{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Imports" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n" ] } ], "source": [ "from datetime import timedelta, datetime\n", "import json\n", "import pprint\n", "from copy import deepcopy\n", "import pytz\n", "\n", "import cufflinks\n", "cufflinks.go_offline()\n", "import numpy as np\n", "from numpy.polynomial import Polynomial\n", "import pandas as pd\n", "from tqdm.notebook import tqdm\n", "\n", "import sys\n", "\n", "import os\n", " \n", "# Path to the folder containing the alternate version of PyRecoy\n", "\n", "alternate_pyrecoy_path = 'C:\\\\Users\\\\z004yn8c\\\\OneDrive - Siemens AG\\\\Documents\\\\GitHub\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n", " \n", "# Add the path to sys.path\n", "\n", "if alternate_pyrecoy_path not in sys.path:\n", "\n", " sys.path.insert(0, alternate_pyrecoy_path)\n", " \n", "# Now import PyRecoy\n", "\n", "import pyrecoy2\n", "\n", "# Check the version or path to confirm\n", "\n", "print(pyrecoy2.__file__)\n", "\n", "\n", "from pyrecoy2.assets_kickstart import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n", "from pyrecoy2.colors import *\n", "from pyrecoy2.converters import *\n", "from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n", "from pyrecoy2.framework import TimeFramework\n", "from pyrecoy2.casestudy import CaseStudy\n", "from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n", "from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n", "from pyrecoy2.sensitivity import SensitivityAnalysis\n", "from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n", "from pyrecoy2.forecasts import Mipf, Forecast\n", "\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Development backlog" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "* aFRR (can improve the optimisation case)\n", "* Report\n", "* COP seems to have fix values, rather than COP curve" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Meeting Notes" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "##### Meeting 25-11-2020" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "* aFRR can help optimisation case\n", "* SDE++ should be included\n", "* Tsource sensitivity really gives interesting insights\n", "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n", "* AP TNO: Update/verify COP curve\n", "* AP TNO: Update CAPEX\n", "* AP Mark: \n", " * Create graphs on COP curve with different Tsource, Tsink\n", " * Generate table and output in .csv (send it to Andrew)\n", "* Investigate opportunity to lower the COP and negative electricity prices\n", " * Technically feasible, but not really needed/possible to do it in this project\n", "* Could be interesting to run this model on a usecase with higher delta T\n", "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "# Kickstart: Heatpump Optimisation Framework\n", "\n", "***\n", "© Shahla Huseynova \n", "July 2024\n", "##### Cases\n", "In this model, 4 cases are compared:\n", "* **Baseline** : All heat is supplied by steam turbine\n", "* **Heatpump case** : All heat is supplied by heatpump\n", "* **Hybrid case** : Steam boiler and heatpump run in hybrid mode, and are optimised on costs in real-time\n", "* **Heat pump & storage cases**: Heat pump only case is compared with different setup of storage +heat pump cases" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "#### Loading config" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "class Config():\n", " start = '2023-01-01'\n", " end = '2023-12-31'\n", " # year = '2019'\n", " # startdate = year + '-01-01'\n", " # enddate = str(year) + '-12-31'\n", " # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n", " # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n", " # start = start.astimezone(pytz.UTC)\n", " # end = end.astimezone(pytz.UTC)\n", " \n", " hp_vdg_e_power = 1.5 # MW\n", " # hp_ndg_e_power = 7.7 # MW\n", " hp_min_load = 0\n", " hp_lifetime = 25\n", " hp_capex = 1_152_000 # EUR/MWth\n", " hp_opex = 0.04 # in % of CAPEX\n", " hp_devex = 0.005 # in % of CAPEX\n", " \n", " gb_power = 5 # MW\n", " gb_efficiency = 0.9\n", " \n", " storage_power = 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 = 0\n", " storage_capex_per_MWh = 100_000\n", " storage_opex_perc_of_capex = 0.03\n", " # storage_initial_level = 5\n", " threshold = 20\n", " \n", " tax_bracket_g = 4 \n", " tax_bracket_e = 4\n", " \n", " include_transport_costs = False\n", " grid_operator = 'Liander'\n", " connection_type = 'TS/MS'\n", " \n", " discount_rate = 0.1\n", " project_duration = 12\n", "\n", " forecast = 'ForeNeg'\n", " gas_price_multiplier = 1\n", " e_price_multiplier = 1\n", " e_price_volatility_multiplier = 1\n", " co2_price_multiplier = 1\n", " tsource_delta = 0\n", " tsink_delta = 0\n", " energy_tax_multiplier = 1\n", " \n", " # Review the SDE implementation\n", " sde_base_amount = 81\n", " longterm_gas_price = 24.00\n", " longterm_co2_price = 37.90\n", " sde_switch_price_correction = 40\n", " \n", " day_ahead_buying_perc = 0.3\n", " \n", " afrr_capacity_fee = 25_000\n", "\n", "c = Config()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "class Store():\n", " pass" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Model set-up" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Define the file paths\n", "excel_file_path = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed3.xlsx'\n", "output_directory = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\data'\n", "output_file_name = 'smurfit_demand_preprocessed3.csv'\n", "csv_file_path = os.path.join(output_directory, output_file_name)\n", "\n", "df = pd.read_excel(excel_file_path)\n", "# df = df.fillna(0) \n", "\n", "# # Save the DataFrame to CSV\n", "df.to_csv(csv_file_path, index=False)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def load_demand_data(c, s):\n", " demand = pd.read_csv('data/smurfit_demand_preprocessed3.csv', delimiter=',', decimal='.')\n", " dt_index = pd.date_range(\n", " start=s.time_fw.start,\n", " end=s.time_fw.start + timedelta(days=365), \n", " freq='1T',\n", " tz='Europe/Amsterdam')\n", " dt_index = dt_index[:len(demand)]\n", " demand.index = dt_index\n", " demand['Total demand'] = demand['MW (VDG)']\n", " \n", " demand = demand[c.start:c.end]\n", " return demand" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "s = Store()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testetss\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n", "\n", "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_29704\\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": 19, "metadata": {}, "outputs": [], "source": [ "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) Total demand\n", "2023-01-01 00:00:00+01:00 70 144 1 1\n", "2023-01-01 00:01:00+01:00 70 144 1 1\n", "2023-01-01 00:02:00+01:00 70 144 1 1\n", "2023-01-01 00:03:00+01:00 70 144 1 1\n", "2023-01-01 00:04:00+01:00 70 144 1 1\n", "... ... ... ... ...\n", "2023-12-31 23:55:00+01:00 70 144 1 1\n", "2023-12-31 23:56:00+01:00 70 144 1 1\n", "2023-12-31 23:57:00+01:00 70 144 1 1\n", "2023-12-31 23:58:00+01:00 70 144 1 1\n", "2023-12-31 23:59:00+01:00 70 144 1 1\n", "\n", "[525600 rows x 4 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.demand" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Load in data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def increase_volatility_by_factor(col, factor):\n", " mean = col.mean()\n", " diff_to_mean = col - mean\n", " new_diff = diff_to_mean * factor\n", " return mean + new_diff\n", "\n", "def multiply_by_factor(col, factor):\n", " mean = col.mean()\n", " diff_to_mean = col - mean\n", " \n", " cond = diff_to_mean > 0\n", " diff_to_mean[cond] *= factor\n", " diff_to_mean[~cond] /= factor\n", " return mean + diff_to_mean" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] } ], "source": [ "def load_data(c, s):\n", " \n", " for case in s.cases:\n", " case.add_gasprices()\n", " case.add_co2prices(perMWh=True)\n", " \n", " case.data['Gas prices (€/MWh)'] *= c.gas_price_multiplier\n", " case.data['CO2 prices (€/MWh)'] *= c.co2_price_multiplier\n", " case.data['CO2 prices (€/ton)'] *= c.co2_price_multiplier\n", " \n", " for case in s.optcases:\n", " case.data['NEG'] = multiply_by_factor(case.data['NEG'], c.e_price_multiplier)\n", " case.data['ForeNeg'] = multiply_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n", " case.data['DAM'] = multiply_by_factor(case.data['DAM'], c.e_price_multiplier)\n", " case.data['DAM_avg'] = case.data.groupby(case.data.index.date)['DAM'].transform('mean')\n", " \n", " case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n", " case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n", "\n", "\n", " s.demand['Tsource (VDG)'] += c.tsource_delta\n", " s.demand['Tsink (VDG)'] += c.tsink_delta\n", " for case in s.cases:\n", " case.data = pd.concat([case.data, s.demand], axis=1) \n", "\n", " s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n", " s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n", " s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n", " s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n", " return s\n", "\n", "s = load_data(c, s)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "75.08224016788466" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['CO2 prices (€/ton)'].mean()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:02:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:03:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40 NaN NaN \n", "... ... ... ... ... ... \n", "2023-12-31 23:55:00+01:00 3.17 -6.81 -6.81 -7.10 -11.16 \n", "2023-12-31 23:56:00+01:00 3.17 -6.81 -6.81 -5.21 -8.12 \n", "2023-12-31 23:57:00+01:00 3.17 -6.81 -6.81 -5.61 -7.59 \n", "2023-12-31 23:58:00+01:00 3.17 -6.81 -6.81 -5.74 -7.87 \n", "2023-12-31 23:59:00+01:00 3.17 -6.81 -6.81 -5.70 -7.27 \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-01-01 00:00:00+01:00 63.998 5.23 \n", "2023-01-01 00:01:00+01:00 63.998 5.23 \n", "2023-01-01 00:02:00+01:00 63.998 5.23 \n", "2023-01-01 00:03:00+01:00 63.998 5.23 \n", "2023-01-01 00:04:00+01:00 63.998 5.23 \n", "... ... ... \n", "2023-12-31 23:55:00+01:00 29.927 77.25 \n", "2023-12-31 23:56:00+01:00 29.927 77.25 \n", "2023-12-31 23:57:00+01:00 29.927 77.25 \n", "2023-12-31 23:58:00+01:00 29.927 77.25 \n", "2023-12-31 23:59:00+01:00 NaN NaN \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) \\\n", "2023-01-01 00:00:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:01:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:02:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:03:00+01:00 1.01 18.144167 70 \n", "2023-01-01 00:04:00+01:00 1.01 18.144167 70 \n", "... ... ... ... \n", "2023-12-31 23:55:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:56:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:57:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:58:00+01:00 14.90 14.683333 70 \n", "2023-12-31 23:59:00+01:00 NaN 14.683333 70 \n", "\n", " Tsink (VDG) MW (VDG) Total demand \n", "2023-01-01 00:00:00+01:00 144 1 1 \n", "2023-01-01 00:01:00+01:00 144 1 1 \n", "2023-01-01 00:02:00+01:00 144 1 1 \n", "2023-01-01 00:03:00+01:00 144 1 1 \n", "2023-01-01 00:04:00+01:00 144 1 1 \n", "... ... ... ... \n", "2023-12-31 23:55:00+01:00 144 1 1 \n", "2023-12-31 23:56:00+01:00 144 1 1 \n", "2023-12-31 23:57:00+01:00 144 1 1 \n", "2023-12-31 23:58:00+01:00 144 1 1 \n", "2023-12-31 23:59:00+01:00 144 1 1 \n", "\n", "[525600 rows x 13 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Assets" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### COP curve" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def cop_curve_new(Tsink, Tsource):\n", " Tsink += 273\n", " Tsource += 273\n", " Tlift = Tsink - Tsource\n", "\n", " c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n", " c1 = 6.7058 \n", " c2 = -1.79\n", " \n", " return Polynomial([c0, c1, c2])" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "text/latex": [ "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$" ], "text/plain": [ "Polynomial([19.2496754, 6.7058 , -1.79 ], domain=[-1, 1], window=[-1, 1], symbol='x')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sourceT = 63 + 273\n", "sinkT = 140 + 273\n", "cop_curve_new(sourceT, sinkT)" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Create and assign assets" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1152000\n" ] } ], "source": [ "def create_and_assign_assets(c, s):\n", " heatpump_vdg = Heatpump(\n", " name='Heatpump VDG',\n", " max_th_power=c.hp_vdg_e_power,\n", " min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n", " cop_curve=2.9\n", " )\n", "\n", " heatpump_pcm = Heatpump(\n", " name='Heatpump PCM',\n", " max_th_power=c.hp_vdg_e_power,\n", " min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n", " cop_curve=2.9\n", " )\n", " \n", " capex_vdg = c.hp_capex * 1\n", " heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n", "\n", " print(capex_vdg)\n", " capex_pcm = c.hp_capex * heatpump_vdg.max_th_power\n", " heatpump_pcm.set_financials(capex=capex_pcm, opex=c.hp_opex*capex_pcm, devex=c.hp_devex*capex_pcm, lifetime=25)\n", " # print(capex_pcm)\n", "\n", " gasboiler = GasBoiler(\n", " name='Gasboiler',\n", " max_th_output=c.gb_power,\n", " efficiency=c.gb_efficiency\n", " )\n", " gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n", " \n", " waterstorage = HotWaterStorage(\n", " name='HotWaterStorage',\n", " rated_power=c.storage_power, \n", " # capacity_per_volume=None,\n", " # volume=None, \n", " temperature=c.storage_temperature,\n", " min_storagelevel=c.storage_min_level,\n", " # initial_storagelevel=c.storage_initial_level\n", " )\n", " capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n", " opex_ws = c.storage_opex_perc_of_capex * capex_ws\n", " waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n", " \n", " s.baseline.add_asset(gasboiler)\n", " s.hpcase.add_asset(heatpump_vdg)\n", " s.storage_case_PCM.add_asset(heatpump_pcm)\n", " s.storage_case_PCM.add_asset(waterstorage)\n", " # s.storage_case_PCM.add_asset(heatpump_vdg)\n", " return s\n", "\n", "s = create_and_assign_assets(c, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "300000" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "capex_ws = c.storage_capex_per_MW * 1 + c.storage_capex_per_MWh * 3\n", "capex_ws" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)" ] }, "execution_count": 33, "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": 36, "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": 37, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " demand_vdg = case.data['MW (VDG)'].to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", "\n", " hp_vdg_input = [0] * len(case.data)\n", " hp_vdg_output = [0] * len(case.data)\n", "\n", " minutes = iter(range(len(case.data)))\n", " for m in minutes:\n", " demand = demand_vdg[m]\n", " if demand != 