{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Imports" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n" ] } ], "source": [ "from datetime import timedelta, datetime\n", "import json\n", "import pprint\n", "from copy import deepcopy\n", "import pytz\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", "import sys\n", "\n", "import os\n", " \n", "# Path to the folder containing the alternate version of PyRecoy\n", "\n", "alternate_pyrecoy_path = 'C:\\\\Users\\\\z004yn8c\\\\OneDrive - Siemens AG\\\\Documents\\\\GitHub\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n", " \n", "# Add the path to sys.path\n", "\n", "if alternate_pyrecoy_path not in sys.path:\n", "\n", " sys.path.insert(0, alternate_pyrecoy_path)\n", " \n", "# Now import PyRecoy\n", "\n", "import pyrecoy2\n", "\n", "# Check the version or path to confirm\n", "\n", "print(pyrecoy2.__file__)\n", "\n", "\n", "from pyrecoy2.assets_kickstart import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n", "from pyrecoy2.colors import *\n", "from pyrecoy2.converters import *\n", "from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n", "from pyrecoy2.framework import TimeFramework\n", "from pyrecoy2.casestudy import CaseStudy\n", "from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n", "from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n", "from pyrecoy2.sensitivity import SensitivityAnalysis\n", "from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n", "from pyrecoy2.forecasts import Mipf, Forecast\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", "* COP seems to have fix values, rather than COP curve" ] }, { "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": [ "# Kickstart: Heatpump Optimisation Framework\n", "\n", "***\n", "© Shahla Huseynova \n", "July 2024\n", "##### Cases\n", "In this model, 4 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 boiler and heatpump run in hybrid mode, and are optimised on costs in real-time\n", "* **Heat pump & storage cases**: Heat pump only case is compared with different setup of storage +heat pump cases" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Loading config" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "class Config():\n", " start = '2023-01-01'\n", " end = '2023-12-31'\n", " # year = '2019'\n", " # startdate = year + '-01-01'\n", " # enddate = str(year) + '-12-31'\n", " # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n", " # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n", " # start = start.astimezone(pytz.UTC)\n", " # end = end.astimezone(pytz.UTC)\n", " \n", " hp_vdg_e_power = 1 # MW\n", " # hp_ndg_e_power = 7.7 # MW\n", " hp_min_load = 0\n", " hp_lifetime = 25\n", " hp_capex = 1_152_000 # EUR/MWth\n", " hp_opex = 0.04 # in % of CAPEX\n", " hp_devex = 0.005 # in % of CAPEX\n", " \n", " gb_power = 5 # MW\n", " gb_efficiency = 0.9\n", " \n", " storage_power = 0.34\n", " # storage_volume = 1000 *3/50\n", " # storage_cap_per_volume = 50 * 1e-3\n", " rated_capacity = 1\n", " storage_lifetime = 25\n", " storage_temperature = 144\n", " # storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n", " storage_min_level = rated_capacity * 0.05\n", " # storage_min_level = rated_capacity\n", " storage_capex_per_MW = 0\n", " storage_capex_per_MWh = 600_000\n", " storage_opex_perc_of_capex = 0.015\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": 13, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "class Store():\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Model set-up" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Define the file paths\n", "excel_file_path = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed3.xlsx'\n", "output_directory = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\data'\n", "output_file_name = 'smurfit_demand_preprocessed3.csv'\n", "csv_file_path = os.path.join(output_directory, output_file_name)\n", "\n", "df = pd.read_excel(excel_file_path)\n", "# df = df.fillna(0) \n", "\n", "# # Save the DataFrame to CSV\n", "df.to_csv(csv_file_path, index=False)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def load_demand_data(c, s):\n", " demand = pd.read_csv('data/smurfit_demand_preprocessed3.csv', delimiter=',', decimal='.')\n", " dt_index = pd.date_range(\n", " start=s.time_fw.start,\n", " end=s.time_fw.start + timedelta(days=365), \n", " freq='1T',\n", " tz='Europe/Amsterdam')\n", " dt_index = dt_index[:len(demand)]\n", " demand.index = dt_index\n", " demand['Total demand'] = demand['MW (VDG)']\n", " \n", " demand = demand[c.start:c.end]\n", " return demand" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "s = Store()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testetss\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: 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", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_15968\\2484316097.py:3: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] } ], "source": [ "def setup_model(c):\n", " s = Store()\n", " \n", " s.time_fw = TimeFramework(start=c.start, end=c.end)\n", " # s.minute_ix = s.time_fw.dt_index(freq='1T')\n", " mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n", " s.mipf = mipf\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.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n", " # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n", " s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n", " s.cases = list(CaseStudy.instances.values())\n", " s.optcases = [s.hpcase, s.storage_case_battery]\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": 19, "metadata": {}, "outputs": [], "source": [ "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) Total demand\n", "2023-01-01 00:00:00+01:00 70 144 1 1\n", "2023-01-01 00:01:00+01:00 70 144 1 1\n", "2023-01-01 00:02:00+01:00 70 144 1 1\n", "2023-01-01 00:03:00+01:00 70 144 1 1\n", "2023-01-01 00:04:00+01:00 70 144 1 1\n", "... ... ... ... ...