " s.cases = list(CaseStudy.instances.values())\n", + " #s.optcases = [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]\n", + " s.optcases = [s.hpcase, s.storage_case]\n", + " #s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n", + " s.sde_cases = []\n", + " \n", + " if hasattr(s, 'afrr_case'):\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", + " if hasattr(s, 'afrr_case'):\n", + " for case in [s.afrr_case]:\n", + " case.data['Hoogste_prijs_opregelen'] = multiply_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_multiplier)\n", + " case.data['Hoogste_prijs_opregelen'] = increase_volatility_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_volatility_multiplier)\n", + " case.data['aFRR_up'] = multiply_by_factor(case.data['aFRR_up'], c.e_price_multiplier)\n", + " case.data['aFRR_up'] = increase_volatility_by_factor(case.data['aFRR_up'], 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', 2022, 4)['EB+ODE'] * c.energy_tax_multiplier\n", + " s.eb_ode_e = get_tax_rate('electricity', 2022, 4)['EB+ODE'] * c.energy_tax_multiplier\n", + " s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2022, 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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DAMPOSNEGRSForePosForeNegForePos_nextQForeNeg_nextQGas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)Total demand
2019-01-01 00:00:00+01:0068.9234.8558.012.046.8352.70NaNNaN20.8334.246.664.990143.3600.020.510147.6200.00.0
2019-01-01 00:01:00+01:0068.9234.8558.012.032.1254.45NaNNaN20.8334.246.664.985143.0890.020.387147.6420.00.0
2019-01-01 00:02:00+01:0068.9234.8558.012.030.9748.64NaNNaN20.8334.246.664.980142.8180.020.264147.6640.00.0
2019-01-01 00:03:00+01:0068.9234.8558.012.051.1348.3557.7173.0520.8334.246.664.975142.5470.020.141147.6860.00.0
2019-01-01 00:04:00+01:0068.9234.8558.012.048.0452.0163.6079.0720.8334.246.664.970142.2760.020.018147.7080.00.0
\n", + "
" + ], + "text/plain": [ + " DAM POS NEG RS ForePos ForeNeg \\\n", + "2019-01-01 00:00:00+01:00 68.92 34.85 58.01 2.0 46.83 52.70 \n", + "2019-01-01 00:01:00+01:00 68.92 34.85 58.01 2.0 32.12 54.45 \n", + "2019-01-01 00:02:00+01:00 68.92 34.85 58.01 2.0 30.97 48.64 \n", + "2019-01-01 00:03:00+01:00 68.92 34.85 58.01 2.0 51.13 48.35 \n", + "2019-01-01 00:04:00+01:00 68.92 34.85 58.01 2.0 48.04 52.01 \n", + "\n", + " ForePos_nextQ ForeNeg_nextQ Gas prices (€/MWh) \\\n", + "2019-01-01 00:00:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:01:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:02:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:03:00+01:00 57.71 73.05 20.83 \n", + "2019-01-01 00:04:00+01:00 63.60 79.07 20.83 \n", + "\n", + " CO2 prices (€/ton) CO2 prices (€/MWh) \\\n", + "2019-01-01 00:00:00+01:00 34.24 6.6 \n", + "2019-01-01 00:01:00+01:00 34.24 6.6 \n", + "2019-01-01 00:02:00+01:00 34.24 6.6 \n", + "2019-01-01 00:03:00+01:00 34.24 6.6 \n", + "2019-01-01 00:04:00+01:00 34.24 6.6 \n", + "\n", + " Tsource (VDG) Tsink (VDG) MW (VDG) \\\n", + "2019-01-01 00:00:00+01:00 64.990 143.360 0.0 \n", + "2019-01-01 00:01:00+01:00 64.985 143.089 0.0 \n", + "2019-01-01 00:02:00+01:00 64.980 142.818 0.0 \n", + "2019-01-01 00:03:00+01:00 64.975 142.547 0.0 \n", + "2019-01-01 00:04:00+01:00 64.970 142.276 0.0 \n", + "\n", + " Tsource (NDG) Tsink (NDG) MW (NDG) Total demand \n", + "2019-01-01 00:00:00+01:00 20.510 147.620 0.0 0.0 \n", + "2019-01-01 00:01:00+01:00 20.387 147.642 0.0 0.0 \n", + "2019-01-01 00:02:00+01:00 20.264 147.664 0.0 0.0 \n", + "2019-01-01 00:03:00+01:00 20.141 147.686 0.0 0.0 \n", + "2019-01-01 00:04:00+01:00 20.018 147.708 0.0 0.0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.hpcase.data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Assets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### COP curve" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def cop_curve(Tsink, Tsource):\n", + " Tsink += 273\n", + " Tsource += 273\n", + "\n", + " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", + " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", + " \n", + " return Polynomial([c2, c1])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$x \\mapsto \\text{1.0829055118110236} + \\text{0.8682755905511811}\\,x$" + ], + "text/plain": [ + "Polynomial([1.08290551, 0.86827559], domain=[-1, 1], window=[-1, 1])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cop_curve(140, 13)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$x \\mapsto \\text{2.052671641791045} + \\text{1.6458358208955224}\\,x$" + ], + "text/plain": [ + "Polynomial([2.05267164, 1.64583582], domain=[-1, 1], window=[-1, 1])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cop_curve(140, 73)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "heatpump = Heatpump(\n", + " name='Heatpump',\n", + " max_th_power=1,\n", + " min_th_power=0,\n", + " cop_curve=cop_curve\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=13)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=99)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "source_Ts = np.arange(25, 75) + 273\n", + "sink_Ts = np.arange(80, 170) + 273\n", + "\n", + "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n", + "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n", + " df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n", + " \n", + "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "loads = np.arange(0, 1, 0.01)\n", + "\n", + "cop_series = pd.Series(index=loads, dtype='float')\n", + "load_series = pd.Series(index=loads, dtype='float')\n", + "for load in loads:\n", + " cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n", + " cop_series[load] = cop\n", + " load_series[load] = load / cop " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgba(70, 165, 121, 1.0)", + "dash": "solid", + "shape": "linear", + "width": 1.3 + }, + "mode": "lines", + "name": "None", + "text": "", + "type": "scatter", + "x": [ + 0, + 0.01, + 0.02, + 0.03, + 0.04, + 0.05, + 0.06, + 0.07, + 0.08, + 0.09, + 0.1, + 0.11, + 0.12, + 0.13, + 0.14, + 0.15, + 0.16, + 0.17, + 0.18, + 0.19, + 0.2, + 0.21, + 0.22, + 0.23, + 0.24, + 0.25, + 0.26, + 0.27, + 0.28, + 0.29, + 0.3, + 0.31, + 0.32, + 0.33, + 0.34, + 0.35000000000000003, + 0.36, + 0.37, + 0.38, + 0.39, + 0.4, + 0.41000000000000003, + 0.42, + 0.43, + 0.44, + 0.45, + 0.46, + 0.47000000000000003, + 0.48, + 0.49, + 0.5, + 0.51, + 0.52, + 0.53, + 0.54, + 0.55, + 0.56, + 0.5700000000000001, + 0.58, + 0.59, + 0.6, + 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"Polynomial([19.2496754, 6.7058 , -1.79 ], domain=[-1, 1], window=[-1, 1])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "cop_curve_new(sourceT, sinkT)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "heatpump = Heatpump(\n", + " name='Heatpump',\n", + " max_th_power=1,\n", + " min_th_power=0,\n", + " cop_curve=cop_curve_new\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "source_Ts = np.arange(25, 75) + 273\n", + "sink_Ts = np.arange(80, 170) + 273\n", + "\n", + "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n", + "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n", + " df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n", + " \n", + "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sourceT = 63 + 273\n", + "sinkT = 140 + 273\n", + "loads = np.arange(0, 1, 0.01)\n", + "\n", + "cop_series = pd.Series(index=loads, dtype='float')\n", + "load_series = pd.Series(index=loads, dtype='float')\n", + "for load in loads:\n", + " cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n", + " cop_series[load] = cop\n", + " load_series[load] = load / cop " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": true + }, + "data": [ + { + "line": { + "color": 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+ "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n", + "fig_load_curve" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Create and assign assets" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "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_curve_new\n", + " )\n", + "\n", + " heatpump_ndg = Heatpump(\n", + " name='Heatpump NDG',\n", + " max_th_power=c.hp_ndg_e_power,\n", + " min_th_power=c.hp_ndg_e_power * c.hp_min_load,\n", + " cop_curve=cop_curve_new\n", + " )\n", + "\n", + " capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n", + " capex_ndg = c.hp_capex*(heatpump_ndg.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", + " heatpump_ndg.set_financials(capex=capex_ndg, opex=c.hp_opex*capex_ndg, devex=c.hp_devex*capex_ndg, lifetime=25)\n", + "\n", + " gasboiler = GasBoiler(\n", + " name='Gasboiler',\n", + " max_th_output=c.gb_power,\n", + " efficiency=c.gb_efficiency\n", + " )\n", + " gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n", + " \n", + " waterstorage = HotWaterStorage(\n", + " name='HotWaterStorage',\n", + " rated_power=c.storage_power, \n", + " capacity_per_volume=c.storage_cap_per_volume,\n", + " volume=c.storage_volume, \n", + " temperature=c.storage_temperature,\n", + " min_storagelevel=c.storage_min_level,\n", + " # initial_storagelevel=c.storage_initial_level\n", + " )\n", + " capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n", + " opex_ws = c.storage_opex_perc_of_capex * capex_ws\n", + " waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n", + " \n", + " s.baseline.add_asset(gasboiler)\n", + " s.hpcase.add_asset(heatpump_vdg)\n", + " s.hpcase.add_asset(heatpump_ndg)\n", + " s.storage_case.add_asset(heatpump_vdg)\n", + " s.storage_case.add_asset(heatpump_ndg)\n", + " s.storage_case.add_asset(waterstorage)\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": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HotWaterStorage(name=HotWaterStorage, rated_power=25, capacity=50.0, temperature=95, min_storagelevel=2.5)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.assets['HotWaterStorage']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Strategies" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def baseline_sim(case):\n", + " gasboiler = list(case.assets.values())[0]\n", + " data = case.data\n", + " demand = (data['MW (VDG)'] + data['MW (NDG)']).to_list()\n", + "\n", + " minutes = iter(range(len(case.data)))\n", + " th_output = [0] * len(case.data)\n", + " gas_input = [0] * len(case.data)\n", + "\n", + " for m in minutes:\n", + " th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n", + "\n", + " data['output_MW_th'] = np.array(th_output)\n", + " data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n", + " data['gb_input_MW'] = np.array(gas_input)\n", + " data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n", + " case.data = data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hponly(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].to_list()\n", + " demand_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + "\n", + " hp_vdg_input = [0] * len(case.data)\n", + " hp_ndg_input = [0] * len(case.data)\n", + " hp_vdg_output = [0] * len(case.data)\n", + " hp_ndg_output = [0] * len(case.data)\n", + "\n", + " minutes = iter(range(len(case.data)))\n", + " for m in minutes:\n", + " demand = demand_vdg[m]\n", + " if demand != 0:\n", + " hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n", + " heat_output=demand,\n", + " Tsink=Tsink_vdg[m],\n", + " Tsource=Tsource_vdg[m]\n", + " )\n", + "\n", + " demand = demand_ndg[m]\n", + " if demand != 0:\n", + " hp_ndg_input[m], hp_ndg_output[m] = hp_ndg.set_heat_output(\n", + " heat_output=demand_ndg[m],\n", + " Tsink=Tsink_ndg[m],\n", + " Tsource=Tsource_ndg[m]\n", + " )\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n", + " case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n", + " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", + " \n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(3)\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "Tref = 0\n", + "Cp = 4190 #J/kgK\n", + "MWtoJs = 1000_000\n", + "\n", + "def power_to_mass_flow(power_MW, Tsink, Tref, Cp):\n", + " return power_MW * MWtoJs /(Cp*(Tsink - Tref))\n", + "def energy_to_storage(hp_heat_output_MW, process_demand_MW):\n", + " return hp_heat_output_MW - process_demand_MW #MW\n", + "\n", + "def Tsource_calculation(Tstorage, discharge_power, Tsource, process_mass_flow): \n", + " discharge_mass_flow = power_to_mass_flow(discharge_power, Tstorage, Tref, Cp)\n", + " \n", + " combined_mass_flow = (discharge_mass_flow + process_mass_flow)\n", + " if combined_mass_flow == 0:\n", + " return 0\n", + " else: \n", + " return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow) / combined_mass_flow" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "52.84691442209342" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "31000000/(140*4190)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# def hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg):\n", + " \n", + "# from_storage_vdg_MW = 0\n", + "# to_storage_vdg_MW = 0 \n", + "# from_storage_ndg_MW = 0\n", + "# to_storage_ndg_MW = 0 \n", + " \n", + "# if neg < dam - c.threshold:\n", + "# # overproduce\n", + "# desired_hp_load_vdg = min(demand_vdg + ws.charging_power_limit, hp_vdg.max_th_power)\n", + "# e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + "# to_storage_vdg_MW = th_load_vdg - demand_vdg\n", + "# to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n", + "\n", + "# extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n", + "\n", + "# desired_hp_load_ndg = min(\n", + "# demand_ndg + min(ws.charging_power_limit, extra_charging_power_constraint), \n", + "# hp_ndg.max_th_power\n", + "# )\n", + "# e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(desired_hp_load_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + "# to_storage_ndg_MW = th_load_ndg - demand_ndg\n", + "# to_storage_ndg_MW = -ws.charge(round(to_storage_ndg_MW, 3))\n", + "\n", + "# elif pos > dam + c.threshold:\n", + "# # take from storage\n", + "# from_process_massflow_vdg = power_to_mass_flow(demand_vdg, tsink_vdg, Tref, Cp)\n", + "# try:\n", + "# from_storage_vdg_MW = ws.discharging_power_limit\n", + "# tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + "# e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + "# except: \n", + "# from_storage_vdg_MW = 0\n", + "# tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + "# e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + "# from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\n", + "# #print({f'from_storage_vdg_MW='})\n", + "# from_process_massflow_ndg = power_to_mass_flow(demand_ndg, tsink_ndg, Tref, Cp)\n", + "\n", + "# try:\n", + "# from_storage_ndg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n", + "# tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n", + "# e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + "# except: \n", + "# from_storage_vdg_MW = 0\n", + "# tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n", + "# e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + "# from_storage_ndg_MW = ws.discharge(round(from_storage_ndg_MW))\n", + "# else:\n", + "# e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n", + "# heat_output=demand_vdg,\n", + "# Tsink=tsink_vdg,\n", + "# Tsource=tsource_vdg\n", + "# )\n", + "\n", + "# e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(\n", + "# heat_output=demand_ndg,\n", + "# Tsink=tsink_ndg,\n", + "# Tsource=tsource_ndg\n", + "# )\n", + "# return e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def hp_storage_opt_enginge(c, ws, hp_ndg, hp_vdg, pos, neg, dam, demand_ndg, demand_vdg, tsource_ndg, tsink_ndg, tsource_vdg, tsink_vdg):\n", + " \n", + " from_storage_ndg_MW = 0\n", + " to_storage_ndg_MW = 0 \n", + " from_storage_vdg_MW = 0\n", + " to_storage_vdg_MW = 0 \n", + " \n", + " if neg < dam - c.threshold:\n", + " # overproduce\n", + " if demand_ndg != 0:\n", + " desired_hp_load_ndg = min(demand_ndg + ws.charging_power_limit, hp_ndg.max_th_power)\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(desired_hp_load_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + " to_storage_ndg_MW = th_load_ndg - demand_ndg\n", + " to_storage_ndg_MW = -ws.charge(round(to_storage_ndg_MW, 3))\n", + "\n", + " extra_charging_power_constraint = ws.max_power - to_storage_ndg_MW\n", + " else:\n", + " e_load_ndg, th_load_ndg = (0,0)\n", + " \n", + " if demand_vdg != 0:\n", + " desired_hp_load_vdg = min(\n", + " demand_vdg + min(ws.charging_power_limit, extra_charging_power_constraint), \n", + " hp_vdg.max_th_power\n", + " )\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + " to_storage_vdg_MW = th_load_vdg - demand_vdg\n", + " to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n", + " else:\n", + " e_load_vdg, th_load_vdg = (0,0)\n", + " \n", + "\n", + " elif pos > dam + c.threshold:\n", + " # take from storage\n", + " if demand_ndg != 0:\n", + " from_process_massflow_ndg = power_to_mass_flow(demand_ndg, tsink_ndg, Tref, Cp)\n", + " try:\n", + " from_storage_ndg_MW = ws.discharging_power_limit\n", + " tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_vdg)\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + " except: \n", + " from_storage_ndg_MW = 0\n", + " tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n", + "\n", + " from_storage_ndg_MW = ws.discharge(round(from_storage_ndg_MW, 3))\n", + " else:\n", + " e_load_ndg, th_load_ndg = (0,0)\n", + " \n", + " #print({f'from_storage_vdg_MW='})\n", + " if demand_vdg != 0:\n", + " from_process_massflow_vdg = power_to_mass_flow(demand_vdg, tsink_vdg, Tref, Cp)\n", + " \n", + " try:\n", + " from_storage_vdg_MW = min(ws.max_power - from_storage_ndg_MW, ws.discharging_power_limit)\n", + " tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + " except: \n", + " from_storage_vdg_MW = 0\n", + " tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n", + " e_load_vdg, th_load_vdg = hp_ndg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n", + "\n", + " from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW))\n", + " else:\n", + " e_load_vdg, th_load_vdg = (0,0)\n", + " else:\n", + " e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(\n", + " heat_output=demand_ndg,\n", + " Tsink=tsink_ndg,\n", + " Tsource=tsource_ndg\n", + " )\n", + "\n", + " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n", + " heat_output=demand_vdg,\n", + " Tsink=tsink_vdg,\n", + " Tsource=tsource_vdg\n", + " )\n", + " return e_load_ndg, th_load_ndg, e_load_vdg, th_load_vdg, tsource_ndg, tsource_vdg, to_storage_ndg_MW, from_storage_ndg_MW, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_ndg, hp_vdg\n", + "# Neg-take price, Pos-feed price" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hponly_with_storage(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " ws = case.assets['HotWaterStorage']\n", + " demands_vdg = case.data['MW (VDG)'].to_list()\n", + " demands_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + " dam_prices = case.data['DAM'].to_list()\n", + " pos_prices = case.data['POS'].to_list()\n", + " neg_prices = case.data['NEG'].to_list()\n", + "\n", + " hp_ndg_input = [0] * len(case.data)\n", + " hp_vdg_input = [0] * len(case.data)\n", + " hp_ndg_output = [0] * len(case.data)\n", + " hp_vdg_output = [0] * len(case.data)\n", + "\n", + " minutes = len(case.data)\n", + " storage_levels = [None] * minutes\n", + " to_storage_list = [0] * minutes\n", + " from_storage_vdg_list = [0] * minutes\n", + " to_storage_vdg_list = [0] * minutes\n", + " from_storage_ndg_list = [0] * minutes\n", + " to_storage_ndg_list = [0] * minutes\n", + " \n", + " new_tsources_ndg = case.data['Tsource (VDG)'].to_list()\n", + " new_tsources_vdg = case.data['Tsource (NDG)'].to_list()\n", + " \n", + " ws.set_chargelevel(ws.min_chargelevel)\n", + " \n", + " for m in range(minutes):\n", + " tsource_ndg = Tsource_ndg[m]\n", + " tsink_ndg = Tsink_ndg[m]\n", + " tsource_vdg = Tsource_vdg[m]\n", + " tsink_vdg = Tsink_vdg[m]\n", + " demand_ndg = demands_ndg[m]\n", + " demand_vdg = demands_vdg[m]\n", + " \n", + " e_load_ndg, th_load_ndg, e_load_vdg, th_load_vdg, tsource_ndg, tsource_vdg, to_storage_ndg_MW, from_storage_ndg_MW, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_ndg, hp_vdg = hp_storage_opt_enginge(\n", + " c, ws, hp_ndg, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_ndg, demand_vdg, \n", + " tsource_ndg, tsink_ndg, tsource_vdg, tsink_vdg\n", + " )\n", + " \n", + " hp_ndg_input[m] = e_load_ndg\n", + " hp_ndg_output[m] = th_load_ndg\n", + " hp_vdg_input[m] = e_load_vdg\n", + " hp_vdg_output[m] = th_load_vdg\n", + " \n", + " storage_levels[m] = ws.chargelevel\n", + " new_tsources_ndg[m] = tsource_ndg\n", + " new_tsources_vdg[m] = tsource_vdg\n", + " to_storage_ndg_list[m] = to_storage_ndg_MW\n", + " from_storage_ndg_list[m] = from_storage_ndg_MW\n", + " to_storage_vdg_list[m] = to_storage_vdg_MW\n", + " from_storage_vdg_list[m] = from_storage_vdg_MW\n", + " \n", + " case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n", + " case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n", + " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", + " case.data['tsource_vdg'] = new_tsources_vdg\n", + " case.data['tsource_ndg'] = new_tsources_ndg\n", + " case.data['to_storage_ndg_MW'] = to_storage_ndg_list\n", + " case.data['from_storage_ndg_MW'] = from_storage_ndg_list\n", + " case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n", + " case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n", + " case.data['to_storage_MW'] = case.data['to_storage_vdg_MW'] + case.data['to_storage_ndg_MW']\n", + " case.data['from_storage_MW'] = case.data['from_storage_vdg_MW'] + case.data['from_storage_ndg_MW']\n", + " case.data['storage_level_MWh'] = storage_levels\n", + " \n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(3)\n", + " return case\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "26711\n" + ] + } + ], + "source": [ + "print(sum(s.demand['Total demand']== 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "s.storage_case = hponly_with_storage(s.storage_case)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + 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DAMPOSNEGRSForePosForeNegForePos_nextQForeNeg_nextQGas prices (€/MWh)CO2 prices (€/ton)...from_storage_MWstorage_level_MWhhp_output_MWhhp_input_MWhto_storage_ndg_MWhfrom_storage_ndg_MWhto_storage_vdg_MWhfrom_storage_vdg_MWhto_storage_MWhfrom_storage_MWh
2019-11-10 22:49:00+01:0044.0826.4626.46-1.014.5917.1530.4330.4315.0522.14...0.07.9820.468-0.0870.00.00.00.00.00.0
2019-09-01 07:52:00+02:0026.4926.7726.771.025.6125.5123.9723.979.4021.77...0.02.5000.000-0.0000.00.00.00.00.00.0
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2 rows × 38 columns

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" + ], + "text/plain": [ + " DAM POS NEG RS ForePos ForeNeg \\\n", + "2019-11-10 22:49:00+01:00 44.08 26.46 26.46 -1.0 14.59 17.15 \n", + "2019-09-01 07:52:00+02:00 26.49 26.77 26.77 1.0 25.61 25.51 \n", + "\n", + " ForePos_nextQ ForeNeg_nextQ Gas prices (€/MWh) \\\n", + "2019-11-10 22:49:00+01:00 30.43 30.43 15.05 \n", + "2019-09-01 07:52:00+02:00 23.97 23.97 9.40 \n", + "\n", + " CO2 prices (€/ton) ... from_storage_MW \\\n", + "2019-11-10 22:49:00+01:00 22.14 ... 0.0 \n", + "2019-09-01 07:52:00+02:00 21.77 ... 0.0 \n", + "\n", + " storage_level_MWh hp_output_MWh hp_input_MWh \\\n", + "2019-11-10 22:49:00+01:00 7.982 0.468 -0.087 \n", + "2019-09-01 07:52:00+02:00 2.500 0.000 -0.000 \n", + "\n", + " to_storage_ndg_MWh from_storage_ndg_MWh \\\n", + "2019-11-10 22:49:00+01:00 0.0 0.0 \n", + "2019-09-01 07:52:00+02:00 0.0 0.0 \n", + "\n", + " to_storage_vdg_MWh from_storage_vdg_MWh \\\n", + "2019-11-10 22:49:00+01:00 0.0 0.0 \n", + "2019-09-01 07:52:00+02:00 0.0 0.0 \n", + "\n", + " to_storage_MWh from_storage_MWh \n", + "2019-11-10 22:49:00+01:00 0.0 0.0 \n", + "2019-09-01 07:52:00+02:00 0.0 0.0 \n", + "\n", + "[2 rows x 38 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.sample(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# s.storage_case.data[['to_storage_MWh'], 'from_storage_MWh'].iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DAMPOSNEGhp_output_MWto_storage_MWTotal demand
count525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000
mean41.19580140.39106042.55408224.3545730.89403723.460537
std11.27817351.22693152.0032527.1519652.6333686.814125
min-9.020000-487.650000-487.6500000.0000000.0000000.000000
25%34.10000024.47000025.53000022.7600000.00000022.676000
50%39.70000031.11000032.16000026.9800000.00000026.441000
75%47.39250040.90000042.54000028.0100000.00000027.690000
max121.460000936.120000936.12000031.00000025.00000030.900000
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" + ], + "text/plain": [ + " DAM POS NEG hp_output_MW \\\n", + "count 525600.000000 525600.000000 525600.000000 525600.000000 \n", + "mean 41.195801 40.391060 42.554082 24.354573 \n", + "std 11.278173 51.226931 52.003252 7.151965 \n", + "min -9.020000 -487.650000 -487.650000 0.000000 \n", + "25% 34.100000 24.470000 25.530000 22.760000 \n", + "50% 39.700000 31.110000 32.160000 26.980000 \n", + "75% 47.392500 40.900000 42.540000 28.010000 \n", + "max 121.460000 936.120000 936.120000 31.000000 \n", + "\n", + " to_storage_MW Total demand \n", + "count 525600.000000 525600.000000 \n", + "mean 0.894037 23.460537 \n", + "std 2.633368 6.814125 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 22.676000 \n", + "50% 0.000000 26.441000 \n", + "75% 0.000000 27.690000 \n", + "max 25.000000 30.900000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DAMPOSNEGhp_output_MWto_storage_MWfrom_storage_MWTotal demand
2019-01-01 00:00:00+01:0068.9234.8558.010.00.00.00.0
2019-01-01 00:01:00+01:0068.9234.8558.010.00.00.00.0
2019-01-01 00:02:00+01:0068.9234.8558.010.00.00.00.0
2019-01-01 00:03:00+01:0068.9234.8558.010.00.00.00.0
2019-01-01 00:04:00+01:0068.9234.8558.010.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " DAM POS NEG hp_output_MW to_storage_MW \\\n", + "2019-01-01 00:00:00+01:00 68.92 34.85 58.01 0.0 0.0 \n", + "2019-01-01 00:01:00+01:00 68.92 34.85 58.01 0.0 0.0 \n", + "2019-01-01 00:02:00+01:00 68.92 34.85 58.01 0.0 0.0 \n", + "2019-01-01 00:03:00+01:00 68.92 34.85 58.01 0.0 0.0 \n", + "2019-01-01 00:04:00+01:00 68.92 34.85 58.01 0.0 0.0 \n", + "\n", + " from_storage_MW Total demand \n", + "2019-01-01 00:00:00+01:00 0.0 0.0 \n", + "2019-01-01 00:01:00+01:00 0.0 0.0 \n", + "2019-01-01 00:02:00+01:00 0.0 0.0 \n", + "2019-01-01 00:03:00+01:00 0.0 0.0 \n", + "2019-01-01 00:04:00+01:00 0.0 0.0 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DAM', 'POS', 'NEG', 'RS', 'ForePos', 'ForeNeg', 'ForePos_nextQ',\n", + " 'ForeNeg_nextQ', 'Gas prices (€/MWh)', 'CO2 prices (€/ton)',\n", + " 'CO2 prices (€/MWh)', 'Tsource (VDG)', 'Tsink (VDG)', 'MW (VDG)',\n", + " 'Tsource (NDG)', 'Tsink (NDG)', 'MW (NDG)', 'Total demand'],\n", + " dtype='object')" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.hpcase.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DAM', 'POS', 'NEG', 'RS', 'ForePos', 'ForeNeg', 'ForePos_nextQ',\n", + " 'ForeNeg_nextQ', 'Gas prices (€/MWh)', 'CO2 prices (€/ton)',\n", + " 'CO2 prices (€/MWh)', 'Tsource (VDG)', 'Tsink (VDG)', 'MW (VDG)',\n", + " 'Tsource (NDG)', 'Tsink (NDG)', 'MW (NDG)', 'Total demand',\n", + " 'hp_output_MW', 'hp_input_MW', 'cop', 'tsource_vdg', 'tsource_ndg',\n", + " 'to_storage_ndg_MW', 'from_storage_ndg_MW', 'to_storage_vdg_MW',\n", + " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", + " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", + " 'to_storage_ndg_MWh', 'from_storage_ndg_MWh', 'to_storage_vdg_MWh',\n", + " 'from_storage_vdg_MWh', 'to_storage_MWh', 'from_storage_MWh'],\n", + " dtype='object')" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n", + "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n", + "storage_selection = s.storage_case.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n", + "\n", + "selection = pd.concat([hp_selection, storage_selection], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DAMPOSNEGTotal demandhp_input_MWhp_output_MWcopto_storage_MWfrom_storage_MW
2019-11-19 16:28:00+01:0070.4332.8432.8427.708-5.36731.0005.7763.2920.0
2019-11-23 06:14:00+01:0032.399.3860.9122.776-4.13822.7765.5040.0000.0
2019-07-03 10:27:00+02:0033.6844.0244.0222.840-4.18322.8405.4600.0000.0
2019-10-17 12:26:00+02:0048.7018.3818.3819.976-3.40119.9765.8730.0000.0
2019-05-21 03:21:00+02:0031.4939.6739.6722.703-3.93222.7035.7730.0000.0
2019-03-20 07:26:00+01:0045.5625.0325.0320.782-3.55020.7825.8550.0000.0
2019-03-23 04:28:00+01:0033.0536.3236.3226.318-5.05426.3185.2080.0000.0
2019-06-30 06:17:00+02:0012.2720.3431.0827.509-5.27327.5095.2170.0000.0
2019-05-15 04:09:00+02:0034.3821.8621.8627.839-5.16927.8395.3860.0000.0
2019-06-26 01:38:00+02:0033.9626.2833.5620.576-3.76320.5765.4690.0000.0
