{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Imports" ] }, { "cell_type": "code", "execution_count": 2, "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": 10, "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_e_power = 1.5 # MW\n", " # hp_ndg_e_power = 7.7 # MW\n", " hp_min_load = 0\n", " hp_lifetime = 25\n", " hp_capex = 200_000 # EUR/MWth\n", " hp_opex = 0.01 # in % of CAPEX\n", " hp_devex = 0.005 # in % of CAPEX\n", " \n", " gb_power = 5 # MW\n", " gb_efficiency = 0.9\n", " \n", " storage_power = 1\n", " # storage_volume = 1000 *3/50\n", " # storage_cap_per_volume = 50 * 1e-3\n", " rated_capacity = 3\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 * 0.00\n", " # storage_min_level = rated_capacity\n", " storage_capex_per_MW = 60_000\n", " storage_capex_per_MWh = 0\n", " storage_opex_perc_of_capex = 0.02\n", " # storage_initial_level = 5\n", " threshold = 0\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": 11, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "class Store():\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Model set-up" ] }, { "cell_type": "code", "execution_count": 13, "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": 14, "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": 15, "metadata": {}, "outputs": [], "source": [ "s = Store()" ] }, { "cell_type": "code", "execution_count": 16, "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_7920\\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_PCM]\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": 17, "metadata": {}, "outputs": [], "source": [ "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()" ] }, { "cell_type": "code", "execution_count": 18, "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": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.demand" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Load in data" ] }, { "cell_type": "code", "execution_count": 20, "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": 21, "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", " \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": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "75.08224016788466" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['CO2 prices (€/ton)'].mean()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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525600 rows × 12 columns

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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) Tsource (VDG) Tsink (VDG) \\\n", "2023-01-01 00:00:00+01:00 1.01 70 144 \n", "2023-01-01 00:01:00+01:00 1.01 70 144 \n", "2023-01-01 00:02:00+01:00 1.01 70 144 \n", "2023-01-01 00:03:00+01:00 1.01 70 144 \n", "2023-01-01 00:04:00+01:00 1.01 70 144 \n", "... ... ... ... \n", "2023-12-31 23:55:00+01:00 14.90 70 144 \n", "2023-12-31 23:56:00+01:00 14.90 70 144 \n", "2023-12-31 23:57:00+01:00 14.90 70 144 \n", "2023-12-31 23:58:00+01:00 14.90 70 144 \n", "2023-12-31 23:59:00+01:00 NaN 70 144 \n", "\n", " MW (VDG) Total demand \n", "2023-01-01 00:00:00+01:00 1 1 \n", "2023-01-01 00:01:00+01:00 1 1 \n", "2023-01-01 00:02:00+01:00 1 1 \n", "2023-01-01 00:03:00+01:00 1 1 \n", "2023-01-01 00:04:00+01:00 1 1 \n", "... ... ... \n", "2023-12-31 23:55:00+01:00 1 1 \n", "2023-12-31 23:56:00+01:00 1 1 \n", "2023-12-31 23:57:00+01:00 1 1 \n", "2023-12-31 23:58:00+01:00 1 1 \n", "2023-12-31 23:59:00+01:00 1 1 \n", "\n", "[525600 rows x 12 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Assets" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### COP curve" ] }, { "cell_type": "code", "execution_count": 26, "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": 27, "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": 27, "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": 29, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def create_and_assign_assets(c, s):\n", " heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=c.hp_e_power,\n", " min_th_power=c.hp_e_power * c.hp_min_load,\n", " cop_curve=2.5\n", " )\n", "\n", "\n", " capex = c.hp_capex*(heatpump.max_th_power) \n", " heatpump.set_financials(capex=capex, opex=c.hp_opex*capex, devex=c.hp_devex*capex, lifetime=25)\n", "\n", " gasboiler = GasBoiler(\n", " name='Gasboiler',\n", " max_th_output=c.gb_power,\n", " efficiency=c.gb_efficiency\n", " )\n", " gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n", " \n", " waterstorage = HotWaterStorage(\n", " name='HotWaterStorage',\n", " rated_power=c.storage_power, \n", " # capacity_per_volume=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 * 1 + c.storage_capex_per_MWh * 3\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)\n", " s.storage_case_PCM.add_asset(heatpump)\n", " s.storage_case_PCM.add_asset(waterstorage)\n", " return s\n", "\n", "s = create_and_assign_assets(c, s)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "60000" ] }, "execution_count": 30, "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": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.assets['HotWaterStorage']" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Optimization" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Strategies" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def baseline_sim(case):\n", " gasboiler = list(case.assets.values())[0]\n", " data = case.data\n", " demand = (data['MW (VDG)']).to_list()\n", "\n", " minutes = iter(range(len(case.data)))\n", " th_output = [0] * len(case.data)\n", " gas_input = [0] * len(case.data)\n", "\n", " for m in