\n", "2023-12-31 23:55:00+01:00 70 144 1 1\n", "2023-12-31 23:56:00+01:00 70 144 1 1\n", "2023-12-31 23:57:00+01:00 70 144 1 1\n", "2023-12-31 23:58:00+01:00 70 144 1 1\n", "2023-12-31 23:59:00+01:00 70 144 1 1\n", "\n", "[525600 rows x 4 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.demand" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Load in data" ] }, { "cell_type": "code", "execution_count": 22, "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": 23, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] } ], "source": [ "def load_data(c, s):\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", " case.data['DAM_avg'] = case.data.groupby(case.data.index.date)['DAM'].transform('mean')\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", "\n", " s.demand['Tsource (VDG)'] += c.tsource_delta\n", " s.demand['Tsink (VDG)'] += 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": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "75.08224016788466" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['CO2 prices (€/ton)'].mean()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:02:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:03:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "... ... ... ... ... ... \n", "2023-12-31 23:55:00+01:00 3.17 -6.81 -6.81 -7.10 -11.16 \n", "2023-12-31 23:56:00+01:00 3.17 -6.81 -6.81 -5.21 -8.12 \n", "2023-12-31 23:57:00+01:00 3.17 -6.81 -6.81 -5.61 -7.59 \n", "2023-12-31 23:58:00+01:00 3.17 -6.81 -6.81 -5.74 -7.87 \n", "2023-12-31 23:59:00+01:00 3.17 -6.81 -6.81 -5.70 -7.27 \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-01-01 00:00:00+01:00 63.998 5.23 \n", "2023-01-01 00:01:00+01:00 63.998 5.23 \n", "2023-01-01 00:02:00+01:00 63.998 5.23 \n", "2023-01-01 00:03:00+01:00 63.998 5.23 \n", "2023-01-01 00:04:00+01:00 63.998 5.23 \n", "... ... ... \n", "2023-12-31 23:55:00+01:00 29.927 77.25 \n", "2023-12-31 23:56:00+01:00 29.927 77.25 \n", "2023-12-31 23:57:00+01:00 29.927 77.25 \n", "2023-12-31 23:58:00+01:00 29.927 77.25 \n", "2023-12-31 23:59:00+01:00 NaN NaN \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) \\\n", "2023-01-01 00:00:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:01:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:02:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:03:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:04:00+01:00 1.01 18.144167 70 \n", "... ... ... ... \n", "2023-12-31 23:55:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:56:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:57:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:58:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:59:00+01:00 NaN 14.683333 70 \n", "\n", " Tsink (VDG) MW (VDG) Total demand \n", "2023-01-01 00:00:00+01:00 144 1 1 \n", "2023-01-01 00:01:00+01:00 144 1 1 \n", "2023-01-01 00:02:00+01:00 144 1 1 \n", "2023-01-01 00:03:00+01:00 144 1 1 \n", "2023-01-01 00:04:00+01:00 144 1 1 \n", "... ... ... ... \n", "2023-12-31 23:55:00+01:00 144 1 1 \n", "2023-12-31 23:56:00+01:00 144 1 1 \n", "2023-12-31 23:57:00+01:00 144 1 1 \n", "2023-12-31 23:58:00+01:00 144 1 1 \n", "2023-12-31 23:59:00+01:00 144 1 1 \n", "\n", "[525600 rows x 13 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Assets" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### COP curve" ] }, { "cell_type": "code", "execution_count": 28, "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": 29, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "text/latex": [ "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$" ], "text/plain": [ "Polynomial([19.2496754, 6.7058 , -1.79 ], domain=[-1, 1], window=[-1, 1], symbol='x')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sourceT = 63 + 273\n", "sinkT = 140 + 273\n", "cop_curve_new(sourceT, sinkT)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Create and assign assets" ] }, { "cell_type": "code", "execution_count": 31, "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=2.9\n", " )\n", "\n", " # heatpump_pcm = Heatpump(\n", " # name='Heatpump PCM',\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=2.9\n", " # )\n", " \n", " capex_vdg = c.hp_capex * heatpump_vdg.max_th_power\n", " heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n", "\n", " # print(capex_vdg)\n", " # capex_pcm = c.hp_capex * heatpump_vdg.max_th_power\n", " # heatpump_pcm.set_financials(capex=capex_pcm, opex=c.hp_opex*capex_pcm, devex=c.hp_devex*capex_pcm, lifetime=25)\n", " # # print(capex_pcm)\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=None,\n", " # volume=None, \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.storage_case_battery.add_asset(heatpump_vdg)\n", " s.storage_case_battery.add_asset(waterstorage)\n", " # s.storage_case_PCM.add_asset(heatpump_vdg)\n", " return s\n", "\n", "s = create_and_assign_assets(c, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "1800000" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "capex_ws = c.storage_capex_per_MW * 1 + c.storage_capex_per_MWh * 3\n", "capex_ws" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "HotWaterStorage(name=HotWaterStorage, rated_power=0.34, capacity=1, temperature=144, min_storagelevel=0.05)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.assets['HotWaterStorage']" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Optimization" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Strategies" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def baseline_sim(case):\n", " gasboiler = list(case.assets.values())[0]\n", " data = case.data\n", " print(data['MW (VDG)'])\n", " demand = (data['MW (VDG)']).to_list()\n", " # demand = data['MW (VDG)'].iloc[:, 0].tolist()\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": 37, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " # demand_vdg = case.data['MW (VDG)'].iloc[:, 0].tolist()\n", " demand_vdg = case.data['MW (VDG)'].to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", "\n", " hp_vdg_input = [0] * len(case.data)\n", " hp_vdg_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", " case.data['hp_output_MW'] = np.array(hp_vdg_output)\n", " case.data['hp_input_MW'] = np.array(hp_vdg_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(5)\n", " return case" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n", " \n", " from_storage_vdg_MW = 0\n", " to_storage_vdg_MW = 0 \n", " \n", " if neg < dam - c.threshold:\n", " # overproduce\n", " if demand_vdg != 0:\n", " storage_charge_power = ws.charging_power_limit\n", " if ws.chargelevel == ws.max_chargelevel:\n", " storage_charge_power = 0\n", " else:\n", " # Adjust charge power if charge level is not at max\n", " storage_charge_power = min(ws.charging_power_limit, (ws.max_chargelevel - ws.chargelevel) * 60)\n", "\n", " # desired_hp_load_vdg = min(demand_vdg + storage_charge_power, hp_vdg.max_th_power)\n", " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg, cop = 2.9)\n", "\n", " to_storage_vdg_MW = storage_charge_power\n", " to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 5))\n", "\n", " else:\n", " e_load_vdg, th_load_vdg = (0,0)\n", " \n", " \n", "\n", " elif pos > dam + c.threshold:\n", " # take from storage\n", " if demand_vdg != 0:\n", " from_storage_vdg_MW = 1/2.9\n", " from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 5))\n", " # from_storage_vdg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n", " desired_hp_load_vdg = min(demand_vdg, 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, cop = 2.9)\n", " else:\n", " e_load_vdg, th_load_vdg = (0,0)\n", " \n", " #print({f'from_storage_vdg_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", " cop = 2.9\n", " )\n", "\n", " return e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg\n", "# Neg-take price, Pos-feed price" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly_with_storage(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " ws = case.assets['HotWaterStorage']\n", " demands_vdg = case.data['MW (VDG)'].to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", " dam_prices = case.data['DAM_avg'].