2019-10-25 15:40:00+02:0035.3325.3125.3129.610-5.39429.6105.4890.0000.0
2019-03-18 22:57:00+01:0041.7029.7829.786.835-2.5266.8352.7060.0000.0
2019-07-23 09:19:00+02:0049.6529.8429.8427.643-5.19827.6435.3180.0000.0
2019-12-10 10:30:00+01:0051.0019.8819.8828.760-5.47631.0005.6612.2400.0
2019-04-24 02:03:00+02:0034.0540.5240.5227.701-5.13127.7015.3990.0000.0
2019-05-02 09:43:00+02:0043.3027.7827.7827.705-5.26227.7055.2650.0000.0
2019-02-16 08:27:00+01:0050.34350.34350.3428.265-5.05328.2655.5940.0000.0
2019-05-15 12:18:00+02:0038.7726.3826.3827.426-5.12627.4265.3510.0000.0
2019-11-21 01:17:00+01:0035.00107.74107.7428.165-5.00228.1655.6310.0000.0
2019-07-01 16:50:00+02:0027.6023.8623.8627.630-5.23827.6305.2750.0000.0
\n", + "
" + ], + "text/plain": [ + " DAM POS NEG Total demand hp_input_MW \\\n", + "2019-11-19 16:28:00+01:00 70.43 32.84 32.84 27.708 -5.367 \n", + "2019-11-23 06:14:00+01:00 32.39 9.38 60.91 22.776 -4.138 \n", + "2019-07-03 10:27:00+02:00 33.68 44.02 44.02 22.840 -4.183 \n", + "2019-10-17 12:26:00+02:00 48.70 18.38 18.38 19.976 -3.401 \n", + "2019-05-21 03:21:00+02:00 31.49 39.67 39.67 22.703 -3.932 \n", + "2019-03-20 07:26:00+01:00 45.56 25.03 25.03 20.782 -3.550 \n", + "2019-03-23 04:28:00+01:00 33.05 36.32 36.32 26.318 -5.054 \n", + "2019-06-30 06:17:00+02:00 12.27 20.34 31.08 27.509 -5.273 \n", + "2019-05-15 04:09:00+02:00 34.38 21.86 21.86 27.839 -5.169 \n", + "2019-06-26 01:38:00+02:00 33.96 26.28 33.56 20.576 -3.763 \n", + "2019-10-25 15:40:00+02:00 35.33 25.31 25.31 29.610 -5.394 \n", + "2019-03-18 22:57:00+01:00 41.70 29.78 29.78 6.835 -2.526 \n", + "2019-07-23 09:19:00+02:00 49.65 29.84 29.84 27.643 -5.198 \n", + "2019-12-10 10:30:00+01:00 51.00 19.88 19.88 28.760 -5.476 \n", + "2019-04-24 02:03:00+02:00 34.05 40.52 40.52 27.701 -5.131 \n", + "2019-05-02 09:43:00+02:00 43.30 27.78 27.78 27.705 -5.262 \n", + "2019-02-16 08:27:00+01:00 50.34 350.34 350.34 28.265 -5.053 \n", + "2019-05-15 12:18:00+02:00 38.77 26.38 26.38 27.426 -5.126 \n", + "2019-11-21 01:17:00+01:00 35.00 107.74 107.74 28.165 -5.002 \n", + "2019-07-01 16:50:00+02:00 27.60 23.86 23.86 27.630 -5.238 \n", + "\n", + " hp_output_MW cop to_storage_MW from_storage_MW \n", + "2019-11-19 16:28:00+01:00 31.000 5.776 3.292 0.0 \n", + "2019-11-23 06:14:00+01:00 22.776 5.504 0.000 0.0 \n", + "2019-07-03 10:27:00+02:00 22.840 5.460 0.000 0.0 \n", + "2019-10-17 12:26:00+02:00 19.976 5.873 0.000 0.0 \n", + "2019-05-21 03:21:00+02:00 22.703 5.773 0.000 0.0 \n", + "2019-03-20 07:26:00+01:00 20.782 5.855 0.000 0.0 \n", + "2019-03-23 04:28:00+01:00 26.318 5.208 0.000 0.0 \n", + "2019-06-30 06:17:00+02:00 27.509 5.217 0.000 0.0 \n", + "2019-05-15 04:09:00+02:00 27.839 5.386 0.000 0.0 \n", + "2019-06-26 01:38:00+02:00 20.576 5.469 0.000 0.0 \n", + "2019-10-25 15:40:00+02:00 29.610 5.489 0.000 0.0 \n", + "2019-03-18 22:57:00+01:00 6.835 2.706 0.000 0.0 \n", + "2019-07-23 09:19:00+02:00 27.643 5.318 0.000 0.0 \n", + "2019-12-10 10:30:00+01:00 31.000 5.661 2.240 0.0 \n", + "2019-04-24 02:03:00+02:00 27.701 5.399 0.000 0.0 \n", + "2019-05-02 09:43:00+02:00 27.705 5.265 0.000 0.0 \n", + "2019-02-16 08:27:00+01:00 28.265 5.594 0.000 0.0 \n", + "2019-05-15 12:18:00+02:00 27.426 5.351 0.000 0.0 \n", + "2019-11-21 01:17:00+01:00 28.165 5.631 0.000 0.0 \n", + "2019-07-01 16:50:00+02:00 27.630 5.275 0.000 0.0 " + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selection.sample(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DAM 41.195801\n", + "POS 40.391060\n", + "NEG 42.554082\n", + "Total demand 23.460537\n", + "hp_input_MW -4.359070\n", + "hp_output_MW 24.354573\n", + "cop 5.574136\n", + "to_storage_MW 0.894037\n", + "from_storage_MW 0.888900\n", + "dtype: float64" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selection.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "########################\n", + "\n", + "# This is where we left\n", + "\n", + "########################" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "tags": [ + "exclude" + ] + }, + "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": 54, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def hybrid_imb_optimisation(case, decimals, s):\n", + " gb = case.assets['Gasboiler']\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", + " demand_ndg = case.data['MW (NDG)'].round(decimals).to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].round(decimals).to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].round(decimals).to_list()\n", + " fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n", + " gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n", + " co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n", + " eb_ode_g = s.eb_ode_g\n", + " eb_ode_e = s.eb_ode_e\n", + " \n", + " gb_input = [0] * len(case.data)\n", + " gb_output = [0] * len(case.data)\n", + "\n", + " minutes = range(len(case.data))\n", + " hp_output = [0] * len(case.data)\n", + " hp_input = [0] * len(case.data)\n", + "\n", + " for m in tqdm(minutes):\n", + " dem_vgd = demand_vdg[m]\n", + " if dem_vgd != 0:\n", + " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", + " min_load = hp_vdg.min_th_power\n", + " Tsink = Tsink_vdg[m]\n", + " Tsource = Tsource_vdg[m]\n", + " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load < cost_min_load:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = (0, 0)\n", + "\n", + " dem_ngd = demand_ndg[m]\n", + " if dem_ngd != 0:\n", + " max_load = min(hp_ndg.max_th_power, dem_ngd)\n", + " min_load = hp_ndg.min_th_power\n", + " Tsink = Tsink_ndg[m]\n", + " Tsource = Tsource_ndg[m]\n", + " cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_ngd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=fore_neg[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load <= cost_min_load:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = (0, 0)\n", + "\n", + " hp_out = hp_vdg_output + hp_ndg_output\n", + " hp_output[m] = hp_out\n", + " hp_input[m] = hp_vdg_input + hp_ndg_input\n", + " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", + " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_output)\n", + " case.data['hp_input_MW'] = np.array(hp_input)\n", + " case.data['gb_output_MW'] = np.array(gb_output)\n", + " case.data['gb_input_MW'] = np.array(gb_input)\n", + "\n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "aFRR\n", + "* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n", + "* Remaining demand is filed in by gasboiler\n", + "* Bid price at switch price?\n", + "* Assume direct response to 0% for now?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def calc_afrr_capacity(case):\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " \n", + " capacity = 0\n", + " for hp in [hp_vdg, hp_ndg]:\n", + " max_th_output = hp.max_th_power\n", + " cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n", + " e_power = max_th_output / cop\n", + " capacity += e_power\n", + " return capacity" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def aFRR_optimisation(case, s):\n", + " s.afrr_case.afrr_capacity = calc_afrr_capacity(s.afrr_case)\n", + " gb = case.assets['Gasboiler']\n", + " hp_vdg = case.assets['Heatpump VDG']\n", + " hp_ndg = case.assets['Heatpump NDG']\n", + " demand_vdg = case.data['MW (VDG)'].to_list()\n", + " demand_ndg = case.data['MW (NDG)'].to_list()\n", + " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", + " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", + " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", + " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", + " afrr_up = case.data['Hoogste_prijs_opregelen'].fillna(-999).to_list()\n", + " gas_prices = case.data['Gas prices (€/MWh)'].to_list()\n", + " co2_prices = case.data['CO2 prices (€/MWh)'].to_list()\n", + " eb_ode_g = s.eb_ode_g\n", + " eb_ode_e = s.eb_ode_e\n", + " \n", + " gb_input = [0] * len(case.data)\n", + " gb_output = [0] * len(case.data)\n", + "\n", + " minutes = range(len(case.data))\n", + " hp_output = [0] * len(case.data)\n", + " hp_input = [0] * len(case.data)\n", + "\n", + " for m in tqdm(minutes):\n", + " dem_vgd = demand_vdg[m]\n", + " if dem_vgd != 0:\n", + " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", + " min_load = hp_vdg.min_th_power\n", + " Tsink = Tsink_vdg[m]\n", + " Tsource = Tsource_vdg[m]\n", + " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e - c.sde_switch_price_correction,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load < cost_min_load:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", + " else:\n", + " hp_vdg_input, hp_vdg_output = (0, 0)\n", + "\n", + " dem_ngd = demand_ndg[m]\n", + " if dem_ngd != 0:\n", + " max_load = min(hp_ndg.max_th_power, dem_ngd)\n", + " min_load = hp_ndg.min_th_power\n", + " Tsink = Tsink_ndg[m]\n", + " Tsource = Tsource_ndg[m]\n", + " cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", + " cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n", + " \n", + " cost_full_load = cost_function(\n", + " th_load=max_load,\n", + " cop=cop_max_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_ngd\n", + " )\n", + " \n", + " cost_min_load = cost_function(\n", + " th_load=min_load,\n", + " cop=cop_min_load,\n", + " electricity_cost=afrr_up[m] + eb_ode_e,\n", + " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", + " demand=dem_vgd\n", + " )\n", + " \n", + " if cost_full_load <= cost_min_load:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n", + " else:\n", + " hp_ndg_input, hp_ndg_output = (0, 0)\n", + "\n", + " hp_out = hp_vdg_output + hp_ndg_output\n", + " hp_output[m] = hp_out\n", + " hp_input[m] = hp_vdg_input + hp_ndg_input\n", + " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", + " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", + "\n", + " case.data['hp_output_MW'] = np.array(hp_output)\n", + " case.data['hp_input_MW'] = np.array(hp_input)\n", + " case.data['gb_output_MW'] = np.array(gb_output)\n", + " case.data['gb_input_MW'] = np.array(gb_input)\n", + "\n", + " for col in case.data.columns:\n", + " if col.endswith('MW'):\n", + " case.data[col + 'h'] = case.data[col] / 60\n", + "\n", + " case.data = case.data.round(5)\n", + " return case" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Run optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def run_optimisation(c, s):\n", + " s.baseline = baseline_sim(s.baseline)\n", + " s.hpcase = hponly(s.hpcase)\n", + " s.storage_case = hponly_with_storage(s.storage_case)\n", + "# s.hpcase_sde.assign_algorithm(hponly)\n", + "# s.hpcase_sde.run()\n", + " \n", + "# s.optcase1.assign_algorithm(hybrid_imb_optimisation)\n", + "# s.optcase1.run(decimals=2, s=s)\n", + " \n", + "# s.afrr_case.assign_algorithm(aFRR_optimisation)\n", + "# s.afrr_case.run(s=s)\n", + " \n", + " for case in [s.hpcase, s.storage_case]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n", + " \n", + " return s\n", + "\n", + "s = run_optimisation(c, s)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.451794715081109" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.58710384808406" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data['hp_output_MW'].sum() /s.storage_case.data['hp_input_MW'].abs().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DAMPOSNEGRSForePosForeNegForePos_nextQForeNeg_nextQGas prices (€/MWh)CO2 prices (€/ton)...MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)Total demandhp_output_MWhp_input_MWcophp_output_MWhhp_input_MWh
2019-01-01 00:00:00+01:0068.9234.8558.012.046.8352.70NaNNaN20.8334.24...0.020.510147.6200.00.00.00.0NaN0.00.0
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2019-01-01 00:02:00+01:0068.9234.8558.012.030.9748.64NaNNaN20.8334.24...0.020.264147.6640.00.00.00.0NaN0.00.0
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5 rows × 23 columns

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" + ], + "text/plain": [ + " DAM POS NEG RS ForePos ForeNeg \\\n", + "2019-01-01 00:00:00+01:00 68.92 34.85 58.01 2.0 46.83 52.70 \n", + "2019-01-01 00:01:00+01:00 68.92 34.85 58.01 2.0 32.12 54.45 \n", + "2019-01-01 00:02:00+01:00 68.92 34.85 58.01 2.0 30.97 48.64 \n", + "2019-01-01 00:03:00+01:00 68.92 34.85 58.01 2.0 51.13 48.35 \n", + "2019-01-01 00:04:00+01:00 68.92 34.85 58.01 2.0 48.04 52.01 \n", + "\n", + " ForePos_nextQ ForeNeg_nextQ Gas prices (€/MWh) \\\n", + "2019-01-01 00:00:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:01:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:02:00+01:00 NaN NaN 20.83 \n", + "2019-01-01 00:03:00+01:00 57.71 73.05 20.83 \n", + "2019-01-01 00:04:00+01:00 63.60 79.07 20.83 \n", + "\n", + " CO2 prices (€/ton) ... MW (VDG) Tsource (NDG) \\\n", + "2019-01-01 00:00:00+01:00 34.24 ... 0.0 20.510 \n", + "2019-01-01 00:01:00+01:00 34.24 ... 0.0 20.387 \n", + "2019-01-01 00:02:00+01:00 34.24 ... 0.0 20.264 \n", + "2019-01-01 00:03:00+01:00 34.24 ... 0.0 20.141 \n", + "2019-01-01 00:04:00+01:00 34.24 ... 