minutes:\n", " th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n", "\n", " data['output_MW_th'] = np.array(th_output)\n", " data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n", " data['gb_input_MW'] = np.array(gas_input)\n", " data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n", " case.data = data.round(5)\n", " return case" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly(case):\n", " hp = case.assets['Heatpump']\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_input = [0] * len(case.data)\n", " hp_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_input[m], hp_output[m] = hp.set_heat_output(\n", " heat_output=demand - 0.66,\n", " Tsink=Tsink_vdg[m],\n", " Tsource=Tsource_vdg[m]\n", " )\n", "\n", " case.data['hp_output_MW'] = np.array(hp_output)\n", " case.data['hp_input_MW'] = np.array(hp_input)\n", " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", " \n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(3)\n", " return case" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def hp_storage_opt_enginge(c, ws, hp, pos, neg, dam, demand, tsource_vdg, tsink_vdg):\n", " \n", " from_storage_MW = 0\n", " to_storage_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 = min(demand_vdg + storage_charge_power - 0.66, hp.max_th_power)\n", " e_load, th_load = hp.set_heat_output(desired_hp_load, tsink_vdg, tsource_vdg)\n", "\n", " to_storage_MW = desired_hp_load - demand_vdg + 0.66\n", " to_storage_MW = -ws.charge(round(to_storage_MW, 3))\n", " # #Added lately\n", " # extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n", " # desired_hp_load_new = min(demand_vdg + min(storage_charge_power, extra_charging_power_constraint) - 0.66, hp_vdg.max_th_power)\n", " # e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_new, tsink_vdg, tsource_vdg)\n", "\n", " else:\n", " e_load_, th_load = (0,0)\n", " \n", " \n", "\n", " elif pos > dam + c.threshold:\n", " # take from storage\n", " if demand_vdg != 0:\n", " from_storage_MW = demand_vdg - 0.66\n", " from_storage_MW = ws.discharge(round(from_storage_MW, 3))\n", " # from_storage_vdg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n", " desired_hp_load = min(demand_vdg - from_storage_MW - 0.66, hp.max_th_power)\n", " e_load, th_load = hp.set_heat_output(desired_hp_load, tsink_vdg, tsource_vdg)\n", " else:\n", " e_load, th_load = (0,0)\n", " \n", " #print({f'from_storage_vdg_MW='})\n", " else:\n", " e_load, th_load = hp.set_heat_output(\n", " heat_output=demand_vdg - 0.66,\n", " Tsink=tsink_vdg,\n", " Tsource=tsource_vdg\n", " )\n", "\n", " return e_load, th_load, tsource, to_storage_MW, from_storage_MW, ws, hp\n", "# Neg-take price, Pos-feed price" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly_with_storage(case):\n", " hp = case.assets['Heatpump']\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'].to_list()\n", " pos_prices = case.data['POS'].to_list()\n", " neg_prices = case.data['NEG'].to_list()\n", "\n", " hp_input = [0] * len(case.data)\n", " hp_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_list = [0] * minutes\n", " to_storage_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, th_load, tsource_vdg, to_storage_MW, from_storage_MW, ws, hp = hp_storage_opt_enginge(\n", " c, ws, hp, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n", " )\n", " \n", " hp_input[m] = e_load\n", " hp_output[m] = th_load\n", " \n", " storage_levels[m] = ws.chargelevel\n", " new_tsources_vdg[m] = tsource_vdg\n", " to_storage_list[m] = to_storage_MW\n", " from_storage_list[m] = from_storage_MW\n", " \n", " case.data['hp_output_MW'] = np.array(hp_output)\n", " case.data['hp_input_MW'] = np.array(hp_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_MW'] = to_storage_list\n", " case.data['from_storage_MW'] = from_storage_list\n", " case.data['to_storage_MW'] = case.data['to_storage_MW']\n", " case.data['from_storage_MW'] = case.data['from_storage_MW']\n", " case.data['storage_level_MWh'] = storage_levels\n", " \n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(3)\n", " return case\n" ] }, { "cell_type": "code", "execution_count": 38, "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": 39, "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'Heatpump VDG'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[39], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s\u001b[38;5;241m.\u001b[39mstorage_case_PCM \u001b[38;5;241m=\u001b[39m \u001b[43mhponly_with_storage\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_case_PCM\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[1;32mIn[37], line 2\u001b[0m, in \u001b[0;36mhponly_with_storage\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhponly_with_storage\u001b[39m(case):\n\u001b[1;32m----> 2\u001b[0m hp_vdg \u001b[38;5;241m=\u001b[39m \u001b[43mcase\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43massets\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHeatpump VDG\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m 3\u001b[0m ws \u001b[38;5;241m=\u001b[39m case\u001b[38;5;241m.\u001b[39massets[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHotWaterStorage\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 4\u001b[0m demands_vdg \u001b[38;5;241m=\u001b[39m case\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMW (VDG)\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_list()\n", "\u001b[1;31mKeyError\u001b[0m: 'Heatpump VDG'" ] } ], "source": [ "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data.sample(2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['cop'].fillna(2.5, inplace = True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data.to_csv('storage_data.