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_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", "\n", " new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n", "\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", " demand_vdg = demands_vdg[m]\n", " \n", " e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg = hp_storage_opt_enginge(\n", " c, ws, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n", " )\n", " \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_vdg[m] = tsource_vdg\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)\n", " case.data['hp_input_MW'] = np.array(hp_vdg_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['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']\n", " case.data['from_storage_MW'] = case.data['from_storage_vdg_MW']\n", " case.data['storage_level_MWh'] = storage_levels\n", " case.data['real_input_system_MW'] = case.data['hp_input_MW'] + case.data['to_storage_vdg_MW'] - case.data['from_storage_vdg_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(5)\n", " return case\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# charge_times = print(sum(s.storage_case_PCM.data['to_storage_MW']!= 0))\n", "# discharge_times = print(sum(s.storage_case_PCM.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": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets_kickstart.py:58: UserWarning:\n", "\n", "Chosen Asset load for HotWaterStorage is out of range. Should be between -0.34 and 0.34. Function will return boundary load level for now.\n", "\n" ] } ], "source": [ "s.storage_case_battery = hponly_with_storage(s.storage_case_battery)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data['cop'].fillna(2.5, inplace = True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGForeNegForePosGas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)DAM_avgTsource (VDG)...from_storage_vdg_MWto_storage_MWfrom_storage_MWstorage_level_MWhhp_output_MWhhp_input_MWhto_storage_vdg_MWhfrom_storage_vdg_MWhto_storage_MWhfrom_storage_MWh
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2 rows × 28 columns

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" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-04-06 16:30:00+02:00 109.87 125.04 125.04 178.87 176.08 \n", "2023-11-16 11:20:00+01:00 116.62 120.07 120.07 163.85 162.51 \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-04-06 16:30:00+02:00 45.521 93.96 \n", "2023-11-16 11:20:00+01:00 46.138 79.42 \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) ... \\\n", "2023-04-06 16:30:00+02:00 18.12 128.53333 70 ... \n", "2023-11-16 11:20:00+01:00 15.32 120.84958 70 ... \n", "\n", " from_storage_vdg_MW to_storage_MW \\\n", "2023-04-06 16:30:00+02:00 0.0 0.0 \n", "2023-11-16 11:20:00+01:00 0.0 0.0 \n", "\n", " from_storage_MW storage_level_MWh hp_output_MWh \\\n", "2023-04-06 16:30:00+02:00 0.0 0.695 0.01667 \n", "2023-11-16 11:20:00+01:00 0.0 0.950 0.01667 \n", "\n", " hp_input_MWh to_storage_vdg_MWh \\\n", "2023-04-06 16:30:00+02:00 -0.00575 0.0 \n", "2023-11-16 11:20:00+01:00 -0.00575 0.0 \n", "\n", " from_storage_vdg_MWh to_storage_MWh \\\n", "2023-04-06 16:30:00+02:00 0.0 0.0 \n", "2023-11-16 11:20:00+01:00 0.0 0.0 \n", "\n", " from_storage_MWh \n", "2023-04-06 16:30:00+02:00 0.0 \n", "2023-11-16 11:20:00+01:00 0.0 \n", "\n", "[2 rows x 28 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data.sample(2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "487.5467199999999" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['to_storage_MWh'].sum()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "s.storage_case_battery.data.to_csv('storage_data_battery.csv')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data.to_csv('storage_data_hp.csv')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGhp_output_MWto_storage_MWTotal demandcop
count525600.000000525600.000000525600.000000525600.0525600.000000525600.05.256000e+05
mean95.81731393.568163103.4389971.00.0556231.02.900000e+00
std49.046697194.977533195.6965510.00.1257490.01.775737e-11
min-500.000000-1549.470000-1549.4700001.0-0.0000001.02.900000e+00
25%76.93500047.00000057.9675001.00.0000001.02.900000e+00
50%99.21500084.13000088.5700001.00.0000001.02.900000e+00
75%122.400000114.020000119.5925001.00.0000001.02.900000e+00
max463.7700002037.7400002037.7400001.00.3400001.02.900000e+00
\n", "
" ], "text/plain": [ " DAM POS NEG hp_output_MW \\\n", "count 525600.000000 525600.000000 525600.000000 525600.0 \n", "mean 95.817313 93.568163 103.438997 1.0 \n", "std 49.046697 194.977533 195.696551 0.0 \n", "min -500.000000 -1549.470000 -1549.470000 1.0 \n", "25% 76.935000 47.000000 57.967500 1.0 \n", "50% 99.215000 84.130000 88.570000 1.0 \n", "75% 122.400000 114.020000 119.592500 1.0 \n", "max 463.770000 2037.740000 2037.740000 1.0 \n", "\n", " to_storage_MW Total demand cop \n", "count 525600.000000 525600.0 5.256000e+05 \n", "mean 0.055623 1.0 2.900000e+00 \n", "std 0.125749 0.0 1.775737e-11 \n", "min -0.000000 1.0 2.900000e+00 \n", "25% 0.000000 1.0 2.900000e+00 \n", "50% 0.000000 1.0 2.900000e+00 \n", "75% 0.000000 1.0 2.900000e+00 \n", "max 0.340000 1.0 2.900000e+00 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand', 'cop']].describe()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGhp_output_MWto_storage_MWfrom_storage_MWTotal demand
2023-02-02 01:36:00+01:0089.96183.52183.5210.000.341
2023-07-20 03:02:00+02:0080.3961.2961.291-0.000.001
2023-01-04 09:30:00+01:00146.00122.28122.2810.000.341
2023-02-19 01:25:00+01:00105.37107.01107.0110.000.001
2023-06-03 00:23:00+02:0084.9559.1959.1910.000.001
2023-03-02 06:09:00+01:00146.0093.6593.6510.340.001
2023-10-18 01:41:00+02:0063.35100.49100.4910.000.341
2023-03-27 14:29:00+02:00-10.02-81.89-81.891-0.000.001
2023-12-29 00:19:00+01:000.00-30.90-30.901-0.000.001
2023-01-03 21:56:00+01:00130.6323.3823.3810.340.001
2023-03-07 16:21:00+01:00111.27113.20113.2010.000.001
2023-11-04 17:27:00+01:0059.00137.49137.4910.000.001
2023-12-28 11:11:00+01:0034.89-25.62-25.6210.340.001
2023-04-04 14:27:00+02:00107.31112.40112.401-0.000.001
2023-04-08 09:34:00+02:00125.59156.79156.7910.000.001
2023-02-11 17:34:00+01:00153.85122.52122.5210.000.001