0.0 20.018 \n", + "\n", + " Tsink (NDG) MW (NDG) Total demand hp_output_MW \\\n", + "2019-01-01 00:00:00+01:00 147.620 0.0 0.0 0.0 \n", + "2019-01-01 00:01:00+01:00 147.642 0.0 0.0 0.0 \n", + "2019-01-01 00:02:00+01:00 147.664 0.0 0.0 0.0 \n", + "2019-01-01 00:03:00+01:00 147.686 0.0 0.0 0.0 \n", + "2019-01-01 00:04:00+01:00 147.708 0.0 0.0 0.0 \n", + "\n", + " hp_input_MW cop hp_output_MWh hp_input_MWh \n", + "2019-01-01 00:00:00+01:00 0.0 NaN 0.0 0.0 \n", + "2019-01-01 00:01:00+01:00 0.0 NaN 0.0 0.0 \n", + "2019-01-01 00:02:00+01:00 0.0 NaN 0.0 0.0 \n", + "2019-01-01 00:03:00+01:00 0.0 NaN 0.0 0.0 \n", + "2019-01-01 00:04:00+01:00 0.0 NaN 0.0 0.0 \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.hpcase.data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "vis_data = s.storage_case.data[[\n", + " 'storage_level_MWh', \n", + " #'to_storage_ndg_MW', \n", + " #'to_storage_vdg_MW', \n", + " 'to_storage_MW', \n", + " #'from_storage_ndg_MW', \n", + " #'from_storage_vdg_MW',\n", + " 'from_storage_MW'\n", + "]]\n", + "\n", + "cops = pd.concat([s.hpcase.data['cop'], s.storage_case.data['cop']], axis=1)\n", + "vis_data = pd.concat([vis_data, cops], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": true + }, + "data": [ + { + "line": { + "color": "rgba(255, 153, 51, 1.0)", + "dash": "solid", + "shape": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Financials" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def calculate_sde_subsidy(c, s, case):\n", + " hp_capacity = abs(case.assets['Heatpump VDG'].max_th_power) + abs(case.assets['Heatpump NDG'].max_th_power)\n", + " case.full_load_hours = abs(case.data['hp_output_MWh'].sum() / hp_capacity)\n", + "\n", + " subsidized_hours = min(case.full_load_hours, 8000)\n", + " subsidized_MWh = subsidized_hours * hp_capacity\n", + "\n", + " base_amount_per_MWh_th = round(c.sde_base_amount*0.173 + c.longterm_gas_price, 2)\n", + " base_subsidy_amount = round(base_amount_per_MWh_th * subsidized_MWh, 2)\n", + " long_term_gas_price_LHV = round(EURperHHV_to_EURperLHV(c.longterm_gas_price), 2)\n", + " base_amount_gas = (2/3)*long_term_gas_price_LHV*0.9\n", + " mean_ttf_price_LHV = round(EURperHHV_to_EURperLHV(case.data['Gas prices (€/MWh)'].mean()), 2)\n", + " correction_amount_gas = max(mean_ttf_price_LHV, base_amount_gas)\n", + " avoided_gas_consumption = subsidized_MWh/0.9\n", + " correction_gas = round(correction_amount_gas * avoided_gas_consumption, 2)\n", + "\n", + " base_amount_co2 = (2/3)*c.longterm_co2_price\n", + " avoided_co2_emission = avoided_gas_consumption * 0.226\n", + " mean_ets_price = round(EURperHHV_to_EURperLHV(case.data['CO2 prices (€/ton)'].mean()), 2)\n", + " correction_amount_co2 = max(base_amount_co2, mean_ets_price)\n", + " correction_co2 = round(correction_amount_co2 * avoided_co2_emission, 2)\n", + "\n", + " sde_subsidy_corrected = max(base_subsidy_amount - (correction_gas + correction_co2), 0)\n", + " sde_per_MWh_th = sde_subsidy_corrected / subsidized_MWh\n", + " sde_per_MWh_e = sde_subsidy_corrected / case.data['hp_input_MWh'].abs().sum()\n", + "\n", + " case.sde_results = {\n", + " 'base_amount': base_subsidy_amount,\n", + " 'correction_gas': correction_gas,\n", + " 'correction_co2': correction_co2,\n", + " 'corrected_amount': sde_subsidy_corrected,\n", + " 'sde_per_MWh_th': sde_per_MWh_th,\n", + " 'sde_per_MWh_e': sde_per_MWh_e\n", + " }\n", + " return case" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['DAM', 'POS', 'NEG', 'RS', 'ForePos', 'ForeNeg', 'ForePos_nextQ',\n", + " 'ForeNeg_nextQ', 'Gas prices (€/MWh)', 'CO2 prices (€/ton)',\n", + " 'CO2 prices (€/MWh)', 'Tsource (VDG)', 'Tsink (VDG)', 'MW (VDG)',\n", + " 'Tsource (NDG)', 'Tsink (NDG)', 'MW (NDG)', 'Total demand',\n", + " 'hp_output_MW', 'hp_input_MW', 'cop', 'tsource_vdg', 'tsource_ndg',\n", + " 'to_storage_ndg_MW', 'from_storage_ndg_MW', 'to_storage_vdg_MW',\n", + " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", + " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", + " 'to_storage_ndg_MWh', 'from_storage_ndg_MWh', 'to_storage_vdg_MWh',\n", + " 'from_storage_vdg_MWh', 'to_storage_MWh', 'from_storage_MWh'],\n", + " dtype='object')" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def collect_cashflows(c, s):\n", + " s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", + " s.storage_case.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n", + " s.storage_case.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n", + "\n", + "# s.hpcase_sde.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", + " \n", + "# s.optcase1.data['DA Nom'] = s.hpcase.data['hp_input_MWh'] * c.day_ahead_buying_perc\n", + "# s.optcase1.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n", + " \n", + "# s.afrr_case.data['POS'] = s.afrr_case.data['aFRR_up'].fillna(value=s.afrr_case.data['POS'])\n", + "# s.afrr_case.data['DA Nom'] = s.hpcase.data['hp_input_MWh']\n", + "# s.afrr_case.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n", + "# s.afrr_case.add_cashflow('aFRR capacity fee (€)', c.afrr_capacity_fee * s.afrr_case.afrr_capacity)\n", + " \n", + " for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n", + " case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n", + " case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n", + " case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n", + " \n", + " for case in [s.hpcase, s.storage_case]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " \n", + " case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n", + " case.add_grid_costs(\n", + " power_MW_col='hp_input_MW',\n", + " grid_operator=c.grid_operator,\n", + " year=2020, \n", + " connection_type=c.connection_type\n", + " )\n", + " \n", + " for case in s.sde_cases:\n", + " case = calculate_sde_subsidy(c=c, s=s, case=case)\n", + " case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n", + " return s\n", + "\n", + "s = collect_cashflows(c, s)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "discount rate 0.1\n", + "cashflows [-6231000.0 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826]\n", + "answer [-5664545.454545454 1971027.9662751092 1791843.6057046445\n", + " 1628948.732458768 1480862.4840534253 1346238.62186675 1223853.2926061363\n", + " 1112593.9023692147 1011449.0021538314 919499.0928671194 835908.2662428358\n", + " 759916.6056753052 690833.277886641]\n", + "discount rate 0.1\n", + "cashflows [-6656000.0 2394564.8828180796 2394564.8828180796 2394564.8828180796\n", + " 2394564.8828180796 2394564.8828180796 2394564.8828180796\n", + " 2394564.8828180796 2394564.8828180796 2394564.8828180796\n", + " 2394564.8828180796 2394564.8828180796 2394564.8828180796]\n", + "answer [-6050909.090909091 1978979.2419984126 1799072.0381803748\n", + " 1635520.0347094317 1486836.3951903924 1351669.4501730837\n", + " 1228790.4092482578 1117082.1902256887 1015529.2638415352\n", + " 923208.4216741228 839280.3833401117 762982.1666728287 693620.1515207533]\n" + ] + } + ], + "source": [ + "def calculate_financials(c, s):\n", + " for case in s.cases:\n", + " case.calculate_ebitda(c.project_duration)\n", + "\n", + " for case in [s.hpcase, s.storage_case]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", + " case.calculate_business_case(\n", + " project_duration=c.project_duration, \n", + " discount_rate=c.discount_rate, \n", + " baseline=s.baseline\n", + " )\n", + " \n", + " return s\n", + "\n", + "s = calculate_financials(c, s)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Visualisations" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgba(70, 165, 121, 1.0)", + "dash": "solid", + "shape": "linear", + "width": 1.3 + }, + "mode": "lines", + "name": "Total demand", + "text": "", + "type": 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", 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'to_storage_vdg_MW',\n", + " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", + " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", + " 'to_storage_ndg_MWh', 'from_storage_ndg_MWh', 'to_storage_vdg_MWh',\n", + " 'from_storage_vdg_MWh', 'to_storage_MWh', 'from_storage_MWh',\n", + " 'nomination_MWh', 'Prod. vol.', 'Cons. vol.', 'Imb. vol.',\n", + " 'Day-Ahead Result', 'POS Result', 'NEG Result', 'Imbalance Result',\n", + " 'Combined Result'],\n", + " dtype='object')" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": true + }, + "data": [ + { + "line": { + "color": "rgba(255, 153, 51, 1.0)", + "dash": "solid", + "shape": "linear", 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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp_comparison = pd.concat([s.storage_case.data[['tsource_ndg', 'tsource_vdg']], s.storage_case.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n", + "temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n", + "temp_comparison.resample('1H').mean().iplot()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "after = s.storage_case.data['tsource_vdg'].round(2)\n", + "before = s.storage_case.data['Tsource (VDG)'].round(2)\n", + "after.equals(before)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean Tsource_vdg before storage: 63.65879788812786\n", + "mean Tsource_vdg after storage: 64.40341691971082\n", + "max Tsource_vdg before storage: 70.07\n", + "max Tsource_vdg after storage: 94.938\n", + "mean Tsource_ndg before storage: 62.17630502283106\n", + "mean Tsource_ndg after storage: 62.17630502283106\n", + "max Tsource increase: 62.179\n", + "charging intensivity: 79011\n", + "discharging intensivity: 18986\n", + "charging intensivity: True\n", + "increase in T(vdg) source: DAM 33.08000\n", + "POS 73.40000\n", + "NEG 73.40000\n", + "RS 1.00000\n", + "ForePos 35.48000\n", + "ForeNeg 48.57000\n", + "ForePos_nextQ NaN\n", + "ForeNeg_nextQ NaN\n", + "Gas prices (€/MWh) 11.40000\n", + "CO2 prices (€/ton) 19.73000\n", + "CO2 prices (€/MWh) 3.81000\n", + "Tsource (VDG) 53.10000\n", + "Tsink (VDG) 127.36000\n", + "MW (VDG) 0.05000\n", + "Tsource (NDG) 63.79000\n", + "Tsink (NDG) 138.86000\n", + "MW (NDG) 0.02000\n", + "Total demand 0.07000\n", + "hp_output_MW 0.07000\n", + "hp_input_MW -0.03000\n", + "cop 2.32100\n", + "tsource_vdg 94.93800\n", + "tsource_ndg 63.79000\n", + "to_storage_ndg_MW 0.00000\n", + "from_storage_ndg_MW 0.00000\n", + "to_storage_vdg_MW 0.00000\n", + "from_storage_vdg_MW 25.00000\n", + "to_storage_MW 0.00000\n", + "from_storage_MW 25.00000\n", + "storage_level_MWh 13.43600\n", + "hp_output_MWh 0.00100\n", + "hp_input_MWh -0.00100\n", + "to_storage_ndg_MWh 0.00000\n", + "from_storage_ndg_MWh 0.00000\n", + "to_storage_vdg_MWh 0.00000\n", + "from_storage_vdg_MWh 0.41700\n", + "to_storage_MWh 0.00000\n", + "from_storage_MWh 0.41700\n", + "nomination_MWh -0.00100\n", + "Prod. vol. 0.00000\n", + "Cons. vol. -0.00100\n", + "Imb. vol. 0.00000\n", + "Day-Ahead Result -0.03308\n", + "POS Result 0.00000\n", + "NEG Result 0.00000\n", + "Imbalance Result 0.00000\n", + "Combined Result -0.03308\n", + "Name: 2019-09-15 00:00:00+02:00, dtype: float64\n", + "4.15989259766242\n" + ] + } + ], + "source": [ + "print(f\"mean Tsource_vdg before storage: {s.storage_case.data['Tsource (VDG)'].mean()}\")\n", + "print(f\"mean Tsource_vdg after storage: {s.storage_case.data['tsource_vdg'].mean()}\")\n", + "print(f\"max Tsource_vdg before storage: {s.storage_case.data['Tsource (VDG)'].max()}\")\n", + "print(f\"max Tsource_vdg after storage: {s.storage_case.data['tsource_vdg'].max()}\")\n", + "print(f\"mean Tsource_ndg before storage: {s.storage_case.data['Tsource (NDG)'].mean()}\")\n", + "print(f\"mean Tsource_ndg after storage: {s.storage_case.data['tsource_ndg'].mean()}\")\n", + "print(f\"max Tsource increase: {(s.storage_case.data['tsource_vdg']-s.storage_case.data['Tsource (VDG)']).max()}\")\n", + "print(f\"charging intensivity: {sum(s.storage_case.data['to_storage_MW']!= 0)}\")\n", + "print(f\"discharging intensivity: {sum(s.storage_case.data['from_storage_MW']!= 0)}\")\n", + "# 98007 times out of 525600, the storage is used to charge/dischage\n", + "# from 39 000 to 110 000 discharge time increases\n", + "print(f\"charging intensivity: {sum(s.storage_case.data['tsource_vdg'])!= sum(s.storage_case.data['Tsource (VDG)'])}\")\n", + "print(f\"increase in T(vdg) source: {s.storage_case.data.loc['2019-09-15 00:00:00']}\")\n", + "print(79013/18994)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tsource_ndg 2019-01-18 19:20:00+01:00\n", + "tsource_vdg 2019-01-22 17:00:00+01:00\n", + "dtype: datetime64[ns, Europe/Amsterdam]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data[['tsource_ndg', 'tsource_vdg']].idxmin()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tsource_ndg 2019-01-16 02:30:00+01:00\n", + "tsource_vdg 2019-09-15 00:00:00+02:00\n", + "dtype: datetime64[ns, Europe/Amsterdam]" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data[['tsource_ndg', 'tsource_vdg']].idxmax()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DAM 33.08000\n", + "POS 73.40000\n", + "NEG 73.40000\n", + "RS 1.00000\n", + "ForePos 35.48000\n", + "ForeNeg 48.57000\n", + "ForePos_nextQ NaN\n", + "ForeNeg_nextQ NaN\n", + "Gas prices (€/MWh) 11.40000\n", + "CO2 prices (€/ton) 19.73000\n", + "CO2 prices (€/MWh) 3.81000\n", + "Tsource (VDG) 53.10000\n", + "Tsink (VDG) 127.36000\n", + "MW (VDG) 0.05000\n", + "Tsource (NDG) 63.79000\n", + "Tsink (NDG) 138.86000\n", + "MW (NDG) 0.02000\n", + "Total demand 0.07000\n", + "hp_output_MW 0.07000\n", + "hp_input_MW -0.03000\n", + "cop 2.32100\n", + "tsource_vdg 94.93800\n", + "tsource_ndg 63.79000\n", + "to_storage_ndg_MW 0.00000\n", + "from_storage_ndg_MW 0.00000\n", + "to_storage_vdg_MW 0.00000\n", + "from_storage_vdg_MW 25.00000\n", + "to_storage_MW 0.00000\n", + "from_storage_MW 25.00000\n", + "storage_level_MWh 13.43600\n", + "hp_output_MWh 0.00100\n", + "hp_input_MWh -0.00100\n", + "to_storage_ndg_MWh 0.00000\n", + "from_storage_ndg_MWh 0.00000\n", + "to_storage_vdg_MWh 0.00000\n", + "from_storage_vdg_MWh 0.41700\n", + "to_storage_MWh 0.00000\n", + "from_storage_MWh 0.41700\n", + "nomination_MWh -0.00100\n", + "Prod. vol. 0.00000\n", + "Cons. vol. -0.00100\n", + "Imb. vol. 0.00000\n", + "Day-Ahead Result -0.03308\n", + "POS Result 0.00000\n", + "NEG Result 0.00000\n", + "Imbalance Result 0.00000\n", + "Combined Result -0.03308\n", + "Name: 2019-09-15 00:00:00+02:00, dtype: float64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.loc['2019-09-15 00:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DAM 89.700\n", + "POS -108.170\n", + "NEG -108.170\n", + "RS -1.000\n", + "ForePos 33.340\n", + "ForeNeg 46.130\n", + "ForePos_nextQ NaN\n", + "ForeNeg_nextQ NaN\n", + "Gas prices (€/MWh) 21.820\n", + "CO2 prices (€/ton) 32.720\n", + "CO2 prices (€/MWh) 6.310\n", + "Tsource (VDG) 25.310\n", + "Tsink (VDG) 152.480\n", + "MW (VDG) 0.000\n", + "Tsource (NDG) 14.300\n", + "Tsink (NDG) 156.670\n", + "MW (NDG) 0.000\n", + "Total demand 0.000\n", + "hp_output_MW 0.000\n", + "hp_input_MW 0.000\n", + "cop NaN\n", + "tsource_vdg 25.310\n", + "tsource_ndg 14.300\n", + "to_storage_ndg_MW 0.000\n", + "from_storage_ndg_MW 0.000\n", + "to_storage_vdg_MW 0.000\n", + "from_storage_vdg_MW 0.000\n", + "to_storage_MW 0.000\n", + "from_storage_MW 0.000\n", + "storage_level_MWh 7.563\n", + "hp_output_MWh 0.000\n", + "hp_input_MWh 0.000\n", + "to_storage_ndg_MWh 0.000\n", + "from_storage_ndg_MWh 0.000\n", + "to_storage_vdg_MWh 0.000\n", + "from_storage_vdg_MWh 0.000\n", + "to_storage_MWh 0.000\n", + "from_storage_MWh 0.000\n", + "nomination_MWh 0.000\n", + "Prod. vol. 0.000\n", + "Cons. vol. 0.000\n", + "Imb. vol. 0.000\n", + "Day-Ahead Result 0.000\n", + "POS Result -0.000\n", + "NEG Result -0.000\n", + "Imbalance Result -0.000\n", + "Combined Result 0.000\n", + "Name: 2019-01-22 17:00:00+01:00, dtype: float64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.loc['2019-01-22 17:00:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DAM 72.46\n", + "POS 44.72\n", + "NEG 44.72\n", + "RS 