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand', 'cop']].describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n", "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n", "storage_selection = s.storage_case_PCM.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n", "\n", "selection = pd.concat([hp_selection, storage_selection], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "selection.sample(20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "none_count = selection['cop'].isnull().sum()\n", "none_count" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "selection.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "########################\n", "\n", "# This is where we left\n", "\n", "########################" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n", " return (\n", " th_load / cop * electricity_cost\n", " + (demand - th_load) * alt_heat_price\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hybrid_imb_optimisation(case, decimals, s):\n", " gb = case.assets['Gasboiler']\n", " hp_vdg = case.assets['Heatpump VDG']\n", " 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": null, "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": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def run_optimisation(c, s):\n", " s.baseline = baseline_sim(s.baseline)\n", " s.hpcase = hponly(s.hpcase)\n", " s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)\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_PCM]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n", " case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n", " \n", " return s\n", "\n", "s = run_optimisation(c, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['hp_output_MW'].sum() /s.storage_case_PCM.data['hp_input_MW'].abs().sum() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['cop'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "vis_data = s.storage_case_PCM.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", " 'NEG'\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": null, "metadata": {}, "outputs": [], "source": [ "vis_data.iplot(subplots=True, shape=(6,1), dimensions=(1000, 800))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "vis_data.min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total_to_storage = s.storage_case_PCM.data['to_storage_MWh'].sum()\n", "total_from_storage = s.storage_case_PCM.data['from_storage_MWh'].sum()\n", "total_to_storage, total_from_storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hp_output = s.storage_case_PCM.data['hp_output_MW'].round(2)\n", "demand_and_to_storage = (s.storage_case_PCM.data['Total demand'] + s.storage_case_PCM.data['to_storage_MW']).round(2)\n", "hp_output.equals(demand_and_to_storage)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Financials" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def collect_cashflows(c, s):\n", " s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", " s.storage_case_PCM.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n", " s.storage_case_PCM.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_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_PCM]: #, 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": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "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_PCM]: #, 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": null, "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))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "charge_times_pcm = print(sum(s.storage_case_PCM.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": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "fig_demands_over_time = s.demand['Total demand'].resample('H').mean().iplot(\n", " title='Smurfit Kappa: Heat demand by Hour in MW', \n", " yTitle='MW', \n", " colors=recoygreen,\n", " asFigure=True,\n", " dimensions=(800, 400)\n", ")\n", "fig_demands_over_time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].resample('H').mean().iplot(\n", " kind='box',\n", " title='Smurfit Kappa: Source and Sink temperatures',\n", " color=recoygreen,\n", " yTitle='Temperature in degrees C',\n", " legend=False,\n", " asFigure=True,\n", " dimensions=(800, 400)\n", ")\n", "demands_fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": null, "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": null, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM.data['tsource_vdg'].idxmin()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "case = s.storage_case_PCM\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": null, "metadata": {}, "outputs": [], "source": [ "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_after = s.storage_case_PCM.