0:\n", " hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n", " heat_output=demand,\n", " Tsink=Tsink_vdg[m],\n", " Tsource=Tsource_vdg[m]\n", " )\n", "\n", " case.data['hp_output_MW'] = np.array(hp_vdg_output)\n", " case.data['hp_input_MW'] = np.array(hp_vdg_input)\n", " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", " \n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(5)\n", " return case" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n", " \n", " from_storage_vdg_MW = 0\n", " to_storage_vdg_MW = 0 \n", " \n", " if neg < dam - c.threshold:\n", " # overproduce\n", " if demand_vdg != 0:\n", " storage_charge_power = ws.charging_power_limit\n", " if ws.chargelevel == ws.max_chargelevel:\n", " storage_charge_power = 0\n", " else:\n", " # Adjust charge power if charge level is not at max\n", " storage_charge_power = min(ws.charging_power_limit, (ws.max_chargelevel - ws.chargelevel) * 60)\n", "\n", " desired_hp_load_vdg = min(demand_vdg + storage_charge_power, hp_vdg.max_th_power)\n", " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg, cop = 2.5)\n", "\n", " to_storage_vdg_MW = desired_hp_load_vdg - demand_vdg\n", " to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 5))\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_vdg, th_load_vdg = (0,0)\n", " \n", " \n", "\n", " elif pos > dam + c.threshold:\n", " # take from storage\n", " if demand_vdg != 0:\n", " from_storage_vdg_MW = demand_vdg\n", " from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 5))\n", " # from_storage_vdg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n", " desired_hp_load_vdg = min(demand_vdg - from_storage_vdg_MW, hp_vdg.max_th_power)\n", " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg, cop = 2.9)\n", " else:\n", " e_load_vdg, th_load_vdg = (0,0)\n", " \n", " #print({f'from_storage_vdg_MW='})\n", " else:\n", " e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n", " heat_output=demand_vdg,\n", " Tsink=tsink_vdg,\n", " Tsource=tsource_vdg,\n", " cop = 2.9\n", " )\n", "\n", " return e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg\n", "# Neg-take price, Pos-feed price" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hponly_with_storage(case):\n", " hp_vdg = case.assets['Heatpump PCM']\n", " ws = case.assets['HotWaterStorage']\n", " demands_vdg = case.data['MW (VDG)'].to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", " dam_prices = case.data['DAM_avg'].to_list()\n", " pos_prices = case.data['POS'].to_list()\n", " neg_prices = case.data['NEG'].to_list()\n", "\n", " hp_vdg_input = [0] * len(case.data)\n", " hp_vdg_output = [0] * len(case.data)\n", "\n", " minutes = len(case.data)\n", " storage_levels = [None] * minutes\n", " to_storage_list = [0] * minutes\n", " from_storage_vdg_list = [0] * minutes\n", " to_storage_vdg_list = [0] * minutes\n", "\n", " new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n", "\n", " \n", " ws.set_chargelevel(ws.min_chargelevel)\n", " \n", " for m in range(minutes):\n", " tsource_vdg = Tsource_vdg[m]\n", " tsink_vdg = Tsink_vdg[m]\n", " demand_vdg = demands_vdg[m]\n", " \n", " e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg = hp_storage_opt_enginge(\n", " c, ws, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n", " )\n", " \n", " hp_vdg_input[m] = e_load_vdg\n", " hp_vdg_output[m] = th_load_vdg\n", " \n", " storage_levels[m] = ws.chargelevel\n", " new_tsources_vdg[m] = tsource_vdg\n", " to_storage_vdg_list[m] = to_storage_vdg_MW\n", " from_storage_vdg_list[m] = from_storage_vdg_MW\n", " \n", " case.data['hp_output_MW'] = np.array(hp_vdg_output)\n", " case.data['hp_input_MW'] = np.array(hp_vdg_input)\n", " case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n", " case.data['tsource_vdg'] = new_tsources_vdg\n", " case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n", " case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n", " case.data['to_storage_MW'] = case.data['to_storage_vdg_MW']\n", " case.data['from_storage_MW'] = case.data['from_storage_vdg_MW']\n", " case.data['storage_level_MWh'] = storage_levels\n", " \n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(5)\n", " return case\n" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "146760\n", "73389\n" ] } ], "source": [ "# charge_times = print(sum(s.storage_case_PCM.data['to_storage_MW']!= 0))\n", "# discharge_times = print(sum(s.storage_case_PCM.data['from_storage_MW']!= 0))\n", "# 154476 times out of 525600, the storage is used to charge/dischage\n", "# from 39 000 to 110 000 discharge time increases\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data['cop'].fillna(2.5, inplace = True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGForeNegForePosGas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)DAM_avgTsource (VDG)...from_storage_vdg_MWto_storage_MWfrom_storage_MWstorage_level_MWhhp_output_MWhhp_input_MWhto_storage_vdg_MWhfrom_storage_vdg_MWhto_storage_MWhfrom_storage_MWh
2023-10-11 01:26:00+02:0024.6123.06123.06129.42128.9438.90381.115.6464.5608370...1.00.01.01.6750.000-0.000.000000.016670.000000.01667
2023-06-02 01:08:00+02:0079.451.0951.0951.8446.2425.78579.215.2778.9137570...0.00.50.00.2000.025-0.010.008330.000000.008330.00000
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2 rows × 28 columns

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" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-10-11 01:26:00+02:00 24.6 123.06 123.06 129.42 128.94 \n", "2023-06-02 01:08:00+02:00 79.4 51.09 51.09 51.84 46.24 \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-10-11 01:26:00+02:00 38.903 81.1 \n", "2023-06-02 01:08:00+02:00 25.785 79.2 \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) ... \\\n", "2023-10-11 01:26:00+02:00 15.64 64.56083 70 ... \n", "2023-06-02 01:08:00+02:00 15.27 78.91375 70 ... \n", "\n", " from_storage_vdg_MW to_storage_MW \\\n", "2023-10-11 01:26:00+02:00 1.0 0.0 \n", "2023-06-02 01:08:00+02:00 0.0 0.5 \n", "\n", " from_storage_MW storage_level_MWh hp_output_MWh \\\n", "2023-10-11 01:26:00+02:00 1.0 1.675 0.000 \n", "2023-06-02 01:08:00+02:00 0.0 0.200 0.025 \n", "\n", " hp_input_MWh to_storage_vdg_MWh \\\n", "2023-10-11 01:26:00+02:00 -0.00 0.00000 \n", "2023-06-02 01:08:00+02:00 -0.01 0.00833 \n", "\n", " from_storage_vdg_MWh to_storage_MWh \\\n", "2023-10-11 01:26:00+02:00 0.01667 0.00000 \n", "2023-06-02 01:08:00+02:00 0.00000 0.00833 \n", "\n", " from_storage_MWh \n", "2023-10-11 01:26:00+02:00 0.01667 \n", "2023-06-02 01:08:00+02:00 0.00000 \n", "\n", "[2 rows x 28 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data.sample(2)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1222.5108" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data['to_storage_MWh'].sum()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data.to_csv('storage_data.csv')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "s.hpcase.data.to_csv('storage_data_hp.csv')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGhp_output_MWto_storage_MWTotal demandcop
count525600.000000525600.000000525600.000000525600.000000525600.000000525600.0452694.000000
mean95.81731393.568163103.4389971.0003000.1396121.02.708758
std49.046697194.977533195.6965510.4571220.2243090.00.199808
min-500.000000-1549.470000-1549.4700000.0000000.0000001.02.500000
25%76.93500047.00000057.9675001.0000000.0000001.02.500000
50%99.21500084.13000088.5700001.0000000.0000001.02.900000
75%122.400000114.020000119.5925001.5000000.5000001.02.900000
max463.7700002037.7400002037.7400001.5000000.5000001.02.900000
\n", "
" ], "text/plain": [ " DAM POS NEG hp_output_MW \\\n", "count 525600.000000 525600.000000 525600.000000 525600.000000 \n", "mean 95.817313 93.568163 103.438997 1.000300 \n", "std 49.046697 194.977533 195.696551 0.457122 \n", "min -500.000000 -1549.470000 -1549.470000 0.000000 \n", "25% 76.935000 47.000000 57.967500 1.000000 \n", "50% 99.215000 84.130000 88.570000 1.000000 \n", "75% 122.400000 114.020000 119.592500 1.500000 \n", "max 463.770000 2037.740000 2037.740000 1.500000 \n", "\n", " to_storage_MW Total demand cop \n", "count 525600.000000 525600.0 452694.000000 \n", "mean 0.139612 1.0 2.708758 \n", "std 0.224309 0.0 0.199808 \n", "min 0.000000 1.0 2.500000 \n", "25% 0.000000 1.0 2.500000 \n", "50% 0.000000 1.0 2.900000 \n", "75% 0.500000 1.0 2.900000 \n", "max 0.500000 1.0 2.900000 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand', 'cop']].describe()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGhp_output_MWto_storage_MWfrom_storage_MWTotal demand
2023-09-30 22:17:00+02:00107.40103.17103.171.00.00.01
2023-09-24 00:07:00+02:0098.7185.02100.211.00.00.01
2023-08-12 09:32:00+02:0088.50127.17127.171.00.00.01
2023-11-08 23:12:00+01:00101.6578.9278.921.00.00.01
2023-09-07 01:19:00+02:0091.3772.9872.981.50.50.01
2023-12-26 04:37:00+01:00-3.80-38.00-38.001.50.50.01
2023-08-10 02:20:00+02:0089.1591.2391.231.00.00.01
2023-11-27 03:24:00+01:0088.82-43.19-43.191.00.00.01
2023-11-11 20:01:00+01:00113.5164.6864.681.00.00.01
2023-03-23 01:08:00+01:0065.93132.60132.600.00.01.01
2023-06-05 22:32:00+02:00101.0269.9369.931.00.00.01
2023-05-11 11:09:00+02:00115.0394.3194.311.00.00.01
2023-09-07 19:25:00+02:00167.27164.49164.490.00.01.01
2023-10-22 10:15:00+02:0051.9419.9819.981.50.50.01
2023-04-21 07:37:00+02:00132.6136.3236.321.50.50.01
2023-09-18 20:47:00+02:00125.65-28.32-28.321.50.50.01
2023-08-21 16:33:00+02:0096.17102.14102.141.00.00.01
2023-03-17 09:53:00+01:00103.90-46.93-46.931.50.50.01
2023-03-16 21:10:00+01:00101.8035.15113.391.00.00.01
2023-04-01 13:37:00+02:0054.90-13.64-13.641.50.50.01
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" ], "text/plain": [ " DAM POS NEG hp_output_MW \\\n", "2023-09-30 22:17:00+02:00 107.40 103.17 103.17 1.0 \n", "2023-09-24 00:07:00+02:00 98.71 85.02 100.21 1.0 \n", "2023-08-12 09:32:00+02:00 88.50 127.17 127.17 1.0 \n", "2023-11-08 23:12:00+01:00 101.65 78.92 78.92 1.0 \n", "2023-09-07 01:19:00+02:00 91.37 72.98 72.98 1.5 \n", "2023-12-26 04:37:00+01:00 -3.80 -38.00 -38.00 1.5 \n", "2023-08-10 02:20:00+02:00 89.15 91.23 91.23 1.0 \n", "2023-11-27 03:24:00+01:00 88.82 -43.19 -43.19 1.0 \n", "2023-11-11 20:01:00+01:00 113.51 64.68 64.68 1.0 \n", "2023-03-23 01:08:00+01:00 65.93 132.60 132.60 0.0 \n", "2023-06-05 22:32:00+02:00 101.02 69.93 69.93 1.0 \n", "2023-05-11 11:09:00+02:00 115.03 94.31 94.31 1.0 \n", "2023-09-07 19:25:00+02:00 167.27 164.49 164.49 0.0 \n", "2023-10-22 10:15:00+02:00 51.94 19.98 19.98 1.5 \n", "2023-04-21 07:37:00+02:00 132.61 36.32 36.32 1.5 \n", "2023-09-18 20:47:00+02:00 125.65 -28.32 -28.32 1.5 \n", "2023-08-21 16:33:00+02:00 96.17 102.14 102.14 1.0 \n", "2023-03-17 09:53:00+01:00 103.90 -46.93 -46.93 1.5 \n", "2023-03-16 21:10:00+01:00 101.80 35.15 113.39 1.0 \n", "2023-04-01 13:37:00+02:00 54.90 -13.64 -13.64 1.5 \n", "\n", " to_storage_MW from_storage_MW Total demand \n", "2023-09-30 22:17:00+02:00 0.0 0.0 1 \n", "2023-09-24 00:07:00+02:00 0.0 0.0 1 \n", "2023-08-12 09:32:00+02:00 0.0 0.0 1 \n", "2023-11-08 23:12:00+01:00 0.0 0.0 1 \n", "2023-09-07 01:19:00+02:00 0.5 0.0 1 \n", "2023-12-26 04:37:00+01:00 0.5 0.0 1 \n", "2023-08-10 02:20:00+02:00 0.0 0.0 1 \n", "2023-11-27 03:24:00+01:00 0.0 0.0 1 \n", "2023-11-11 20:01:00+01:00 0.0 0.0 1 \n", "2023-03-23 01:08:00+01:00 0.0 1.0 1 \n", "2023-06-05 22:32:00+02:00 0.0 0.0 1 \n", "2023-05-11 11:09:00+02:00 0.0 0.0 1 \n", "2023-09-07 19:25:00+02:00 0.0 1.0 1 \n", "2023-10-22 10:15:00+02:00 0.5 0.0 1 \n", "2023-04-21 07:37:00+02:00 0.5 0.0 1 \n", "2023-09-18 20:47:00+02:00 0.5 0.0 1 \n", "2023-08-21 16:33:00+02:00 0.0 0.0 1 \n", "2023-03-17 09:53:00+01:00 0.5 0.0 1 \n", "2023-03-16 21:10:00+01:00 0.0 0.0 1 \n", "2023-04-01 13:37:00+02:00 0.5 0.0 1 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "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": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n", " dtype='object')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data.columns" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n", " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", " 'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n", "hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n", "storage_selection = s.storage_case_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": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGTotal demandhp_input_MWhp_output_MWcopto_storage_MWfrom_storage_MW
2023-02-25 16:33:00+01:0093.90-14.32210.371-0.344831.02.90.00.0
2023-10-15 11:59:00+02:00-1.01-34.08-34.081-0.600001.52.50.50.0
2023-05-02 04:20:00+02:0081.2279.8679.861-0.600001.52.50.50.0
2023-01-16 02:37:00+01:0062.72-10.62-10.621-0.400001.02.50.00.0
2023-12-02 19:29:00+01:00129.56101.58103.941-0.344831.02.90.00.0
2023-02-18 13:37:00+01:0083.01-13.66-13.661-0.400001.02.50.00.0
2023-02-18 07:34:00+01:0091.14-22.29-22.291-0.600001.52.50.50.0
2023-05-30 23:39:00+02:0089.6568.6168.611-0.344831.02.90.00.0
2023-10-25 05:21:00+02:00100.01136.07136.071-0.344831.02.90.00.0
2023-09-03 23:04:00+02:00101.1782.4582.451-0.344831.02.90.00.0
2023-02-22 12:35:00+01:00118.67128.45128.451-0.344831.02.90.00.0
2023-09-19 08:55:00+02:00109.70-31.39-31.391-0.600001.52.50.50.0
2023-07-25 05:44:00+02:0094.2182.5282.521-0.344831.02.90.00.0
2023-03-01 20:14:00+01:00153.84106.91145.861-0.344831.02.90.00.0
2023-03-21 14:58:00+01:0096.0973.3673.361-0.600001.52.50.50.0
2023-09-12 21:43:00+02:00136.6288.3888.381-0.600001.52.50.50.0
2023-06-30 03:06:00+02:0096.3471.5571.551-0.400001.02.50.00.0
2023-07-05 16:20:00+02:00-3.52-35.66-35.661-0.600001.52.50.50.0
2023-12-30 20:44:00+01:0062.01-8.81-8.811-0.600001.52.50.50.0
2023-04-09 04:17:00+02:00118.24108.87108.871-0.344831.02.90.00.0
\n", "