0.00\n", + "ForePos 42.17\n", + "ForeNeg 42.91\n", + "ForePos_nextQ 43.74\n", + "ForeNeg_nextQ 51.74\n", + "Gas prices (€/MWh) 22.45\n", + "CO2 prices (€/ton) 32.96\n", + "CO2 prices (€/MWh) 6.36\n", + "Tsource (VDG) 26.10\n", + "Tsink (VDG) 117.21\n", + "MW (VDG) 0.00\n", + "Tsource (NDG) 13.54\n", + "Tsink (NDG) 116.88\n", + "MW (NDG) 0.00\n", + "Total demand 0.00\n", + "hp_output_MW 0.00\n", + "hp_input_MW 0.00\n", + "cop NaN\n", + "tsource_vdg 26.10\n", + "tsource_ndg 13.54\n", + "to_storage_ndg_MW 0.00\n", + "from_storage_ndg_MW 0.00\n", + "to_storage_vdg_MW 0.00\n", + "from_storage_vdg_MW 0.00\n", + "to_storage_MW 0.00\n", + "from_storage_MW 0.00\n", + "storage_level_MWh 47.50\n", + "hp_output_MWh 0.00\n", + "hp_input_MWh 0.00\n", + "to_storage_ndg_MWh 0.00\n", + "from_storage_ndg_MWh 0.00\n", + "to_storage_vdg_MWh 0.00\n", + "from_storage_vdg_MWh 0.00\n", + "to_storage_MWh 0.00\n", + "from_storage_MWh 0.00\n", + "nomination_MWh 0.00\n", + "Prod. vol. 0.00\n", + "Cons. vol. 0.00\n", + "Imb. vol. 0.00\n", + "Day-Ahead Result 0.00\n", + "POS Result 0.00\n", + "NEG Result 0.00\n", + "Imbalance Result 0.00\n", + "Combined Result 0.00\n", + "Name: 2019-01-18 19:20:00+01:00, dtype: float64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.loc['2019-01-18 19:20:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DAM 68.400\n", + "POS 157.120\n", + "NEG 157.120\n", + "RS 1.000\n", + "ForePos 35.650\n", + "ForeNeg 43.850\n", + "ForePos_nextQ NaN\n", + "ForeNeg_nextQ NaN\n", + "Gas prices (€/MWh) 22.280\n", + "CO2 prices (€/ton) 33.090\n", + "CO2 prices (€/MWh) 6.380\n", + "Tsource (VDG) 29.635\n", + "Tsink (VDG) 134.840\n", + "MW (VDG) 0.000\n", + "Tsource (NDG) 21.420\n", + "Tsink (NDG) 118.755\n", + "MW (NDG) 0.000\n", + "Total demand 0.000\n", + "hp_output_MW 0.000\n", + "hp_input_MW 0.000\n", + "cop NaN\n", + "tsource_vdg 29.635\n", + "tsource_ndg 21.420\n", + "to_storage_ndg_MW 0.000\n", + "from_storage_ndg_MW 0.000\n", + "to_storage_vdg_MW 0.000\n", + "from_storage_vdg_MW 0.000\n", + "to_storage_MW 0.000\n", + "from_storage_MW 0.000\n", + "storage_level_MWh 47.500\n", + "hp_output_MWh 0.000\n", + "hp_input_MWh 0.000\n", + "to_storage_ndg_MWh 0.000\n", + "from_storage_ndg_MWh 0.000\n", + "to_storage_vdg_MWh 0.000\n", + "from_storage_vdg_MWh 0.000\n", + "to_storage_MWh 0.000\n", + "from_storage_MWh 0.000\n", + "nomination_MWh 0.000\n", + "Prod. vol. 0.000\n", + "Cons. vol. 0.000\n", + "Imb. vol. 0.000\n", + "Day-Ahead Result 0.000\n", + "POS Result 0.000\n", + "NEG Result 0.000\n", + "Imbalance Result 0.000\n", + "Combined Result 0.000\n", + "Name: 2019-01-04 07:45:00+01:00, dtype: float64" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s.storage_case.data.loc['2019-01-04 07:45:00']" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 29.635,\n", + " 21.42,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " HotWaterStorage(name=HotWaterStorage, rated_power=25, capacity=50.0, temperature=95, min_storagelevel=2.5),\n", + " Heatpump(name='Heatpump VDG', max_thermal_power=23.3, cop_curve=, min_th_power=0.0),\n", + " Heatpump(name='Heatpump NDG', max_thermal_power=7.7, cop_curve=, min_th_power=0.0))" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "case = s.storage_case\n", + "hp_vdg = case.assets['Heatpump VDG']\n", + "hp_ndg = case.assets['Heatpump NDG']\n", + "ws = case.assets['HotWaterStorage']\n", + "ws.storage_level = 47.083+25\n", + "pos = 157.120\n", + "neg = 157.120\n", + "dam = 68.400\n", + "demand_vdg = 0\n", + "demand_ndg = 0\n", + "tsource_vdg = 29.635\n", + "tsink_vdg = 134.840\n", + "tsource_ndg = 21.420\n", + "tsink_ndg = 118.755\n", + "\n", + "hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "test_hp = Heatpump(\n", + " name='Heatpump for Testing',\n", + " max_th_power=1,\n", + " min_th_power=0.3,\n", + " cop_curve=cop_curve\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# Tsrc_vdg, Tsnk_vdg, Tsrc_ndg, Tsnk_ndg = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].mean().to_list()\n", + "# mean_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].mean() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].mean()\n", + "# )\n", + "\n", + "# max_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].max() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].max()\n", + "# )\n", + "\n", + "# min_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].min() \n", + "# + s.eb_ode_g \n", + "# + s.optcase1.data['CO2 prices (€/MWh)'].min()\n", + "# )\n", + "\n", + "# Tsrc_vdg_min, Tsnk_vdg_min, Tsrc_ndg_min, Tsnk_ndg_min = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].min().to_list()\n", + "# Tsrc_vdg_max, Tsnk_vdg_max, Tsrc_ndg_max, Tsnk_ndg_max = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].max().to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "import plotly.graph_objs as go\n", + "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n", + " loads = []\n", + " eprices = list(range(1000))\n", + " for eprice in eprices:\n", + " _, load = test_hp.set_opt_load(\n", + " electricity_cost=eprice + s.eb_ode_e,\n", + " alt_heat_price=gas_price / 0.9,\n", + " demand=1, \n", + " Tsink=Tsnk,\n", + " Tsource=Tsrc\n", + " )\n", + " loads.append(load)\n", + " trace = go.Scatter(x=eprices, y=loads, name=name)\n", + " return trace" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# import plotly.graph_objects as go\n", + "# import numpy as np\n", + "\n", + "# fig = go.Figure()\n", + "\n", + "# configs = {\n", + "# 'mean': [mean_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'unfav_gas': [min_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'fav_gas': [max_gas_price, Tsnk_vdg, Tsrc_vdg],\n", + "# 'unfav_all': [min_gas_price, Tsnk_vdg_max, Tsrc_vdg_min],\n", + "# 'fav_all': [max_gas_price, Tsnk_vdg_min, Tsrc_vdg_max],\n", + "# }\n", + "\n", + "# for name, config in configs.items():\n", + "# trace = create_load_trace(*config, name)\n", + "# fig.add_trace(trace)\n", + "\n", + "# fig.update_layout(title='Switch prices for different configurations')\n", + "# fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "date = s.baseline.data.index[0].strftime('%Y-%m-%d')" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "rgba(14, 41, 59, 1.0)", + "dash": "solid", + "shape": "linear", + "width": 1.3 + }, + "mode": "lines", + "name": "Total demand", + "text": "", + "type": "scatter", + "x": [ + "2019-01-01 00:00:00+01:00", + "2019-01-01 00:01:00+01:00", + "2019-01-01 00:02:00+01:00", + "2019-01-01 00:03:00+01:00", + "2019-01-01 00:04:00+01:00", + "2019-01-01 00:05:00+01:00", + "2019-01-01 00:06:00+01:00", + "2019-01-01 00:07:00+01:00", + "2019-01-01 00:08:00+01:00", + "2019-01-01 00:09:00+01:00", + "2019-01-01 00:10:00+01:00", + "2019-01-01 00:11:00+01:00", + "2019-01-01 00:12:00+01:00", + 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Heatpump onlyHeatpump + Storage
Total net volume (MWh)-37,696-38,187
Total exported to grid (MWh)--
Total consumed from grid (MWh)37,69638,187
Total nominated volume (MWh)-37,696-37,696
Absolute imbalance volume (MWh)-805
Mean selling price (€/MWh)--
Mean buying price (€/MWh)4140
Total day-ahead result (€)-1,553,699-1,553,699
Total POS result (€)-23,481
Total NEG result (€)--5,581
Total imbalance result (€)-17,901
Total combined result (€)-1,553,699-1,535,798
\n", + "
" + ], + "text/plain": [ + " Heatpump only Heatpump + Storage\n", + "Total net volume (MWh) -37,696 -38,187\n", + "Total exported to grid (MWh) - -\n", + "Total consumed from grid (MWh) 37,696 38,187\n", + "Total nominated volume (MWh) -37,696 -37,696\n", + "Absolute imbalance volume (MWh) - 805\n", + "Mean selling price (€/MWh) - -\n", + "Mean buying price (€/MWh) 41 40\n", + "Total day-ahead result (€) -1,553,699 -1,553,699\n", + "Total POS result (€) - 23,481\n", + "Total NEG result (€) - -5,581\n", + "Total imbalance result (€) - 17,901\n", + "Total combined result (€) -1,553,699 -1,535,798" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "report = ComparisonReport(\n", + " cases=s.optcases, \n", + " kind='electr_market_results',\n", + ")\n", + "report.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Heatpump onlyHeatpump + Storage
Gasboiler OPEX (€)--
Gas consumption costs (€)3,090,1643,090,164
CO2 emission costs (€)1,124,5721,124,572
Gas taxes (€)790,305790,305
Heatpump VDG OPEX (€)-46,600-46,600
Heatpump NDG OPEX (€)-15,400-15,400
Result on electricity market (€)-1,553,699-1,535,798
Electricity taxes (€)-35,811-36,277
Grid transport costs (€)-273,944-275,718
HotWaterStorage OPEX (€)--8,500
EBITDA (€)3,079,5863,086,747
Depreciation (€)-519,250-554,667
EBITDA + depr (€)2,560,3362,532,081
\n", + "
" + ], + "text/plain": [ + " Heatpump only Heatpump + Storage\n", + "Gasboiler OPEX (€) - -\n", + "Gas consumption costs (€) 3,090,164 3,090,164\n", + "CO2 emission costs (€) 1,124,572 1,124,572\n", + "Gas taxes (€) 790,305 790,305\n", + "Heatpump VDG OPEX (€) -46,600 -46,600\n", + "Heatpump NDG OPEX (€) -15,400 -15,400\n", + "Result on electricity market (€) -1,553,699 -1,535,798\n", + "Electricity taxes (€) -35,811 -36,277\n", + "Grid transport costs (€) -273,944 -275,718\n", + "HotWaterStorage OPEX (€) - -8,500\n", + "EBITDA (€) 3,079,586 3,086,747\n", + "Depreciation (€) -519,250 -554,667\n", + "EBITDA + depr (€) 2,560,336 2,532,081" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n", + "casereport.show(presentation_format=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12
CAPEX (€)-6,231,000
Regular Earnings (€)3,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,592
Irregular Cashflows (€)------------
EBITDA (€)3,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,5923,079,592
Depreciations (€) -/--248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000
EBIT (€)2,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,5922,831,592
Income tax (Vpb.) (€)-694,648-694,648-694,648-694,648-694,648-694,648-694,648-694,648-694,648-694,648-694,648-694,648
NOPLAT (€)2,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,9442,136,944
Depreciations (€) +/+248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000
Free Cash Flow (€)-6,231,0002,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,9442,384,944
Simple Payback Period2.6 years
IRR (%)37%
WACC (%)10%
NPV of explicit period (€)9,108,429
Discounted residual value (€)1,037,143
NPV (€)9,108,429
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" + ], + "text/plain": [ + " Year 0 Year 1 Year 2 Year 3 \\\n", + "CAPEX (€) -6,231,000 \n", + "Regular Earnings (€) 3,079,592 3,079,592 3,079,592 \n", + "Irregular Cashflows (€) - - - \n", + "EBITDA (€) 3,079,592 3,079,592 3,079,592 \n", + "Depreciations (€) -/- -248,000 -248,000 -248,000 \n", + "EBIT (€) 2,831,592 2,831,592 2,831,592 \n", + "Income tax (Vpb.) (€) -694,648 -694,648 -694,648 \n", + "NOPLAT (€) 2,136,944 2,136,944 2,136,944 \n", + "Depreciations (€) +/+ 248,000 248,000 248,000 \n", + "Free Cash Flow (€) -6,231,000 2,384,944 2,384,944 2,384,944 \n", + "Simple Payback Period 2.6 years \n", + "IRR (%) 37% \n", + "WACC (%) 10% \n", + "NPV of explicit period (€) 9,108,429 \n", + "Discounted residual value (€) 1,037,143 \n", + "NPV (€) 9,108,429 \n", + "\n", + " Year 4 Year 5 Year 6 Year 7 \\\n", + "CAPEX (€) \n", + "Regular Earnings (€) 3,079,592 3,079,592 3,079,592 3,079,592 \n", + "Irregular Cashflows (€) - - - - \n", + "EBITDA (€) 3,079,592 3,079,592 3,079,592 3,079,592 \n", + "Depreciations (€) -/- -248,000 -248,000 -248,000 -248,000 \n", + "EBIT (€) 2,831,592 2,831,592 2,831,592 2,831,592 \n", + "Income tax (Vpb.) (€) -694,648 -694,648 -694,648 -694,648 \n", + "NOPLAT (€) 2,136,944 2,136,944 2,136,944 2,136,944 \n", + "Depreciations (€) +/+ 248,000 248,000 248,000 248,000 \n", + "Free Cash Flow (€) 2,384,944 2,384,944 2,384,944 2,384,944 \n", + "Simple Payback Period \n", + "IRR (%) \n", + "WACC (%) \n", + "NPV of explicit period (€) \n", + "Discounted residual value (€) \n", + "NPV (€) \n", + "\n", + " Year 8 Year 9 Year 10 Year 11 \\\n", + "CAPEX (€) \n", + "Regular Earnings (€) 3,079,592 3,079,592 3,079,592 3,079,592 \n", + "Irregular Cashflows (€) - - - - \n", + "EBITDA (€) 3,079,592 3,079,592 3,079,592 3,079,592 \n", + "Depreciations (€) -/- -248,000 -248,000 -248,000 -248,000 \n", + "EBIT (€) 2,831,592 2,831,592 2,831,592 2,831,592 \n", + "Income tax (Vpb.) (€) -694,648 -694,648 -694,648 -694,648 \n", + "NOPLAT (€) 2,136,944 2,136,944 2,136,944 2,136,944 \n", + "Depreciations (€) +/+ 248,000 248,000 248,000 248,000 \n", + "Free Cash Flow (€) 2,384,944 2,384,944 2,384,944 2,384,944 \n", + "Simple Payback Period \n", + "IRR (%) \n", + "WACC (%) \n", + "NPV of explicit period (€) \n", + "Discounted residual value (€) \n", + "NPV (€) \n", + "\n", + " Year 12 \n", + "CAPEX (€) \n", + "Regular Earnings (€) 3,079,592 \n", + "Irregular Cashflows (€) - \n", + "EBITDA (€) 3,079,592 \n", + "Depreciations (€) -/- -248,000 \n", + "EBIT (€) 2,831,592 \n", + "Income tax (Vpb.) (€) -694,648 \n", + "NOPLAT (€) 2,136,944 \n", + "Depreciations (€) +/+ 248,000 \n", + "Free Cash Flow (€) 2,384,944 \n", + "Simple Payback Period \n", + "IRR (%) \n", + "WACC (%) \n", + "NPV of explicit period (€) \n", + "Discounted residual value (€) \n", + "NPV (€) " + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BusinessCaseReport(s.hpcase).show()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": true + }, + "data": [ + { + "line": { + "color": "rgba(14, 41, 59, 1.0)", + "dash": "solid", + "shape": "linear", + "width": 1.3 + }, + "mode": "lines", + "name": "Gas prices (€/MWh)", + "text": "", + "type": "scatter", + "x": [ + "2019-01-01 00:00:00+01:00", + "2019-01-02 00:00:00+01:00", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#price chart\n", + "from copy import deepcopy\n", + "\n", + "data = deepcopy(s.baseline.data)\n", + "data = data[data.columns[:2]]\n", + "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n", + "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n", + "\n", + "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "# #price chart\n", + "# from copy import deepcopy\n", + "\n", + "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n", + "\n", + "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n", + "# _total_output = _source_output.sum(axis=1)\n", + "# _data = _source_output.divide(_total_output, axis=0).rename(\n", + "# columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n", + "# ) * 