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_before.equals(Tsource_VDG_after)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "test_hp = Heatpump(\n", " name='Heatpump for Testing',\n", " max_th_power=1,\n", " min_th_power=0.3,\n", " cop_curve=2.5\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "import plotly.graph_objs as go\n", "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n", " loads = []\n", " eprices = list(range(1000))\n", " for eprice in eprices:\n", " _, load = test_hp.set_opt_load(\n", " electricity_cost=eprice + s.eb_ode_e,\n", " alt_heat_price=gas_price / 0.9,\n", " demand=1, \n", " Tsink=Tsnk,\n", " Tsource=Tsrc\n", " )\n", " loads.append(load)\n", " trace = go.Scatter(x=eprices, y=loads, name=name)\n", " return trace" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "date = s.baseline.data.index[0].strftime('%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n", " subplots=True, \n", " title=f'Steamboiler only case on {date}',\n", " subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n", " legend=False,\n", " dimensions=(800, 400),\n", " colors=[recoydarkblue, recoygreen], \n", " asFigure=True,\n", " shape = (2, 1)\n", ")\n", "fig_steamboiler" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n", " subplots=True, \n", " title=f'Heatpump only case on {date}', \n", " subplot_titles=['Total demand', 'Heatpump output (MW)'],\n", " legend=False,\n", " dimensions=(800, 400),\n", " colors=[recoydarkblue, recoygreen], \n", " asFigure=True,\n", " shape = (2, 1)\n", ")\n", "fig_heatpump" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "report = ComparisonReport(\n", " cases=s.optcases, \n", " kind='electr_market_results',\n", ")\n", "report.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n", "casereport.show(presentation_format=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "BusinessCaseReport(s.hpcase).show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "BusinessCaseReport(s.storage_case_PCM).show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "#price chart\n", "from copy import deepcopy\n", "\n", "data = deepcopy(s.baseline.data)\n", "data = data[data.columns[:2]]\n", "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n", "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n", "\n", "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "# #price chart\n", "# from copy import deepcopy\n", "\n", "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n", "\n", "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n", "# _total_output = _source_output.sum(axis=1)\n", "# _data = _source_output.divide(_total_output, axis=0).rename(\n", "# columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n", "# ) * 100\n", "\n", "# production_fig = _data.iplot(\n", "# kind='bar',\n", "# barmode='stack',\n", "# colors=[recoydarkblue, recoygreen],\n", "# title='Hybrid case: Heat production per Month by Source in % share',\n", "# yTitle='Share of production in %',\n", "# dimensions=(600, 400),\n", "# asFigure=True\n", "# )\n", "\n", "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n", "# production_fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "report = ComparisonReport(s.cases, kind='capex').report\n", "report = report[report.index.str.contains('CAPEX')].T\n", "capex_fig = report.iplot(\n", " kind='bar', \n", " barmode='relative',\n", " colors=recoycolors,\n", " title='CAPEX by Casestudy',\n", " yTitle='CAPEX in €',\n", " dimensions=(600, 400),\n", " asFigure=True,\n", ")\n", "\n", "capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n", "capex_fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n", "\n", "fig = cashflow_report.report.T.iplot(\n", " kind='bar',\n", " barmode='relative',\n", " colors=recoycolors,\n", " title='OPEX breakdown',\n", " asFigure=True,\n", " yTitle='€',\n", " dimensions=(800, 600)\n", ")\n", "\n", "ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n", "scat = go.Scatter(\n", " mode='markers',\n", " y=ebitda_report.report.loc['EBITDA (€)', :].values, \n", " x=ebitda_report.report.columns, \n", " line=go.scatter.Line(color=recoydarkgrey),\n", " marker=dict(\n", " color=recoydarkgrey,\n", " size=20,\n", " line=dict(\n", " color=recoydarkgrey,\n", " width=3\n", " ),\n", " symbol='line-ew'\n", " ),\n", " name='EBITDA (€)'\n", ")\n", "fig.add_trace(scat)\n", "fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# ebitda_report.show(comparison='relative')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n", "ebitda_graph = _series.iplot(\n", " kind='bar',\n", " title='Yearly EBITDA by Casestudy in €',\n", " colors=recoygreen,\n", " dimensions=(600, 400), \n", " yTitle='EBITDA in €',\n", " asFigure=True,\n", " yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n", ")\n", "\n", "ebitda_graph.update_traces(\n", " text=_series.values/1000_000, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f}M €\", \n", ")\n", "\n", "ebitda_graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n", "\n", "npv_graph = _series.iplot(\n", " kind='bar',\n", " title='NPV by Casestudy in €',\n", " colors=recoygreen,\n", " dimensions=(600, 400), \n", " yTitle='NPV in €',\n", " asFigure=True,\n", " yrange=[0, _series.max() * 1.1]\n", ")\n", "\n", "npv_graph.update_traces(\n", " text=_series.values/1000_000, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f}M €\", \n", ")\n", "\n", "npv_graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n", "irr_fig = irr_report.iplot(\n", " kind='bar',\n", " title='IRR by Casestudy in %',\n", " colors=recoygreen,\n", " dimensions=(600, 400),\n", " yTitle='IRR in %',\n", " asFigure=True,\n", " yrange=[0, irr_report.max() * 1.2]\n", ")\n", "\n", "irr_fig.update_traces(\n", " text=irr_report.values, \n", " textposition='outside', \n", " texttemplate=\"%{text:.0f} %\", \n", ")\n", "\n", "irr_fig" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n", "spt_fig = _series.iplot(\n", " kind='bar',\n", " title='Simple Payback Time by Casestudy in Years',\n", " colors=recoygreen,\n", " dimensions=(600, 400),\n", " yTitle='Years',\n", " asFigure=True,\n", " yrange=[0, _series.max() * 1.2]\n", ")\n", "\n", "spt_fig.update_traces(\n", " text=_series.values, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f} years\", \n", ")\n", "\n", "spt_fig " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n", "cop_fig = mean_cops.iplot(\n", " kind='bar',\n", " title='Mean COP by Casestudy',\n", " colors=recoygreen,\n", " dimensions=(600, 400),\n", " yTitle='COP',\n", " asFigure=True,\n", " yrange=[0, mean_cops.max() * 1.2]\n", ")\n", "\n", "cop_fig.update_traces(\n", " text=mean_cops.values, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f}\", \n", ")\n", "\n", "cop_fig " ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Sensitivity analysis" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n", "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n", "2. aFRR prices +/- 30%\n", "3. CAPEX\n", "4. CO2 price\n", "5. Tsource +/- 10 degrees Celsius\n", "6. Tsink\n", " * Roam off the peak / lower pressure\n", " * Stabalize / running average per hour/ 2 hours\n", "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n", "8. Energy Tax and ODE +/- 30%" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def setup():\n", " _c = Config()\n", " s = setup_model(c=_c)\n", " return s\n", "\n", "def routine(c, s):\n", " s = load_data(c=c, s=s)\n", " s = create_and_assign_assets(c=c, s=s)\n", " #s = preprocessing(c=c, s=s)\n", " s = run_optimisation(c=c, s=s)\n", " #s = postprocessing(c=c, s=s)\n", " s = collect_cashflows(c=c, s=s)\n", " s = calculate_financials(c=c, s=s)\n", " return s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%time _s = setup()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%time result = routine(c, _s)\n", "npv = result.hpcase.npv\n", "npv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# sensitivity:Storage temperature\n", "values = range(80, 120, 10)\n", "param = 'storage_temperature'\n", "kpis = ['npv','irr','spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: Storage volume',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Water storage volume" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time _s = setup()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(100, 500, 100)\n", "param = 'storage_volume'\n", "kpis = ['npv','irr','spp']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[case.name for case in s.cases][1:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: Storage volume',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='irr in %',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: IRR',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='COP',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: COP',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: CAPEX per MW storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(4_000, 10_000, 2_000)\n", "param = 'storage_capex_per_MW'\n", "kpis = ['npv', 'irr', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX',\n", " yTitle='NPV in Euro',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: NPV in euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MW',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: CAPEX per MWh storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(1_000, 5_000, 1000)\n", "param = 'storage_capex_per_MWh'\n", "kpis = ['npv', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MWh',\n", " yTitle='NPV',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: NPV in Euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MWh',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: Payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: threshold price" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(10, 60, 10)\n", "param = 'threshold'\n", "kpis = ['npv', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='NPV',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: NPV in Euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: SPP in years',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='COP',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: SOP',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Heat Pump CAPEX" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3, 2]\n", "for value in values:\n", " _c = Config()\n", " _c.hp_capex *= value\n", " configs[value*100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "\n", "sens_capex = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX factor (%)',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Heat Pump CAPEX (€)',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_capex" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: CO2 prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "mean = 24.86\n", "co2_prices = [10, 25, 50, 100]\n", "\n", "for price in co2_prices:\n", " _c = Config()\n", " multiplier = price / mean\n", " _c.co2_price_multiplier = multiplier\n", " configs[price] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='EBITDA in €',\n", " yrange=[output.min().min() * 1.1, 0],\n", " title='Sensitivity: CO2 prices > EBITDA',\n", " colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_co2_spp = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='Payback Period in Years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: CO2 prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_co2_spp" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min() * 10, output.max().max()*1.1],\n", " title='Sensitivity: CO2 prices > NPV',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Gas prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.gas_price_multiplier = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Gas price factor (%)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.5, output.max().max()*1.1],\n", " title='Sensitivity: Gas prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Day Ahead buying amount" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0, 0.5, 1]\n", "for value in values:\n", " _c = Config()\n", " _c.day_ahead_buying_perc = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Volume in %',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Volume bought on Day-Ahead market in %',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Electricity prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.e_price_multiplier = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_eprices = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Electricity price factor in %',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Electricity prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_eprices" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Electricity price volatility" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.e_price_volatility_multiplier = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_evol = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Volatility factor',\n", " yTitle='Payback period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: E-price volatility',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_evol" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: aFRR capacity fee" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [10_000, 25_000, 50_000]\n", "for value in values:\n", " _c = Config()\n", " _c.afrr_capacity_fee = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_affr_fee = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='aFRR capacity fee in €/MW',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: aFRR capacity fee',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_affr_fee" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Energy tax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.energy_tax_multiplier = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Factor',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Energy taxes',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Tsource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [-25, -10, 0, 10, 25]\n", "for value in values:\n", " _c = Config()\n", " _c.tsource_delta = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.loc[-25, 'Heatpump only'] = np.nan\n", "sens_tsource = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsource delta',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsource',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsource delta',\n", " yTitle='COP',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsource > Mean COP',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [-25, -10, 0, 10, 25]\n", "for value in values:\n", " _c = Config()\n", " _c.tsink_delta = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_tsink = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsink delta',\n", " yTitle='Payback Period in Years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsink',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_tsink_cop = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsink delta',\n", " yTitle='COP',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsink > Mean COP',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsink_cop" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Time period" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def routine2(c, s):\n", " s = setup_model(c=c)\n", " s = load_data(c=c, s=s)\n", " s = create_and_assign_assets(c=c, s=s)\n", " s = run_optimisation(c=c, s=s)\n", " s = collect_cashflows(c=c, s=s)\n", " s = calculate_financials(c=c, s=s)\n", " return s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "configs = {}\n", "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n", "\n", "for value in start_values:\n", " _c = Config()\n", " _c.start = value\n", " _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine2, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "result.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Start date',\n", " yTitle='NPV in €',\n", " yrange=[0, result.max().max()*1.1],\n", " title='Sensitivity: Modelled time period',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Report" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "renderer = 'svg'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# __Presentation Flexible Heatpumps__\n", "\n", "ENCORE meeting: 17-12-2020\n", "Mark Kremer\n", "\n", " \n", " \n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## __Central question:__\n", "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Flexible operations on imbalance market__\n", "\n", "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n", " title='Day-Ahead prices on arbitrary day in €/MWh',\n", " yrange=[-100, 200],\n", " colors=recoygreen, \n", " yTitle='Price in €/MWh',\n", " xTitle='Time of Day',\n", " dimensions=(800, 400),\n", " asFigure=True\n", ")\n", "\n", "fig_example_1.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n", " title='Imbalance Prices on arbitrary day in €/MWh',\n", " colors=[recoygreen, recoydarkblue], \n", " yTitle='Price in €/MWh',\n", " xTitle='Time of Day',\n", " dimensions=(800, 400),\n", " asFigure=True\n", ")\n", "\n", "fig_example_2.