2023-04-08 15:10:00+02:0083.39121.70121.7010.000.001
2023-06-29 08:56:00+02:00130.31100.81100.8110.000.001
2023-02-09 07:32:00+01:00167.84106.83153.8010.000.001
2023-02-25 06:27:00+01:00100.10448.91448.9110.000.001
\n", "
" ], "text/plain": [ " DAM POS NEG hp_output_MW \\\n", "2023-02-02 01:36:00+01:00 89.96 183.52 183.52 1 \n", "2023-07-20 03:02:00+02:00 80.39 61.29 61.29 1 \n", "2023-01-04 09:30:00+01:00 146.00 122.28 122.28 1 \n", "2023-02-19 01:25:00+01:00 105.37 107.01 107.01 1 \n", "2023-06-03 00:23:00+02:00 84.95 59.19 59.19 1 \n", "2023-03-02 06:09:00+01:00 146.00 93.65 93.65 1 \n", "2023-10-18 01:41:00+02:00 63.35 100.49 100.49 1 \n", "2023-03-27 14:29:00+02:00 -10.02 -81.89 -81.89 1 \n", "2023-12-29 00:19:00+01:00 0.00 -30.90 -30.90 1 \n", "2023-01-03 21:56:00+01:00 130.63 23.38 23.38 1 \n", "2023-03-07 16:21:00+01:00 111.27 113.20 113.20 1 \n", "2023-11-04 17:27:00+01:00 59.00 137.49 137.49 1 \n", "2023-12-28 11:11:00+01:00 34.89 -25.62 -25.62 1 \n", "2023-04-04 14:27:00+02:00 107.31 112.40 112.40 1 \n", "2023-04-08 09:34:00+02:00 125.59 156.79 156.79 1 \n", "2023-02-11 17:34:00+01:00 153.85 122.52 122.52 1 \n", "2023-04-08 15:10:00+02:00 83.39 121.70 121.70 1 \n", "2023-06-29 08:56:00+02:00 130.31 100.81 100.81 1 \n", "2023-02-09 07:32:00+01:00 167.84 106.83 153.80 1 \n", "2023-02-25 06:27:00+01:00 100.10 448.91 448.91 1 \n", "\n", " to_storage_MW from_storage_MW Total demand \n", "2023-02-02 01:36:00+01:00 0.00 0.34 1 \n", "2023-07-20 03:02:00+02:00 -0.00 0.00 1 \n", "2023-01-04 09:30:00+01:00 0.00 0.34 1 \n", "2023-02-19 01:25:00+01:00 0.00 0.00 1 \n", "2023-06-03 00:23:00+02:00 0.00 0.00 1 \n", "2023-03-02 06:09:00+01:00 0.34 0.00 1 \n", "2023-10-18 01:41:00+02:00 0.00 0.34 1 \n", "2023-03-27 14:29:00+02:00 -0.00 0.00 1 \n", "2023-12-29 00:19:00+01:00 -0.00 0.00 1 \n", "2023-01-03 21:56:00+01:00 0.34 0.00 1 \n", "2023-03-07 16:21:00+01:00 0.00 0.00 1 \n", "2023-11-04 17:27:00+01:00 0.00 0.00 1 \n", "2023-12-28 11:11:00+01:00 0.34 0.00 1 \n", "2023-04-04 14:27:00+02:00 -0.00 0.00 1 \n", "2023-04-08 09:34:00+02:00 0.00 0.00 1 \n", "2023-02-11 17:34:00+01:00 0.00 0.00 1 \n", "2023-04-08 15:10:00+02:00 0.00 0.00 1 \n", "2023-06-29 08:56:00+02:00 0.00 0.00 1 \n", "2023-02-09 07:32:00+01:00 0.00 0.00 1 \n", "2023-02-25 06:27:00+01:00 0.00 0.00 1 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n", " dtype='object')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data.columns" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', '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_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data.columns" ] }, { "cell_type": "code", "execution_count": 51, "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_battery.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": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGTotal demandhp_input_MWhp_output_MWcopto_storage_MWfrom_storage_MW
2023-10-20 07:59:00+02:00108.1283.5783.571-0.3448312.90.000.00
2023-02-03 19:49:00+01:00180.0019.9719.971-0.3448312.90.340.00
2023-06-20 13:41:00+02:0089.90184.83184.831-0.3448312.90.000.34
2023-07-04 10:50:00+02:0033.64-12.31-12.311-0.3448312.9-0.000.00
2023-06-09 23:17:00+02:0086.0051.0851.081-0.3448312.9-0.000.00
2023-06-01 17:44:00+02:0071.9983.7483.741-0.3448312.90.000.00
2023-10-26 11:34:00+02:00140.0092.2092.201-0.3448312.9-0.000.00
2023-11-03 13:47:00+01:0060.85-22.40-22.401-0.3448312.90.340.00
2023-03-23 10:58:00+01:0041.40-258.95-258.951-0.3448312.90.340.00
2023-03-15 18:29:00+01:00182.30138.74138.741-0.3448312.90.000.00
2023-04-16 22:13:00+02:00129.9797.56113.821-0.3448312.90.000.00
2023-11-18 02:48:00+01:0090.6197.7097.701-0.3448312.90.000.00
2023-08-31 21:45:00+02:00125.6775.9175.911-0.3448312.90.340.00
2023-11-21 03:06:00+01:0091.1784.4084.401-0.3448312.9-0.000.00
2023-06-04 13:49:00+02:00-81.54-10.03-10.031-0.3448312.90.340.00
2023-07-06 21:13:00+02:00175.10116.12116.121-0.3448312.90.000.00
2023-06-21 10:18:00+02:00103.021269.001269.001-0.3448312.90.000.34
2023-12-28 07:03:00+01:0016.06-136.80112.131-0.3448312.90.000.00
2023-01-25 08:02:00+01:00225.04-189.55169.361-0.3448312.90.000.00
2023-01-30 20:09:00+01:00171.00326.17326.171-0.3448312.90.000.00
\n", "
" ], "text/plain": [ " DAM POS NEG Total demand \\\n", "2023-10-20 07:59:00+02:00 108.12 83.57 83.57 1 \n", "2023-02-03 19:49:00+01:00 180.00 19.97 19.97 1 \n", "2023-06-20 13:41:00+02:00 89.90 184.83 184.83 1 \n", "2023-07-04 10:50:00+02:00 33.64 -12.31 -12.31 1 \n", "2023-06-09 23:17:00+02:00 86.00 51.08 51.08 1 \n", "2023-06-01 17:44:00+02:00 71.99 83.74 83.74 1 \n", "2023-10-26 11:34:00+02:00 140.00 92.20 92.20 1 \n", "2023-11-03 13:47:00+01:00 60.85 -22.40 -22.40 1 \n", "2023-03-23 10:58:00+01:00 41.40 -258.95 -258.95 1 \n", "2023-03-15 18:29:00+01:00 182.30 138.74 138.74 1 \n", "2023-04-16 22:13:00+02:00 129.97 97.56 113.82 1 \n", "2023-11-18 02:48:00+01:00 90.61 97.70 97.70 1 \n", "2023-08-31 21:45:00+02:00 125.67 75.91 75.91 1 \n", "2023-11-21 03:06:00+01:00 91.17 84.40 84.40 1 \n", "2023-06-04 13:49:00+02:00 -81.54 -10.03 -10.03 1 \n", "2023-07-06 21:13:00+02:00 175.10 116.12 116.12 1 \n", "2023-06-21 10:18:00+02:00 103.02 1269.00 1269.00 1 \n", "2023-12-28 07:03:00+01:00 16.06 -136.80 112.13 1 \n", "2023-01-25 08:02:00+01:00 225.04 -189.55 169.36 1 \n", "2023-01-30 20:09:00+01:00 171.00 326.17 326.17 1 \n", "\n", " hp_input_MW hp_output_MW cop to_storage_MW \\\n", "2023-10-20 07:59:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-02-03 19:49:00+01:00 -0.34483 1 2.9 0.34 \n", "2023-06-20 13:41:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-07-04 10:50:00+02:00 -0.34483 1 2.9 -0.00 \n", "2023-06-09 23:17:00+02:00 -0.34483 1 2.9 -0.00 \n", "2023-06-01 17:44:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-10-26 11:34:00+02:00 -0.34483 1 2.9 -0.00 \n", "2023-11-03 13:47:00+01:00 -0.34483 1 2.9 0.34 \n", "2023-03-23 10:58:00+01:00 -0.34483 1 2.9 0.34 \n", "2023-03-15 18:29:00+01:00 -0.34483 1 2.9 0.00 \n", "2023-04-16 22:13:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-11-18 02:48:00+01:00 -0.34483 1 2.9 0.00 \n", "2023-08-31 21:45:00+02:00 -0.34483 1 2.9 0.34 \n", "2023-11-21 03:06:00+01:00 -0.34483 1 2.9 -0.00 \n", "2023-06-04 13:49:00+02:00 -0.34483 1 2.9 0.34 \n", "2023-07-06 21:13:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-06-21 10:18:00+02:00 -0.34483 1 2.9 0.00 \n", "2023-12-28 07:03:00+01:00 -0.34483 1 2.9 0.00 \n", "2023-01-25 08:02:00+01:00 -0.34483 1 2.9 0.00 \n", "2023-01-30 20:09:00+01:00 -0.34483 1 2.9 0.00 \n", "\n", " from_storage_MW \n", "2023-10-20 07:59:00+02:00 0.00 \n", "2023-02-03 19:49:00+01:00 0.00 \n", "2023-06-20 13:41:00+02:00 0.34 \n", "2023-07-04 10:50:00+02:00 0.00 \n", "2023-06-09 23:17:00+02:00 