100\n", + "\n", + "# production_fig = _data.iplot(\n", + "# kind='bar',\n", + "# barmode='stack',\n", + "# colors=[recoydarkblue, recoygreen],\n", + "# title='Hybrid case: Heat production per Month by Source in % share',\n", + "# yTitle='Share of production in %',\n", + "# dimensions=(600, 400),\n", + "# asFigure=True\n", + "# )\n", + "\n", + "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n", + "# production_fig" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "rgba(14, 41, 59, 0.6)", + "line": { + "color": "rgba(14, 41, 59, 1.0)", + "width": 1 + } + }, + "name": "Gasboiler 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A4Dh/Y3sgQIAAAQIECBAgQIBA1QIGx6rTZ/EECBAgQIAAAQIECBCYv4DBcf7G9kCAAAECBAgQIECAAIGqBQyOVafP4gkQIECAAAECBAgQIDB/AYPj/I3tgQABAgQIECBAgAABAlULGByrTp/FEyBAgAABAgQIECBAYP4CBsf5G9sDAQIECBAgQIAAAQIEqhYwOFadPosnQIAAAQIECBAgQIDA/AUMjvM3tgcCBAgQIECAAAECBAhULWBwrDp9Fk+AAAECBAgQIECAAIH5Cxgc529sDwQIECBAgAABAgQIEKhawOBYdfosngABAgQIECBAgAABAvMXMDjO39geCBAgQIAAAQIECBAgULWAwbHq9Fk8AQIECBAgQIAAAQIE5i9gcJy/sT0QIECAAAECBAgQIECgagGDY9Xps3gCBAgQIECAAAECBAjMX8DgOH9jeyBAgAABAgQIECBAgEDVAgbHqtNn8QQIECBAgAABAgQIEJi/gMFx/sb2QIAAAQIECBAgQIAAgaoFDI5Vp8/iCRAgQIAAAQIECBAgMH8Bg+P8je2BAAECBAgQIECAAAECVQsYHKtOn8UTIECAAAECBAgQIEBg/gIGx/kb2wMBAgQIECBAgAABAgSqFjA4Vp0+iydAgAABAgQIECBAgMD8BQyO8ze2BwIECBAgQIAAAQIECFQtYHCsOn0WT4AAAQIECBAgQIAAgfkLGBznb2wPBAgQIECAAAECBAgQqFrA4Fh1+iyeAAECBAgQIECAAAEC8xcwOM7f2B4IECBAgAABAgQIECBQtYDBser0WTwBAgQIECBAgAABAgTmL2BwnL+xPRAgQIAAAQIECBAgQKBqAYNj1emzeAIECBAgQIAAAQIECMxfwOA4f2N7IECAAAECBAgQIECAQNUCBseq02fxBAgQIECAAAECBAgQmL+AwXH+xvZAgAABAgQIECBAgACBqgUMjlWnz+IJECBAgAABAgQIECAwfwGD4/yN7YEAAQIECBAgQIAAAQJVCxgcq06fxRMgQIAAAQIECBAgQGD+AgbH+RvbAwECBAgQIECAAAECBKoWMDhWnT6LJ0CAAAECBAgQIECAwPwFDI7zN7YHAgQIECBAgAABAgQIVC1gcKw6fRZPYHECN3/j9uaoIw5Z3A7tiQABAgTmLqC2z53YDggMRsDgOJhUOhAC8xXQXMzX19YJECCwDAG1fRnq9kmgTgGDY515s2oCCxfQXCyc3A4JECAwdwG1fe7EdkBgMAIGx8Gk0oEQmK+A5mK+vrZOgACBZQio7ctQt08CdQoYHOvMm1UTWLiA5mLh5HZIgACBuQuo7XMntgMCgxEwOA4mlQ6EwHwFNBfz9bV1AgQILENAbV+Gun0SqFPA4Fhn3qyawMIFNBcLJ7dDAgQIzF1AbZ87sR0QGIyAwXEwqXQgBOYroLmYr6+tEyBAYBkCavsy1O2TQJ0CBsc682bVBBYuoLlYOLkdEiBAYO4Cavvcie2AwGAEDI6DSaUDITBfAc3FfH1tnQABAssQUNuXoW6fBOoUMDjWmTerJrBwAc3FwsntkAABAnMXUNvnTmwHBAYjYHAcTCodCIH5Cmgu5utr6wQIEFiGgNq+DHX7JFCngMGxzrxZNYGFC2guFk5uhwQIEJi7gNo+d2I7IDAYAYPjYFLpQAjMV0BzMV9fWydAgMAyBNT2ZajbJ4E6BQyOdebNqgksXEBzsXByOyRAgMDcBdT2uRPbAYHBCBgcB5NKB0JgvgKai/n62joBAgSWIaC2L0PdPgnUKWBwrDNvVk1g4QKai4WT2yEBAgTmLqC2z53YDggMRsDgOJhUOhAC8xXQXMzX19YJECCwDAG1fRnq9kmgTgGDY515s2oCCxfQXCyc3A4JECAwdwG1fe7EdkBgMAIGx21SeeMXv9y88rWXNrfddvso8rRTT2nOP29Pc/CuXYM5CRwIgURAc5EoiSFAgEBdAmp7XfmyWgLLFDA4bqN/zYeubY4/9ujm1IedMoq8/Ip3jf6755ynLzNv9k1g4QKai4WT2yEBAgTmLqC2z53YDggMRsDgWJjKdpC8/rOfc9Wx0E14/QKai/pz6AgIECAwLqC2OycIEEgFDI6pVNM0d+3d27zussubo4480hXHAjehwxDQXAwjj46CAIHVF3jD2//v5sr3fGBzoS87/5nNU57041MX/o1bb2su+PU3Njd8/n+PYp751Cc1zz/3Z6IDVdsjJkEECDRNY3AMT4P2FtUPX3vdfu9xvPkbd4RbEEagboHvf//7zUEHHVT3QVg9AQIEVlzgrr3fbt559Qean/nJM5rdhx7S3PjXX2sues1bmguf83PNw0/+h/utvo1//Vv/Y3PaQx/UPOmx/6QZ//N2h6u2byfk34ckcNQR9xzS4Sz8WAyOheTjt6p+v/l+4RaEE6hT4G+/eUfzDw5XcOvMnlUTIFCrwN/d9e3mN1//e82jTntI85Qn/dh+h9FebXzRr7+ped65ZzX/x8NPGv37G99+9ei/zzv37P3ix//tr2/6RvPm3716c/vd9rqrl//+NS/a3O77P/CJ5pWvu3Jzm/1/a7d7x513Nbff8XfNBz76yeal5z+zeeiPPKA5/2VvaL729b8dvaa9EjppTbXmxrrrEzio8QvwA8mawbFQr33K6tuufHfzouc/uzl892GFrxZOoF4BtzPVmzsrJ0CgXoHuNtTnn3v25gA3fjTv/8AfNf/2dVc2b37Nhc0DTjy+efnF72heuOepzQPue9x+B/5XX/xq8/rL39O8/MJnNUfsPrT5yCc+3Vz5nmuaS37jeaPY9pbXs888fXRrbD+2fZr8f/7AHzX/4kk/3vzQwfdo2n1efc11o9e122lvr/3gxz7VvP7fvmC0342B98rmp888fbTu9s/919ebESsnsL4CBsdtcn/Vf3pf8+P/5FHNifc9YRTZ3rJ68y23eDjO+n7PrO2RGxzXNvUOnACBJQq0A1n7tdV7FtsB7xUXv2MUd8Off2Hb9zi22zzx3seMhsN/d9m7m4c88ITR///3P/t884a3X705DI4Pf32Gbp8XXfis0aA4vs5u4O2G0CUS2jUBAjMSMDhuA3n9Z25oXnXxmzajfI7jjM48m6lOwOBYXcosmACBygXaYezrf/ON5tde+MzRVb5JX+2A1r/C2A17R9/riKnDZjsg/v411zXPfdZZzW9ccmXz4l9+2mj4a//+Ob9y8X67aa9ktlcN22HxhS+7dPPW02OP/gebVxgnDbjj8d12Kk+L5RNYWwGD49qm3oETKBMwOJZ5iSZAgMCBCCRDY7v98VtP279rbyP9b5/+n1MHzm64vPX2O5sTjju6+TfnPW201G6gnDSodkPgyy/8vzaHyPYq57QrjuPHPn4180BsvJYAgeUIGByX426vBKoTMDhWlzILJkCgUoHtbk/tD5XtR4X135eYXHHshsv2fZGveel5zeN+7LSR1KTbS9uBr/064vDDRrfDdoNi+/cvv/h3p15xbLf1wY99svmXT/nnm0Np/zbYSlNj2QTWWsDguNbpd/AEcgGDY24lkgABAjsVGP9Mxm47T37sP968gjh+NXL8ltDkcxy7q4v/6pyzmxOOPXJzueP7P+Wk++/zAJzu8yVPefD9Rq+ZdsWxG2D/8KOfHMX1b2vdqY3XESCwXAGD43L97Z1ANQIGx2pSZaEECBDYVqAdPh/9yIc2J97n3s1RRxyybbwAAgQIGBydAwQIRAIGx4hJEAECBFZeoP++yO9+7yCD48pnzAIJrIaAwXE18mAVBFZewOC48imyQAIECBQLqO3FZF5AYG0FDI5rm3oHTqBMQHNR5iWaAAECNQio7TVkyRoJrIaAwXE18mAVBHYk0L5HpXtQQbuBl53/zNGHOG/11X+IQsnDCjQXO0qRFxEgQGClBdT2lU6PxRFYKQGD40qlw2II5ALtE+suf+cfND//M09sjth96OYHM3efsTVpS+3Q2H+cer63ptFclGiJJTB7gUs+dXXz+Vu+NPsN2+JaC3zvu99rfuBuP7DWBg5+9gInHXGf5oJHnT37DdviUgUMjkvlt3MCsxPoHn3+qNMeMvGqY/fvP33m6aMPb97ua/xzxL78tVua37ni6qbb/vgj29/8mgs3t9t++HT7+WDdV//f2u3ecefe5o477mzax7S3V0kf+iMPbF74skubr339b0cvSR4lv936/TuBoQmc9b6XNz9275OHdliOZ8kCe7/9nWbXPe6+5FXY/dAEPvHXn2ved9bLh3ZYa388Bse1PwUADEWgG+Sef+7ZEwfDSZ8N1v9csHGH/lP32iuaH/nEp5sr33PN6PO82q/+B073Yw/etav5zx/4o+ZfPOnHmx86+B5NO0Refc11+3wO2Ac/9qnND40eH2jbP/dfP5T8OA4CByrQDo5PvN8jDnQzXk9gH4G79v59c/CuH6RCYKYCH/zCnxocZyq6GhszOK5GHqyCwAELjF8h3G4Q7Aa2o+91RPP8c39m4v7bbZ5472NGVzD/3WXvbh7ywBNG/99+cPQb3n715jC41dXM8dtjx9fZDbRnn3n6tu/PPGAkGyBQsYDBseLkrfDSDY4rnJyKl2ZwrDh5Wyzd4DjMvDqqNRNoh7Gv/803ml974TNHV/kmfY1fQWxjxgfA8de1//7711zXPPdZZzW/ccmVzYt/+WnNA+573Oh1z/mVi/fbTXdLav8BPG1Q/yE8kwbc8fj+ra1rlkqHS2CqgMHRyTEPAYPjPFRt0+A4zHPA4DjMvDqqNRJIhsaWo72y9/KL39G8cM9TR8NfNzi2g+G0gbO7knjr7Xc2Jxx3dPNvznvatq/rhsDuIT3bXXGcNKz2r2auUSodKoEtBQyOTpB5CBgc56FqmwbHYZ4DBsdh5tVRrYnAdrenjg+V/T+3RL/5+is3H3Yzjax70M1rXnpe87gfO20UNun20vYqZPt1xOGH7fPk1vbvX37x726+p3HSraof/Ngnm3/5lH++OZQaHNfkBHaYRQIGxyIuwaGAwTGEElYkYHAs4qom2OBYTaoslMC+ApMedtNG9B94Mz44dlcQ26eZtl/J00u721X/1TlnNycce+TmIsb3f8pJ99/nATjd50ue8uD7jV5z0YXPGl3pHB8cx9dU8tmSzgkC6yRgcFynbC/uWA2Oi7Nepz0ZHIeZbYPjMPPqqAjMTKAd9B79yIc2J97n3s1RRxwys+3aEAECZQIGxzIv0ZmAwTFzElUmYHAs86ol2uBYS6ask8ASBPoP1Pnu9w4yOC4hB3ZJoBMwODoX5iFgcJyHqm0aHId5Dhgch5lXR0Vg5gI3f+N2g+PMVW2QQC5gcMytROYCBsfcSmQuYHDMrWqKNDjWlC1rJbBEAYPjEvHtmkDTNAZHp8E8BAyO81C1TYPjMM8Bg+Mw8+qoCMxcwOA4c1IbJFAkYHAs4hIcChgcQyhhRQIGxyKuaoINjtWkKl/o2/7sD5vP3/Kl/AUiCQQC3/nOd5u73/1uQaQQArnASUfep/nFhz85f8EaRxoc1zj5czx0g+Mccdd40wbHYSbf4DjAvLbNxSOPfdAAj8whLVPg23//3eYeP2hwXGYOhrjvT33tL5r3nfXyIR7azI/J4DhzUhtsmsbg6DSYh4DBcR6qy9+mwXH5OZj5CjQXMye1Qc2Fc2BOApqLHFZtz61E5gIGx9xKZC6gtudWNUUaHGvKVrhWzUUIJaxIQHNRxCU4FNBchFAejpNDiSwSUNuLuASHAmp7CFVZmMGxsoQlyzU4JkpiSgU0F6Vi4hMBzUWitBGjtudWInMBtT23EpkLqO25VU2RBseashWuVXMRQgkrEtBcFHEJDgU0FyGUwTGHElkkoLYXcQkOBdT2EKqyMINjZQlLlmtwTJTElApoLkrFxCcCmotEaSNGbc+tROYCantuJTIXUNtzq5oiDY41ZStcq+YihBJWJKC5KOISHApoLkIog2MOJbJIQG0v4hIcCqjtIVRlYQbHyhKWLNfgmCiJKRWdn6Z5AAAgAElEQVTQXJSKiU8ENBeJ0kaM2p5bicwF1PbcSmQuoLbnVjVFGhxryla4Vs1FCCWsSEBzUcQlOBTQXIRQBsccSmSRgNpexCU4FFDbQ6jKwgyOlSUsWa7BMVESUyqguSgVE58IaC4SpY0YtT23EpkLqO25lchcQG3PrWqKNDjWlK1wrZqLEEpYkYDmoohLcCiguQihDI45lMgiAbW9iEtwKKC2h1CVhRkcK0tYslyDY6IkplRAc1EqJj4R0FwkShsxantuJTIXUNtzK5G5gNqeW9UUaXCsKVvhWjUXIZSwIgHNRRGX4FBAcxFCGRxzKJFFAmp7EZfgUEBtD6EqCzM4VpawZLkGx0RJTKmA5qJUTHwioLlIlDZi1PbcSmQuoLbnViJzAbU9t6op0uBYU7bCtWouQihhRQKaiyIuwaGA5iKEMjjmUCKLBNT2Ii7BoYDaHkJVFmZwrCxhyXINjomSmFIBzUWpmPhEQHORKG3EqO25lchcQG3PrUTmAmp7blVTpMGxpmyFa9VchFDCigQ0F0VcgkMBzUUIZXDMoUQWCajtRVyCQwG1PYSqLMzgWFnCkuUaHBMlMaUCmotSMfGJgOYiUdqIUdtzK5G5gNqeW4nMBdT23KqmSINjTdkK16q5CKGEFQloLoq4BIcCmosQyuCYQ4ksElDbi7gEhwJqewhVWZjBsbKEJcs1OCZKYkoFNBelYuITAc1ForQRo7bnViJzAbU9txKZC6jtuVVNkQbHmrIVrlVzEUIJKxLQXBRxCQ4FNBchlMExhxJZJKC2F3EJDgXU9hCqsjCDY2UJS5ZrcEyUxJQKaC5KxcQnApqLRGkjRm3PrUTmAmp7biUyF1Dbc6uaIg2ONWUrXKvmIoQSViSguSjiEhwKaC5CKINjDiWySEBtL+ISHAqo7SFUZWEGx8oSlizX4JgoiSkV0FyUiolPBDQXidJGjNqeW4nMBdT23EpkLqC251Y1RRoca8pWuFbNRQglrEhAc1HEJTgU0FyEUAbHHEpkkYDaXsQlOBRQ20OoysIMjpUlLFmuwTFRElMqoLkoFROfCGguEqWNGLU9txKZC6jtuZXIXEBtz61qijQ41pStcK2aixBKWJGA5qKIS3AooLkIoQyOOZTIIgG1vYhLcCigtodQlYUZHCtLWLJcg2OiJKZUQHNRKiY+EdBcJEobMWp7biUyF1DbcyuRuYDanlvVFGlwrClb4Vo1FyGUsCIBzUURl+BQQHMRQhkccyiRRQJqexGX4FBAbQ+hKgszOFaWsGS5BsdESUypgOaiVEx8IqC5SJQ2YtT23EpkLqC251YicwG1PbeqKdLgWFO2wrVqLkIoYUUCmosiLsGhgOYihDI45lAiiwTU9iIuwaGA2h5CVRZmcKwsYclyDY6JkphSAc1FqZj4REBzkShtxKjtuZXIXEBtz61E5gJqe25VU6TBsaZshWvVXIRQwooENBdFXIJDAc1FCGVwzKFEFgmo7UVcgkMBtT2EqizM4FhZwpLlGhwTJTGlApqLUjHxiYDmIlHaiFHbcyuRuYDanluJzAXU9tyqpkiD4zbZuv4zNzSvuvhNm1GnnXpKc/55e5qDd+1a2TxrLlY2NVUvTHNRdfpWdvGaizw1antuJTIXUNtzK5G5gNqeW9UUaXDcJlvXfOja5vhjj25OfdgpzV179zavu+zy5qgjj2z2nPP0lc2z5mJlU1P1wjQXVadvZRevuchTo7bnViJzAbU9txKZC6jtuVVNkQbHwmy1g+T1n/3cSl911FwUJlV4JKC5iJgEFQpoLnIwtT23EpkLqO25lchcQG3PrWqKNDgWZuvyK941eoUrjoVwwqsX0FxUn8KVPADNRZ4Wg2NuJTIXUNtzK5G5gNqeW9UUaXAsyFb7fscr3vne5qKXXNAcvvuw0SvvvOvbBVtYTOgzrvnN5nH3OW0xO7OXtRH4zne+29z97ndbm+N1oIsR+MiXPt1cdeavLWZnle9Fba88gSu6fLV9RRNT+bJWtbb/8MH3qFx2ucs3OIb+7dD4xrdc0bz0xS9oTrzvCZuvuuPOveEWFhf28x94VXPGCacubof2tBYC3/3u95q73e0H1uJYHeTiBK798vXNf3jSry5uhxXvSW2vOHkrvHS1fYWTU/HSVrW23/OHV/fhljWk2+AYZGna0Bi8dCkhbmdaCvvgd+p2psGneCkH6HamnF1tz61E5gJqe24lMhdQ23OrmiINjttka9LtqaueYM3FqmeozvVpLurM26qvWnORZ0htz61E5gJqe24lMhdQ23OrmiINjttkq30YzoevvW6fqEMPPWS/W1ZXKemai1XKxnDWorkYTi5X6Ug0F3k21PbcSmQuoLbnViJzAbU9t6opcqUHx3Zou/mWW0YffdF+tZ+h+Onrb2hWfXBb9gmguVh2Boa5f83FMPO67KPSXOQZUNtzK5G5gNqeW4nMBdT23KqmyJUdHL9567eaV7z6kuacn/vZ5tSHndK0t4xe8+GPjobIz//5/9r8/4N3eZPr+AmnuajpW7CetWou6slVTSvVXOTZUttzK5G5gNqeW4nMBdT23KqmyJUeHH/7DW9pfvGZTxs9xbT/+Yk3fvHLzduufHfzouc/e/NjMWpCn/daNRfzFl7P7Wsu1jPv8z5qzUUurLbnViJzAbU9txKZC6jtuVVNkSs7ON61d+/o1tQzH//Y5sT73me/q4/jn6dYE/q816q5mLfwem5fc7GeeZ/3UWsucmG1PbcSmQuo7bmVyFxAbc+taopc2cGxRWyvLL7ytZc2t912e/P4M05v9pzz9Ka7hfXkhzx49Gdf+wtoLpwV8xDQXMxD1TY1F/k5oLbnViJzAbU9txKZC6jtuVVNkSs9ONYEuUpr1VysUjaGsxbNxXByuUpHornIs6G251YicwG1PbcSmQuo7blVTZErOzi2Vxb773GsCXXZa9VcLDsDw9y/5mKYeV32UWku8gyo7bmVyFxAbc+tROYCantuVVOkwbGmbIVr1VyEUMKKBDQXRVyCQwHNRQjVNI3anluJzAXU9txKZC6gtudWNUWu7ODYIrZPUn3UIx4++jgOX7mA5iK3EpkLaC5yK5G5gOYit1LbcyuRuYDanluJzAXU9tyqpsiVHhzbh+O8/798sPmlZz2j8XmN+WmlucitROYCmovcSmQuoLnIrdT23EpkLqC251YicwG1PbeqKXJlB8fu6alf+epNEz2PP+6Y5qKXXOBzHCfoaC5q+hasZ62ai3pyVdNKNRd5ttT23EpkLqC251YicwG1PbeqKXJlB8eaEFdtrZqLVcvIMNajuRhGHlftKDQXeUbU9txKZC6gtudWInMBtT23qinS4FhTtsK1ai5CKGFFApqLIi7BoYDmIoTycJwcSmSRgNpexCU4FFDbQ6jKwlZ6cGzf4/jK117a3Hbb7fuxulV1+plmcKzsu7CS5WouKklUZcvUXOQJU9tzK5G5gNqeW4nMBdT23KqmyJUdHO/au7d53WWXN6c+9OTm5JMetM9DcjxtdetTTHNR07dgPWvVXNSTq5pWqrnIs6W251YicwG1PbcSmQuo7blVTZErOzi2D8f57Te8pfnFZz5t5Pm2K9/dvOj5zx49DOf6z9zQXPPhjzbnn7fH01YnnG2ai5q+BetZq+ainlzVtFLNRZ4ttT23EpkLqO25lchcQG3PrWqKrGJw3L37sObNb/+95jnn/sJocGxvYe0PkjWBL2KtmotFKK/fPjQX65fzRRyx5iJXVttzK5G5gNqeW4nMBdT23KqmyJUdHPu3qp75hDOa9vbUex9/bNP+/zUfura5/rOfc8VxypmmuajpW7CetWou6slVTSvVXOTZUttzK5G5gNqeW4nMBdT23KqmyJUdHMcR+5/reOihhzQvffELmhPve0JN1gtbq+ZiYdRrtSPNxVqle2EHq7nIqdX23EpkLqC251YicwG1PbeqKbKawbEm1GWvVXOx7AwMc/+ai2HmddlHpbnIM6C251YicwG1PbcSmQuo7blVTZEGx5qyFa5VcxFCCSsS0FwUcQkOBTQXIZTPccyhRBYJqO1FXIJDAbU9hKosbKUHx+59jp++/oamuz31mGPutfkxHe37HX3tL2BwdFbMQ0BzMQ9V29Rc5OeA2p5bicwF1PbcSmQuoLbnVjVFrvTg2D0Q54zHPLp56zuuap7yE08cva/Rx3FsfYppLmr6FqxnrZqLenJV00o1F3m21PbcSmQuoLbnViJzAbU9t6opcmUHx/7nOLZXGfuDo4/jMDjW9E02lLVqLoaSydU6Ds1Fng+DY24lMhdQ23MrkbmA2p5b1RRZ5eDoiqPBsaZvsqGsVXMxlEyu1nFoLvJ8GBxzK5G5gNqeW4nMBdT23KqmyJUdHFvE7vMan3PuLzS/9673jm5V3b37sOYVr76kecLjHjP6TEdf+wtoLpwV8xDQXMxD1TY1F/k5oLbnViJzAbU9txKZC6jtuVVNkSs9OLaQ7dXFV138pn1Mf/XC5zanPuyUmpwXulbNxUK512Znmou1SfVCD1RzkXOr7bmVyFxAbc+tROYCantuVVPkyg2O7Xsbr/34Hzdn/dSTa3JcqbVqLlYqHYNZjOZiMKlcqQPRXOTpUNtzK5G5gNqeW4nMBdT23KqmyJUcHNtbUW+7/Y7mpS9+wegpqr7KBDQXZV6iMwHNReYkqkxAc5F7qe25lchcQG3PrUTmAmp7blVT5MoNjh1e+/7GK656T3POM57qvYyFZ5TmohBMeCSguYiYBBUKaC5yMLU9txKZC6jtuZXIXEBtz61qilzZwbFFbG9bba8+Hn30Uc355+1pDt61qybbpa1Vc7E0+kHvWHMx6PQu7eA0Fzm92p5bicwF1PbcSmQuoLbnVjVFrvTg2EF2Vx/7sMcfd0xz0UsuaA7ffVhN3gtZq+ZiIcxrtxPNxdqlfCEHrLnImdX23EpkLqC251YicwG1PbeqKXKlB8fuimMLakjMTyvNRW4lMhfQXORWInMBzUVupbbnViJzAbU9txKZC6jtuVVNkSs7OHZXGR9/xunNnnOeXpPp0tequVh6Cga5AM3FINO69IPSXOQpUNtzK5G5gNqeW4nMBdT23KqmyJUbHLurjJ6quvPTSHOxczuvnC6guXB2zENAc5Grqu25lchcQG3PrUTmAmp7blVT5EoOjj7H8cBOIc3Fgfl59WQBzYUzYx4CmotcVW3PrUTmAmp7biUyF1Dbc6uaIlducKwJb1XXqrlY1czUvS7NRd35W9XVay7yzKjtuZXIXEBtz61E5gJqe25VU6TBsaZshWvVXIRQwooENBdFXIJDAc1FCNU0jdqeW4nMBdT23EpkLqC251Y1RRoca8pWuFbNRQglrEhAc1HEJTgU0FyEUAbHHEpkkYDaXsQlOBRQ20OoysIMjpUlLFmuwTFRElMqoLkoFROfCGguEqWNGLU9txKZC6jtuZXIXEBtz61qijQ41pStcK2aixBKWJGA5qKIS3AooLkIoQyOOZTIIgG1vYhLcCigtodQlYUZHCtLWLJcg2OiJKZUQHNRKiY+EdBcJEobMWp7biUyF1DbcyuRuYDanlvVFGlwrClb4Vo1FyGUsCIBzUURl+BQQHMRQhkccyiRRQJqexGX4FBAbQ+hKgszOFaWsGS5BsdESUypgOaiVEx8IqC5SJQ2YtT23EpkLqC251YicwG1PbeqKdLgWFO2wrVqLkIoYUUCmosiLsGhgOYihDI45lAiiwTU9iIuwaGA2h5CVRZmcKwsYclyDY6JkphSAc1FqZj4REBzkShtxKjtuZXIXEBtz61E5gJqe25VU6TBsaZshWvVXIRQwooENBdFXIJDAc1FCGVwzKFEFgmo7UVcgkMBtT2EqizM4FhZwpLlGhwTJTGlApqLUjHxiYDmIlHaiFHbcyuRuYDanluJzAXU9tyqpkiDY03ZCtequQihhBUJaC6KuASHApqLEMrgmEOJLBJQ24u4BIcCansIVVmYwbGyhCXLNTgmSmJKBTQXpWLiEwHNRaK0EaO251YicwG1PbcSmQuo7blVTZEGx5qyFa5VcxFCCSsS0FwUcQkOBTQXIZTBMYcSWSSgthdxCQ4F1PYQqrIwg2NlCUuWa3BMlMSUCmguSsXEJwKai0RpI0Ztz61E5gJqe24lMhdQ23OrmiINjjVlK1yr5iKEElYkoLko4hIcCmguQiiDYw4lskhAbS/iEhwKqO0hVGVhBsfKEpYs1+CYKIkpFdBclIqJTwQ0F4nSRozanluJzAXU9txKZC6gtudWNUUaHGvKVrhWzUUIJaxIQHNRxCU4FNBchFAGxxxKZJGA2l7EJTgUUNtDqMrCDI6VJSxZrsExURJTKqC5KBUTnwhoLhKljRi1PbcSmQuo7bmVyFxAbc+taoo0ONaUrXCtmosQSliRgOaiiEtwKKC5CKEMjjmUyCIBtb2IS3AooLaHUJWFGRzDhN34xS83b7vy3c2Lnv/s5vDdh4WvWk6YwXE57kPfq+Zi6BlezvFpLnJ3tT23EpkLqO25lchcQG3PrWqKNDhuk61v3vqt5hWvvqT5yldvao4/7pjmopdcYHCs6Qy31pkJaC5mRmlDPQHNRX46GBxzK5G5gNqeW4nMBdT23KqmSINjmC1XHEMoYYMV0FwMNrVLPTDNRc5vcMytROYCantuJTIXUNtzq5oiDY5htgyOIZSwwQpoLgab2qUemOYi5zc45lYicwG1PbcSmQuo7blVTZEGxzBb0wbHm79xe7iFxYX90kd/q3nMcQ9b3A7tiQABAjsU+PhXP9O89bH/eoevXq+Xqe3rlW9HS6BmgVWt7UcdcUjNrEtfu8ExTMG0wfH73/9+uIXFhZ39+7/RPOF+j1jcDu1pLQT27v37ZteuH1yLY3WQixP40Bf+tLn6p399cTsM93TQQQeFkYsLc8Vxcdar95N9fseuts/Pdp23rLYPM/sGxzCvblUNoYQNVsDtTINN7VIPzO1MOb/BMbcSmQuo7bmVyFxAbc+taoo0OIbZMjiGUMIGK6C5GGxql3pgmouc3+CYW4nMBdT23EpkLqC251Y1RRoct8lW/+M4utDHn3F6s+ecp69snjUXK5uaqhemuag6fSu7eM1Fnhq1PbcSmQuo7bmVyFxAbc+taoo0ONaUrXCtmosQSliRgOaiiEtwKKC5CKGaplHbcyuRuYDanluJzAXU9tyqpkiDY03ZCtequQihhBUJaC6KuASHApqLEMrgmEOJLBJQ24u4BIcCansIVVmYwbGyhCXLNTgmSmJKBTQXpWLiEwHNRaK0EaO251YicwG1PbcSmQuo7blVTZEGx5qyFa5VcxFCCSsS0FwUcQkOBTQXIZTBMYcSWSSgthdxCQ4F1PYQqrIwg2NlCUuWa3BMlMSUCmguSsXEJwKai0RpI0Ztz61E5gJqe24lMhdQ23OrmiINjjVlK1yr5iKEElYkoLko4hIcCmguQiiDYw4lskhAbS/iEhwKqO0hVGVhBsfKEpYs1+CYKIkpFdBclIqJTwQ0F4nSRozanluJzAXU9txKZC6gtudWNUUaHGvKVrhWzUUIJaxIQHNRxCU4FNBchFAGxxxKZJGA2l7EJTgUUNtDqMrCDI6VJSxZrsExURJTKqC5KBUTnwhoLhKljRi1PbcSmQuo7bmVyFxAbc+taoo0ONaUrXCtmosQSliRgOaiiEtwKKC5CKEMjjmUyCIBtb2IS3AooLaHUJWFGRwrS1iyXINjoiSmVEBzUSomPhHQXCRKGzFqe24lMhdQ23MrkbmA2p5b1RRpcKwpW+FaNRchlLAiAc1FEZfgUEBzEUIZHHMokUUCansRl+BQQG0PoSoLMzhWlrBkuQbHRElMqYDmolRMfCKguUiUNmLU9txKZC6gtudWInMBtT23qinS4FhTtsK1ai5CKGFFApqLIi7BoYDmIoQyOOZQIosE1PYiLsGhgNoeQlUWZnCsLGHJcg2OiZKYUgHNRamY+ERAc5EobcSo7bmVyFxAbc+tROYCantuVVOkwbGmbIVr1VyEUMKKBDQXRVyCQwHNRQhlcMyhRBYJqO1FXIJDAbU9hKoszOBYWcKS5RocEyUxpQKai1Ix8YmA5iJR2ohR23MrkbmA2p5bicwF1PbcqqZIg2NN2QrXqrkIoYQVCWguirgEhwKaixDK4JhDiSwSUNuLuASHAmp7CFVZmMGxsoQlyzU4JkpiSgU0F6Vi4hMBzUWitBGjtudWInMBtT23EpkLqO25VU2RBseashWuVXMRQgkrEtBcFHEJDgU0FyGUwTGHElkkoLYXcQkOBdT2EKqyMINjZQlLlmtwTJTElApoLkrFxCcCmotEaSNGbc+tROYCantuJTIXUNtzq5oiDY41ZStcq+YihBJWJKC5KOISHApoLkIog2MOJbJIQG0v4hIcCqjtIVRlYQbHyhKWLNfgmCiJKRXQXJSKiU8ENBeJ0kaM2p5bicwF1PbcSmQuoLbnVjVFGhxryla4Vs1FCCWsSEBzUcQlOBTQXIRQBsccSmSRgNpexCU4FFDbQ6jKwgyOlSUsWa7BMVESUyqguSgVE58IaC4SpY0YtT23EpkLqO25lchcQG3PrWqKNDjWlK1wrZqLEEpYkYDmoohLcCiguQihDI45lMgiAbW9iEtwKKC2h1CVhRkcK0tYslyDY6IkplRAc1EqJj4R0FwkShsxantuJTIXUNtzK5G5gNqeW9UUaXCsKVvhWjUXIZSwIgHNRRGX4FBAcxFCGRxzKJFFAmp7EZfgUEBtD6EqCzM4VpawZLkGx0RJTKmA5qJUTHwioLlIlDZi1PbcSmQuoLbnViJzAbU9t6op0uBYU7bCtWouQihhRQKaiyIuwaGA5iKEMjjmUCKLBNT2Ii7BoYDaHkJVFmZwrCxhyXINjomSmFIBzUWpmPhEQHORKG3EqO25lchcQG3PrUTmAmp7blVTpMGxpmyFa9VchFDCigQ0F0VcgkMBzUUIZXDMoUQWCajtRVyCQwG1PYSqLMzgWFnCkuUaHBMlMaUCmotSMfGJgOYiUdqIUdtzK5G5gNqeW4nMBdT23KqmSINjTdkK16q5CKGEFQloLoq4BIcCmosQyuCYQ4ksElDbi7gEhwJqewhVWZjBsbKEJcs1OCZKYkoFNBelYuITAc1ForQRo7bnViJzAbU9txKZC6jtuVVNkQbHmrIVrlVzEUIJKxLQXBRxCQ4FNBchlMExhxJZJKC2F3EJDgXU9hCqsjCDY2UJS5ZrcEyUxJQKaC5KxcQnApqLRGkjRm3PrUTmAmp7biUyF1Dbc6uaIg2ONWUrXKvmIoQSViSguSjiEhwKaC5CKINjDiWySEBtL+ISHAqo7SFUZWEGx8oSlizX4JgoiSkV0FyUiolPBDQXidJGjNqeW4nMBdT23EpkLqC251Y1RRoca8pWuFbNRQglrEhAc1HEJTgU0FyEUAbHHEpkkYDaXsQlOBRQ20OoysIMjpUlLFmuwTFRElMqoLkoFROfCGguEqWNGLU9txKZC6jtuZXIXEBtz61qijQ41pStcK2aixBKWJGA5qKIS3AooLkIoQyOOZTIIgG1vYhLcCigtodQlYUZHCtLWLJcg2OiJKZUQHNRKiY+EdBcJEobMWp7biUyF1DbcyuRuYDanlvVFGlwrClb4Vo1FyGUsCIBzUURl+BQQHMRQhkccyiRRQJqexGX4FBAbQ+hKgszOFaWsGS5BsdESUypgOaiVEx8IqC5SJQ2YtT23EpkLqC251YicwG1PbeqKdLgWFO2wrVqLkIoYUUCmosiLsGhgOYihDI45lAiiwTU9iIuwaGA2h5CVRZmcKwsYclyDY6JkphSAc1FqZj4REBzkShtxKjtuZXIXEBtz61E5gJqe25VU6TBsaZshWvVXIRQwooENBdFXIJDAc1FCGVwzKFEFgmo7UVcgkMBtT2EqizM4FhZwpLlGhwTJTGlApqLUjHxiYDmIlHaiFHbcyuRuYDanluJzAXU9tyqpkiDY03ZCtequQihhBUJaC6KuASHApqLEMrgmEOJLBJQ24u4BIcCansIVVmYwbGyhCXLNTgmSmJKBTQXpWLiEwHNRaK0EaO251YicwG1PbcSmQuo7blVTZEGx5qyFa5VcxFCCSsS0FwUcQkOBTQXIZTBMYcSWSSgthdxCQ4F1PYQqrIwg2NlCUuWa3BMlMSUCmguSsXEJwKai0RpI0Ztz61E5gJqe24lMhdQ23OrmiINjjVlK1yr5iKEElYkoLko4hIcCmguQiiDYw4lskhAbS/iEhwKqO0hVGVhBsfKEpYs1+CYKIkpFdBclIqJTwQ0F4nSRozanluJzAXU9txKZC6gtudWNUUaHGvKVrhWzUUIJaxIQHNRxCU4FNBchFAGxxxKZJGA2l7EJTgUUNtDqMrCDI5Bwq750LXNFVe9ZxR52qmnNOeft6c5eNeu4JXLCTE4Lsd96HvVXAw9w8s5Ps1F7q6251YicwG1PbcSmQuo7blVTZEGx22ydf1nbmiueOd7m4teckFz+O7DmsuveNfoFXvOefrK5llzsbKpqXphmouq07eyi9dc5KlR23MrkbmA2p5bicwF1PbcqqZIg+M22WoHxXsff2xz5hPOGEWOD5KrmGzNxSpmpf41aS7qz+EqHoHmIs+K2p5bicwF1PbcSmQuoLbnVjVFGhy3yNZde/c2r7vs8ubUh568OTje+MUvN6+/7PLmheftaU687wkrmWvNxUqmpfpFaS6qT+FKHoDmIk+L2p5bicwF1PbcSmQuoLbnVjVFGhyDwfHMxz+2OfVhp4wixwfHm79x+8rl+5c++lvN/Q49ZuXWZUEECBAYF/jCbTc1b33svwYTCKjtAZIQAgRWQmBVa/tRRxyyEj61LsLgGAyOW11xXMXBsdaT0boJECBAgAABAgQIzEvA4HhgsgbHbfxqfI/jgZ0SXk2AAAECBBjrkc0AAAwgSURBVAgQIECAAIF9BQyO25wRNT5V1UlOgAABAgQIECBAgACBWQoYHAPN2j7HMTgkIQSqF6jhQVXVIzsAAgQIECBAgMD/L2BwdCoQWILApI91+eat32pe8epLmnN+7mc3H8a0k6XNajs72fciX2NwXKS2fREYtsC0ujmLz25el5rcniGt14evvW7zZDn00EOal774BaOn0Le/hL/+s59rzj9vT3Pwrl3DPqEcHYGBChgcB5pYh7XaAgbHA8+PwfHADW2BAIENAYPjgZ8JkwbD9mfdV7729dFHmhkcD9zYFggsW8DguOwM2P9aCqSDYxv3qovfNDI6/rhjmoteckFz+O7DRn/u/2a3/1vd8d/4nvOMpzYnn/Sg0eePnv7oH23+49V/MHr94884vdlzztM3t9X+T/fndr/XfPijo98M33TT34xee//73bf5xP/7qc3XPuoRD99cW39bXXNwzx/6oc34X73wuVteRe2vOd1Wf3BsFzX++aqTjNfyZHPQBAhsK5AOjl3cV75602ib/drWf1tL/99mXZPvumvv6O6Ukx/y4M2re6edekpz1k8+ufmtS3+nue2225v2z92VvZ3U5A6srbPv/y8fbH7pWc/Y8irhpM+97qO323nlay8drW3851la/9ufDT/70//n6Ng7//5xttsd308/D90aP339DaM1tD8b24HWFwECuYDBMbcSSWBmAsng2Ma88S1X7HObz19/5Wuj4a5tXq79+B83Z/3UkzcHv5tvuWXUKHRNRf+W1+6H6Y8+8h9tvr5/W+z47Vjjg2P7A//sp5w5+iE7vq3xK39d89Q1VNsNcP3fQrcH87rLLm+OOvLI0Tq32tatt35rn2Gx/wTk7ZqYmSXShggQGIRAMjh2MU943GM2a+Hbrnx386LnP7s5+OBdzTUf+Ehz5pMeNxqw2tr1oY98fPTLvvZr/G0IB1KT+4Nj9/Og3f7RRx818WdAaU0eH/iSwbF9TVuD/+RT/2PzZ9b4iTHpimNJ/W+31/48ab/az9Yez8e0P7c/C0968D/c52dLG/vbb3hL84vPfNroNlpfBAhkAgbHzEkUgZkK9K8kjm+4G7jGh7m20eialO6qY/fa/nA2rUkZvyLX3/52g2P/teMN1vifx5uDrd7fM2nA6x/Lf/3kf9/nPTF9g/HBcfzfplnNNJE2RoDAIATGryT2D6q7C2L8l2Bd/Trz8Y/d746K/i/Udu8+bOLguNOaPP7LwfE6Ov7nkpo8nsz0imP3uvGrq5OufHZXQkvr/6QTrf+zazw//Z89h+/evd9dKeMftzaIE9lBEJizgMFxzsA2T2CSQHLFcfwHcLud/u2q48Nn92/p4Ng2E90VzHkOjls1V5P+rT8AlgyO/W2176npjs0ZSIAAge0EkiuO037h1/2yb/w2ye4tBOngmNbkAx0ct6rJrdP4Lbd9u/G3TGzl2pm2t9R2d5D0H45TWv+nDaf9wb57i0V71Xd8cOzfKttty+2q231n+HcC+woYHJ0RBJYgkA6O9z7+2InvwRi/jdUVx41bjUYOV71n9P8vPG+PW5CWcG7bJYEaBdLBsT+Y9I+zGxqf9+xzRlcf1/WK43ju+1c727dXTBocT33oyZs/57a646TddvtLzu5tGe1wWHLF0V0oNX5nWvOqCRgcVy0j1rMWAsngOD4ctjDv+4M/bM54zKObG7/4peaKd75382E5/ffTtO+1ad8n2P9hPP4+xEnvS+x+oLf7aV/ffvUfjtMNYqW3qm73JL1Ja++/x7HfaGx1q2q73m5t3Xt9PPJ9Lb6dHCSBAxZIBsfx99B1v6xq/zt+K2S/fh9zzL1mWpMP9IrjdjV5fCBO3uPYXj186zuuap7yE0/c/IXd+K2ok37uldT/bnBs/9tewey23/28GM9hd4W4vSI8/h7Hdhvtz5Mv3Pil5p+d/k8P+PyxAQLrImBwXJdMO86VEkgGx64p6Z6q2v65uyVn/OlwD3rg/Zs77rxzc5Ds31LVPVV1/Dad/tMA+9trb6/6iSec0Xz+L/9qx4Njd9WvXXNya9N2T9Xr3hOz3eC43S1YK3USWAwBAisjkAyO/V9OdU/17Ne3fh07/tijR8fW/cJtljV5J4NjaU3uElPyHsdJt7j2bwXt/5yZ5jbpqdr9z33s3w7c/qw69J4/3Jz8Iyft80Tw7mdm+/7KO+/4u80H4Iz/3Ow/jXxlTkQLIbDiAgbHFU+Q5RGYhcAiP/Ow5LfZszi2nfx2fNb7tT0CBAiUCKxLTS4xmXXsVg+Um/W+bI/AuggYHNcl045zrQXWpUnxlLy1Ps0dPIFqBNalJi86IdM+33jR67A/AkMVMDgONbOOiwABAgQIECBAgAABAjMSMDjOCNJmCBAgQIAAAQIECBAgMFQBg+NQM+u4CBAgQIAAAQIECBAgMCMBg+OMIG2GAAECBAgQIECAAAECQxUwOA41s46LAAECBAgQIECAAAECMxIwOM4I0mYIECBAgAABAgQIECAwVAGD41Az67gIECBAgAABAgQIECAwIwGD44wgbYYAAQIECBAgQIAAAQJDFTA4DjWzjosAAQIECBAgQIAAAQIzEjA4zgjSZggQIECAAAECBAgQIDBUAYPjUDPruAgQIECAAAECBAgQIDAjAYPjjCBthgABAgQIECBAgAABAkMVMDgONbOOiwABAgQIECBAgAABAjMSMDjOCNJmCBAgQIAAAQIECBAgMFQBg+NQM+u4CBAgQIAAAQIECBAgMCMBg+OMIG2GAAECBAgQIECAAAECQxUwOA41s46LAAECBAgQIECAAAECMxIwOM4I0mYIECBAgAABAgQIECAwVAGD41Az67gIECBAgAABAgQIECAwIwGD44wgbYYAAQIECBAgQIAAAQJDFTA4DjWzjosAAQIECBAgQIAAAQIzEjA4zgjSZggQIECAAAECBAgQIDBUAYPjUDPruAgQIECAAAECBAgQIDAjAYPjjCBthgABAgQIECBAgAABAkMVMDgONbOOiwABAgQIECBAgAABAjMSMDjOCNJmCBAgQIAAAQIECBAgMFQ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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n", + "cop_fig = mean_cops.iplot(\n", + " kind='bar',\n", + " title='Mean COP by Casestudy',\n", + " colors=recoygreen,\n", + " dimensions=(600, 400),\n", + " yTitle='COP',\n", + " asFigure=True,\n", + " yrange=[0, mean_cops.max() * 1.2]\n", + ")\n", + "\n", + "cop_fig.update_traces(\n", + " text=mean_cops.values, \n", + " textposition='outside', \n", + " texttemplate=\"%{text:.1f}\", \n", + ")\n", + "\n", + "cop_fig " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Sensitivity analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n", + "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n", + "2. aFRR prices +/- 30%\n", + "3. CAPEX\n", + "4. CO2 price\n", + "5. Tsource +/- 10 degrees Celsius\n", + "6. Tsink\n", + " * Roam off the peak / lower pressure\n", + " * Stabalize / running average per hour/ 2 hours\n", + "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n", + "8. Energy Tax and ODE +/- 30%" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def setup():\n", + " _c = Config()\n", + " s = setup_model(c=_c)\n", + " return s\n", + "\n", + "def routine(c, s):\n", + " s = load_data(c=c, s=s)\n", + " s = create_and_assign_assets(c=c, s=s)\n", + " #s = preprocessing(c=c, s=s)\n", + " s = run_optimisation(c=c, s=s)\n", + " #s = postprocessing(c=c, s=s)\n", + " s = collect_cashflows(c=c, s=s)\n", + " s = calculate_financials(c=c, s=s)\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n", + "\n", + "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Entries missing from dataset 'imbalanceRT_nl.csv'.\n", + "Issue solved: Dataset was reindexed automatically.\n", + "CPU times: total: 5.5 s\n", + "Wall time: 12 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_42308\\332434720.py:3: FutureWarning:\n", + "\n", + "Argument `closed` is deprecated in favor of `inclusive`.\n", + "\n" + ] + } + ], + "source": [ + "%time _s = setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "discount rate 0.1\n", + "cashflows [-6231000.0 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826\n", + " 2384943.8391928826 2384943.8391928826 2384943.8391928826]\n", + "answer [-5664545.454545454 1971027.9662751092 1791843.6057046445\n", + " 1628948.732458768 1480862.4840534253 1346238.62186675 1223853.2926061363\n", + " 1112593.9023692147 1011449.0021538314 919499.0928671194 835908.2662428358\n", + " 759916.6056753052 690833.277886641]\n", + "discount rate 0.1\n", + "cashflows [-6656000.0 2394552.0227854503 2394552.0227854503 2394552.0227854503\n", + " 2394552.0227854503 2394552.0227854503 2394552.0227854503\n", + " 2394552.0227854503 2394552.0227854503 2394552.0227854503\n", + " 2394552.0227854503 2394552.0227854503 2394552.0227854503]\n", + "answer [-6050909.090909091 1978968.6138722727 1799062.3762475203\n", + " 1635511.2511341095 1486828.4101219175 1351662.1910199248\n", + " 1228783.8100181133 1117076.1909255574 1015523.8099323249\n", + " 923203.4635748408 839275.8759771279 762978.0690701163 693616.4264273783]\n", + "CPU times: total: 14.8 s\n", + "Wall time: 38.9 s\n" + ] + }, + { + "data": { + "text/plain": [ + "9108429.395614326" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%time result = routine(c, _s)\n", + "npv = result.hpcase.npv\n", + "npv" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "84fd024585d24066b648efed61f43c86", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/4 [00:00
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: Storage volume',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: Water storage volume" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.0, 0.0, 0.0]" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n", + "\n", + "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Entries missing from dataset 'imbalanceRT_nl.csv'.\n", + "Issue solved: Dataset was reindexed automatically.\n", + "CPU times: total: 6.92 s\n", + "Wall time: 12.3 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_42308\\332434720.py:3: FutureWarning:\n", + "\n", + "Argument `closed` is deprecated in favor of `inclusive`.\n", + "\n" + ] + } + ], + "source": [ + "%time _s = setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Store' object has no attribute 'cases'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[114], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m param \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstorage_volume\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 3\u001b[0m kpis \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnpv\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mirr\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspp\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 5\u001b[0m sens \u001b[38;5;241m=\u001b[39m \u001b[43mSensitivityAnalysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_s\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroutine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparam\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkpis\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\users\\shahla.huseynova\\documents\\asset_studies_recoy\\asset-case-studies\\pyrecoy\\pyrecoy\\pyrecoy\\sensitivity.py:25\u001b[0m, in \u001b[0;36mSensitivityAnalysis.__init__\u001b[1;34m(self, c, s, routine, param, values, output_kpis)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, c, s, routine, param, values, output_kpis):\n\u001b[0;32m 24\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfigs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_generate_configs(c, param, values)\n\u001b[1;32m---> 25\u001b[0m output_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_output_dict(\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcases\u001b[49m, output_kpis)\n\u001b[0;32m 26\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkpis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sensitivities(s, routine, output_kpis, output_dict)\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Store' object has no attribute 'cases'" + ] + } + ], + "source": [ + "values = range(100, 500, 100)\n", + "param = 'storage_volume'\n", + "kpis = ['npv','irr','spp']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "[case.name for case in s.cases][1:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: Storage volume',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='irr in %',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: IRR',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='volume(m3)',\n", + " yTitle='COP',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: COP',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: CAPEX per MW storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# values = range(4_000, 10_000, 2_000)\n", + "# param = 'storage_capex_per_MW'\n", + "# kpis = ['npv', 'irr', 'spp', 'mean_cop']\n", + "\n", + "# sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX',\n", + " yTitle='NPV in Euro',\n", + " yrange=[output.min().min()*1.1, 0],\n", + " title='Sensitivity: NPV in euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MW',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: CAPEX per MWh storage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(1_000, 5_000, 1000)\n", + "param = 'storage_capex_per_MWh'\n", + "kpis = ['npv', 'spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MWh',\n", + " yTitle='NPV',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: NPV in Euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX/MWh',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: Payback time',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sensitivity: threshold price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "values = range(10, 60, 10)\n", + "param = 'threshold'\n", + "kpis = ['npv', 'spp', 'mean_cop']\n", + "\n", + "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='NPV',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: NPV in Euro',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='years',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: SPP in years',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='threshold',\n", + " yTitle='COP',\n", + " yrange=[output.min().min()*1.2, 0],\n", + " title='Sensitivity: SOP',\n", + " colors=recoycolors,\n", + " dimensions=(600, 400),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Heat Pump CAPEX" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3, 2]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.hp_capex *= value\n", + " configs[value*100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "\n", + "sens_capex = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='CAPEX factor (%)',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Heat Pump CAPEX (€)',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_capex" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: CO2 prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "mean = 24.86\n", + "co2_prices = [10, 25, 50, 100]\n", + "\n", + "for price in co2_prices:\n", + " _c = Config()\n", + " multiplier = price / mean\n", + " _c.co2_price_multiplier = multiplier\n", + " configs[price] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='EBITDA in €',\n", + " yrange=[output.min().min() * 1.1, 0],\n", + " title='Sensitivity: CO2 prices > EBITDA',\n", + " colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_co2_spp = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='Payback Period in Years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: CO2 prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_co2_spp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Mean CO2 price in €',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min() * 10, output.max().max()*1.1],\n", + " title='Sensitivity: CO2 prices > NPV',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Gas prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.gas_price_multiplier = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Gas price factor (%)',\n", + " yTitle='NPV in €',\n", + " yrange=[output.min().min()*1.5, output.max().max()*1.1],\n", + " title='Sensitivity: Gas prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Day Ahead buying amount" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0, 0.5, 1]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.day_ahead_buying_perc = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Volume in %',\n", + " yTitle='NPV in €',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Volume bought on Day-Ahead market in %',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Electricity prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.e_price_multiplier = value\n", + " configs[value * 100] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_eprices = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Electricity price factor in %',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Electricity prices',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_eprices" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Electricity price volatility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.e_price_volatility_multiplier = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_evol = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Volatility factor',\n", + " yTitle='Payback period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: E-price volatility',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_evol" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: aFRR capacity fee" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [10_000, 25_000, 50_000]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.afrr_capacity_fee = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_affr_fee = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='aFRR capacity fee in €/MW',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: aFRR capacity fee',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_affr_fee" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Energy tax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [0.7, 1, 1.3]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.energy_tax_multiplier = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Factor',\n", + " yTitle='NPV in €',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Energy taxes',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Tsource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [-25, -10, 0, 10, 25]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.tsource_delta = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.loc[-25, 'Heatpump only'] = np.nan\n", + "sens_tsource = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsource delta',\n", + " yTitle='Payback Period in years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsource',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsource" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsource delta',\n", + " yTitle='COP',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsource > Mean COP',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "_s = setup()\n", + "configs = {}\n", + "values = [-25, -10, 0, 10, 25]\n", + "for value in values:\n", + " _c = Config()\n", + " _c.tsink_delta = value\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "sens = SensitivityAnalysis(_s, routine, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_tsink = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsink delta',\n", + " yTitle='Payback Period in Years',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsink',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "sens_tsink_cop = output.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Tsink delta',\n", + " yTitle='COP',\n", + " yrange=[0, output.max().max()*1.1],\n", + " title='Sensitivity: Tsink > Mean COP',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400),\n", + " asFigure=True\n", + ")\n", + "sens_tsink_cop" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "### Sensitivity: Time period" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "def routine2(c, s):\n", + " s = setup_model(c=c)\n", + " s = load_data(c=c, s=s)\n", + " s = create_and_assign_assets(c=c, s=s)\n", + " s = run_optimisation(c=c, s=s)\n", + " s = collect_cashflows(c=c, s=s)\n", + " s = calculate_financials(c=c, s=s)\n", + " return s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "configs = {}\n", + "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n", + "\n", + "for value in start_values:\n", + " _c = Config()\n", + " _c.start = value\n", + " _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n", + " configs[value] = _c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "%%time\n", + "sens = SensitivityAnalysis(_s, routine2, configs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", + "result.iplot(\n", + " mode='lines+markers',\n", + " symbol='circle-dot',\n", + " size=10,\n", + " xTitle='Start date',\n", + " yTitle='NPV in €',\n", + " yrange=[0, result.max().max()*1.1],\n", + " title='Sensitivity: Modelled time period',\n", + " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", + " dimensions=(600, 400)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "exclude" + ] + }, + "source": [ + "## Report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "exclude" + ] + }, + "outputs": [], + "source": [ + "renderer = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# __Presentation Flexible Heatpumps__\n", + "\n", + "ENCORE meeting: 17-12-2020\n", + "Mark Kremer\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## __Central question:__\n", + "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Flexible operations on imbalance market__\n", + "\n", + "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n", + " title='Day-Ahead prices on arbitrary day in €/MWh',\n", + " yrange=[-100, 200],\n", + " colors=recoygreen, \n", + " yTitle='Price in €/MWh',\n", + " xTitle='Time of Day',\n", + " dimensions=(800, 400),\n", + " asFigure=True\n", + ")\n", + "\n", + "fig_example_1.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n", + " title='Imbalance Prices on arbitrary day in €/MWh',\n", + " colors=[recoygreen, recoydarkblue], \n", + " yTitle='Price in €/MWh',\n", + " xTitle='Time of Day',\n", + " dimensions=(800, 400),\n", + " asFigure=True\n", + ")\n", + "\n", + "fig_example_2.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is possible to benefit from these fluctiations, if you have __flexibility__\n", + "* Storage options\n", + "* Hybrid installations (e.g. with gas-powered assets)\n", + "\n", + "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Simulations & mathematical modelling__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* To answer the central question, we have build a simulation model\n", + "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n", + "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n", + "* We are taking into account all major investment and operating costs, including:\n", + " * Asset CAPEX\n", + " * Commodity costs for gas and electricity\n", + " * Energy taxes\n", + " * Grid transport costs\n", + " * SDE++ subsidies\n", + " * Maintenance costs\n", + " * CO2 allowances\n", + "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Casestudies__\n", + "5 main scenario's" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "1. Steamboiler only (baseline, baseload)\n", + "2. Heatpump only (stand-alone, baseload, without SDE++)\n", + "3. Heatpump + SDE\n", + "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n", + "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n", + "\n", + "Besides that, we modelled 11 sensitivities:\n", + "* Heatpump CAPEX\n", + "* Gas & CO2 prices\n", + "* Electricity prices & volatility\n", + "* Energy taxes\n", + "* Bidding strategies\n", + "* Source temperatures (affecting COP)\n", + "* Sink temperatures (affecting COP)\n", + "* Time period (2018, 2019, 2020)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Smurfit Kappa case__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model is based on the context of Smurfit Kappa (paper factory)\n", + "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n", + "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n", + "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n", + "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_demands_over_time.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Source temperatures of around 65 degrees C\n", + "* Sink temperatures of 125-170 degrees C \n", + "* Average Temperature lift of about 85 degrees C" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "demands_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Heat pump__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def cop_curve(Tsink, Tsource):\n", + " Tsink += 273\n", + " Tsource += 273\n", + "\n", + " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", + " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", + " \n", + " return Polynomial([c2, c1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sourceT = 63\n", + "sinkT = 140\n", + "cop_curve(sourceT, sinkT)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_cop_curve.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Optimisation__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* At each moment in time, we calculate the cheapest option to produce the required heat. \n", + "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n", + "* Taking into account fluctuating market prices (electricity, gas, CO2)\n", + "* We are predicting real-time electricity prices using our forecasting models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Some example days:__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Steamboiler only is following demand pattern" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_steamboiler.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar pattern for heatpump only case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_heatpump.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_optcase.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_optcase2.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Business case__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "capex_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n", + "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ebitda_graph.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spt_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "production_fig.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The heatpump is filling in 95-98 % of the demand. \n", + "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n", + "* Switch price is on average around 90€/MWh (excluding subsidies)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Sensitivities__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If CAPEX is 200%, subsidy is needed to keep a good Payback Time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_capex.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Subsidies are protecting the business case againsts low CO2 prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_co2_spp.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n", + "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n", + "* When the temperature lift is higher, the COP will decrease and the business case will degrade" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsource.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsink.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_tsink_cop.show(renderer=renderer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sens_eprices.show(renderer=renderer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### __Conclusions__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* The business case for a Heat Pump seems favourable\n", + "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n", + "* However, this only results in a marginal improvement of the business case\n", + "* SDE++ has a very favourable effect on the business case, but is not critical\n", + "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "fea6e7ab4c0ed1d184e153838ac4b2d24a0985a5f17e99f4f437a114b66796b8" + }, + "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": "3.10.9" + }, + "toc-autonumbering": false, + "toc-showmarkdowntxt": false, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + 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