" ], "text/plain": [ " DAM POS NEG Total demand hp_input_MW \\\n", "2023-02-25 16:33:00+01:00 93.90 -14.32 210.37 1 -0.34483 \n", "2023-10-15 11:59:00+02:00 -1.01 -34.08 -34.08 1 -0.60000 \n", "2023-05-02 04:20:00+02:00 81.22 79.86 79.86 1 -0.60000 \n", "2023-01-16 02:37:00+01:00 62.72 -10.62 -10.62 1 -0.40000 \n", "2023-12-02 19:29:00+01:00 129.56 101.58 103.94 1 -0.34483 \n", "2023-02-18 13:37:00+01:00 83.01 -13.66 -13.66 1 -0.40000 \n", "2023-02-18 07:34:00+01:00 91.14 -22.29 -22.29 1 -0.60000 \n", "2023-05-30 23:39:00+02:00 89.65 68.61 68.61 1 -0.34483 \n", "2023-10-25 05:21:00+02:00 100.01 136.07 136.07 1 -0.34483 \n", "2023-09-03 23:04:00+02:00 101.17 82.45 82.45 1 -0.34483 \n", "2023-02-22 12:35:00+01:00 118.67 128.45 128.45 1 -0.34483 \n", "2023-09-19 08:55:00+02:00 109.70 -31.39 -31.39 1 -0.60000 \n", "2023-07-25 05:44:00+02:00 94.21 82.52 82.52 1 -0.34483 \n", "2023-03-01 20:14:00+01:00 153.84 106.91 145.86 1 -0.34483 \n", "2023-03-21 14:58:00+01:00 96.09 73.36 73.36 1 -0.60000 \n", "2023-09-12 21:43:00+02:00 136.62 88.38 88.38 1 -0.60000 \n", "2023-06-30 03:06:00+02:00 96.34 71.55 71.55 1 -0.40000 \n", "2023-07-05 16:20:00+02:00 -3.52 -35.66 -35.66 1 -0.60000 \n", "2023-12-30 20:44:00+01:00 62.01 -8.81 -8.81 1 -0.60000 \n", "2023-04-09 04:17:00+02:00 118.24 108.87 108.87 1 -0.34483 \n", "\n", " hp_output_MW cop to_storage_MW from_storage_MW \n", "2023-02-25 16:33:00+01:00 1.0 2.9 0.0 0.0 \n", "2023-10-15 11:59:00+02:00 1.5 2.5 0.5 0.0 \n", "2023-05-02 04:20:00+02:00 1.5 2.5 0.5 0.0 \n", "2023-01-16 02:37:00+01:00 1.0 2.5 0.0 0.0 \n", "2023-12-02 19:29:00+01:00 1.0 2.9 0.0 0.0 \n", "2023-02-18 13:37:00+01:00 1.0 2.5 0.0 0.0 \n", "2023-02-18 07:34:00+01:00 1.5 2.5 0.5 0.0 \n", "2023-05-30 23:39:00+02:00 1.0 2.9 0.0 0.0 \n", "2023-10-25 05:21:00+02:00 1.0 2.9 0.0 0.0 \n", "2023-09-03 23:04:00+02:00 1.0 2.9 0.0 0.0 \n", "2023-02-22 12:35:00+01:00 1.0 2.9 0.0 0.0 \n", "2023-09-19 08:55:00+02:00 1.5 2.5 0.5 0.0 \n", "2023-07-25 05:44:00+02:00 1.0 2.9 0.0 0.0 \n", "2023-03-01 20:14:00+01:00 1.0 2.9 0.0 0.0 \n", "2023-03-21 14:58:00+01:00 1.5 2.5 0.5 0.0 \n", "2023-09-12 21:43:00+02:00 1.5 2.5 0.5 0.0 \n", "2023-06-30 03:06:00+02:00 1.0 2.5 0.0 0.0 \n", "2023-07-05 16:20:00+02:00 1.5 2.5 0.5 0.0 \n", "2023-12-30 20:44:00+01:00 1.5 2.5 0.5 0.0 \n", "2023-04-09 04:17:00+02:00 1.0 2.9 0.0 0.0 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection.sample(20)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72906" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "none_count = selection['cop'].isnull().sum()\n", "none_count" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DAM 95.817313\n", "POS 93.568163\n", "NEG 103.438997\n", "Total demand 1.000000\n", "hp_input_MW -0.375354\n", "hp_output_MW 1.000300\n", "cop 2.708758\n", "to_storage_MW 0.139612\n", "from_storage_MW 0.139312\n", "dtype: float64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection.mean()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "########################\n", "\n", "# This is where we left\n", "\n", "########################" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n", " return (\n", " th_load / cop * electricity_cost\n", " + (demand - th_load) * alt_heat_price\n", " )" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def hybrid_imb_optimisation(case, decimals, s):\n", " gb = case.assets['Gasboiler']\n", " hp_vdg = case.assets['Heatpump VDG']\n", " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n", " fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n", " gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n", " co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n", " eb_ode_g = s.eb_ode_g\n", " eb_ode_e = s.eb_ode_e\n", " \n", " gb_input = [0] * len(case.data)\n", " gb_output = [0] * len(case.data)\n", "\n", " minutes = range(len(case.data))\n", " hp_output = [0] * len(case.data)\n", " hp_input = [0] * len(case.data)\n", "\n", " for m in tqdm(minutes):\n", " dem_vgd = demand_vdg[m]\n", " if dem_vgd != 0:\n", " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", " min_load = hp_vdg.min_th_power\n", " Tsink = Tsink_vdg[m]\n", " Tsource = Tsource_vdg[m]\n", " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", " \n", " cost_full_load = cost_function(\n", " th_load=max_load,\n", " cop=cop_max_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_vgd\n", " )\n", " \n", " cost_min_load = cost_function(\n", " th_load=min_load,\n", " cop=cop_min_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_vgd\n", " )\n", " \n", " if cost_full_load < cost_min_load:\n", " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", " else:\n", " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", " else:\n", " hp_vdg_input, hp_vdg_output = (0, 0)\n", "\n", "\n", " hp_out = hp_vdg_output\n", " hp_output[m] = hp_out\n", " hp_input[m] = hp_vdg_input\n", " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", "\n", " case.data['hp_output_MW'] = np.array(hp_output)\n", " case.data['hp_input_MW'] = np.array(hp_input)\n", " case.data['gb_output_MW'] = np.array(gb_output)\n", " case.data['gb_input_MW'] = np.array(gb_input)\n", "\n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(5)\n", " return case" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "aFRR\n", "* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n", "* Remaining demand is filed in by gasboiler\n", "* Bid price at switch price?\n", "* Assume direct response to 0% for now?\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def calc_afrr_capacity(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " \n", " capacity = 0\n", " for hp in hp_vdg:\n", " max_th_output = hp.max_th_power\n", " cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n", " e_power = max_th_output / cop\n", " capacity += e_power\n", " return capacity" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Run optimisation" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "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": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.899979700142095" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() " ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-181242.64800000025" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.34483" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-197286.05060999998" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data['hp_input_MW'].sum()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.6" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data['hp_input_MW'].min()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.664950199846266" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "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": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DAMPOSNEGForeNegForePosGas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)DAM_avgTsource (VDG)Tsink (VDG)MW (VDG)Total demandhp_output_MWhp_input_MWcophp_output_MWhhp_input_MWh
2023-01-01 00:00:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:01:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:02:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:03:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
2023-01-01 00:04:00+01:00-3.61-209.4-209.4NaNNaN63.9985.231.0118.1441770144111-0.344832.90.01667-0.00575
\n", "
" ], "text/plain": [ " DAM POS NEG ForeNeg ForePos \\\n", "2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n", "\n", " Gas prices (€/MWh) CO2 prices (€/ton) \\\n", "2023-01-01 00:00:00+01:00 63.998 5.23 \n", "2023-01-01 00:01:00+01:00 63.998 5.23 \n", "2023-01-01 00:02:00+01:00 63.998 5.23 \n", "2023-01-01 00:03:00+01:00 63.998 5.23 \n", "2023-01-01 00:04:00+01:00 63.998 5.23 \n", "\n", " CO2 prices (€/MWh) DAM_avg Tsource (VDG) \\\n", "2023-01-01 00:00:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:01:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:02:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:03:00+01:00 1.01 18.14417 70 \n", "2023-01-01 00:04:00+01:00 1.01 18.14417 70 \n", "\n", " Tsink (VDG) MW (VDG) Total demand hp_output_MW \\\n", "2023-01-01 00:00:00+01:00 144 1 1 1 \n", "2023-01-01 00:01:00+01:00 144 1 1 1 \n", "2023-01-01 00:02:00+01:00 144 1 1 1 \n", "2023-01-01 00:03:00+01:00 144 1 1 1 \n", "2023-01-01 00:04:00+01:00 144 1 1 1 \n", "\n", " hp_input_MW cop hp_output_MWh hp_input_MWh \n", "2023-01-01 00:00:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:01:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:02:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:03:00+01:00 -0.34483 2.9 0.01667 -0.00575 \n", "2023-01-01 00:04:00+01:00 -0.34483 2.9 0.01667 -0.00575 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.hpcase.data.head()" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72906" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data['cop'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 70, "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", " 'hp_input_MW'\n", "]]\n", "\n", "# cops = pd.concat([s.hpcase.data['cop'], s.storage_case_PCM.data['cop']], axis=1)\n", "# vis_data = pd.concat([vis_data, cops], axis=1)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "storage_level_MWh", "text": "", "type": "scatter", "x": [ "2023-03-01 00:00:00+01:00", "2023-03-01 00:01:00+01:00", "2023-03-01 00:02:00+01:00", "2023-03-01 00:03:00+01:00", "2023-03-01 00:04:00+01:00", "2023-03-01 00:05:00+01:00", "2023-03-01 00:06:00+01:00", "2023-03-01 00:07:00+01:00", "2023-03-01 00:08:00+01:00", "2023-03-01 00:09:00+01:00", "2023-03-01 00:10:00+01:00", "2023-03-01 00:11:00+01:00", "2023-03-01 00:12:00+01:00", "2023-03-01 00:13:00+01:00", "2023-03-01 00:14:00+01:00", "2023-03-01 00:15:00+01:00", "2023-03-01 00:16:00+01:00", "2023-03-01 00:17:00+01:00", "2023-03-01 00:18:00+01:00", "2023-03-01 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"outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n", " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", " 'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hp_output = s.storage_case_PCM.data['hp_output_MW'].round(2)\n", "demand_and_to_storage = 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Financials" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n", " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", " 'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh'],\n", " dtype='object')" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n" ] } ], "source": [ "def collect_cashflows(c, s):\n", " s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", " s.storage_case_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": 80, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] } ], "source": [ "def calculate_financials(c, s):\n", " for case in s.cases:\n", " case.calculate_ebitda(c.project_duration)\n", "\n", " for case in [s.hpcase, s.storage_case_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": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "146760\n", "73389\n" ] } ], "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": 82, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-197286.05061\n", "-181242.64800000002\n" ] } ], "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": 84, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_29704\\1396106630.py:1: FutureWarning:\n", "\n", "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", "\n", "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "rgba(70, 165, 121, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "Total demand", "text": "", "type": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-01 01:00:00+01:00", "2023-01-01 02:00:00+01:00", "2023-01-01 03:00:00+01:00", "2023-01-01 04:00:00+01:00", "2023-01-01 05:00:00+01:00", "2023-01-01 06:00:00+01:00", "2023-01-01 07:00:00+01:00", "2023-01-01 08:00:00+01:00", "2023-01-01 09:00:00+01:00", "2023-01-01 10:00:00+01:00", "2023-01-01 11:00:00+01:00", "2023-01-01 12:00:00+01:00", "2023-01-01 13:00:00+01:00", "2023-01-01 14:00:00+01:00", "2023-01-01 15:00:00+01:00", "2023-01-01 16:00:00+01:00", "2023-01-01 17:00:00+01:00", "2023-01-01 18:00:00+01:00", "2023-01-01 19:00:00+01:00", "2023-01-01 20:00:00+01:00", "2023-01-01 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].resample('H').mean().iplot(\n", " kind='box',\n", " title='Smurfit Kappa: Source and Sink temperatures',\n", " color=recoygreen,\n", " yTitle='Temperature in degrees C',\n", " legend=False,\n", " asFigure=True,\n", " dimensions=(800, 400)\n", ")\n", "demands_fig" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'DAM_avg', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n", " 'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n", " 'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n", " 'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n", " 'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n", " 'from_storage_MWh', 'nomination_MWh', 'Prod. vol.', 'Cons. vol.',\n", " 'Imb. vol.', 'Day-Ahead Result', 'POS Result', 'NEG Result',\n", " 'Imbalance Result', 'Combined Result'],\n", " dtype='object')" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data.columns" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "# temp_comparison = pd.concat([s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']], s.storage_case_PCM.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n", "# temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n", "# temp_comparison.resample('15T').mean().iplot()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.storage_case_PCM.data['tsource_vdg'].idxmin()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0,\n", " 0,\n", " 29.635,\n", " 0,\n", " 0,\n", " HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0),\n", " Heatpump(name='Heatpump PCM', max_thermal_power=1.5, cop_curve=2.9, min_th_power=0.0))" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "case = s.storage_case_PCM\n", "hp_vdg = case.assets['Heatpump PCM']\n", "ws = case.assets['HotWaterStorage']\n", "ws.storage_level = 47.083 + 25\n", "pos = 35.650\n", "neg = 43.850\n", "dam = 68.400\n", "demand_vdg = 0\n", "tsource_vdg = 29.635\n", "tsink_vdg = 134.840\n", "\n", "hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [], "source": [ "#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_after = s.storage_case_PCM.data['Tsource (VDG)'].round(2)\n", "Tsource_VDG_before.equals(Tsource_VDG_after)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "test_hp = Heatpump(\n", " name='Heatpump for Testing',\n", " max_th_power=1,\n", " min_th_power=0.3,\n", " cop_curve=2.5\n", ")" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "import plotly.graph_objs as go\n", "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n", " loads = []\n", " eprices = list(range(1000))\n", " for eprice in eprices:\n", " _, load = test_hp.set_opt_load(\n", " electricity_cost=eprice + s.eb_ode_e,\n", " alt_heat_price=gas_price / 0.9,\n", " demand=1, \n", " Tsink=Tsnk,\n", " Tsource=Tsrc\n", " )\n", " loads.append(load)\n", " trace = go.Scatter(x=eprices, y=loads, name=name)\n", " return trace" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "date = s.baseline.data.index[0].strftime('%Y-%m-%d')" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "rgba(14, 41, 59, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "Total demand", "text": "", "type": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-01 00:01:00+01:00", "2023-01-01 00:02:00+01:00", "2023-01-01 00:03:00+01:00", "2023-01-01 00:04:00+01:00", "2023-01-01 00:05:00+01:00", "2023-01-01 00:06:00+01:00", "2023-01-01 00:07:00+01:00", "2023-01-01 00:08:00+01:00", "2023-01-01 00:09:00+01:00", "2023-01-01 00:10:00+01:00", "2023-01-01 00:11:00+01:00", "2023-01-01 00:12:00+01:00", "2023-01-01 00:13:00+01:00", "2023-01-01 00:14:00+01:00", "2023-01-01 00:15:00+01:00", "2023-01-01 00:16:00+01:00", "2023-01-01 00:17:00+01:00", "2023-01-01 00:18:00+01:00", "2023-01-01 00:19:00+01:00", "2023-01-01 00:20:00+01:00", "2023-01-01 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Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "rgba(14, 41, 59, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "Total demand", "text": "", "type": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-01 00:01:00+01:00", "2023-01-01 00:02:00+01:00", "2023-01-01 00:03:00+01:00", "2023-01-01 00:04:00+01:00", "2023-01-01 00:05:00+01:00", "2023-01-01 00:06:00+01:00", "2023-01-01 00:07:00+01:00", "2023-01-01 00:08:00+01:00", "2023-01-01 00:09:00+01:00", "2023-01-01 00:10:00+01:00", "2023-01-01 00:11:00+01:00", "2023-01-01 00:12:00+01:00", "2023-01-01 00:13:00+01:00", "2023-01-01 00:14:00+01:00", "2023-01-01 00:15:00+01:00", "2023-01-01 00:16:00+01:00", "2023-01-01 00:17:00+01:00", "2023-01-01 00:18:00+01:00", "2023-01-01 00:19:00+01:00", "2023-01-01 00:20:00+01:00", "2023-01-01 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n", " subplots=True, \n", " title=f'Heatpump only case on {date}', \n", " subplot_titles=['Total demand', 'Heatpump output (MW)'],\n", " legend=False,\n", " dimensions=(800, 400),\n", " colors=[recoydarkblue, recoygreen], \n", " asFigure=True,\n", " shape = (2, 1)\n", ")\n", "fig_heatpump" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Heatpump onlyHeatpump + Storage_PCM
Total net volume (MWh)-3,022-3,289
Total exported to grid (MWh)--
Total consumed from grid (MWh)3,0223,289
Total nominated volume (MWh)-3,022-3,022
Absolute imbalance volume (MWh)-1,109
Mean selling price (€/MWh)--
Mean buying price (€/MWh)9657
Total day-ahead result (€)-289,579-289,579
Total POS result (€)-114,467
Total NEG result (€)--10,793
Total imbalance result (€)-103,675
Total combined result (€)-289,579-185,904
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" ], "text/plain": [ " Heatpump only Heatpump + Storage_PCM\n", "Total net volume (MWh) -3,022 -3,289\n", "Total exported to grid (MWh) - -\n", "Total consumed from grid (MWh) 3,022 3,289\n", "Total nominated volume (MWh) -3,022 -3,022\n", "Absolute imbalance volume (MWh) - 1,109\n", "Mean selling price (€/MWh) - -\n", "Mean buying price (€/MWh) 96 57\n", "Total day-ahead result (€) -289,579 -289,579\n", "Total POS result (€) - 114,467\n", "Total NEG result (€) - -10,793\n", "Total imbalance result (€) - 103,675\n", "Total combined result (€) -289,579 -185,904" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report = ComparisonReport(\n", " cases=s.optcases, \n", " kind='electr_market_results',\n", ")\n", "report.show()" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Heatpump onlyHeatpump + Storage_PCM
Gasboiler OPEX (€)--
Gas consumption costs (€)393,966393,966
CO2 emission costs (€)140,949140,949
Gas taxes (€)33,68933,689
Heatpump VDG OPEX (€)-46,080-
Result on electricity market (€)-289,579-185,904
Electricity taxes (€)-2,871-3,125
Grid transport costs (€)-28,883-50,256
Heatpump PCM OPEX (€)--69,120
HotWaterStorage OPEX (€)--9,000
EBITDA (€)201,192251,200
Depreciation (€)-46,080-81,120
EBITDA + depr (€)155,112170,080
\n", "
" ], "text/plain": [ " Heatpump only Heatpump + Storage_PCM\n", "Gasboiler OPEX (€) - -\n", "Gas consumption costs (€) 393,966 393,966\n", "CO2 emission costs (€) 140,949 140,949\n", "Gas taxes (€) 33,689 33,689\n", "Heatpump VDG OPEX (€) -46,080 -\n", "Result on electricity market (€) -289,579 -185,904\n", "Electricity taxes (€) -2,871 -3,125\n", "Grid transport costs (€) -28,883 -50,256\n", "Heatpump PCM OPEX (€) - -69,120\n", "HotWaterStorage OPEX (€) - -9,000\n", "EBITDA (€) 201,192 251,200\n", "Depreciation (€) -46,080 -81,120\n", "EBITDA + depr (€) 155,112 170,080" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n", "casereport.show(presentation_format=True)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12
CAPEX (€)-1,157,760
Regular Earnings (€)201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192
Irregular Cashflows (€)------------
EBITDA (€)201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192201,192
Depreciations (€) -/--46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080-46,080
EBIT (€)155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112155,112
Income tax (Vpb.) (€)-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267-23,267
NOPLAT (€)131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845131,845
Depreciations (€) +/+46,08046,08046,08046,08046,08046,08046,08046,08046,08046,08046,08046,080
Free Cash Flow (€)-1,157,760177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925177,925
Simple Payback Period6.507001039280803 years
IRR (%)11%
WACC (%)10%
NPV of explicit period (€)49,607
Discounted residual value (€)192,708
NPV (€)49,607
\n", "
" ], "text/plain": [ " Year 0 Year 1 Year 2 \\\n", "CAPEX (€) -1,157,760 \n", "Regular Earnings (€) 201,192 201,192 \n", "Irregular Cashflows (€) - - \n", "EBITDA (€) 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 \n", "Free Cash Flow (€) -1,157,760 177,925 177,925 \n", "Simple Payback Period 6.507001039280803 years \n", "IRR (%) 11% \n", "WACC (%) 10% \n", "NPV of explicit period (€) 49,607 \n", "Discounted residual value (€) 192,708 \n", "NPV (€) 49,607 \n", "\n", " Year 3 Year 4 Year 5 Year 6 Year 7 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 201,192 201,192 201,192 201,192 201,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 201,192 201,192 201,192 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 -46,080 -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 155,112 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 -23,267 -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 131,845 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 46,080 46,080 46,080 \n", "Free Cash Flow (€) 177,925 177,925 177,925 177,925 177,925 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) \n", "\n", " Year 8 Year 9 Year 10 Year 11 Year 12 \n", "CAPEX (€) \n", "Regular Earnings (€) 201,192 201,192 201,192 201,192 201,192 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 201,192 201,192 201,192 201,192 201,192 \n", "Depreciations (€) -/- -46,080 -46,080 -46,080 -46,080 -46,080 \n", "EBIT (€) 155,112 155,112 155,112 155,112 155,112 \n", "Income tax (Vpb.) (€) -23,267 -23,267 -23,267 -23,267 -23,267 \n", "NOPLAT (€) 131,845 131,845 131,845 131,845 131,845 \n", "Depreciations (€) +/+ 46,080 46,080 46,080 46,080 46,080 \n", "Free Cash Flow (€) 177,925 177,925 177,925 177,925 177,925 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) " ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BusinessCaseReport(s.hpcase).show()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n", "\n", "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", "\n" ] }, { "data": { "text/html": [ "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12
CAPEX (€)-2,036,640
Regular Earnings (€)251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200
Irregular Cashflows (€)------------
EBITDA (€)251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200251,200
Depreciations (€) -/--81,120-81,120-81,120-81,120-81,120-81,120-81,120-81,120-81,120-81,120-81,120-81,120
EBIT (€)170,080170,080170,080170,080170,080170,080170,080170,080170,080170,080170,080170,080
Income tax (Vpb.) (€)-25,512-25,512-25,512-25,512-25,512-25,512-25,512-25,512-25,512-25,512-25,512-25,512
NOPLAT (€)144,568144,568144,568144,568144,568144,568144,568144,568144,568144,568144,568144,568
Depreciations (€) +/+81,12081,12081,12081,12081,12081,12081,12081,12081,12081,12081,12081,120
Free Cash Flow (€)-2,036,640225,688225,688225,688225,688225,688225,688225,688225,688225,688225,688225,688225,688
Simple Payback Period9.0241276072978 years
IRR (%)5%
WACC (%)10%
NPV of explicit period (€)-453,518
Discounted residual value (€)338,768
NPV (€)-453,518
\n", "
" ], "text/plain": [ " Year 0 Year 1 Year 2 \\\n", "CAPEX (€) -2,036,640 \n", "Regular Earnings (€) 251,200 251,200 \n", "Irregular Cashflows (€) - - \n", "EBITDA (€) 251,200 251,200 \n", "Depreciations (€) -/- -81,120 -81,120 \n", "EBIT (€) 170,080 170,080 \n", "Income tax (Vpb.) (€) -25,512 -25,512 \n", "NOPLAT (€) 144,568 144,568 \n", "Depreciations (€) +/+ 81,120 81,120 \n", "Free Cash Flow (€) -2,036,640 225,688 225,688 \n", "Simple Payback Period 9.0241276072978 years \n", "IRR (%) 5% \n", "WACC (%) 10% \n", "NPV of explicit period (€) -453,518 \n", "Discounted residual value (€) 338,768 \n", "NPV (€) -453,518 \n", "\n", " Year 3 Year 4 Year 5 Year 6 Year 7 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 251,200 251,200 251,200 251,200 251,200 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 251,200 251,200 251,200 251,200 251,200 \n", "Depreciations (€) -/- -81,120 -81,120 -81,120 -81,120 -81,120 \n", "EBIT (€) 170,080 170,080 170,080 170,080 170,080 \n", "Income tax (Vpb.) (€) -25,512 -25,512 -25,512 -25,512 -25,512 \n", "NOPLAT (€) 144,568 144,568 144,568 144,568 144,568 \n", "Depreciations (€) +/+ 81,120 81,120 81,120 81,120 81,120 \n", "Free Cash Flow (€) 225,688 225,688 225,688 225,688 225,688 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) \n", "\n", " Year 8 Year 9 Year 10 Year 11 Year 12 \n", "CAPEX (€) \n", "Regular Earnings (€) 251,200 251,200 251,200 251,200 251,200 \n", "Irregular Cashflows (€) - - - - - \n", "EBITDA (€) 251,200 251,200 251,200 251,200 251,200 \n", "Depreciations (€) -/- -81,120 -81,120 -81,120 -81,120 -81,120 \n", "EBIT (€) 170,080 170,080 170,080 170,080 170,080 \n", "Income tax (Vpb.) (€) -25,512 -25,512 -25,512 -25,512 -25,512 \n", "NOPLAT (€) 144,568 144,568 144,568 144,568 144,568 \n", "Depreciations (€) +/+ 81,120 81,120 81,120 81,120 81,120 \n", "Free Cash Flow (€) 225,688 225,688 225,688 225,688 225,688 \n", "Simple Payback Period \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BusinessCaseReport(s.storage_case_PCM).show()" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n", "\n", "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(14, 41, 59, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "Gas prices (€/MWh)", "text": "", "type": "scatter", "x": [ "2023-01-01 00:00:00+01:00", "2023-01-02 00:00:00+01:00", "2023-01-03 00:00:00+01:00", "2023-01-04 00:00:00+01:00", "2023-01-05 00:00:00+01:00", "2023-01-06 00:00:00+01:00", "2023-01-07 00:00:00+01:00", "2023-01-08 00:00:00+01:00", "2023-01-09 00:00:00+01:00", "2023-01-10 00:00:00+01:00", "2023-01-11 00:00:00+01:00", "2023-01-12 00:00:00+01:00", "2023-01-13 00:00:00+01:00", "2023-01-14 00:00:00+01:00", "2023-01-15 00:00:00+01:00", "2023-01-16 00:00:00+01:00", "2023-01-17 00:00:00+01:00", "2023-01-18 00:00:00+01:00", "2023-01-19 00:00:00+01:00", "2023-01-20 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#price chart\n", "from copy import deepcopy\n", "\n", "data = deepcopy(s.baseline.data)\n", "data = data[data.columns[:2]]\n", "data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n", "data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n", "\n", "data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "# #price chart\n", "# from copy import deepcopy\n", "\n", "# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n", "\n", "# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n", "# _total_output = _source_output.sum(axis=1)\n", "# _data = _source_output.divide(_total_output, axis=0).rename(\n", "# columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n", "# ) * 100\n", "\n", "# production_fig = _data.iplot(\n", "# kind='bar',\n", "# barmode='stack',\n", "# colors=[recoydarkblue, recoygreen],\n", "# title='Hybrid case: Heat production per Month by Source in % share',\n", "# yTitle='Share of production in %',\n", "# dimensions=(600, 400),\n", "# asFigure=True\n", "# )\n", "\n", "# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n", "# production_fig" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(14, 41, 59, 0.6)", "line": { "color": "rgba(14, 41, 59, 1.0)", "width": 1 } }, "name": "Gasboiler CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, 0, 0 ] }, { "marker": { "color": "rgba(31, 131, 118, 0.6)", "line": { "color": "rgba(31, 131, 118, 1.0)", "width": 1 } }, "name": "Heatpump VDG CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, 1152000, 0 ] }, { "marker": { "color": "rgba(221, 67, 59, 0.6)", "line": { "color": "rgba(221, 67, 59, 1.0)", "width": 1 } }, "name": "Heatpump PCM CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, 0, 1728000 ] }, { "marker": { "color": "rgba(243, 210, 104, 0.6)", "line": { "color": "rgba(243, 210, 104, 1.0)", "width": 1 } }, "name": "HotWaterStorage CAPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, 0, 300000 ] } ], "layout": { "barmode": "relative", "height": 400, "legend": { "bgcolor": "#F5F6F9", "font": { "color": "#4D5663" }, "traceorder": "reversed" }, "paper_bgcolor": 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"color": "rgba(31, 131, 118, 0.6)", "line": { "color": "rgba(31, 131, 118, 1.0)", "width": 1 } }, "name": "Gas consumption costs (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ -393966.04, 0, 0 ] }, { "marker": { "color": "rgba(221, 67, 59, 0.6)", "line": { "color": "rgba(221, 67, 59, 1.0)", "width": 1 } }, "name": "CO2 emission costs (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ -140949.49, 0, 0 ] }, { "marker": { "color": "rgba(243, 210, 104, 0.6)", "line": { "color": "rgba(243, 210, 104, 1.0)", "width": 1 } }, "name": "Gas taxes (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ -33689.26, 0, 0 ] }, { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "Heatpump VDG OPEX (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, -46080, 0 ] }, { "marker": { "color": "rgba(109, 82, 107, 0.6)", "line": { "color": "rgba(109, 82, 107, 1.0)", "width": 1 } }, "name": "Result on electricity market (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, -289579.08, -185904.47 ] }, { "marker": { "color": "rgba(242, 165, 65, 0.6)", "line": { "color": "rgba(242, 165, 65, 1.0)", "width": 1 } }, "name": "Electricity taxes (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, -2871.09, -3124.55 ] }, { "marker": { "color": "rgba(20, 85, 97, 0.6)", "line": { "color": "rgba(20, 85, 97, 1.0)", "width": 1 } }, "name": "Grid transport costs (€)", "orientation": "v", "text": "", "type": "bar", "x": [ "Baseline", "Heatpump only", "Heatpump + Storage_PCM" ], "y": [ 0, -28882.91, -50255.9 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n", "\n", "fig = cashflow_report.report.T.iplot(\n", " kind='bar',\n", " barmode='relative',\n", " colors=recoycolors,\n", " title='OPEX breakdown',\n", " asFigure=True,\n", " yTitle='€',\n", " dimensions=(800, 600)\n", ")\n", "\n", "ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n", "scat = go.Scatter(\n", " mode='markers',\n", " y=ebitda_report.report.loc['EBITDA (€)', :].values, \n", " x=ebitda_report.report.columns, \n", " line=go.scatter.Line(color=recoydarkgrey),\n", " marker=dict(\n", " color=recoydarkgrey,\n", " size=20,\n", " line=dict(\n", " color=recoydarkgrey,\n", " width=3\n", " ),\n", " symbol='line-ew'\n", " ),\n", " name='EBITDA (€)'\n", ")\n", "fig.add_trace(scat)\n", "fig" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "# 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n", "\n", "npv_graph = _series.iplot(\n", " kind='bar',\n", " title='NPV by Casestudy in €',\n", " colors=recoygreen,\n", " dimensions=(600, 400), \n", " yTitle='NPV in €',\n", " asFigure=True,\n", " yrange=[0, _series.max() * 1.1]\n", ")\n", "\n", "npv_graph.update_traces(\n", " text=_series.values/1000_000, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f}M €\", \n", ")\n", "\n", "npv_graph" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "IRR", "orientation": "v", "text": [ 10.952734299520062, 4.682490260946448 ], "textposition": "outside", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n", "irr_fig = irr_report.iplot(\n", " kind='bar',\n", " title='IRR by Casestudy in %',\n", " colors=recoygreen,\n", " dimensions=(600, 400),\n", " yTitle='IRR in %',\n", " asFigure=True,\n", " yrange=[0, irr_report.max() * 1.2]\n", ")\n", "\n", "irr_fig.update_traces(\n", " text=irr_report.values, \n", " textposition='outside', \n", " texttemplate=\"%{text:.0f} %\", \n", ")\n", "\n", "irr_fig" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "marker": { "color": "rgba(70, 165, 121, 0.6)", "line": { "color": "rgba(70, 165, 121, 1.0)", "width": 1 } }, "name": "Simple Payback Time", "orientation": "v", "text": [ 6.507001039280803, 9.0241276072978 ], "textposition": 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CBAjkKiCA59oZ4yJAgAABAgQIECBAgACBuRIQwOeqnU6GAAECBAgQIECAAAECBHIVEMBz7YxxESBAgAABAgQIECBAgMBcCQjgc9VOJ0OAAAECBAgQIECAAAECuQoI4Ll2xrgIECBAgAABAgQIECBAYK4EBPC5aqeTIUCAAAECBAgQIECAAIFcBQTwXDtjXAQIECBAgAABAgQIECAwVwIC+Fy108kQIECAAAECBAgQIECAQK4CAniunTEuAgQIECBAgAABAgQIEJgrAQF8rtrpZAgQIECAAAECBAgQIEAgVwEBPNfOGBcBAgQIECBAgAABAgQIzJWAAD5X7XQyBAgQIECAAAECBAgQIJCrgACea2eMiwABAgQIECBAgAABAgTmSkAAn6t2OhkCBAgQIECAAAECBAgQyFVAAM+1M8ZFgAABAgQIECBAgAABAnMlIIDPVTudDAECBAgQIECAAAECBAjkKiCA59oZ4yJAgAABAgQIECBAgACBuRIQwOeqnU6GAAECBAgQIECAAAECBHIVEMBz7YxxESBAgAABAgQIECBAgMBcCQjgc9VOJ0OAAAECBAgQIECAAAECuQoI4Ll2xrgIECBAgAABAgQIECBAYK4EBPC5aqeTIUCAAAECBAgQIECAAIFcBQTwXDtjXAQIECBAgAABAgQIECAwVwIC+Fy108kQIECAAAECBAgQIECAQK4CAniunTEuAgQIECBAgAABAgQIEJgrAQF8rtrpZAgQIECAAAECBAgQIEAgVwEBPNfOGBcBAgQIECBAgAABAgQIzJWAAD5X7XQyBAgQIECAAAECBAgQIJCrgACea2eMiwABAgQIECBAgAABAgTmSkAAn6t2OhkCBAgQIECAAAECBAgQyFVAAM+1M8ZFgAABAgQIECBAgAABAnMlIIDPVTudDAECBAgQIECAAAECBAjkKiCA59oZ4yJAgAABAgQIECBAgACBuRIQwOeqnU6GAAECBAgQIECAAAECBHIVEMBz7YxxESBAgAABAgQIECBAgMBcCQjgc9VOJ0OAAAECBAgQIECAAAECuQoI4Ll2xrgIECBAgAABAgQIECBAYK4EBPC5aqeTIUCAAAECBAgQIECAAIFcBQTwXDtjXAQIECBAgAABAgQIECAwVwIC+Fy108kQ6I/AD+/9UUp7pPSQtXv3Z9BGSoAAAQK9FzD/9L6FToBArwUE8F63z+AJ9FfAAqi/vTNyAgQI9FnA/NPn7hk7gf4LCOD976EzINBLAQugXrbNoAkQINB7AfNP71voBAj0WkAA73X7DJ5AfwUsgPrbOyMnQIBAnwXMP33unrET6L+AAN7/HjoDAr0UsADqZdsMmgABAr0XMP/0voVOgECvBQTwXrfP4An0V8ACqL+9M3ICBAj0WcD80+fuGTuB/gsI4P3voTMg0EsBC6Bets2gCRAg0HsB80/vW+gECPRaQADvdfsMnkB/BSyA+ts7IydAgECfBcw/fe6esRPov4AA3v8eOgMCvRSwAOpl2wyaAAECvRcw//S+hU6AQK8FBPBet8/gCfRXwAKov70zcgIECPRZwPzT5+4ZO4H+Cwjg/e+hMyDQSwELoF62zaAJECDQewHzT+9b6AQI9FpAAO91+wyeQH8FLID62zsjJ0CAQJ8FzD997p6xE+i/gABe6eGWrR9Ihx92aNp4yknLOnvtddvS1vddPfi3J2w4Np175ua0ds2a/nffGRBYRQELoFXEd2gCBAgssID5Z4Gb79QJZCAggKeUqgF702kvXBbAt3/+prT1/R9KF15wXjpg/f6pCOnF1+ZNL86gfYZAoL8CFkD97Z2REyBAoM8C5p8+d8/YCfRfQACv9HDYHfD6v9UDef8vAWdAYHUELIBWx91RCRAgsOgC5p9FvwKcP4HVFRDAxwTw+3bsSJdcsSVteNwxS3fFb77lG+nSK7akc87cnI468ojV7Z6jE+ixgAVQj5tn6AQIEOixgPmnx80zdAJzICCANwjgG09+Rtpw3LGDLesB/Ad33zsHl4FTIDB7gfvv3zk46F577Tn7gzsiAQIECCysgPlnYVvvxIME9t9vn6BKi1lGAG8QwMfdAd/xo58s5pXjrAm0FBj87OyR0poHP6hlJbsTIECAAIHmAuaf5la2JDBMYM3e1m5trgwBfEwAL77lPeBtLi/7Ehgt4BFAVwcBAgQIrIaA+Wc11B2TAIFSQACfEMB9CrofFgLdCFgAdeOqKgECBAiMFzD/uEIIEFhNAQG89mfIimasW7dfesPrz176kDV/B3w1L1HHnlcBC6B57azzIkCAQN4C5p+8+2N0BOZdQACf9w47PwKZClgAZdoYwyJAgMCcC5h/5rzBTo9A5gICeOYNMjwC8ypgATSvnXVeBAgQWJnA5e/5cLrq6k8Odn7OM45P/+mc09M+a/ceWeyaT34mvfmSqxpvXxYy/6ysP/YiQCBGQACPcVSFAIEpBSyApgSzOQECBOZYoAjTf3fjPw5Cd/H1Xy+9Kh388Iems8745aFn/ff/8KV0+Xs+ki7+L69ND12/LhXhvfgatX21iPlnji8kp0agBwICeA+aZIgE5lHAAmgeu+qcCBAgML3AHXfelc77z+9MZ51xanry4x8zKFAP2PWqReA+6vBD0vOf/fSJ23/1llvTmy56b7rw/Jeno498RCrmnxtv+nK68o+uWQrwo+6mF/ue81uXpW/d9r3BcU5/4bOXQn75osG++z4kffjjnxp876W//KzBudz0pa8Ntm9yJ396MXsQINBnAQG8z90zdgI9FhDAe9w8QydAgECgwLAAXg/N1cPde9+PBnfIn/KExy4F8HHbF/tWA/v37rg7/fbvvj897cnHDPYvgvRHrr1h2d30Mtxff8Pn0tFHHT4I7mUYf+P5vzp4oaAM7b/39vOXXjiovzDwx9f8ZXrWLxw/uEvviwABAoWAAO46IEBgVQQE8FVhd1ACBAhkKVB/hLxJAP/FjScsBd9JAbz4/qVbrk5vPP/l6dbb7khX/MFH05t//Yy0ds2a3cJ8cff9Y9fesNt70OvBv/rYfPFe9fL74x6dzxLfoAgQmKmAAD5TbgcjQKAUEMBdCwQIECBQCpR3wctHt4t/P/Yxj1y6K12VWskd8HKfIrR/7ZZb09e/eVs671W/shSaP/Gpv13WjPLR8eIfi7vt1e//1rmnL905L9+3Xn5YXP08ym11mgABAqWAAO5aIEBgVQQE8FVhd1ACBAj0QqC4C/3Zz31h5IeqTfMe8PKEB+8rf/eH086dD6QLzn5ZOubRRy4F8Ord9HL7+h3tSXfA67CT7sr3ohEGSYBAuIAAHk6qIAECTQQE8CZKtiFAgMDiCQwLrvX3aa/kU9DLu9OPOPjA9LozX5IOPGC/AW69dhG0/+STnxm8d/vi//bBpfeal/ufuvGEoXfAy/3+w7OfPvjzaQL44l27zphAEwEBvImSbQgQCBcQwMNJFSRAgEBvBaqfNj7s0fN6SC6D8zR/B7y8g73xmU9LTzju0ekhlb8xXv0U9KJ2+eh4EfRf/RsXDVwPPfjAdOAB69Kpzz1x5CPo1b9lXuxT/YC23jbHwAkQCBUQwEM5FSNAoKmAAN5UynYECBAgECFQhPz/fs229MrTnp/22WfvZQE8or4aBAgQaCIggDdRsg0BAuECAng4qYIECBAgMEagfN/4yScen9IeSQB3tRAgsCoCAviqsDsoAQICuGuAAAECBFZDwPyzGuqOSYBAKSCAuxYIEFgVAQugVWF3UAIECCy8gPln4S8BAARWVUAAX1V+ByewuAIWQIvbe2dOgACB1RQw/6ymvmMTICCAuwYIEFgVAQugVWF3UAIzFfj97R9Pn/vWl9Mee+wx0+M6GIFxAg/sfGDwHnDXpeskJ4GdDzyQnnrYY9MrjntOTsMylg4EBPAOUJUkQGCygAA+2cgWBPou8NI/e3v6twcdkdbs+eC+n4rxz5HA/ff/awDfa08vDM1RW3t/Kjvu/3H60u3/kq76v3+99+fiBMYLCOCuEAIEVkVAAF8VdgclMFOBl3387elJhzwqrd1r75ke18EIjBP4yU92DgL4g/baExSBbATuu/9H6cZvfzVtFcCz6UlXAxHAu5JVlwCBsQICuAuEwPwLCODz3+M+nqEA3seuzf+YBfD573F5hgL44vTamRLISkAAz6odBkOgEwEBvBNWRVsKCOAtAe3eiYAA3glrlkUF8CzbYlAE5l9AAJ//HjtDAgK4ayBHAQE8x64YkwC+ONeAAL44vXamBLISEMCzaofBEOhEQADvhFXRlgICeEtAu3ciIIB3wpplUQE8y7YYFIH5FxDA57/HzpCAAO4ayFFAAM+xK8YkgC/ONSCAL06vnSmBrAQE8KzaYTAEOhEQwDthVbSlgADeEtDunQgI4J2wZllUAM+yLQZFYP4FBPD577EzJCCAuwZyFBDAc+yKMQngi3MNCOCL02tnSiArAQE8q3YYDIFOBATwTlgVbSkggLcEtHsnAgJ4J6xZFhXAs2yLQRGYfwEBfP577AwJCOCugRwFBPAcu2JMAvjiXAMC+OL02pkSyEpAAM+qHQZDoBMBAbwTVkVbCgjgLQHt3omAAN4Ja5ZFBfAs22JQBOZfQACf/x47QwICuGsgRwEBPMeuGJMAvjjXgAC+OL12pgSyEhDAs2qHwRDoREAA74RV0ZYCAnhLQLt3IiCAd8KaZVEBPMu2GBSB+RcQwOe/x86QgADuGshRQADPsSvGJIAvzjUggC9Or50pgawEBPCs2mEwBDoREMA7YVW0pYAA3hLQ7p0ICOCdsGZZVABv0JYtWz+Qrt92w2DLwx5xSLrwgvPSAev3b7CnTQgQGCUggLs2CMy/gAA+/z3u4xkK4H3s2vyPWQCf/x6XZyiAT+j1tddtS9u/8MV07pmb09o1a1L9vxfnUnGmBGIFBPBYT9UI5CgggOfYFWMSwF0DOQoI4Dl2pZsxCeATXIu738XX5k0vHvz/7Z+/KW19/4fcBe/melR1gQQE8AVqtlNdWAEBfGFbn/WJC+BZt2dhByeAL07rBfAJvb75lm+kt7zjsvTUn33iIIQXgfzwww5NG085aXGuEmdKoAOBe+/70aDqPmv37qC6kgQI5CAggOfQBWOoC/zk/p2Df3rQXnvCIZCNgACeTSs6H4gAPoH4vh070iVXbEn33P3D9OWvfG2394B/9467O2+SAxCYR4EHHkgp7TH4P18ECMypwLmfeWd63IE/ndbs+eA5PUOn1UeBYvopvsw/feze/I55x/0/TjfdcXO6+N+9JvuTPOih+2U/xpwHKIBP6E79jnfxHvDr/urTHkHP+ao2tl4IeAS9F20ySAKtBNwBb8Vn544EPILeEayyrQTcAW/F16udBfAx7Srvfm88+Rlpw3HHDrYsHkm/9Iot6ZwzN6ejjjyiV802WAI5CQjgOXXDWAh0IyCAd+OqajsBAbydn727ERDAu3HNsaoAPqErxR3w795++7JPQXcHPMdL2Zj6JiCA961jxktgegEBfHoze3QvIIB3b+wI0wsI4NOb9XUPAXxC58q74Dduv2mwpb8D3tdL3bhzExDAc+uI8RCIFxDA401VbC8ggLc3VCFeQACPN821ogCea2eMi8CcCwjgc95gp0cgpSSAuwxyFBDAc+yKMQngi3MNCOCL02tnSiArAQE8q3YYDIFOBATwTlgVbSkggLcEtHsnAgJ4J6xZFhXAs2yLQRGYfwEBfP577AwJCOCugRwFBPAcu2JMAvjiXAMC+OL02pkSyEpAAM+qHQZDoBMBAbwTVkVbCgjgLQHt3omAAN4Ja5ZFBfAs22JQBOZfQACf/x47QwICuGsgRwEBPMeuGJMAvjjXgAC+OL12pgSyEhDAs2qHwRDoREAA74RV0ZYCAnhLQLt3IiCAd8KaZVEBPMu2GBSB+RcQwOe/x86QgADuGshRQADPsSvGJIAvzjUggC9Or50pgawEBPCs2mEwBDoREMA7YVW0pYAA3hLVf4ttAAAgAElEQVTQ7p0ICOCdsGZZVADPsi0GRWD+BQTw+e+xMyQggLsGchQQwHPsijEJ4ItzDQjgi9NrZ0ogKwEBPKt2GAyBTgQE8E5YFW0pIIC3BLR7JwICeCesWRYVwLNsi0ERmH8BAXz+e+wMCQjgroEcBQTwHLtiTAL44lwDAvji9NqZEmgkcO99P0r/9dKr0ic+9beD7X/r3NPT85/99KH7fvWWW9M5v3VZ+tZt31v6/rGPeWS6+L+8Nj10/bqxxxPAG7XDRgR6LSCA97p9czt4AXxuW9vrExPAe92+qQYvgE/FZWMC8y1Qhu+nPOGxI0N3VaAI4G+66L3pwvNfno4+8hFT4QjgU3HZmEAvBQTwXrZt7gctgM99i3t5ggJ4L9u2okEL4CtisxOB+RS45pOfSTf/y7fTWWf8cqMTnCaA17ctAviNN305XflH1yzdMS+O/+ZLrhoc+znPOD79p3NOT/us3TvV77Sf/sJnL42x2OfvbvzHtO++D0kf/vinUvG9l/7ys9J5//md6aYvfW23Wo1OzEYECIQICOAhjIoECwjgwaDKhQgI4CGMvSgigPeiTQZJYDYCl7/nw+mqqz+5dLBDDz4wXfrms0fe3a4H40mPnxf1jzr8kMHd9e/dcXf67d99f3rak48Z/HcRpD9y7Q1LYby67fU3fC4dfdThg3GUx3zj+b+anvz4xwz2K0L77739/MF/F1/VfYv//uNr/jI96xeOn/hY/GyUHYXA4ggI4IvT6z6dqQDep24tzlgF8MXptQC+OL12pgTGCpSPn//ixhOWgmw9FE8iLILvbd+5Y+nOdX37IjxfuuXq9MbzX55uve2OdMUffDS9+dfPSGvXrBm877z66Pvf/8OX0seuvWG3WvXH5Ms74OXd8vL7Bz/8oY3v5E86L98nQGBlAgL4ytzs1a2AAN6tr+orExDAV+bWx70E8D52zZgJdCAwLIDfceddg0e5zzrj1KVQPu7Q1YA97EPYqsf42i23pq9/87Z03qt+JdU/+K08RvkYevHf1Q+GK/67/HC4egAvvleOu3wEfdwHyXVAqSQBArsEBHCXQo4CAniOXTEmAXxxrgEBfHF67UwJTBSoP7pdBNk3XvTedM7mFzb6kLVJAbwYQHFn+/J3fzjt3PlAuuDsl6VjHn3kUgCv3n0vB1u/oz3pDnj9JKd5n/pEIBsQIDCVgAA+FZeNZyQggM8I2mGmEhDAp+Lq9cYCeK/bZ/AEYgWKcPzGi/5g6X3f9bvL9UfSq+/NLkZSBPjia9yHuJV3px9x8IHpdWe+JB14wH6Dfeq1i6D9J5/8zOC92xf/tw8uPZ5e7n/qxhOW3jtefAhb9RH0Yr//8OynL32A20o/qT1WVzUCiycggC9ez/twxgJ4H7q0eGMUwBen5wL44vTamRJoJFD9JPL6h6rVQ3IR2F/9Gxct1a1+cvmog5V3sDc+82npCcc9Oj1k7d5Lm1aPXfxj+eh49TjFB8MdeMC6dOpzTxwawMsXAqofJlf9gLZGCDYiQCBEQAAPYVQkWEAADwZVLkRAAA9h7EURAbwXbTJIAvMjUDwS/t+v2ZZeedrz0z777L0sgM/PWToTAgQKAQHcdZCjgACeY1eMSQBfnGtAAF+cXjtTAlkIlO8zP/nE41PaIwngWXTFIAh0IyCAd+OqajsBAbydn727ERDAu3HNsaoAnmNXjInAAgj88N4fCeAL0GenuNgCAvhi9z/XsxfAc+3MYo9LAF+c/gvgi9NrZ0ogKwEBPKt2GAyBTgQE8E5YFW0pIIC3BLR7JwICeCesWRYVwLNsi0ERmH8BAXz+e+wMCQjgroEcBQTwHLtiTAL44lwDAvgC9Ppj//uz6XO3/lPxdltfBLIRuH/nA4NH0Pfaw5WZTVMMJO184IH0c0f82/S8o3+ORoCAAB6AqES4gAAeTqpggIAAHoDYkxICeE8a1WaYp/3Z29LR6w9JD97rQW3K2JdAqMDO+x8Y1NtzLwE8FFaxVgI/2vmTdPP3b0t/+O9/o1UdO/+rgADuSshRQADPsSvGJIAvzjUggC9Ary2AFqDJPTxFC6AeNm0BhmwBFNtk80+sp2oxAuafGEdVYgXMP7GeOVcTwHPuTtDYLICCIJUJFbAACuVULEjAAigIclcZ80+sp2oxAuafGEdVYgXMP7GeOVcTwHPuTtDYLICCIJUJFbAACuVULEjAAigIUgCPhVQtVMD8E8qpWJCA+ScIsgdlBPAeNKntEAXwtoL270LAAqgLVTXbClgAtRVcvr/5J9ZTtRgB80+MoyqxAuafWM+cq2UXwLds/UD67u23p3PP3Dxwu+SKLenG7Teldev2S294/dnpqCOPmLnnfTt2LI2jOPim016YNp5y0szHsdIDWgCtVM5+XQpYAHWpq/ZKBSyAVio3fD/zT6ynajEC5p8YR1ViBcw/sZ45V8sqgH//zh+kN73t4rTpJS9IG447Nm3//E3p2us/NQjjX/qn/730v9euWTMz0zJ8b3jcMb0K3VUgC6CZXS4ONIWABdAUWDadmYAFUCy1+SfWU7UYAfNPjKMqsQLmn1jPnKtlF8B/5/Ir0ytOf9HgTndxN7z42rzpxenmW76R3n3VB9OvnfWqdMD6/Wdmeu1129K/fPNbgzH09csCqK+dm+9xWwDNd3/7enYWQLGdM//EeqoWI2D+iXFUJVbA/BPrmXO1rAJ4ebd548nPSEcd+VO73Q3f+v4PpQsvOG+mAbx4EeD6bTcs9XA1H4Vf6YVkAbRSOft1KWAB1KWu2isVsABaqdzw/cw/sZ6qxQiYf2IcVYkVMP/EeuZcLasAXkAVd7rf8o7L0l133Z1OPumEwZ3n8tH0Yx77MzO9E119QaB4JL74Ku6IX/dXn156IeC+H/0k5/4OxvbKv/jttOHgf5PW7PXg7MdqgIsjsHPnzsHJ7rnnnotz0s40e4H77v9x+vx3vpquPOXXsh9rHwZo/ulDlxZvjOafxet5H864T/PP2r0f1AfSbMeYXQDPSWpYAK+/T/3ue+7LachDx/LqbZekDQcdLYBn36nFGuDOnQ/sCuB7LNaJO9usBYoF0Be++8/pipPOznqcfRmc+acvnVqscZp/FqvffTnbPs0/++27ti+sWY4zqwBehNvqe8BzECseQT/8sEOXPoAtxzFOcvII4CQh318NAY8Aroa6Y04S8AjgJKHpvm/+mc7L1rMRMP/MxtlRphMw/0zn1eetBfAJ3Ss+if2dV25d+hNoxSPo27/wxcEns8/y09jbXGQWQG307NuVgAVQV7LqthGwAGqjt/u+5p9YT9ViBMw/MY6qxAqYf2I9c66WVQAvoIo7zk950uMHf4Ysl68idG9939WD4Rz2iENm/kFwbR0sgNoK2r8LAQugLlTVbCtgAdRWcPn+5p9YT9ViBMw/MY6qxAqYf2I9c66WXQAvPoTtmj//i/TKl5/WmzvMOTe4GJsFUO4dWszxWQAtZt9zP2sLoNgOmX9iPVWLETD/xDiqEitg/on1zLlaVgG8/ICzb9767aFmfbz7nEPzLYBy6IIx1AUsgFwTOQpYAMV2xfwT66lajID5J8ZRlVgB80+sZ87VsgrgOUP1eWwWQH3u3vyO3QJofnvb5zOzAIrtnvkn1lO1GAHzT4yjKrEC5p9Yz5yrCeA5dydobBZAQZDKhApYAIVyKhYkYAEUBLmrjPkn1lO1GAHzT4yjKrEC5p9Yz5yrZRfAi/eAv+Udl6W77rp7NzePoK/sUrIAWpmbvboVsADq1lf1lQlYAK3MbdRe5p9YT9ViBMw/MY6qxAqYf2I9c66WVQC/b8eOdMkVW9KGxx2TjnnMo5d9GFuOn46ec2OrY7MA6kunFmucFkCL1e++nK0FUGynzD+xnqrFCJh/YhxViRUw/8R65lwtqwBefAjb71x+ZXrF6S8amL37qg+mXzvrVemA9fun4u9xX3v9p3r197dzabwFUC6dMI6qgAWQ6yFHAQug2K6Yf2I9VYsRMP/EOKoSK2D+ifXMuVq2AXz9+v3T773nD9Orz3jZIIAXj6ZXA3nOqLmNzQIot44YTyFgAeQ6yFHAAii2K+afWE/VYgTMPzGOqsQKmH9iPXOullUArz6CvvGUk1Lx2Pnhhx2aiv997XXb0vYvfNEd8BVcTRZAK0CzS+cCFkCdEzvACgQsgFaANmYX80+sp2oxAuafGEdVYgXMP7GeOVfLKoDXoap/F3zduv3SG15/djrqyCNy9sxybBZAWbZl4QdlAbTwl0CWABZAsW0x/8R6qhYjYP6JcVQlVsD8E+uZc7WsA3jOcH0amwVQn7q1OGO1AFqcXvfpTC2AYrtl/on1VC1GwPwT46hKrID5J9Yz52oCeM7dCRqbBVAQpDKhAhZAoZyKBQlYAAVB7ipj/on1VC1GwPwT46hKrID5J9Yz52rZBfDyfeA3br8plY+dH3LIw5f+PFnxfnBf0wlYAE3nZevZCFgAzcbZUaYTsACazmvS1uafSUK+vxoC5p/VUHfMSQLmn0lC8/P97AJ4+cFrJ5348+n33/u+9PznPmvwvm9/hmzlF50F0Mrt7NmdgAVQd7Yqr1zAAmjldsP2NP/EeqoWI2D+iXFUJVbA/BPrmXO1rAJ49e+AF3e9qwHcnyFb+WVkAbRyO3t2J2AB1J2tyisXsABauZ0AHmunWncC5p/ubFVeuYD5Z+V2fduzNwHcHfCVX1oC+Mrt7NmdgAVQd7Yqr1zAAmjldgJ4rJ1q3QmYf7qzVXnlAuafldv1bc+sAniBV/6971ef8bL0hx/40OAR9PXr909vetvF6ZRnnjj4m+C+phMQwKfzsvVsBCyAZuPsKNMJWABN5zVpa/PPJCHfXw0B889qqDvmJAHzzySh+fl+dgG8oC3udr/1onctU/7N81+TNhx37PzIz/BMLIBmiO1QjQUsgBpT2XCGAhZAsdjmn1hP1WIEzD8xjqrECph/Yj1zrpZFAC/e+73t059Nv/S85+Rs1duxWQD1tnVzPXALoLlub29PzgIotnXmn1hP1WIEzD8xjqrECph/Yj1zrpZNAC8eMb/r7nvSG15/9uBTz33FCVgAxVmqFCdgARRnqVKcgAVQnGVRyfwT66lajID5J8ZRlVgB80+sZ87VsgjgJVDx/u+t77s6bTrthd7rHXjVWAAFYioVJmABFEapUKCABVAgpgAei6lamID5J4xSoUAB808gZualsgrghVXxOHpxN/zggw9K5565Oa1dsyZzwvyHJ4Dn36NFHKEF0CJ2Pf9ztgCK7ZH5J9ZTtRgB80+MoyqxAuafWM+cq2UXwEus8m54Fe+wRxySLrzgvHTA+v1zNs1ubBZA2bXEgFJKFkAugxwFLIBiu2L+ifVULUbA/BPjqEqsgPkn1jPnatkF8PIOeIEmbMdcOhZAMY6qxApYAMV6qhYjYAEU41hWMf/EeqoWI2D+iXFUJVbA/BPrmXO1rAJ4edf75JNOSJs3vThnt16NzQKoV+1amMFaAC1Mq3t1ohZAse0y/8R6qhYjYP6JcVQlVsD8E+uZc7UsAnh519unoHdzqVgAdeOqajsBC6B2fvbuRsACKNbV/BPrqVqMgPknxlGVWAHzT6xnztWyCeD+Dnh3l4kFUHe2Kq9cwAJo5Xb27E7AAijW1vwT66lajID5J8ZRlVgB80+sZ87VsgjgOQPNw9gsgOahi/N3DhZA89fTeTgjC6DYLpp/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZC7yph/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZACeCykaqEC5p9QTsWCBMw/QZA9KCOA96BJbYcogLcVtH8XAhZAXaiq2VbAAqit4PL9zT+xnqrFCJh/YhxViRUw/8R65lxNAM+5O0FjswAKglQmVMACKJRTsSABC6AgyF1lzD+xnqrFCJh/YhxViRUw/8R65lxNAJ+iO9s/f1N660XvSr95/mvShuOOnWLP1d3UAmh1/R19uIAFkCsjRwELoNiumH9iPVWLETD/xDiqEitg/on1zLmaAN6wO2X4LjYXwBui2YzAGAELIJdHjgIWQLFdEcBjPVWLETD/xDiqEitg/on1zLmaAN6gOzff8o307qs+mF5x+ovTpb+7JW16yQvcAW/gZhMC4wQsgFwfOQpYAMV2RQCP9VQtRsD8E+OoSqyA+SfWM+dqAviE7hTh+9IrtqRzztyc1q/fP73pbRcL4Dlf0cbWGwELoN60aqEGagEU224BPNZTtRgB80+MoyqxAuafWM+cqwngY7rz/Tt/kH7n8ivTK05/UTrqyCNS8d/1AP79H/ww5/4Oxvbav74sHXfQI9OaPR+c/VgNcHEEHth1qnsszik70x4I3Hf/j9NNt/9zuvzEs3ow2vyHaP7Jv0eLOELzzyJ2Pf9z7tP8c8D+D8kfNOMRCuBjmlPc/X7LOy5Ld911925ble8D//FP7s+4vf86tDM+8Y70xIP/TVqzlwCefbMWaIA/2blzcLYP2nPPBTprp5q7QLEA2v6dr6Z3P/v83Ifai/GZf3rRpoUbpPln4VreixPu0/zz4Aft1QvTXAcpgE/RmWF3wKfYfdU29QjgqtE78BgBjwC6PHIU8AhgbFfMP7GeqsUImH9iHFWJFTD/xHrmXE0An6I7AvgUWDYlMEHAAsglkqOABVBsVwTwWE/VYgTMPzGOqsQKmH9iPXOuJoBP0R0BfAosmxIQwF0DPRSwAIptmgAe66lajIAAHuOoSqyA+SfWM+dqAnjO3QkamwVQEKQyoQIWQKGcigUJWAAFQe4qY/6J9VQtRsD8E+OoSqyA+SfWM+dqAnjO3QkamwVQEKQyoQIWQKGcigUJWAAFQQrgsZCqhQqYf0I5FQsSMP8EQfagjADegya1HaIA3lbQ/l0IWAB1oapmWwELoLaCy/c3/8R6qhYjYP6JcVQlVsD8E+uZczUBPOfuBI3NAigIUplQAQugUE7FggQsgIIgd5Ux/8R6qhYjYP6JcVQlVsD8E+uZczUBPOfuBI3NAigIUplQAQugUE7FggQsgIIgBfBYSNVCBcw/oZyKBQmYf4Ige1BGAO9Bk9oOUQBvK2j/LgQsgLpQVbOtgAVQW8Hl+5t/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZC7yph/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZACeCykaqEC5p9QTsWCBMw/QZA9KCOA96BJbYcogLcVtH8XAhZAXaiq2VbAAqit4PL9zT+xnqrFCJh/YhxViRUw/8R65lxNAM+5O0FjswAKglQmVMACKJRTsSABC6AgyF1lzD+xnqrFCJh/YhxViRUw/8R65lxNAM+5O0FjswAKglQmVMACKJRTsSABC6AgSAE8FlK1UAHzTyinYkEC5p8gyB6UEcB70KS2QxTA2wravwsBC6AuVNVsK2AB1FZw+f7mn1hP1WIEzD8xjqrECph/Yj1zriaA59ydoLFZAAVBKhMqYAEUyqlYkIAFUBDkrjLmn1hP1WIEzD8xjqrECph/Yj1zriaA59ydoLFZAAVBKhMqYAEUyqlYkIAFUBCkAB4LqVqogPknlFOxIAHzTxBkD8oI4D1oUtshCuBtBe3fhYAFUBeqarYVsABqK7h8f/NPrKdqMQLmnxhHVWIFzD+xnjlXE8Bz7k7Q2CyAgiCVCRWwAArlVCxIwAIoCHJXGfNPrKdqMQLmnxhHVWIFzD+xnjlXE8Bz7k7Q2CyAgiCVCRWwAArlVCxIwAIoCFIAj4VULVTA/BPKqViQgPknCLIHZQTwHjSp7RAF8LaC9u9CwAKoC1U12wpYALUVXL6/+SfWU7UYAfNPjKMqsQLmn1jPnKsJ4Dl3J2hsFkBBkMqEClgAhXIqFiRgARQEuauM+SfWU7UYAfNPjKMqsQLmn1jPnKsJ4Dl3J2hsFkBBkMqEClgAhXIqFiRgARQEKYDHQqoWKmD+CeVULEjA/BME2YMyAngPmtR2iAJ4W0H7dyFgAdSFqpptBSyA2gou39/8E+upWoyA+SfGUZVYAfNPrGfO1QTwnLsTNDYLoCBIZUIFLIBCORULErAACoLcVcb8E+upWoyA+SfGUZVYAfNPrGfO1QTwnLsTNDYLoCBIZUIFLIBCORULErAACoIUwGMhVQsVMP+EcioWJGD+CYLsQRkBvAdNajtEAbytoP27ELAA6kJVzbYCFkBtBZfvb/6J9VQtRsD8E+OoSqyA+SfWM+dqAnjO3QkamwVQEKQyoQIWQKGcigUJWAAFQe4qY/6J9VQtRsD8E+OoSqyA+SfWM+dqAnjO3QkamwVQEKQyoQIWQKGcigUJWAAFQQrgsZCqhQqYf0I5FQsSMP8EQfagjADegya1HaIA3lbQ/l0IWAB1oapmWwELoLaCy/c3/8R6qhYjYP6JcVQlVsD8E+uZczUBPOfuBI3NAigIUplQAQugUE7FggQsgIIgd5Ux/8R6qhYjYP6JcVQlVsD8E+uZczUBPOfuBI3NAigIUplQAQugUE7FggQsgIIgBfBYSNVCBcw/oZyKBQmYf4Ige1BGAO9Bk9oOUQBvK2j/LgQsgLpQVbOtgAVQW8Hl+5t/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZC7yph/Yj1VixEw/8Q4qhIrYP6J9cy5mgCec3eCxmYBFASpTKiABVAop2JBAhZAQZACeCykaqEC5p9QTsWCBMw/QZA9KCOAT2jS9s/flN560buWtnrChmPTuWduTmvXrOlBe/91iAJ4b1q1UAO1AFqodvfmZC2AYltl/on1VC1GwPwT46hKrID5J9Yz52oC+ITuXHvdtnTYoQenDccdm+7bsSNdcsWWdNDDHpY2b3pxzn1dNjYLoN60aqEGagG0UO3uzclaAMW2yvwT66lajID5J8ZRlVgB80+sZ87VBPApu1ME8u1f+GKv7oJbAE3ZZJvPRMACaCbMDjKlgAXQlGATNjf/xHqqFiNg/olxVCVWwPwT65lzNQF8yu5s2fqBwR7lHfD77985ZYXZb/6r174jPfGQR6U1ez149gd3RAIjBMqfnb322pMRgWwE7rv/x2n7bV9N733O6yaO6YGJW9jg5eYfF0GGAuafDJtiSGma+We1uazd2nVAAJ/Cr3g/+Nb3fyhdeMF56YD1+w/2vP3Oe6aosDqbnv3py9PjDnpkWrOnAL46HXDU4QJlfNkDEIFsBHbc/+N00+3/nC494bUTx+TKnUiUzvr05ek4889kqBVs4QWgFaAt7WL+aaNn324Eppl/uhlB86oPW79v841tuZuAAN7woijC9zuv3Jre8Pqz01FHHtFwrzw28whgHn0wiuUCHgF0ReQo4BHA2K6Yf2I9VYsRMP/EOKoSK2D+ifXMuZoA3qA7fQ7fxelZADVosk1mLmABNHNyB2wgYAHUAGmKTcw/U2DZdGYC5p+ZUTvQFALmnymwer6pAD6hgcMeO+9bzy2A+taxxRivBdBi9LlvZ2kBFNsx80+sp2oxAuafGEdVYgXMP7GeOVcTwCd0p/jQteu33bBsq3Xr9uvVo+gWQDn/CC7u2CyAFrf3OZ+5BVBsd8w/sZ6qxQiYf2IcVYkVMP/EeuZcTQDPuTtBY7MACoJUJlTAAiiUU7EgAQugIMhdZcw/sZ6qxQiYf2IcVYkVMP/EeuZcTQDPuTtBY7MACoJUJlTAAiiUU7EgAQugIEgBPBZStVAB808op2JBAuafIMgelBHAe9CktkMUwNsK2r8LAQugLlTVbCtgAdRWcPn+5p9YT9ViBMw/MY6qxAqYf2I9c64mgOfcnaCxWQAFQSoTKmABFMqpWJCABVAQ5K4y5p9YT9ViBMw/MY6qxAqYf2I9c64mgOfcnaCxWQAFQSoTKmABFMqpWJCABVAQpAAeC6laqID5J5RTsSAB808QZA/KCOA9aFLbIQrgbQXt34WABVAXqmq2FbAAaiu4fH/zT6ynajEC5p8YR1ViBcw/sZ45VxPAc+5O0NgsgIIglQkVsAAK5VQsSMACKAhyVxnzT6ynajEC5p8YR1ViBcw/sZ45VxPAc+5O0NgsgIIglQkVsAAK5VQsSMACKAhSAI+FVC1UwPwTyqlYkID5JwiyB2UE8B40qe0QBfC2gvbvQsACqAtVNdsKWAC1FVy+v/kn1lO1GAHzT4yjKrEC5p9Yz5yrCeA5dydobBZAQZDKhApYAIVyKhYkYAEUBLmrjPkn1lO1GAHzT4yjKrEC5p9Yz5yrCeA5dydobBZAQZDKhApYAIVyKhYkYAEUBCmAx0KqFipg/gnlVCxIwPwTBNmDMgJ4D5rUdogCeFtB+3chYAHUhaqabQUsgNoKLt/f/BPrqVqMgPknxlGVWAHzT6xnztUE8Jy7EzQ2C6AgSGVCBSyAQjkVCxKwAAqC3FXG/BPrqVqMgPknxlGVWAHzT6xnztUE8Jy7EzQ2C6AgSGVCBSyAQjkVCxKwAAqCFMBjIVULFTD/hHIqFiRg/gmC7EEZAbwHTWo7RAG8raD9uxCwAOpCVc22AhZAbQWX72/+ifVULUbA/BPjqEqsgPkn1jPnagJ4zt0JGpsFUBCkMqECFkChnIoFCVgABUHuKmP+ifVULUbA/BPjqEqsgPkn1jPnagJ4zt0JGpsFUBCkMqECFkChnIoFCVgABUEK4LGQqoUKmH9CORULEjD/BEH2oIwA3oMmtR2iAN5W0P5dCFgAdaGqZlsBC6C2gsv3N//EeqoWI2D+iXFUJVbA/BPrmXM1ATzn7gSNzQIoCFKZUAELoFBOxYIELICCIHeVMf/EeqoWI2D+iXFUJVbA/BPrmXM1ATzn7gSNzQIoCFKZUJEWEs4AABHnSURBVAELoFBOxYIELICCIAXwWEjVQgXMP6GcigUJmH+CIHtQRgDvQZPaDlEAbyto/y4ELIC6UFWzrYAFUFvB5fubf2I9VYsRMP/EOKoSK2D+ifXMuZoAnnN3gsZmARQEqUyogAVQKKdiQQIWQEGQu8qYf2I9VYsRMP/EOKoSK2D+ifXMuZoAnnN3gsZmARQEqUyogAVQKKdiQQIWQEGQAngspGqhAuafUE7FggTMP0GQPSgjgPegSW2HKIC3FbR/FwIWQF2oqtlWwAKoreDy/c0/sZ6qxQiYf2IcVYkVMP/EeuZcTQDPuTtBY7MACoJUJlTAAiiUU7EgAQugIMhdZcw/sZ6qxQiYf2IcVYkVMP/EeuZcTQDPuTtBY7MACoJUJlTAAiiUU7EgAQugIEgBPBZStVAB808op2JBAuafIMgelBHAe9CktkMUwNsK2r8LAQugLlTVbCtgAdRWcPn+5p9YT9ViBMw/MY6qxAqYf2I9c64mgOfcnaCxWQAFQSoTKmABFMqpWJCABVAQ5K4y5p9YT9ViBMw/MY6qxAqYf2I9c64mgOfcnaCxWQAFQSoTKmABFMqpWJCABVAQpAAeC6laqID5J5RTsSAB808QZA/KCOA9aFLbIQrgbQXt34WABVAXqmq2FbAAaiu4fH/zT6ynajEC5p8YR1ViBcw/sZ45VxPAc+5O0NgsgIIglQkVsAAK5VQsSMACKAhyVxnzT6ynajEC5p8YR1ViBcw/sZ45VxPAG3Tn2uu2pa3vu3qw5RM2HJvOPXNzWrtmTYM989jEAiiPPhjFcgELIFdEjgIWQLFdMf/EeqoWI2D+iXFUJVbA/BPrmXM1AXxCd7Z//qa09f0fShdecF46YP3+acvWDwz22LzpxTn3ddnYLIB606qFGqgF0EK1uzcnawEU2yrzT6ynajEC5p8YR1ViBcw/sZ45VxPAJ3SnCNyHH3Zo2njKSYMt64E85+aWY7MA6kOXFm+MFkCL1/M+nLEFUGyXzD+xnqrFCJh/YhxViRUw/8R65lxNAB/Tnft27EiXXLElbXjcMUsB/OZbvpEuvWJLOufMzemoI4/IubdLY7MA6kWbFm6QFkAL1/JenLAFUGybzD+xnqrFCJh/YhxViRUw/8R65lxNAG8QwDee/Iy04bhjB1vWA/h377g75/4Oxnb2DZelA/fZPz1oj72yH6sBEiBAYDUFfrLz/vS9HXely55+1moOY26Obf6Zm1Y6EQIEOhbo0/xz0EP361hjvssL4A0C+Lg74H0I4PN9CTs7AgQIECBAgAABAgRmJSCAt5MWwCf4zcN7wNtdIvYmQIAAAQIECBAgQIAAgQgBAXyC4jx8CnrEhaIGAQIECBAgQIAAAQIECLQTEMAb+PX974A3OEWbECCQUi//zKDGESBAgAABAgQI9EdAAO9Pr4yUwEiBYZ/YX2xcvHi0/QtfTOeeuTmtXbNmxYJRdVY8gBntWLzlpPjavOnFMzqiwxAgQGBlAsP+LOr37/xBetPbLk6bXvKCpQ+PXUn1qDorOfas9yl+71+/7Yalw65bt196w+vPHvylm77OCWX/vnnrt5fOa9NpL1z6iz7FPxYfKvyWd1yW7rrr/3yY8G+e/5rBdVPeeCr/uyxSWtX/fdY9czwCfRcQwPveQeMnkFISwGMug74utmLOXhUCBPokIIC379awF5cL129+67ZBWO3rnFB/AaX871OeeeLgvIpzfOtF70rVIF1s86GPfTy99EWnpm2f/my67i//Oj3yp49Mr3z5aYMX8IvAXnh867bvpNe+alOrF3jad04FAv0WEMD73T+jJzAQaBrAy+1u3H7TYL/qK+L1V8NPPumEwZ3g+r8f9ohD0oUXnDeYqO+99950z733pqJe9a5B/c/1lcct/6RfMYl/9/bb0223fTcVr9AX+77u7P+YPvpnn9itVrlwOOFpx6c/v27b4NX6J2w4duxd/eqYq+OaVKu62KovvEYZuwQJECCwGgJNA3gZtooxlr+/D1i//2DI1bu/9Tu/1bvCxVxxzGMenS69Yks64eefmv74I3862L+cJ8paxf8vnyAqjnvt9Z8a/K7+9re/M9i3CHT/4//93NK+T3nS4wdBsF6rDMb77rPP0vZN77oW+xZfRdAc9zXpd3rVrahTzjv33bdj8JRBeXe5Oq7Cszovlm7VO81Vs6Ju9W2O9R6Nm7PHnduwJxiK4/zLN781CNiXXLElVf/CT71WuW3x70WPirvixbk9+uifTn/y53/R+gmL1fh5cUwCOQkI4Dl1w1gIrFCgSQAvSheT7kEPe9hggVRM0L9z+ZXpFae/aPCo3V/f8Dfpp4/6qcH/LgNs+Sr3sLsExWT8Pz/3v5Ye1atuUy62zjlz86DesAD+xX/8p0GQLxaC9VrV8FsuJA4++KDBQq48j1GLh/rCo1hEvfPKrYNxrl+//2DhNKpW9biFwbuv+mD6tbNeNRjjsMXuCttlNwIECLQWaBLAq7//it/FZbAq54DiTucvPe85g7GUL4wWv2fLkFl9lL2cF576s09cmkOqj7vXX7SsB/AihJ76/I2DYFyvVX/Rtv4I9DS/f5sG8PKcq/NYvSmTXoitz5X1uayoN25urfenep5r164ZO2dPG8DLcznlpBMGL4aU8/OwOuV1Umx7zZ//RfqVU5+X/vTa69PzNp6c3n7xFQJ4659eBRZdQABf9CvA+c+FQP1V8upJla/a10Nxufg4/LBDd7tTUA/0owJ4UaO821FdQBX/Xp3ghwXw6r71+tX/HrYQHPee9PpCrXouTzv+ybu9P7L6pwbri63694ZZzcUF5CQIEOidQP0ObfUEyruy9d9p9RcWq/tUf3cW/15/L3k9JJdzSPm7fFIAr84Jox6RLgN//Xf8NO9JnyaAl+dQvdtffcJqmF89vI57cqp+UdXn1mFmW9//ocGL03fe+YPdgnL9T+OOumjrXtUXCg5Yv37Zi8vjAngxvxfHLPZ/wS8+Nx115E+FfMZA737YDJhAsIAAHgyqHIHVEGhyB7wI4PUPXCnGWj6GPizEl99rEsCrd9S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gQIAAAQIECBAgQIAAAQO4e4AAAQIECBAgQIAAAQIECPQgYADvAdkpCBA4WuBrd3+9lD2lfOe+++MhQIAAAQK9Cag/vVE7EQECCwQM4G4LAgTWIqABWgu7kxIgQGD0AurP6G8BAATWKmAAXyu/kxMYr4AGaLx778oJECCwTgH1Z536zk2AgAHcPUCAwFoENEBrYXdSAgQIjF5A/Rn9LQCAwFoFDOBr5XdyAuMV0ACNd+9dOQECBNYpoP6sU9+5CRAwgLsHCBBYi4AGaC3sTkqAAIHRC6g/o78FABBYq4ABfK38Tk5gvAIaoPHuvSsnQIDAOgXUn3XqOzcBAgZw9wABAmsR0ACthd1JCRAgMHoB9Wf0twAAAmsVMICvld/JCYxXQAM03r135QQIEFingPqzTn3nJkDAAO4eIEBgLQIaoLWwOykBAgRGL6D+jP4WAEBgrQIG8LXyOzmB8QpogMa7966cAAEC6xRQf9ap79wECBjA3QMECKxFQAO0FnYnJUCAwOgF1J/R3wIACKxVwAC+Vn4nJzBeAQ3QePfelRMgQGCdAurPOvWdmwABA7h7gACBtQhogNbC7qQECBAYvYD6M/pbAACBtQoYwNfK7+QExiugARrv3rtyAgQIrFNA/VmnvnMTIGAAdw8QILAWAQ3QWtidlAABAqMXUH9GfwsAILBWAQP4WvmdnMB4BTRA4917V06AAIF1Cqg/69R3bgIEDODuAQIE1iKgAVoLu5MSIEBg9ALqz+hvAQAE1ipgAF8rv5MTGK+ABmi8e+/KCRAgsE4B9Wed+s5NgIAB3D1AgMBaBDRAa2F3UgIECIxeQP0Z/S0AgMBaBQzga+V3cgLjFdAAjXfvXTkBAgTWKaD+rFPfuQkQMIC7BwgQWIuABmgt7E5KgACB0QuoP6O/BQAQWKuAAXyt/E5OYPcKvOm3/l255l0fPHKBv3bpBeVZP/VjR/57vgH64p13lcv+xZvLTTf/5eQx84/fvVKujAABAgT6FDCA96ntXAQIzAsYwN0TBAjEBe6+5+vl4DveX57/s88oD95/QvnkrZ8tL/21K8uvv/wXyw884bGT8802QM3jf+ON15SnPPFxkyF90ePjixRIgAABAqMUMICPcttdNIFqBAzg1WyFhRDYvQLzA/b8AP7fP3ZzedNvvadc8S9/aTKwN7+aV9CbX5e88Gd3L4wrI0CAAIHeBQzgvZM7IQECMwIGcLcDAQIrF5i+vfySFz574Svgiwbw933wj8t/++iflX/20gvKA/bdf+VrdAICBAgQGIeAAXwc++wqCdQqYADfZmduufVT5VWvvbLcdddXJo984plnlEsvPlD27d1b655aF4HqBBa9mj3bAC0a0A3g1W2jBREgQGBXCBjAd8U2uggCgxUwgG+zddddf0M59ZSTy5mPP2PyyIOHfnvyvwcuPH+wm27hBPoUaIbv2z//xaNeyZ5vgJpXwf/P/+t1m5Z2wXN+ylvQ+9ws5yJAgMAIBAzgI9hkl0igYgED+A43pxnIb/zTT3gVfIduHj5OgWXDd6OxXQPUHPsjP/j9R96yPk5BV02AAAECaYHt6k/6fPIIECAwK2AA38H9cM/hw+UNVx0sJz3kIUdeAf/mvffuIMFDCYxH4C1vf+/kYl/8gn+w8KKvPPjucvsdXyr//NKjP+P9/t//T+UjN95cXnHJ8+v+/Pd949lPV0qAAIHdInDP4W+UsqeUfff/juFe0p7hLt3Khy9wv+OPH/5FrPEKDOAt8Zu3nn/ohg8f9RnwL975tZYJHkZgPAJ3fvkr5Vf/1VvLzf/fLZsu+id/9Mnl0n/03LJv3/3LW699X7njr79ULrvo2//9ezf8l/L6//udk8f//LP+bjnwCz9TP5gGqP49skICBAjMCXzrW99+9vS44wb8j7gngN3XaxR48P7vXOPZh39qA/gO99Bb0HcI5uEElgh4C6BbgwABAgTWIaD+rEPdOQkQmAoYwHd4LzTfiv62a95ZXnbJReVB+0/c4dEeToDAVEAD5F4gQIAAgXUIqD/rUHdOAgQM4C3vgWv/7XvLj/3QU8rppz1ickTzVvQ7vvAFX8LW0s/DCCwT0AC5NwgQIEBgHQLqzzrUnZMAAQN4y3vgxo/fVF79urccebSfA94SzsMIbCOgAXKLECBAgMA6BNSfdag7JwECBnD3AAECaxXQAK2V38kJECAwWgH1Z7Rb78IJVCHgM+BVbMNqF/HWGz9QPvK5Py979gz42z5XSyR9DQL3Nd9Cu6e4L9dg75TLBb51333laac+rrzo8T+NiQCBXSpgAN+lG+uyCAxEwAA+kI3qsszn/85vlr9z0iPK3uMG/PMuuwA4tkqBe+/99gB+/JB/DEyVshbVReDwvd8oN3/h0+Wav/dPu8Q4lgCBNQq86bf+XbnmXR88soJfu/SC8qyf+rEj/z07gH/xzrvKZf/izeWmm/9y04rPeOx3lyv+5S+VB+8/YY1X4tQECOxGAQP4btzVuWv6hx/4zfLkhz6m7Dv+/iO4Wpc4FIFvfvNbkwH8fscfN5QlW+cIBO659+vlo7d9shwygI9gt13ibhS4+56vl4PveH95/s8+YzI8f/LWz5aX/tqV5ddf/ovlB57w2Mklb/cK+Ps++Mfllk/fVi554c/uRiLXRIDAmgUM4GvegD5ObwDvQ9k5dipgAN+pmMf3IWAA70PZOQj0J9AM5L/xxmvKU574uCOvgm81gDeviP/6695eXnrgOeXRpz2sv4U6EwECoxEwgI9gqw3gI9jkAV6iAXyAmzaCJRvAR7DJLnFUAtO3mF/ywme3egXcq9+juj1cLIG1CBjA18Le70kN4P16O1s7AQN4OyeP6lfAAN6vt7MRWLVA83nw5tfs28mXvQLu1e9V74Z8AgQaAQP4CO4DA/gINnmAl2gAH+CmjWDJBvARbLJLHI1AM3zf/vkvln/20gvKA/b9zffgLBvAFw3ro8FyoQQI9CZgAO+Nen0nMoCvz96ZlwsYwN0dNQoYwGvcFWsisHOBZcN3k7RoAG++rO2Vr3t7ufzlL/DZ751zO4IAgR0IGMB3gDXUhxrAh7pzu3vdBvDdvb9DvToD+FB3zroJ/I3Adq9kX3H1vy233/GlcvnLfvHIK+PbHcOXAAECKQEDeEqy4hwDeMWbM+KlGcBHvPkVX7oBvOLNsTQCLQSW/Vzvn376U4+8FX1+AP/vH7u5vOm33uPnfrfw9RACBLoLGMC7G1afYACvfotGuUAD+Ci3vfqLNoBXv0UWSKCzwHY/B7zzCQQQIEBgCwED+AhuDwP4CDZ5gJdoAB/gpo1gyQbwEWyySxy9gAF89LcAAAJrFTCAr5W/n5MbwPtxdpadCRjAd+bl0f0IGMD7cXYWAusUMICvU9+5CRAwgI/gHjCAj2CTB3iJBvABbtoIlmwAH8Emu8TRCxjAR38LACCwVgED+Fr5+zm5AbwfZ2fZmYABfGdeHt2PgAG8H2dnIbBOAQP4OvWdmwABA/gI7gED+Ag2eYCXaAAf4KaNYMkG8Owmv+pP3lH+x+f+PBsqjQABArtQ4L5yX3nqwx5bfuWHzt+FV+eSZgUM4CO4HwzgI9jkAV6iAXyAmzaCJRvAs5v8Dz/wmvKkhz6m7Dv+/tlgaQQ6CKg/HfAcujKBe+79RvnobZ8s1/y9f7qycwiuQ8AAXsc+rHQVBvCV8go/RgEN0DHCOWylAgbwLK/6k/WUlhFQfzKOUrIC6k/Ws+Y0A3jNuxNamwYoBCkmKqABinIKCwlogEKQGzHqT9ZTWkZA/ck4SskKqD9Zz5rTDOA1705obRqgEKSYqIAGKMopLCSgAQpBGsCzkNKiAupPlFNYSED9CUEOIMYAPoBN6rpEA3hXQcevQkADtApVmV0FNEBdBTcfr/5kPaVlBNSfjKOUrID6k/WsOc0AXvPuhNamAQpBiokKaICinMJCAhqgEORGjPqT9ZSWEVB/Mo5SsgLqT9az5jQDeM27E1qbBigEKSYqoAGKcgoLCWiAQpAG8CyktKiA+hPlFBYSUH9CkAOIMYAPYJO6LtEA3lXQ8asQ0ACtQlVmVwENUFfBzcerP1lPaRkB9SfjKCUroP5kPWtOM4DXvDuhtWmAQpBiogIaoCinsJCABigEuRGj/mQ9pWUE1J+Mo5SsgPqT9aw5zQBe8+6E1qYBCkGKiQpogKKcwkICGqAQpAE8CyktKqD+RDmFhQTUnxDkAGIM4APYpK5LNIB3FXT8KgQ0QKtQldlVQAPUVXDz8epP1lNaRkD9yThKyQqoP1nPmtMM4DXvTmhtGqAQpJiogAYoyiksJKABCkFuxKg/WU9pGQH1J+MoJSug/mQ9a04zgNe8O6G1aYBCkGKiAhqgKKewkIAGKARpAM9CSosKqD9RTmEhAfUnBDmAGAP4ADap6xIN4F0FHb8KAQ3QKlRldhXQAHUV3Hy8+pP1lJYRUH8yjlKyAupP1rPmNAN4zbsTWpsGKAQpJiqgAYpyCgsJaIBCkBsx6k/WU1pGQP3JOErJCqg/Wc+a0wzgNe9OaG0aoBCkmKiABijKKSwkoAEKQRrAs5DSogLqT5RTWEhA/QlBDiDGAD6ATeq6RAN4V0HHr0JAA7QKVZldBTRAXQU3H6/+ZD2lZQTUn4yjlKyA+pP1rDnNAF7z7oTWpgEKQYqJCmiAopzCQgIaoBDkRoz6k/WUlhFQfzKOUrIC6k/Ws+Y0A3jNuxNamwYoBCkmKqABinIKCwlogEKQBvAspLSogPoT5RQWElB/QpADiDGAD2CTui7RAN5V0PGrENAArUJVZlcBDVBXwc3Hqz9ZT2kZAfUn4yglK6D+ZD1rTjOA17w7obVpgEKQYqICGqAop7CQgAYoBLkRo/5kPaVlBNSfjKOUrID6k/WsOc0AXvPuhNamAQpBiokKaICinMJCAhqgEKQBPAspLSqg/kQ5hYUE1J8Q5ABiDOAD2KSuSzSAdxV0/CoENECrUJXZVUAD1FVw8/HqT9ZTWkZA/ck4SskKqD9Zz5rTDOA1705obRqgEKSYqIAGKMopLCSgAQpBbsSoP1lPaRkB9SfjKCUroP5kPWtOM4DXvDuhtWmAQpBiogIaoCinsJCABigEaQDPQkqLCqg/UU5hIQH1JwQ5gBgD+AA2qesSDeBdBR2/CgEN0CpUZXYV0AB1Fdx8vPqT9ZSWEVB/Mo5SsgLqT9az5jQDeM27E1qbBigEKSYqoAGKcgoLCWiAQpAbMepP1lNaRkD9yThKyQqoP1nPmtMM4DXvTmhtGqAQpJiogAYoyiksJKABCkEawLOQ0qIC6k+UU1hIQP0JQQ4gxgA+gE3qukQDeFdBx69CQAO0ClWZXQU0QF0FNx+v/mQ9pWUE1J+Mo5SsgPqT9aw5zQBe8+6E1qYBCkGKiQpogKKcwkICGqAQ5EaM+pP1lJYRUH8yjlKyAupP1rPmNAN4zbsTWpsGKAQpJiqgAYpyCgsJaIBCkAbwLKS0qID6E+UUFhJQf0KQA4gxgA9gk7ou0QDeVdDxqxDQAK1CVWZXAQ1QV8HNx6s/WU9pGQH1J+MoJSug/mQ9a04zgNe8O6G1aYBCkGKiAhqgKKewkIAGKAS5EaP+ZD2lZQTUn4yjlKyA+pP1rDnNAF7z7oTWpgEKQYqJCmiAopzCQgIaoBCkATwLKS0qoP5EOYWFBNSfEOQAYgzgA9ikrks0gHcVdPwqBDRAq1CV2VVAA9RVcPPx6k/WU1pGQP3JOErJCqg/Wc+a0wzgNe9OaG0aoBCkmKiABijKKSwkoAEKQW7EqD9ZT2kZAfUn4yglK6D+ZD1rTjOA17w7obVpgEKQYqICGqAop7CQgAYoBGkAz0JKiwqoP1FOYSEB9ScEOYAYA/g2m3Tjx28qr37dW4486olnnlEuvfhA2bd37wC299tLNIAPZqtGtVAN0Ki2ezAXqwHKbpX6k/WUlhFQfzKOUrIC6k/Ws+Y0A/g2u3Pd9TeUU085uZz5+DPKPYcPlzdcdbCc9JDSjRsoAAAgAElEQVSHlAMXnl/zvm5amwZoMFs1qoVqgEa13YO5WA1QdqvUn6yntIyA+pNxlJIVUH+ynjWnGcB3uDvNQH7jn35iUK+Ca4B2uMke3ouABqgXZifZoYAGaIdg2zxc/cl6SssIqD8ZRylZAfUn61lzmgF8h7tz8NBvT47wCvgO4TycwJyABsgtUaOABii7KwbwrKe0jID6k3GUkhVQf7KeNacZwHewO83nwQ+9493l8ldcVh60/8TJkXd88Ss7SFjPQy/94zeX7/9bjyp7j/uO9SzAWQkQIDAQgcP3fqPc9MVbyhU/+uKBrLjuZao/de+P1REgUI/AkOrPSQ9+YD1wA1yJAbzlpjXD95uvPlR+9ZdfUk4/7RFHjrrvvvtaJqzvYRd84F+VJ53ymLLv+PuvbxHOTGBO4JvfvHfyO/e73/FsCFQjcOQViGf+cjVrGvJC1J8h797uXbv6s3v3dshXNqT6s2fPniFTr33tBvAWW7Bs+G5xaBUP8RbAKrbBIo4awL9Vyp5S7nf8cWwIVCPgLYDZrVB/sp7SMgLegp5xlJIVUH+ynjWnDWoAbz5//aEbPjzxPOfss3r5HPait53XvKGL1qYBGtqOjWO9GqBx7PPQrlIDlN0x9SfrKS0joP5kHKVkBdSfrGfNaYMZwOe/fbwZxh9+6inlvHPPXqnv7NA/PdEJJzzwqLeir3QRHcM1QB0BHb4SAQ3QSliFdhTQAHUEnDtc/cl6SssIqD8ZRylZAfUn61lz2iAG8OnP3z7vnKdPfh538+uWWz9V3nbNO8vLLrnoyBei1Qy9zrVpgNap79zLBDRA7o0aBTRA2V1Rf7Ke0jIC6k/GUUpWQP3JetacNogB/Et3frm8/k1Xlxdd8NwjX4C26Pdqhl7n2jRA69R3bgO4e2BIAhqg7G6pP1lPaRkBA3jGUUpWQP3JetacNugB/JWvuaJc+As/d+RV8Zqh17k2DdA69Z3bAO4eGJKABii7W+pP1lNaRsAAnnGUkhVQf7KeNacZwGvendDaNEAhSDFRAQ1QlFNYSEADFILciFF/sp7SMgLqT8ZRSlZA/cl61pw2mAG8ebX7M5+9bUvLUx/20HL5Ky7zmfA5JQ1QzX8Fx7s2DdB4977mK9cAZXdH/cl6SssIqD8ZRylZAfUn61lz2iAG8JoBh7A2DdAQdml8a9QAjW/Ph3DFGqDsLqk/WU9pGQH1J+MoJSug/mQ9a04zgNe8O6G1aYBCkGKiAhqgKKewkIAGKAS5EaP+ZD2lZQTUn4yjlKyA+pP1rDltcAP4op/Lfc7ZZ5UDF55fs/Na16YBWiu/ky8R0AC5NWoU0ABld0X9yXpKywioPxlHKVkB9SfrWXPaYAbw5seONZ8DP/nkk8qlFx8o+/bunbhOf0b47bff4fPfS+40DVDNfwXHuzYN0Hj3vuYr1wBld0f9yXpKywioPxlHKVkB9SfrWXPaYAbw5pXv5teyV7q3+/OaN2HVa9MArVpY/rEIaICORc0xqxbQAGWF1Z+sp7SMgPqTcZSSFVB/sp41pw1iAJ++yn3eOU9f+jO/b/z4TeW6D/3BplfHa4bvc20aoD61nautgAaorZTH9SmgAcpqqz9ZT2kZAfUn4yglK6D+ZD1rThvEAN68/fz1b7q6vOiC55bTT3vEQs9bbv1Ueds17ywvu+QiP4ZsTkgDVPNfwfGuTQM03r2v+co1QNndUX+yntIyAupPxlFKVkD9yXrWnDaIAdwr4N1uIQ1QNz9Hr0ZAA7QaV6ndBDRA3fzmj1Z/sp7SMgLqT8ZRSlZA/cl61pw2iAG8AdzuM97b/XnNm7DqtWmAVi0s/1gENEDHouaYVQtogLLC6k/WU1pGQP3JOErJCqg/Wc+a0wYzgPsW9GO/jTRAx27nyNUJaIBWZyv52AU0QMdut+hI9SfrKS0joP5kHKVkBdSfrGfNaYMZwKeI111/Qzl07bs2mfo54FvfYhqgmv8KjndtGqDx7n3NV64Byu6O+pP1lJYRUH8yjlKyAupP1rPmtMEN4DVj1ro2DVCtOzPudWmAxr3/tV69Bii7M+pP1lNaRkD9yThKyQqoP1nPmtMM4DXvTmhtGqAQpJiogAYoyiksJKABCkFuxKg/WU9pGQH1J+MoJSug/mQ9a04bzADe/JzvV7/uLeVXXv7io34W+FZ/VjN+X2vTAPUl7Tw7EdAA7UTLY/sS0ABlpdWfrKe0jID6k3GUkhVQf7KeNacNZgDf7lvOt/vzmjdh1WvTAK1aWP6xCGiAjkXNMasW0ABlhdWfrKe0jID6k3GUkhVQf7KeNacNYgCffgP6hb/wc0e9+j3FbV4FP/SOd5fLX3FZedD+E2s2731tGqDeyZ2whYAGqAWSh/QuoAHKkqs/WU9pGQH1J+MoJSug/mQ9a04bzAD++jddXV50wXPL6ac9YqHnLbd+qrztmneWl11ykQF8TkgDVPNfwfGuTQM03r2v+co1QNndUX+yntIyAupPxlFKVkD9yXrWnDaIAfyew4fLG646WM475+lbvgJ+3Yf+oFx68YGyb+/ems17X5sGqHdyJ2whoAFqgeQhvQtogLLk6k/WU1pGQP3JOErJCqg/Wc+a0wYxgDeAzc///vRnPlcOXHj+Qs/mM+APP/WUct65Z9fsvZa1aYDWwu6k2whogNwiNQpogLK7ov5kPaVlBNSfjKOUrID6k/WsOW0wA/j0VfAGc/ZV7unv3377HT7/veRO0wDV/FdwvGvTAI1372u+cg1QdnfUn6yntIyA+pNxlJIVUH+ynjWnDWYAnyI2r4QfuvZdm0wvfN5zvPK9xV2mAar5r+B416YBGu/e13zlGqDs7qg/WU9pGQH1J+MoJSug/mQ9a04b3ABeM2ata9MA1boz416XBmjc+1/r1WuAsjuj/mQ9pWUE1J+Mo5SsgPqT9aw5zQBe8+6E1qYBCkGKiQpogKKcwkICGqAQ5EaM+pP1lJYRUH8yjlKyAupP1rPmNAN4zbsTWpsGKAQpJiqgAYpyCgsJaIBCkAbwLKS0qID6E+UUFhJQf0KQA4gxgA9gk7ou0QDeVdDxqxDQAK1CVWZXAQ1QV8HNx6s/WU9pGQH1J+MoJSug/mQ9a04zgNe8O6G1aYBCkGKiAhqgKKewkIAGKAS5EaP+ZD2lZQTUn4yjlKyA+pP1rDnNAF7z7oTWpgEKQYqJCmiAopzCQgIaoBCkATwLKS0qoP5EOYWFBNSfEOQAYgzgA9ikrks0gHcVdPwqBDRAq1CV2VVAA9RVcPPx6k/WU1pGQP3JOErJCqg/Wc+a0wzgNe9OaG0aoBCkmKiABijKKSwkoAEKQW7EqD9ZT2kZAfUn4yglK6D+ZD1rTjOA17w7obVpgEKQYqICGqAop7CQgAYoBGkAz0JKiwqoP1FOYSEB9ScEOYAYA/gANqnrEg3gXQUdvwoBDdAqVGV2FdAAdRXcfLz6k/WUlhFQfzKOUrIC6k/Ws+Y0A3jNuxNamwYoBCkmKqABinIKCwlogEKQGzHqT9ZTWkZA/ck4SskKqD9Zz5rTDOA1705obRqgEKSYqIAGKMopLCSgAQpBGsCzkNKiAupPlFNYSED9CUEOIMYAPoBN6rpEA3hXQcevQkADtApVmV0FNEBdBTcfr/5kPaVlBNSfjKOUrID6k/WsOc0AXvPuhNamAQpBiokKaICinMJCAhqgEORGjPqT9ZSWEVB/Mo5SsgLqT9az5jQDeM27E1qbBigEKSYqoAGKcgoLCWiAQpAG8CyktKiA+hPlFBYSUH9CkAOIMYAPYJO6LtEA3lXQ8asQ0ACtQlVmVwENUFfBzcerP1lPaRkB9SfjKCUroP5kPWtOM4DXvDuhtWmAQpBiogIaoCinsJCABigEuRGj/mQ9pWUE1J+Mo5SsgPqT9aw5zQBe8+6E1qYBCkGKiQpogKKcwkICGqAQpAE8CyktKqD+RDmFhQTUnxDkAGIM4APYpK5LNIB3FXT8KgQ0QKtQldlVQAPUVXDz8epP1lNaRkD9yThKyQqoP1nPmtMM4DXvTmhtGqAQpJiogAYoyiksJKABCkFuxKg/WU9pGQH1J+MoJSug/mQ9a04zgNe8O6G1aYBCkGKiAhqgKKewkIAGKARpAM9CSosKqD9RTmEhAfUnBDmAGAP4ADap6xIN4F0FHb8KAQ3QKlRldhXQAHUV3Hy8+pP1lJYRUH8yjlKyAupP1rPmNAN4zbsTWpsGKAQpJiqgAYpyCgsJaIBCkBsx6k/WU1pGQP3JOErJCqg/Wc+a0wzgNe9OaG0aoBCkmKiABijKKSwkoAEKQRrAs5DSogLqT5RTWEhA/QlBDiDGAD6ATeq6RAN4V0HHr0JAA7QKVZldBTRAXQU3H6/+ZD2lZQTUn4yjlKyA+pP1rDnNAF7z7oTWpgEKQYqJCmiAopzCQgIaoBDkRoz6k/WUlhFQfzKOUrIC6k/Ws+Y0A3jNuxNamwYoBCkmKqABinIKCwlogEKQBvAspLSogPoT5RQWElB/QpADiDGAD2CTui7RAN5V0PGrENAArUJVZlcBDVBXwc3Hqz9ZT2kZAfUn4yglK6D+ZD1rTjOAt9ydW279VHnbNe8sL7vkovKg/Se2PKqOh2mA6tgHq9gsoAFyR9QooAHK7or6k/WUlhFQfzKOUrIC6k/Ws+Y0A/g2u/OlO79cXvmaK8pnPntbOfVhDy2Xv+IyA3jNd7S1DUZAAzSYrRrVQjVA2e02gGc9pWUE1J+Mo5SsgPqT9aw5zQDecne8At4SysMItBTQALWE8rBeBTRAWW4DeNZTWkZA/ck4SskKqD9Zz5rTDOAtd8cA3hLKwwi0FNAAtYTysF4FNEBZbgN41lNaRkD9yThKyQqoP1nPmtMM4C13Z9kAfuddd7dMWN/DXvyHbyxPOOnRZe9x37G+RTgzgTmBb9133+R3jtuzhw2BagTuufcb5U//+i/Lm3/iJdWsacgLUX+GvHu7d+3qz+7d2yFf2ZDqz/4THjBk6rWv3QDecguWDeBf/8Y3Wyas72Ev+uC/Lk88+W+XvccbwNe3C848L3Dvvd8qZU8pxx93HBwC1Qg0DdDHPv/JcvAZL9t2TXuaG9ivLQVe+MHXliepPyu5S779FKZfxyKg/hyLmmNWLbCT+rPqtWyXf//vuN92D/HnWwgYwFveHt6C3hLKwwi0FPAWwJZQHtargLcAZrm9BT3rKS0joP5kHKVkBdSfrGfNaQbwlrtjAG8J5WEEWgpogFpCeVivAhqgLLcBPOspLSOg/mQcpWQF1J+sZ81pBvBtdmf2x5BNH3rO2WeVAxeeX/O+blqbBmgwWzWqhWqARrXdg7lYDVB2q9SfrKe0jID6k3GUkhVQf7KeNacZwGvendDaNEAhSDFRAQ1QlFNYSEADFILciFF/sp7SMgLqT8ZRSlZA/cl61pxmAK95d0Jr0wCFIMVEBTRAUU5hIQENUAjSAJ6FlBYVUH+inMJCAupPCHIAMQbwAWxS1yUawLsKOn4VAhqgVajK7CqgAeoquPl49SfrKS0joP5kHKVkBdSfrGfNaQbwmncntDYNUAhSTFRAAxTlFBYS0ACFIDdi1J+sp7SMgPqTcZSSFVB/sp41pxnAa96d0No0QCFIMVEBDVCUU1hIQAMUgjSAZyGlRQXUnyinsJCA+hOCHECMAXwAm9R1iQbwroKOX4WABmgVqjK7CmiAugpuPl79yXpKywioPxlHKVkB9SfrWXOaAbzm3QmtTQMUghQTFdAARTmFhQQ0QCHIjRj1J+spLSOg/mQcpWQF1J+sZ81pBvCadye0Ng1QCFJMVEADFOUUFhLQAIUgDeBZSGlRAfUnyiksJKD+hCAHEGMAH8AmdV2iAbyroONXIaABWoWqzK4CGqCugpuPV3+yntIyAupPxlFKVkD9yXrWnGYAr3l3QmvTAIUgxUQFNEBRTmEhAQ1QCHIjRv3JekrLCKg/GUcpWQH1J+tZc5oBvObdCa1NAxSCFBMV0ABFOYWFBDRAIUgDeBZSWlRA/YlyCgsJqD8hyAHEGMAHsEldl2gA7yro+FUIaIBWoSqzq4AGqKvg5uPVn6yntIyA+pNxlJIVUH+ynjWnGcBr3p3Q2jRAIUgxUQENUJRTWEhAAxSC3IhRf7Ke0jIC6k/GUUpWQP3JetacZgCveXdCa9MAhSDFRAU0QFFOYSEBDVAI0gCehZQWFVB/opzCQgLqTwhyADEG8AFsUtclGsC7Cjp+FQIaoFWoyuwqoAHqKrj5ePUn6yktI6D+ZBylZAXUn6xnzWkG8Jp3J7Q2DVAIUkxUQAMU5RQWEtAAhSA3YtSfrKe0jID6k3GUkhVQf7KeNacZwGvendDaNEAhSDFRAQ1QlFNYSEADFII0gGchpUUF1J8op7CQgPoTghxAjAF8AJvUdYkG8K6Cjl+FgAZoFaoyuwpogLoKbj5e/cl6SssIqD8ZRylZAfUn61lzmgG85t0JrU0DFIIUExXQAEU5hYUENEAhyI0Y9SfrKS0joP5kHKVkBdSfrGfNaQbwmncntDYNUAhSTFRAAxTlFBYS0ACFIA3gWUhpUQH1J8opLCSg/oQgBxBjAB/AJnVdogG8q6DjVyGgAVqFqsyuAhqgroKbj1d/sp7SMgLqT8ZRSlZA/cl61pxmAK95d0Jr0wCFIMVEBTRAUU5hIQENUAhyI0b9yXpKywioPxlHKVkB9SfrWXOaAbzm3QmtTQMUghQTFdAARTmFhQQ0QCFIA3gWUlpUQP2JcgoLCag/IcgBxBjAB7BJXZdoAO8q6PhVCGiAVqEqs6uABqir4Obj1Z+sp7SMgPqTcZSSFVB/sp41pxnAa96d0No0QCFIMVEBDVCUU1hIQAMUgtyIUX+yntIyAupPxlFKVkD9yXrWnGYAr3l3QmvTAIUgxUQFNEBRTmEhAQ1QCNIAnoWUFhVQf6KcwkIC6k8IcgAxBvABbFLXJRrAuwo6fhUCGqBVqMrsKqAB6iq4+Xj1J+spLSOg/mQcpWQF1J+sZ81pBvCadye0Ng1QCFJMVEADFOUUFhLQAIUgN2LUn6yntIyA+pNxlJIVUH+ynjWnGcBr3p3Q2jRAIUgxUQENUJRTWEhAAxSCNIBnIaVFBdSfKKewkID6E4IcQIwBfACb1HWJBvCugo5fhYAGaBWqMrsKaIC6Cm4+Xv3JekrLCKg/GUcpWQH1J+tZc5oBvObdCa1NAxSCFBMV0ABFOYWFBDRAIciNGPUn6yktI6D+ZBylZAXUn6xnzWkG8Jp3J7Q2DVAIUkxUQAMU5RQWEtAAhSAN4FlIaVEB9SfKKSwkoP6EIAcQYwAfwCZ1XaIBvKug41choAFaharMrgIaoK6Cm49Xf7Ke0jIC6k/GUUpWQP3JetacZgCveXdCa9MAhSDFRAU0QFFOYSEBDVAIciNG/cl6SssIqD8ZRylZAfUn61lzmgG85t0JrU0DFIIUExXQAEU5hYUENEAhSAN4FlJaVED9iXIKCwmoPyHIAcQYwAewSV2XaADvKuj4VQhogFahKrOrgAaoq+Dm49WfrKe0jID6k3GUkhVQf7KeNacZwGvendDaNEAhSDFRAQ1QlFNYSEADFILciFF/sp7SMgLqT8ZRSlZA/cl61pxmAK95d0Jr0wCFIMVEBTRAUU5hIQENUAjSAJ6FlBYVUH+inMJCAupPCHIAMQbwAWxS1yUawLsKOn4VAhqgVajK7CqgAeoquPl49SfrKS0joP5kHKVkBdSfrGfNaQbwmncntDYNUAhSTFRAAxTlFBYS0ACFIDdi1J+sp7SMgPqTcZSSFVB/sp41pxnAa96d0No0QCFIMVEBDVCUU1hIQAMUgjSAZyGlRQXUnyinsJCA+hOCHECMAXwAm9R1iQbwroKOX4WABmgVqjK7CmiAugpuPl79yXpKywioPxlHKVkB9SfrWXOaAbzm3QmtTQMUghQTFdAARTmFhQQ0QCHIjRj1J+spLSOg/mQcpWQF1J+sZ81pBvCadye0Ng1QCFJMVEADFOUUFhLQAIUgDeBZSGlRAfUnyiksJKD+hCAHEGMAH8AmdV2iAbyroONXIaABWoWqzK4CGqCugpuPV3+yntIyAupPxlFKVkD9yXrWnGYAr3l3QmvTAIUgxUQFNEBRTmEhAQ1QCHIjRv3JekrLCKg/GUcpWQH1J+tZc5oBvObdCa1NAxSCFBMV0ABFOYWFBDRAIUgDeBZSWlRA/YlyCgsJqD8hyAHEGMAHsEldl2gA7yro+FUIaIBWoSqzq4AGqKvg5uPVn6yntIyA+pNxlJIVUH+ynjWnGcBr3p3Q2jRAIUgxUQENUJRTWEhAAxSC3IhRf7Ke0jIC6k/GUUpWQP3JetacZgCveXdCa9MAhSDFRAU0QFFOYSEBDVAI0gCehZQWFVB/opzCQgLqTwhyADEG8AFsUtclGsC7Cjp+FQIaoFWoyuwqoAHqKrj5ePUn6yktI6D+ZBylZAXUn6xnzWkG8Jp3J7Q2DVAIUkxUQAMU5RQWEtAAhSA3YtSfrKe0jID6k3GUkhVQf7KeNacZwGvendDaNEAhSDFRAQ1QlFNYSEADFII0gGchpUUF1J8op7CQgPoTghxAjAF8AJvUdYkG8K6Cjl+FgAZoFaoyuwpogLoKbj5e/cl6SssIqD8ZRylZAfUn61lzmgG8xe5cd/0N5dC175o88olnnlEuvfhA2bd3b4sj63iIBqiOfbCKzQIaIHdEjQIaoOyuqD9ZT2kZAfUn4yglK6D+ZD1rTjOAb7M7N378pnLoHe8ul7/isvKg/SeWg4d+e3LEgQvPr3lfN61NAzSYrRrVQjVAo9ruwVysBii7VepP1lNaRkD9yThKyQqoP1nPmtMM4NvsTjNwP/zUU8p55549eeT8QF7z5k7XpgEawi6Nb40aoPHt+RCuWAOU3SX1J+spLSOg/mQcpWQF1J+sZ81pBvAtdueew4fLG646WM78/u87MoDfcuunyhuvOlheevGBcvppj6h5b4+sTQM0iG0a3SI1QKPb8kFcsAYou03qT9ZTWkZA/ck4SskKqD9Zz5rTDOAtBvDzznl6OfPxZ0weOT+A3/HFr9S8v5O1veTDV5a/9YATy/32HF/9Wi2QAAEC6xT45rfuLX99+K5y5Y9dss5l7Jpzqz+7ZitdCAECKxYYUv056cEPXLHG7o43gLcYwLd6BXwIA/juvoVdHQECBAgQIECAAAECfQkYwLtJG8C38dsNnwHvdos4mgABAgQIECBAgAABAgQSAgbwbRR3w7egJ24UGQQIECBAgAABAgQIECDQTcAA3sJv6D8HvMUleggBAqUM8scM2jgCBAgQIECAAIHhCBjAh7NXVkpgqcCib+xvHtw8eXTjn36iXHrxgbJv795jFkzlHPMCejqw+chJ8+vAhef3dEanIUCAwLEJLPqxqF+688vlla+5olz4Cz935MtjjyU9lXMs5+77mObf/Q/d8OEjpz3hhAeWX/3ll0x+0s1Qa8J0/z7z2duOXNeFz3vOkZ/o0/xm86XCr3rtleWuu/7my4R/5eUvntw30xeepv89DZlazf9+33vmfASGLmAAH/oOWj+BUooBPHMbDLXZyly9FAIEhiRgAO++W4ueXG5cP/O52yfD6lBrwvwTKNP/Pvcnf3xyXc01vvp1bymzg3TzmHf/+w+U5z/32eWGP/rP5fr/9w/Ldz/qtPKPXvC8yRP4zcDeeHzu9s+XX7rowk5P8HTfOQkEhi1gAB/2/lk9gYlA2wF8+riP3njT5LjZZ8Tnnw0/5+yzJq8Ez//+qQ97aLn8FZdNCvXdd99dvnr33aXJm33VYP7H9U3PO/2Rfk0Rv+MLXyi3335HaZ6hb479Jy/5x+W9v/N7R2VNG4ezfvip5Xevv2HybP0Tzzxjy1f1Z9c8u67tsmabrfnGa5mxW5AAAQLrEGg7gE+HrWaN03+/H7T/xMmSZ1/9nX/ld/ZV4aZWfN9jv6e88aqD5awfeVr5N+95/+T4aZ2YZjX/O30HUXPe6z70B5N/q2+77fOTY5uB7j/9l48cOfYpT37CZBCcz5oOxt/1gAcceXzbV12bY5tfzaC51a/t/k2fdWtypnXnnnsOT95lMH11eXZdjedsXZy6zb7SPGvW5M5+zHF+j7aq2Vtd26J3MDTn+fRnPjcZsN9w1cEy+xN+5rOmj21+v9mj5lXx5tq+59GPKv/hd3+/8zss1vH3xTkJ1CRgAK9pN6yFwDEKtBnAm+im6J70kIdMGqSmQL/+TVeXF13w3Mlb7f7ww39SHnX6Iyf/fzrATp/lXvQqQVOM/+tH/ueRt+rNPmbabL304gOTvEUD+Cf+7H9NBvmmEZzPmh1+p43EySefNGnkptexrHmYbzyaJurNVx+arHP//hMnjdOyrNnzNgZvu+ad5WWXXDRZ46Jm9xi3y2EECBDoLNBmAJ/996/5t3g6WE1rQPNK5z/4mZ+erGX6xGjz7+x0yJx9K/u0LjztB590pIbMvt19/knL+QG8GUKf/azzJoPxfNb8k7bzb4Heyb+/bQfw6TXP1rH5Tdnuidj5Wjlfy5q8rWrr/P7MXue+fXu3rNk7HcCn13Lu2WdNngyZ1udFOdP7pHns+37398vPP/tnyvuv+1D5mfPOKb95xVUG8M5/ewWMXcAAPvY7wPXvCoH5Z8lnL2r6rP38UDxtPh5+6ilHvVIwP9AvG8CbjOmrHbMNVPP7swV+0QA+e+x8/ux/L2oEt/pM+nyjNnstP/zUHzjq85GzP2pwvtma/7NFVrviBnIRBAgMTmD+FdrZC5i+Kjv/b9r8E4uzx8z+29n8/vxnyeeH5GkNmf5bvt0APlsTlr1Fejrwz/8bv5PPpO9kAJ9ew+yr/bPvsFrkNz+8bvXOqfmbar62LjI79I53T56cvvPOLx81KM//aNxlN+281+wTBQ/av3/Tk8tbDeBNfW/O2Rz/c3//meX00x4Z+Y6BwZLTBnoAAAlFSURBVP1ls2ACYQEDeBhUHIF1CLR5BbwZwOe/cKVZ6/Rt6IuG+OmftRnAZ19RX/UAPvvKyvyXyy36s2nTstMBfJr1guf9fHnL1YeOvFtgHXvsnAQIEFg2ME/fUj4/eM1/wVhz/Ozb0OeH+OmftR3AZ19RX+UAPv8k7qzDVk9AN4+bf8v3VnfR1O/7Hve9kyeX2zyBsZVBc66tauv8QD37JEgzgG9Vs9tcx+yXsE2flFn0RMp81uw1zdbURU+I+1tJgMDOBQzgOzdzBIHqBNoO4LNvqV7UwEzfnu4V8G9/C/rU4atf+drkrfS+Hb26W9+CCIxWoM1b0Ld6xXSrtz+3HcC3evV30WfAp297rukV8EXD5/Snh/w/73zP5I8XvdOrqQnNrzbfHbKstm73Cviymr3dTb/VOwa2++x7kz07gM+eayfvRNhujf6cwJgFDOBj3n3XvmsE2gzgzcXOfga8+e/mmfD//5a/Kk976pM3fSnLom9Mnb4tbvbLe2Ybk0Wf256+nXD+G1fnm46dvAV9uwZg0drnPwM++7nGrd6CPm1EDl37rk3fFrtrbhwXQoDAYAXaDODzQ3Zzse99/++Vs3/8R8ott/5Vmf13vfl3+Pr/+EeTtz9PP388+10b86+cLvrc9nRwndab5n9nv4TtWAfwnfwozLZvQW/q5lvffm151jOfMXmCtfm13ZPP0z+fDtSLPgM+Wxfn87arrU09mn4/yvxnwGdr9k+c9cNb3rfb1ck234LefGHb/JPO2+UO9i+ThRPoWcAA3jO40xFYhUCbAbx5q/b8W+Fmv/V29q2Ize+f8F3fWc79uz8x+Xz47HGz34K+7HNzzTXO5v3oD/3g5NvSZ78FfbZJaTOAb/XzTOdNt/sW9J0M4Fu93X0VeymTAAECbQTaDODz/xY3/z19S/Z8Pfiex3x3+erXvnbkyzFn/w1f9G3eTdbsN4DP5jU15Jnnnl1u/otPHvMA3jzxOf01/+3tW/m0HcCbjPlvIG9+b/ang8z+PO2234I+W9vm/edra/Pnsx8TaPam+QkhzZMW29XsrQzaDMrb/RxwA3ibv4UeQ+DYBAzgx+bmKAKjF5h/FXtVIG0aiVWde9ocTX8MyyrPI5sAAQI1C7T57HBq/Tt5xTt1zhpylr31u4a1WQMBAjkBA3jOUhKBUQmMYQDf6huDR7XZLpYAgdELGMDzt8DsK+xN+uw3sC872/wx84/byZfO5a9IIgECbQQM4G2UPIYAAQIECBAgQIAAAQIECHQUMIB3BHQ4AQIECBAgQIAAAQIECBBoI2AAb6PkMQQIECBAgAABAgQIECBAoKOAAbwjoMMJECBAgAABAgQIECBAgEAbAQN4GyWPIUCAAAECBAgQIECAAAECHQUM4B0BHU6AAAECBAgQIECAAAECBNoIGMDbKHkMAQIECBAgQIAAAQIECBDoKGAA7wjocAIECBAgQIAAAQIECBAg0EbAAN5GyWMIECBAgAABAgQIECBAgEBHAQN4R0CHEyBAgAABAgQIECBAgACBNgIG8DZKHkOAAAECBAgQIECAAAECBDoKGMA7AjqcAAECBAgQIECAAAECBAi0ETCAt1HyGAIECBAgQIAAAQIECBAg0FHAAN4R0OEECBAgQIAAAQIECBAgQKCNgAG8jZLHECBAgAABAgQIECBAgACBjgIG8I6ADidAgAABAgQIECBAgAABAm0EDOBtlDyGAAECBAgQIECAAAECBAh0FDCAdwR0OAECBAgQIECAAAECBAgQaCNgAG+j5DEECBAgQIAAAQIECBAgQKCjgAG8I6DDCRAgQIAAAQIECBAgQIBAGwEDeBsljyFAgAABAgQIECBAgAABAh0FDOAdAR1OgAABAgQIECBAgAABAgTaCBjA2yh5DAECBAgQIECAAAECBAgQ6ChgAO8I6HACBAgQIECAAAECBAgQINBGwADeRsljCBAgQIAAAQIECBAgQIBARwEDeEdAhxMgQIAAAQIECBAgQIAAgTYCBvA2Sh5DgAABAgQIECBAgAABAgQ6ChjAOwI6nAABAgQIECBAgAABAgQItBEwgLdR8hgCBAgQIECAAAECBAgQINBRwADeEdDhBAgQIECAAAECBAgQIECgjYABvI2SxxAgQIAAAQIECBAgQIAAgY4CBvCOgA4nQIAAAQIECBAgQIAAAQJtBAzgbZQ8hgABAgQIECBAgAABAgQIdBQwgHcEdDgBAgQIECBAgAABAgQIEGgjYABvo+QxBAgQIECAAAECBAgQIECgo4ABvCOgwwkQIECAAAECBAgQIECAQBsBA3gbJY8hQIAAAQIECBAgQIAAAQIdBQzgHQEdToAAAQIECBAgQIAAAQIE2ggYwNsoeQwBAgQIECBAgAABAgQIEOgoYADvCOhwAgQIECBAgAABAgQIECDQRsAA3kbJYwgQIECAAAECBAgQIECAQEcBA3hHQIcTIECAAAECBAgQIECAAIE2AgbwNkoeQ4AAAQIECBAgQIAAAQIEOgoYwDsCOpwAAQIECBAgQIAAAQIECLQRMIC3UfIYAgQIECBAgAABAgQIECDQUcAA3hHQ4QQIECBAgAABAgQIECBAoI2AAbyNkscQIECAAAECBAgQIECAAIGOAgbwjoAOJ0CAAAECBAgQIECAAAECbQQM4G2UPIYAAQIECBAgQIAAAQIECHQUMIB3BHQ4AQIECBAgQIAAAQIECBBoI2AAb6PkMQQIECBAgAABAgQIECBAoKOAAbwjoMMJECBAgAABAgQIECBAgEAbAQN4GyWPIUCAAAECBAgQIECAAAECHQUM4B0BHU6AAAECBAgQIECAAAECBNoIGMDbKHkMAQIECBAgQIAAAQIECBDoKGAA7wjocAIECBAgQIAAAQIECBAg0EbAAN5GyWMIECBAgAABAgQIECBAgEBHAQN4R0CHEyBAgAABAgQIECBAgACBNgIG8DZKHkOAAAECBAgQIECAAAECBDoKGMA7AjqcAAECBAgQIECAAAECBAi0ETCAt1HyGAIECBAgQIAAAQIECBAg0FHAAN4R0OEECBAgQIAAAQIECBAgQKCNgAG8jZLHECBAgAABAgQIECBAgACBjgIG8I6ADidAgAABAgQIECBAgAABAm0EDOBtlDyGAAECBAgQIECAAAECBAh0FPjfKOaht6v2A10AAAAASUVORK5CYII=", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n", "cop_fig = mean_cops.iplot(\n", " kind='bar',\n", " title='Mean COP by Casestudy',\n", " colors=recoygreen,\n", " dimensions=(600, 400),\n", " yTitle='COP',\n", " asFigure=True,\n", " yrange=[0, mean_cops.max() * 1.2]\n", ")\n", "\n", "cop_fig.update_traces(\n", " text=mean_cops.values, \n", " textposition='outside', \n", " texttemplate=\"%{text:.1f}\", \n", ")\n", "\n", "cop_fig " ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Sensitivity analysis" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n", "1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n", "2. aFRR prices +/- 30%\n", "3. CAPEX\n", "4. CO2 price\n", "5. Tsource +/- 10 degrees Celsius\n", "6. Tsink\n", " * Roam off the peak / lower pressure\n", " * Stabalize / running average per hour/ 2 hours\n", "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n", "8. Energy Tax and ODE +/- 30%" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def setup():\n", " _c = Config()\n", " s = setup_model(c=_c)\n", " return s\n", "\n", "def routine(c, s):\n", " s = load_data(c=c, s=s)\n", " s = create_and_assign_assets(c=c, s=s)\n", " #s = preprocessing(c=c, s=s)\n", " s = run_optimisation(c=c, s=s)\n", " #s = postprocessing(c=c, s=s)\n", " s = collect_cashflows(c=c, s=s)\n", " s = calculate_financials(c=c, s=s)\n", " return s" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testetss\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n", "\n", "The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_29704\\2484316097.py:3: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: total: 5.73 s\n", "Wall time: 17.4 s\n" ] } ], "source": [ "%time _s = setup()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "tags": [ "exclude" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "1152000\n", "CPU times: total: 1.53 s\n", "Wall time: 5.91 s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n", "\n", "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n", "\n", "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n", "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] }, { "data": { "text/plain": [ "49607.31309321511" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time result = routine(c, _s)\n", "npv = result.hpcase.npv\n", "npv" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "289d49ef97ff4372ba6dbe732a18a67c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00 6\u001b[1;33m \u001b[0msens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSensitivityAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m 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s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;31m#s = postprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcollect_cashflows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_29704\\3686723783.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mgasboiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m 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\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6298\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_list'" ] } ], "source": [ "# sensitivity:Storage temperature\n", "values = range(80, 120, 10)\n", "param = 'storage_temperature'\n", "kpis = ['npv','irr','spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: Storage volume',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Water storage volume" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%time _s = setup()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(100, 500, 100)\n", "param = 'storage_volume'\n", "kpis = ['npv','irr','spp']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[case.name for case in s.cases][1:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: Storage volume',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='irr in %',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: IRR',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='volume(m3)',\n", " yTitle='COP',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: COP',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: CAPEX per MW storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(4_000, 10_000, 2_000)\n", "param = 'storage_capex_per_MW'\n", "kpis = ['npv', 'irr', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX',\n", " yTitle='NPV in Euro',\n", " yrange=[output.min().min()*1.1, 0],\n", " title='Sensitivity: NPV in euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MW',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: CAPEX per MWh storage" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(1_000, 5_000, 1000)\n", "param = 'storage_capex_per_MWh'\n", "kpis = ['npv', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MWh',\n", " yTitle='NPV',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: NPV in Euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX/MWh',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: Payback time',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: threshold price" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "values = range(10, 60, 10)\n", "param = 'threshold'\n", "kpis = ['npv', 'spp', 'mean_cop']\n", "\n", "sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='NPV',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: NPV in Euro',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='years',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: SPP in years',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='threshold',\n", " yTitle='COP',\n", " yrange=[output.min().min()*1.2, 0],\n", " title='Sensitivity: SOP',\n", " colors=recoycolors,\n", " dimensions=(600, 400),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Heat Pump CAPEX" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3, 2]\n", "for value in values:\n", " _c = Config()\n", " _c.hp_capex *= value\n", " configs[value*100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "\n", "sens_capex = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX factor (%)',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Heat Pump CAPEX (€)',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_capex" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: CO2 prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "mean = 24.86\n", "co2_prices = [10, 25, 50, 100]\n", "\n", "for price in co2_prices:\n", " _c = Config()\n", " multiplier = price / mean\n", " _c.co2_price_multiplier = multiplier\n", " configs[price] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='EBITDA in €',\n", " yrange=[output.min().min() * 1.1, 0],\n", " title='Sensitivity: CO2 prices > EBITDA',\n", " colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_co2_spp = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='Payback Period in Years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: CO2 prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_co2_spp" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Mean CO2 price in €',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min() * 10, output.max().max()*1.1],\n", " title='Sensitivity: CO2 prices > NPV',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Gas prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.gas_price_multiplier = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Gas price factor (%)',\n", " yTitle='NPV in €',\n", " yrange=[output.min().min()*1.5, output.max().max()*1.1],\n", " title='Sensitivity: Gas prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Day Ahead buying amount" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0, 0.5, 1]\n", "for value in values:\n", " _c = Config()\n", " _c.day_ahead_buying_perc = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Volume in %',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Volume bought on Day-Ahead market in %',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Electricity prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.e_price_multiplier = value\n", " configs[value * 100] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_eprices = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Electricity price factor in %',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Electricity prices',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_eprices" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Electricity price volatility" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.e_price_volatility_multiplier = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_evol = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Volatility factor',\n", " yTitle='Payback period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: E-price volatility',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_evol" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: aFRR capacity fee" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [10_000, 25_000, 50_000]\n", "for value in values:\n", " _c = Config()\n", " _c.afrr_capacity_fee = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_affr_fee = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='aFRR capacity fee in €/MW',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: aFRR capacity fee',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_affr_fee" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Energy tax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.energy_tax_multiplier = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Factor',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Energy taxes',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Tsource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [-25, -10, 0, 10, 25]\n", "for value in values:\n", " _c = Config()\n", " _c.tsource_delta = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.loc[-25, 'Heatpump only'] = np.nan\n", "sens_tsource = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsource delta',\n", " yTitle='Payback Period in years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsource',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsource" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsource delta',\n", " yTitle='COP',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsource > Mean COP',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [-25, -10, 0, 10, 25]\n", "for value in values:\n", " _c = Config()\n", " _c.tsink_delta = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "sens = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_tsink = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsink delta',\n", " yTitle='Payback Period in Years',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsink',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "sens_tsink_cop = output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsink delta',\n", " yTitle='COP',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsink > Mean COP',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400),\n", " asFigure=True\n", ")\n", "sens_tsink_cop" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "### Sensitivity: Time period" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "def routine2(c, s):\n", " s = setup_model(c=c)\n", " s = load_data(c=c, s=s)\n", " s = create_and_assign_assets(c=c, s=s)\n", " s = run_optimisation(c=c, s=s)\n", " s = collect_cashflows(c=c, s=s)\n", " s = calculate_financials(c=c, s=s)\n", " return s" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "configs = {}\n", "start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n", "\n", "for value in start_values:\n", " _c = Config()\n", " _c.start = value\n", " _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "%%time\n", "sens = SensitivityAnalysis(_s, routine2, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n", "result.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Start date',\n", " yTitle='NPV in €',\n", " yrange=[0, result.max().max()*1.1],\n", " title='Sensitivity: Modelled time period',\n", " colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "tags": [ "exclude" ] }, "source": [ "## Report" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "exclude" ] }, "outputs": [], "source": [ "renderer = 'svg'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# __Presentation Flexible Heatpumps__\n", "\n", "ENCORE meeting: 17-12-2020\n", "Mark Kremer\n", "\n", " \n", " \n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## __Central question:__\n", "#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Flexible operations on imbalance market__\n", "\n", "Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n", " title='Day-Ahead prices on arbitrary day in €/MWh',\n", " yrange=[-100, 200],\n", " colors=recoygreen, \n", " yTitle='Price in €/MWh',\n", " xTitle='Time of Day',\n", " dimensions=(800, 400),\n", " asFigure=True\n", ")\n", "\n", "fig_example_1.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n", " title='Imbalance Prices on arbitrary day in €/MWh',\n", " colors=[recoygreen, recoydarkblue], \n", " yTitle='Price in €/MWh',\n", " xTitle='Time of Day',\n", " dimensions=(800, 400),\n", " asFigure=True\n", ")\n", "\n", "fig_example_2.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to benefit from these fluctiations, if you have __flexibility__\n", "* Storage options\n", "* Hybrid installations (e.g. with gas-powered assets)\n", "\n", "In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Simulations & mathematical modelling__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* To answer the central question, we have build a simulation model\n", "* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n", "* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n", "* We are taking into account all major investment and operating costs, including:\n", " * Asset CAPEX\n", " * Commodity costs for gas and electricity\n", " * Energy taxes\n", " * Grid transport costs\n", " * SDE++ subsidies\n", " * Maintenance costs\n", " * CO2 allowances\n", "* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Casestudies__\n", "5 main scenario's" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "1. Steamboiler only (baseline, baseload)\n", "2. Heatpump only (stand-alone, baseload, without SDE++)\n", "3. Heatpump + SDE\n", "4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n", "5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n", "\n", "Besides that, we modelled 11 sensitivities:\n", "* Heatpump CAPEX\n", "* Gas & CO2 prices\n", "* Electricity prices & volatility\n", "* Energy taxes\n", "* Bidding strategies\n", "* Source temperatures (affecting COP)\n", "* Sink temperatures (affecting COP)\n", "* Time period (2018, 2019, 2020)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Smurfit Kappa case__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model is based on the context of Smurfit Kappa (paper factory)\n", "* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n", "* We add a 31 MW heatpump (to make sure it can cover entire demand)\n", "* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n", "* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_demands_over_time.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Source temperatures of around 65 degrees C\n", "* Sink temperatures of 125-170 degrees C \n", "* Average Temperature lift of about 85 degrees C" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "demands_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Heat pump__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "COP roughly between 4 and 1.5, depending on load, Tsource and Tsink" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def cop_curve(Tsink, Tsource):\n", " Tsink += 273\n", " Tsource += 273\n", "\n", " c1 = 0.267 * Tsink / (Tsink - Tsource)\n", " c2 = 0.333 * Tsink / (Tsink - Tsource)\n", " \n", " return Polynomial([c2, c1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sourceT = 63\n", "sinkT = 140\n", "cop_curve(sourceT, sinkT)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_cop_curve.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Optimisation__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* At each moment in time, we calculate the cheapest option to produce the required heat. \n", "* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n", "* Taking into account fluctuating market prices (electricity, gas, CO2)\n", "* We are predicting real-time electricity prices using our forecasting models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Some example days:__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Steamboiler only is following demand pattern" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_steamboiler.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similar pattern for heatpump only case" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_heatpump.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_optcase.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig_optcase2.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Business case__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "capex_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n", "* The optimisation on aFRR allows for a 30-40% improvement in EBITDA" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ebitda_graph.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "spt_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "production_fig.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The heatpump is filling in 95-98 % of the demand. \n", "* Because of its high COP, it is almost always cheaper to run than the steam boiler\n", "* Switch price is on average around 90€/MWh (excluding subsidies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Sensitivities__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If CAPEX is 200%, subsidy is needed to keep a good Payback Time" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_capex.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subsidies are protecting the business case againsts low CO2 prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_co2_spp.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n", "* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n", "* When the temperature lift is higher, the COP will decrease and the business case will degrade" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsource.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsink.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_tsink_cop.show(renderer=renderer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sens_eprices.show(renderer=renderer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### __Conclusions__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* The business case for a Heat Pump seems favourable\n", "* Flexible operation, using aFRR, can improve the operational results by 30-40%\n", "* However, this only results in a marginal improvement of the business case\n", "* SDE++ has a very favourable effect on the business case, but is not critical\n", "* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case." ] } ], "metadata": { "interpreter": { "hash": "fea6e7ab4c0ed1d184e153838ac4b2d24a0985a5f17e99f4f437a114b66796b8" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": 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