0.00 \n", "2023-06-01 17:44:00+02:00 0.00 \n", "2023-10-26 11:34:00+02:00 0.00 \n", "2023-11-03 13:47:00+01:00 0.00 \n", "2023-03-23 10:58:00+01:00 0.00 \n", "2023-03-15 18:29:00+01:00 0.00 \n", "2023-04-16 22:13:00+02:00 0.00 \n", "2023-11-18 02:48:00+01:00 0.00 \n", "2023-08-31 21:45:00+02:00 0.00 \n", "2023-11-21 03:06:00+01:00 0.00 \n", "2023-06-04 13:49:00+02:00 0.00 \n", "2023-07-06 21:13:00+02:00 0.00 \n", "2023-06-21 10:18:00+02:00 0.34 \n", "2023-12-28 07:03:00+01:00 0.00 \n", "2023-01-25 08:02:00+01:00 0.00 \n", "2023-01-30 20:09:00+01:00 0.00 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection.sample(20)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "none_count = selection['cop'].isnull().sum()\n", "none_count" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DAM 95.817313\n", "POS 93.568163\n", "NEG 103.438997\n", "Total demand 1.000000\n", "hp_input_MW -0.344830\n", "hp_output_MW 1.000000\n", "cop 2.900000\n", "to_storage_MW 0.055623\n", "from_storage_MW 0.055521\n", "dtype: float64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection.mean()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "########################\n", "\n", "# This is where we left\n", "\n", "########################" ] }, { "cell_type": "code", "execution_count": 56, "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": 57, "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", " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].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", "\n", " hp_out = hp_vdg_output\n", " hp_output[m] = hp_out\n", " hp_input[m] = hp_vdg_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": 59, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def calc_afrr_capacity(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " \n", " capacity = 0\n", " for hp in hp_vdg:\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": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Run optimisation" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2023-01-01 00:00:00+01:00 1\n", "2023-01-01 00:01:00+01:00 1\n", "2023-01-01 00:02:00+01:00 1\n", "2023-01-01 00:03:00+01:00 1\n", "2023-01-01 00:04:00+01:00 1\n", " ..\n", "2023-12-31 23:55:00+01:00 1\n", "2023-12-31 23:56:00+01:00 1\n", "2023-12-31 23:57:00+01:00 1\n", "2023-12-31 23:58:00+01:00 1\n", "2023-12-31 23:59:00+01:00 1\n", "Freq: min, Name: MW (VDG), Length: 525600, dtype: int64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets_kickstart.py:58: UserWarning:\n", "\n", "Chosen Asset load for HotWaterStorage is out of range. Should be between -0.34 and 0.34. Function will return boundary load level for now.\n", "\n" ] } ], "source": [ "def run_optimisation(c, s):\n", " s.baseline = baseline_sim(s.baseline)\n", " s.hpcase = hponly(s.hpcase)\n", " s.storage_case_battery = hponly_with_storage(s.storage_case_battery)\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_battery]: # [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": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.899979700142095" ] }, "execution_count": 62, "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": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-181242.64800000025" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.34483" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-181242.64800000025" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.34483" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.899979700142095" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['hp_output_MW'].sum() /s.storage_case_battery.data['hp_input_MW'].abs().sum() " ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGForeNegForePosGas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)DAM_avgTsource (VDG)Tsink (VDG)MW (VDG)Total demandhp_output_MWhp_input_MWcophp_output_MWhhp_input_MWh
2023-01-01 00:00:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:01:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:02:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:03:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:04:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
\n", "
" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-01-01 00:00:00+01:00 63.998 5.23 \n", "2023-01-01 00:01:00+01:00 63.998 5.23 \n", "2023-01-01 00:02:00+01:00 63.998 5.23 \n", "2023-01-01 00:03:00+01:00 63.998 5.23 \n", "2023-01-01 00:04:00+01:00 63.998 5.23 \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) \\\n", "2023-01-01 00:00:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:01:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:02:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:03:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:04:00+01:00 1.01 18.14417 70 \n", "\n", " Tsink (VDG) MW (VDG) Total demand hp_output_MW \\\n", "2023-01-01 00:00:00+01:00 144 1 1 1 \n", "2023-01-01 00:01:00+01:00 144 1 1 1 \n", "2023-01-01 00:02:00+01:00 144 1 1 1 \n", "2023-01-01 00:03:00+01:00 144 1 1 1 \n", "2023-01-01 00:04:00+01:00 144 1 1 1 \n", "\n", " hp_input_MW cop hp_output_MWh hp_input_MWh \n", "2023-01-01 00:00:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:01:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:02:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:03:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:04:00+01:00 -0.34483 2.9 0.01667 -0.00575 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data.head()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['cop'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "vis_data = s.storage_case_battery.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", " 'hp_input_MW'\n", "]]\n", "\n", "# cops = pd.concat([s.hpcase.data['cop'], s.storage_case_PCM.data['cop']], axis=1)\n", "# vis_data = pd.concat([vis_data, cops], axis=1)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "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": "storage_level_MWh", "text": "", "type": "scatter", "x": [ "2023-03-01 00:00:00+01:00", "2023-03-01 00:01:00+01:00", "2023-03-01 00:02:00+01:00", "2023-03-01 00:03:00+01:00", "2023-03-01 00:04:00+01:00", "2023-03-01 00:05:00+01:00", "2023-03-01 00:06:00+01:00", "2023-03-01 00:07:00+01:00", "2023-03-01 00:08:00+01:00", "2023-03-01 00:09:00+01:00", "2023-03-01 00:10:00+01:00", "2023-03-01 00:11:00+01:00", "2023-03-01 00:12:00+01:00", "2023-03-01 00:13:00+01:00", "2023-03-01 00:14:00+01:00", "2023-03-01 00:15:00+01:00", "2023-03-01 00:16:00+01:00", "2023-03-01 00:17:00+01:00", "2023-03-01 00:18:00+01:00", "2023-03-01 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"execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', '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_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data.columns" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hp_output = s.storage_case_battery.data['hp_output_MW'].round(2)\n", "demand_and_to_storage = (s.storage_case_battery.data['Total demand'] + s.storage_case_battery.data['to_storage_MW']).round(2)\n", "hp_output.equals(demand_and_to_storage)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_15968\\4223154146.py:1: FutureWarning:\n", "\n", "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", "\n" ] }, { "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": "hp_output_MW", "text": "", "type": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-01 01:00:00+01:00", "2023-01-01 02:00:00+01:00", 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"text/html": [ "
" ] }, "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": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', '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_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data.columns" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n" ] } ], "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_battery.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n", " s.storage_case_battery.generate_electr_market_results(nom_col='nomination_MWh', real_col='real_input_system_MWh')\n", "\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_battery]: #, 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=2023, \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": 80, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\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_battery]: #, 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": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "86016\n", "85857\n" ] } ], "source": [ "charge_times = print(sum(s.storage_case_battery.data['to_storage_MW']!= 0))\n", "discharge_times = print(sum(s.storage_case_battery.data['from_storage_MW']!= 0))" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-181242.64800000002\n", "-181242.64800000002\n" ] } ], "source": [ "charge_times_battery = print(sum(s.storage_case_battery.data['hp_input_MW']))\n", "charge_times_hp = print(sum(s.hpcase.data['hp_input_MW']))" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Visualisations" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_15968\\1396106630.py:1: FutureWarning:\n", "\n", "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", "\n", "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "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": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-01 01:00:00+01:00", "2023-01-01 02:00:00+01:00", "2023-01-01 03:00:00+01:00", "2023-01-01 04:00:00+01:00", "2023-01-01 05:00:00+01:00", "2023-01-01 06:00:00+01:00", "2023-01-01 07:00:00+01:00", "2023-01-01 08:00:00+01:00", "2023-01-01 09:00:00+01:00", "2023-01-01 10:00:00+01:00", "2023-01-01 11:00:00+01:00", "2023-01-01 12:00:00+01:00", "2023-01-01 13:00:00+01:00", "2023-01-01 14:00:00+01:00", "2023-01-01 15:00:00+01:00", "2023-01-01 16:00:00+01:00", "2023-01-01 17:00:00+01:00", "2023-01-01 18:00:00+01:00", "2023-01-01 19:00:00+01:00", "2023-01-01 20:00:00+01:00", "2023-01-01 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].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": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', '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_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh', 'nomination_MWh', 'Prod. vol.', 'Cons. vol.',\n", " 'Imb. vol.', 'Day-Ahead Result', 'POS Result', 'NEG Result',\n", " 'Imbalance Result', 'Combined Result'],\n", " dtype='object')" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data.columns" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "# temp_comparison = pd.concat([s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']], s.storage_case_PCM.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": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_battery.data['tsource_vdg'].idxmin()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0,\n", " 0,\n", " 29.635,\n", " 0,\n", " 0,\n", " HotWaterStorage(name=HotWaterStorage, rated_power=0.34, capacity=1, temperature=144, min_storagelevel=0.05),\n", " Heatpump(name='Heatpump VDG', max_thermal_power=1, cop_curve=2.9, min_th_power=0))" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case = s.storage_case_battery\n", "hp_vdg = case.assets['Heatpump VDG']\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", "tsource_vdg = 29.635\n", "tsink_vdg = 134.840\n", "\n", "hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg)" ] }, { "cell_type": "code", "execution_count": 91, "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": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_after = s.storage_case_battery.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_before.equals(Tsource_VDG_after)" ] }, { "cell_type": "code", "execution_count": 93, "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=2.9\n", ")" ] }, { "cell_type": "code", "execution_count": 94, "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": 95, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "date = s.baseline.data.index[0].strftime('%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "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": [ "2023-01-01 00:00:00+01:00", "2023-01-01 00:01:00+01:00", "2023-01-01 00:02:00+01:00", "2023-01-01 00:03:00+01:00", "2023-01-01 00:04:00+01:00", "2023-01-01 00:05:00+01:00", "2023-01-01 00:06:00+01:00", "2023-01-01 00:07:00+01:00", "2023-01-01 00:08:00+01:00", "2023-01-01 00:09:00+01:00", "2023-01-01 00:10:00+01:00", "2023-01-01 00:11:00+01:00", "2023-01-01 00:12:00+01:00", "2023-01-01 00:13:00+01:00", "2023-01-01 00:14:00+01:00", "2023-01-01 00:15:00+01:00", "2023-01-01 00:16:00+01:00", "2023-01-01 00:17:00+01:00", "2023-01-01 00:18:00+01:00", "2023-01-01 00:19:00+01:00", "2023-01-01 00:20:00+01:00", "2023-01-01 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Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "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": [ "2023-01-01 00:00:00+01:00", "2023-01-01 00:01:00+01:00", "2023-01-01 00:02:00+01:00", "2023-01-01 00:03:00+01:00", "2023-01-01 00:04:00+01:00", "2023-01-01 00:05:00+01:00", "2023-01-01 00:06:00+01:00", "2023-01-01 00:07:00+01:00", "2023-01-01 00:08:00+01:00", "2023-01-01 00:09:00+01:00", "2023-01-01 00:10:00+01:00", "2023-01-01 00:11:00+01:00", "2023-01-01 00:12:00+01:00", "2023-01-01 00:13:00+01:00", "2023-01-01 00:14:00+01:00", "2023-01-01 00:15:00+01:00", "2023-01-01 00:16:00+01:00", "2023-01-01 00:17:00+01:00", "2023-01-01 00:18:00+01:00", "2023-01-01 00:19:00+01:00", "2023-01-01 00:20:00+01:00", "2023-01-01 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 98, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Heatpump onlyHeatpump + Storage_battery
Total net volume (MWh)-3,022-3,022
Total exported to grid (MWh)--
Total consumed from grid (MWh)3,0223,022
Total nominated volume (MWh)-3,022-3,022
Absolute imbalance volume (MWh)--
Mean selling price (€/MWh)--
Mean buying price (€/MWh)9696
Total day-ahead result (€)-289,579-289,579
Total POS result (€)--
Total NEG result (€)--
Total imbalance result (€)--
Total combined result (€)-289,579-289,579
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" ], "text/plain": [ " Heatpump only Heatpump + Storage_battery\n", "Total net volume (MWh) -3,022 -3,022\n", "Total exported to grid (MWh) - -\n", "Total consumed from grid (MWh) 3,022 3,022\n", "Total nominated volume (MWh) -3,022 -3,022\n", "Absolute imbalance volume (MWh) - -\n", "Mean selling price (€/MWh) - -\n", "Mean buying price (€/MWh) 96 96\n", "Total day-ahead result (€) -289,579 -289,579\n", "Total POS result (€) - -\n", "Total NEG result (€) - -\n", "Total imbalance result (€) - -\n", "Total combined result (€) -289,579 -289,579" ] }, "execution_count": 98, "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": 99, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Heatpump onlyHeatpump + Storage_battery
Gasboiler OPEX (€)--
Gas consumption costs (€)393,966393,966
CO2 emission costs (€)140,949140,949
Gas taxes (€)33,68933,689
Heatpump VDG OPEX (€)-46,080-46,080
Result on electricity market (€)-289,579-289,579
Electricity taxes (€)-2,871-2,871
Grid transport costs (€)-28,883-28,883
HotWaterStorage OPEX (€)--9,000
EBITDA (€)201,192192,192
Depreciation (€)-46,080-70,080
EBITDA + depr (€)155,112122,112
\n", "
" ], "text/plain": [ " Heatpump only Heatpump + Storage_battery\n", "Gasboiler OPEX (€) - -\n", "Gas consumption costs (€) 393,966 393,966\n", "CO2 emission costs (€) 140,949 140,949\n", "Gas taxes (€) 33,689 33,689\n", "Heatpump VDG OPEX (€) -46,080 -46,080\n", "Result on electricity market (€) -289,579 -289,579\n", "Electricity taxes (€) -2,871 -2,871\n", "Grid transport costs (€) -28,883 -28,883\n", "HotWaterStorage OPEX (€) - -9,000\n", "EBITDA (€) 201,192 192,192\n", "Depreciation (€) -46,080 -70,080\n", "EBITDA + depr (€) 155,112 122,112" ] }, "execution_count": 99, "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": 100, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12
CAPEX (€)-1,157,760
Regular Earnings (€)201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192
Irregular Cashflows (€)------------
EBITDA (€)201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192
Depreciations (€) -/--46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080
EBIT (€)155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112
Income tax (Vpb.) (€)-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267
NOPLAT (€)131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845
Depreciations (€) +/+46,08046,08046,08046,08046,08046,08046,08046,08046,08046,08046,08046,080
Free Cash Flow (€)-1,157,760177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925
Simple Payback Period6.507001039280803 years
IRR (%)11%
WACC (%)10%
NPV of explicit period (€)49,607
Discounted residual value (€)192,708
NPV (€)49,607
\n", "
" ], "text/plain": [ " Year 0 Year 1 Year 2 \\\n", "CAPEX (€) -1,157,760 \n", "Regular Earnings (€) 201,192 201,192 \n", "Irregular Cashflows (€) - - \n", "EBITDA (€) 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 \n", "Free Cash Flow (€) -1,157,760 177,925 177,925 \n", "Simple Payback Period 6.507001039280803 years \n", "IRR (%) 11% \n", "WACC (%) 10% \n", "NPV of explicit period (€) 49,607 \n", "Discounted residual value (€) 192,708 \n", "NPV (€) 49,607 \n", "\n", " Year 3 Year 4 Year 5 Year 6 Year 7 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 201,192 201,192 201,192 201,192 201,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 201,192 201,192 201,192 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 -46,080 -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 155,112 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 -23,267 -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 131,845 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 46,080 46,080 46,080 \n", "Free Cash Flow (€) 177,925 177,925 177,925 177,925 177,925 \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 Year 12 \n", "CAPEX (€) \n", "Regular Earnings (€) 201,192 201,192 201,192 201,192 201,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 201,192 201,192 201,192 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 -46,080 -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 155,112 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 -23,267 -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 131,845 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 46,080 46,080 46,080 \n", "Free Cash Flow (€) 177,925 177,925 177,925 177,925 177,925 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) " ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BusinessCaseReport(s.hpcase).show()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12
CAPEX (€)-1,757,760
Regular Earnings (€)192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192
Irregular Cashflows (€)------------
EBITDA (€)192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192192,192
Depreciations (€) -/--70,080-70,080-70,080-70,080-70,080-70,080-70,080-70,080-70,080-70,080-70,080-70,080
EBIT (€)122,112122,112122,112122,112122,112122,112122,112122,112122,112122,112122,112122,112
Income tax (Vpb.) (€)-18,317-18,317-18,317-18,317-18,317-18,317-18,317-18,317-18,317-18,317-18,317-18,317
NOPLAT (€)103,795103,795103,795103,795103,795103,795103,795103,795103,795103,795103,795103,795
Depreciations (€) +/+70,08070,08070,08070,08070,08070,08070,08070,08070,08070,08070,08070,080
Free Cash Flow (€)-1,757,760173,875173,875173,875173,875173,875173,875173,875173,875173,875173,875173,875173,875
Simple Payback Period10.109315970302973 years
IRR (%)3%
WACC (%)10%
NPV of explicit period (€)-520,934
Discounted residual value (€)292,121
NPV (€)-520,934
\n", "
" ], "text/plain": [ " Year 0 Year 1 Year 2 \\\n", "CAPEX (€) -1,757,760 \n", "Regular Earnings (€) 192,192 192,192 \n", "Irregular Cashflows (€) - - \n", "EBITDA (€) 192,192 192,192 \n", "Depreciations (€) -/- -70,080 -70,080 \n", "EBIT (€) 122,112 122,112 \n", "Income tax (Vpb.) (€) -18,317 -18,317 \n", "NOPLAT (€) 103,795 103,795 \n", "Depreciations (€) +/+ 70,080 70,080 \n", "Free Cash Flow (€) -1,757,760 173,875 173,875 \n", "Simple Payback Period 10.109315970302973 years \n", "IRR (%) 3% \n", "WACC (%) 10% \n", "NPV of explicit period (€) -520,934 \n", "Discounted residual value (€) 292,121 \n", "NPV (€) -520,934 \n", "\n", " Year 3 Year 4 Year 5 Year 6 Year 7 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 192,192 192,192 192,192 192,192 192,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 192,192 192,192 192,192 192,192 192,192 \n", "Depreciations (€) -/- -70,080 -70,080 -70,080 -70,080 -70,080 \n", "EBIT (€) 122,112 122,112 122,112 122,112 122,112 \n", "Income tax (Vpb.) (€) -18,317 -18,317 -18,317 -18,317 -18,317 \n", "NOPLAT (€) 103,795 103,795 103,795 103,795 103,795 \n", "Depreciations (€) +/+ 70,080 70,080 70,080 70,080 70,080 \n", "Free Cash Flow (€) 173,875 173,875 173,875 173,875 173,875 \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 Year 12 \n", "CAPEX (€) \n", "Regular Earnings (€) 192,192 192,192 192,192 192,192 192,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 192,192 192,192 192,192 192,192 192,192 \n", "Depreciations (€) -/- -70,080 -70,080 -70,080 -70,080 -70,080 \n", "EBIT (€) 122,112 122,112 122,112 122,112 122,112 \n", "Income tax (Vpb.) (€) -18,317 -18,317 -18,317 -18,317 -18,317 \n", "NOPLAT (€) 103,795 103,795 103,795 103,795 103,795 \n", "Depreciations (€) +/+ 70,080 70,080 70,080 70,080 70,080 \n", "Free Cash Flow (€) 173,875 173,875 173,875 173,875 173,875 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BusinessCaseReport(s.storage_case_battery).show()" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "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": [ "2023-01-01 00:00:00+01:00", "2023-01-02 00:00:00+01:00", "2023-01-03 00:00:00+01:00", "2023-01-04 00:00:00+01:00", "2023-01-05 00:00:00+01:00", "2023-01-06 00:00:00+01:00", "2023-01-07 00:00:00+01:00", "2023-01-08 00:00:00+01:00", "2023-01-09 00:00:00+01:00", "2023-01-10 00:00:00+01:00", "2023-01-11 00:00:00+01:00", "2023-01-12 00:00:00+01:00", "2023-01-13 00:00:00+01:00", "2023-01-14 00:00:00+01:00", "2023-01-15 00:00:00+01:00", "2023-01-16 00:00:00+01:00", "2023-01-17 00:00:00+01:00", "2023-01-18 00:00:00+01:00", "2023-01-19 00:00:00+01:00", "2023-01-20 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"text/html": [ "
" ] }, "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": 103, "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": 104, "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 CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_battery" ], "y": [ 0, 0, 0 ] }, { "marker": { "color": "rgba(31, 131, 118, 0.6)", "line": { "color": "rgba(31, 131, 118, 1.0)", "width": 1 } }, "name": "Heatpump VDG CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_battery" ], "y": [ 0, 1152000, 1152000 ] }, { "marker": { "color": "rgba(221, 67, 59, 0.6)", "line": { "color": "rgba(221, 67, 59, 1.0)", "width": 1 } }, "name": "HotWaterStorage CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_battery" ], "y": [ 0, 0, 600000 ] } ], "layout": { "barmode": "relative", "height": 400, "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" }, "traceorder": "reversed" }, "paper_bgcolor": "#F5F6F9", "plot_bgcolor": "#F5F6F9", "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", "width": 0.5 }, "pattern": { "fillmode": "overlay", "size": 10, 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 106, "metadata": {}, "outputs": [], "source": [ "# 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 108, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "NPV", "orientation": "v", "text": [ 0.04960731309321511, 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 109, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "IRR", "orientation": "v", "text": [ 10.952734299520062, 2.7415872622115867 ], "textposition": "outside", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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": 110, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "Simple Payback Time", "orientation": "v", "text": [ 6.507001039280803, 10.109315970302973 ], "textposition": 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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": 114, "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": 115, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testetss\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: 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", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: total: 7.52 s\n", "Wall time: 15.6 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_15968\\2484316097.py:3: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] } ], "source": [ "%time _s = setup()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2023-01-01 00:00:00+01:00 1\n", "2023-01-01 00:01:00+01:00 1\n", "2023-01-01 00:02:00+01:00 1\n", "2023-01-01 00:03:00+01:00 1\n", "2023-01-01 00:04:00+01:00 1\n", " ..\n", "2023-12-31 23:55:00+01:00 1\n", "2023-12-31 23:56:00+01:00 1\n", "2023-12-31 23:57:00+01:00 1\n", "2023-12-31 23:58:00+01:00 1\n", "2023-12-31 23:59:00+01:00 1\n", "Freq: min, Name: MW (VDG), Length: 525600, dtype: int64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets_kickstart.py:58: UserWarning:\n", "\n", "Chosen Asset load for HotWaterStorage is out of range. Should be between -0.34 and 0.34. Function will return boundary load level for now.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: total: 2.42 s\n", "Wall time: 5.44 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] }, { "data": { "text/plain": [ "49607.31309321511" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time result = routine(c, _s)\n", "npv = result.hpcase.npv\n", "npv" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "82e551df4a854ab09abe4bbb5bf44028", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00 6\u001b[1;33m \u001b[0msens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSensitivityAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m 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49\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcase\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkpi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15968\\3648020848.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mdef\u001b[0m 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s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;31m#s = postprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcollect_cashflows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_15968\\1226049738.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mgasboiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m 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"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": 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