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to benefit from these fluctiations, if you have __flexibility__\n", "* Storage options\n", "* Hybrid installations (e.g. with gas-powered assets)\n", "\n", "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Simulations & mathematical modelling__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* To answer the central question, we have build a simulation model\n", "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n", "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n", "* We are taking into account all major investment and operating costs, including:\n", " * Asset CAPEX\n", " * Commodity costs for gas and electricity\n", " * Energy taxes\n", " * Grid transport costs\n", " * SDE++ subsidies\n", " * Maintenance costs\n", " * CO2 allowances\n", "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Casestudies__\n", "5 main scenario's" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "1. Steamboiler only (baseline, baseload)\n", "2. Heatpump only (stand-alone, baseload, without SDE++)\n", "3. Heatpump + SDE\n", "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n", "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n", "\n", "Besides that, we modelled 11 sensitivities:\n", "* Heatpump CAPEX\n", "* Gas & CO2 prices\n", "* Electricity prices & volatility\n", "* Energy taxes\n", "* Bidding strategies\n", "* Source temperatures (affecting COP)\n", "* Sink temperatures (affecting COP)\n", "* Time period (2018, 2019, 2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Smurfit Kappa case__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is based on the context of Smurfit Kappa (paper factory)\n", "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n", "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n", "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n", "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_demands_over_time.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Source temperatures of around 65 degrees C\n", "* Sink temperatures of 125-170 degrees C \n", "* Average Temperature lift of about 85 degrees C" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "demands_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Heat pump__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def cop_curve(Tsink, Tsource):\n", " Tsink += 273\n", " Tsource += 273\n", "\n", " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", " \n", " return Polynomial([c2, c1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceT = 63\n", "sinkT = 140\n", "cop_curve(sourceT, sinkT)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_cop_curve.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Optimisation__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* At each moment in time, we calculate the cheapest option to produce the required heat. \n", "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n", "* Taking into account fluctuating market prices (electricity, gas, CO2)\n", "* We are predicting real-time electricity prices using our forecasting models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Some example days:__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Steamboiler only is following demand pattern" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_steamboiler.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar pattern for heatpump only case" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_heatpump.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_optcase.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_optcase2.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Business case__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "capex_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n", "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ebitda_graph.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "spt_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "production_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The heatpump is filling in 95-98 % of the demand. \n", "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n", "* Switch price is on average around 90€/MWh (excluding subsidies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Sensitivities__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If CAPEX is 200%, subsidy is needed to keep a good Payback Time" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_capex.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subsidies are protecting the business case againsts low CO2 prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_co2_spp.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n", "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n", "* When the temperature lift is higher, the COP will decrease and the business case will degrade" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsource.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsink.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsink_cop.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_eprices.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Conclusions__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The business case for a Heat Pump seems favourable\n", "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n", "* However, this only results in a marginal improvement of the business case\n", "* SDE++ has a very favourable effect on the business case, but is not critical\n", "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case." ] } ], "metadata": { "interpreter": { "hash": "fea6e7ab4c0ed1d184e153838ac4b2d24a0985a5f17e99f4f437a114b66796b8" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" }, "toc-autonumbering": false, "toc-showmarkdowntxt": false, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }