{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from datetime import timedelta\n", "import json\n", "import pprint\n", "from copy import deepcopy\n", "\n", "import cufflinks\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from numpy.polynomial import Polynomial\n", "import pandas as pd\n", "from tqdm.notebook import tqdm\n", "\n", "from pyrecoy.assets import Heatpump, Eboiler, GasBoiler\n", "from pyrecoy.colors import *\n", "from pyrecoy.converters import *\n", "from pyrecoy.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n", "from pyrecoy.framework import TimeFramework, CaseStudy\n", "from pyrecoy.plotting import ebitda_bar_chart, npv_bar_chart\n", "from pyrecoy.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n", "from pyrecoy.sensitivity import SensitivityAnalysis\n", "\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Development backlog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* SDE++\n", "* aFRR (can improve the optimisation case)\n", "* Simple payback time (relevant for ENCORE, because its the main metric)\n", "* APX inkoop toevoegen (baseload inkopen, bijv. 30%)\n", "* Add explanations/conclusions to analysis/graph --> Create report-like output\n", "* Graphs\n", " * EBITDA vs. baseline (earnings vs baseline)\n", "* Show COP curves in different cases, just for illustration\n", "* Energy report --> Check + add gas\n", "* Fix comparison reports\n", "* Model verification" ] }, { "cell_type": "markdown", "metadata": { "collapsed": "true" }, "source": [ "#### Meeting Notes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Meeting 25-11-2020" ] }, { "cell_type": "markdown", "metadata": {}, "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": {}, "source": [ "# ENCORE : Heatpump Optimisation Framework\n", "\n", "***\n", "© Mark Kremer \n", "July 2020\n", "##### Cases\n", "In this model, 3 cases are compared:\n", "* **Baseline** : All heat is supplied by steam turbine\n", "* **Heatpump case** : All heat is supplied by heatpump\n", "* **Hybrid case** : Steam turbine and heatpump run in hybrid mode, and are optimised on costs in real-time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Loading config" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class Config():\n", " start = '2019-01-01'\n", " end = '2019-12-31'\n", " \n", " hp_vdg_e_power = 23.3 # MW\n", " hp_ndg_e_power = 7.7 # MW\n", " hp_min_load = 0.3\n", " hp_lifetime = 25\n", " hp_capex = 200_000 # EUR/MWth\n", " hp_opex = 0.01 # in % of CAPEX\n", " hp_devex = 0.005 # in % of CAPEX\n", " \n", " gb_power = 35 # MW\n", " gb_efficiency = 0.9\n", " \n", " tax_bracket_g = 4 \n", " tax_bracket_e = 4\n", " \n", " include_transport_costs = False\n", " grid_operator = 'Liander'\n", " connection_type = 'TS/MS'\n", " \n", " discount_rate = 0.1\n", " project_duration = 15\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", " energy_tax_multiplier = 1\n", " \n", "c = Config()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Store():\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model set-up" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def load_demand_data(c, s):\n", " demand = pd.read_csv('data/smurfit_demand_preprocessed.csv', delimiter=';', decimal=',')\n", " dt_index = pd.date_range(\n", " start=s.time_fw.start,\n", " end=s.time_fw.start + timedelta(days=365), freq='1T',\n", " closed='left',\n", " tz='Europe/Amsterdam')\n", " demand.index = dt_index\n", " demand['Total demand'] = demand['MW (VDG)'] + demand['MW (NDG)']\n", " demand = demand[c.start:c.end]\n", " return demand" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def setup_model(c):\n", " s = Store()\n", " s.time_fw = TimeFramework(start=c.start, end=c.end)\n", "\n", " s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n", " s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', forecast='mipf')\n", " s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', forecast='mipf')\n", " s.optcase2 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation 2', forecast='mipf')\n", " \n", " s.cases = list(CaseStudy.get_instances().values())\n", " s.optcases = [s.hpcase, s.optcase1, s.optcase2]\n", " \n", " s.demand = load_demand_data(c, s)\n", " return s\n", "\n", "s = setup_model(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load in data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def increase_volatility_by_factor(col, factor):\n", " mean = col.mean()\n", " diff_to_mean = col - mean\n", " new_diff = diff_to_mean * factor\n", " return mean + new_diff\n", "\n", "def increase_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": 7, "metadata": {}, "outputs": [], "source": [ "def load_data(c, s):\n", " for case in [s.baseline, s.optcase1, s.optcase2]:\n", " case.add_gasprices()\n", " case.add_co2prices(perMWh=True)\n", " \n", " case.data['Gas prices (€/MWh)'] = increase_by_factor(case.data['Gas prices (€/MWh)'], c.gas_price_multiplier)\n", " case.data['CO2 prices (€/MWh)'] = increase_by_factor(case.data['CO2 prices (€/MWh)'], c.co2_price_multiplier)\n", " \n", " for case in s.optcases:\n", " case.data['NEG'] = increase_by_factor(case.data['NEG'], c.e_price_multiplier)\n", " case.data['ForeNeg'] = increase_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n", " case.data['DAM'] = increase_by_factor(case.data['DAM'], c.e_price_multiplier)\n", " \n", " case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n", " case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n", " \n", " s.demand[['Tsource (VDG)', 'Tsource (NDG)']] += c.tsource_delta\n", " 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": "markdown", "metadata": {}, "source": [ "## Assets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### COP curve" ] }, { "cell_type": "code", "execution_count": 8, "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": 9, "metadata": {}, "outputs": [], "source": [ "heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=1,\n", " min_th_power=0,\n", " cop_curve=cop_curve\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "\n", "source_Ts = np.arange(25, 75)\n", "sink_Ts = np.arange(80, 170)\n", "\n", "df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n", "for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n", " df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n", " \n", "#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(70, 165, 121, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "None", "text": "", "type": "scatter", "x": [ 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sourceT = 63\n", "sinkT = 140\n", "loads = np.arange(0, 1, 0.01)\n", "\n", "series = pd.Series(index=loads, dtype='float')\n", "for load in loads:\n", " series[load] = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n", " \n", "series.iplot(title='Heatpump COP curve', yTitle='COP', xTitle='Thermal load in %', colors=recoygreen)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": "true" }, "source": [ "### Create and assign assets" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def create_and_assign_assets(c, s):\n", " heatpump_vdg = Heatpump(\n", " name='Heatpump VDG',\n", " max_th_power=c.hp_vdg_e_power,\n", " min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n", " cop_curve=cop_curve\n", " )\n", "\n", " heatpump_ndg = Heatpump(\n", " name='Heatpump NDG',\n", " max_th_power=c.hp_ndg_e_power,\n", " min_th_power=c.hp_ndg_e_power * c.hp_min_load,\n", " cop_curve=cop_curve\n", " )\n", "\n", " capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n", " capex_ndg = c.hp_capex*(heatpump_ndg.max_th_power)\n", " heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n", " heatpump_ndg.set_financials(capex=capex_ndg, opex=c.hp_opex*capex_ndg, devex=c.hp_devex*capex_ndg, lifetime=25)\n", "\n", " gasboiler = GasBoiler(\n", " name='Gasboiler',\n", " max_th_output=c.gb_power,\n", " efficiency=c.gb_efficiency\n", " )\n", " gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n", " \n", " s.baseline.add_asset(gasboiler)\n", " s.hpcase.add_asset(heatpump_vdg)\n", " s.hpcase.add_asset(heatpump_ndg)\n", " s.optcase1.add_asset(heatpump_vdg)\n", " s.optcase1.add_asset(heatpump_ndg)\n", " s.optcase1.add_asset(gasboiler)\n", " s.optcase2.add_asset(heatpump_vdg)\n", " s.optcase2.add_asset(heatpump_ndg)\n", " s.optcase2.add_asset(gasboiler)\n", " return s\n", "\n", "s = create_and_assign_assets(c, s)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Gas prices (€/MWh)CO2 prices (€/ton)CO2 prices (€/MWh)Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)Total demand
count525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000525600.000000
mean13.53874524.8640184.68314463.658798138.63767417.63671262.176305145.3516455.82382423.460537
std3.6447792.1425740.4038276.42277314.3285805.12325210.25255514.8960561.6916976.814125
min7.53000018.7000003.52000025.31000082.4800000.00000013.540000106.3300000.0000000.000000
25%10.38000023.6700004.46000064.892000126.07400017.01400063.516000131.5515005.61200022.676000
50%12.85000025.0800004.72000064.991000142.45000019.89200064.313000148.4900006.54000026.441000
75%15.60000026.2900004.95000065.076000151.73000020.81900065.546000158.1000006.88000027.690000
max22.67000029.7700005.61000070.070000166.02000023.25000073.470000172.0500007.65000030.900000
\n", "
" ], "text/plain": [ " Gas prices (€/MWh) CO2 prices (€/ton) CO2 prices (€/MWh) \\\n", "count 525600.000000 525600.000000 525600.000000 \n", "mean 13.538745 24.864018 4.683144 \n", "std 3.644779 2.142574 0.403827 \n", "min 7.530000 18.700000 3.520000 \n", "25% 10.380000 23.670000 4.460000 \n", "50% 12.850000 25.080000 4.720000 \n", "75% 15.600000 26.290000 4.950000 \n", "max 22.670000 29.770000 5.610000 \n", "\n", " Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n", "count 525600.000000 525600.000000 525600.000000 525600.000000 \n", "mean 63.658798 138.637674 17.636712 62.176305 \n", "std 6.422773 14.328580 5.123252 10.252555 \n", "min 25.310000 82.480000 0.000000 13.540000 \n", "25% 64.892000 126.074000 17.014000 63.516000 \n", "50% 64.991000 142.450000 19.892000 64.313000 \n", "75% 65.076000 151.730000 20.819000 65.546000 \n", "max 70.070000 166.020000 23.250000 73.470000 \n", "\n", " Tsink (NDG) MW (NDG) Total demand \n", "count 525600.000000 525600.000000 525600.000000 \n", "mean 145.351645 5.823824 23.460537 \n", "std 14.896056 1.691697 6.814125 \n", "min 106.330000 0.000000 0.000000 \n", "25% 131.551500 5.612000 22.676000 \n", "50% 148.490000 6.540000 26.441000 \n", "75% 158.100000 6.880000 27.690000 \n", "max 172.050000 7.650000 30.900000 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.baseline.data.describe()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=1,\n", " min_th_power=0,\n", " cop_curve=cop_curve\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Strategies" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "def baseline_sim(case):\n", " gasboiler = list(case.assets.values())[0]\n", " data = case.data\n", " demand = (data['MW (VDG)'] + data['MW (NDG)']).to_list()\n", "\n", " minutes = iter(range(len(case.data)))\n", " th_output = [0] * len(case.data)\n", " gas_input = [0] * len(case.data)\n", "\n", " for m in minutes:\n", " th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n", "\n", " data['output_MW_th'] = np.array(th_output)\n", " data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n", " data['gb_input_MW'] = np.array(gas_input)\n", " data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n", " case.data = data.round(5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def hponly(case):\n", " hp_vdg = case.assets['Heatpump VDG']\n", " hp_ndg = case.assets['Heatpump NDG']\n", " demand_vdg = case.data['MW (VDG)'].to_list()\n", " demand_ndg = case.data['MW (NDG)'].to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n", " Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n", " Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n", "\n", " hp_vdg_input = [0] * len(case.data)\n", " hp_ndg_input = [0] * len(case.data)\n", " hp_vdg_output = [0] * len(case.data)\n", " hp_ndg_output = [0] * len(case.data)\n", "\n", " minutes = iter(range(len(case.data)))\n", " for m in minutes:\n", " demand = demand_vdg[m]\n", " if demand != 0:\n", " hp_vdg_output[m], hp_vdg_input[m] = hp_vdg.set_heat_output(\n", " heat_output=demand,\n", " Tsink=Tsink_vdg[m],\n", " Tsource=Tsource_vdg[m]\n", " )\n", "\n", " demand = demand_ndg[m]\n", " if demand != 0:\n", " hp_ndg_output[m], hp_ndg_input[m] = hp_ndg.set_heat_output(\n", " heat_output=demand_ndg[m],\n", " Tsink=Tsink_ndg[m],\n", " Tsource=Tsource_ndg[m]\n", " )\n", "\n", " case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n", " case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n", "\n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(3)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def strategy(case, decimals, tolerance, s):\n", " gb = case.assets['Gasboiler']\n", " hp_vdg = case.assets['Heatpump VDG']\n", " hp_ndg = case.assets['Heatpump NDG']\n", " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", " demand_ndg = case.data['MW (NDG)'].round(decimals).to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", " Tsink_ndg = case.data['Tsink (NDG)'].round(decimals).to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n", " Tsource_ndg = case.data['Tsource (NDG)'].round(decimals).to_list()\n", " fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n", " gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n", " co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n", " eb_ode_g = s.eb_ode_g\n", " eb_ode_e = s.eb_ode_e\n", " \n", " gb_input = [0] * len(case.data)\n", " gb_output = [0] * len(case.data)\n", "\n", " minutes = range(len(case.data))\n", " hp_output = [0] * len(case.data)\n", " hp_input = [0] * len(case.data)\n", "\n", " for m in tqdm(minutes):\n", " dem_vgd = demand_vdg[m]\n", " if dem_vgd != 0:\n", " hp_vdg_input, hp_vdg_output = hp_vdg.set_opt_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", " Tsink=Tsink_vdg[m],\n", " Tsource=Tsource_vdg[m],\n", " tolerance=tolerance\n", " )\n", " else:\n", " hp_vdg_input, hp_vdg_output = (0, 0)\n", "\n", " dem_ngd = demand_ndg[m]\n", " if dem_ngd != 0:\n", " hp_ndg_input, hp_ndg_output = hp_ndg.set_opt_load(\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=(gas_prices[m] + co2_prices[m] + eb_ode_g)/case.assets['Gasboiler'].efficiency,\n", " demand=dem_ngd,\n", " Tsink=Tsink_ndg[m],\n", " Tsource=Tsource_ndg[m],\n", " tolerance=tolerance\n", " )\n", " else:\n", " hp_ndg_input, hp_ndg_output = (0, 0)\n", "\n", " hp_out = hp_vdg_output + hp_ndg_output\n", " hp_output[m] = hp_out\n", " hp_input[m] = hp_vdg_input + hp_ndg_input\n", " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", "\n", " case.data['hp_output_MW'] = np.array(hp_output)\n", " case.data['hp_input_MW'] = np.array(hp_input)\n", " case.data['gb_output_MW'] = np.array(gb_output)\n", " case.data['gb_input_MW'] = np.array(gb_input)\n", "\n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "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": 19, "metadata": {}, "outputs": [], "source": [ "def strategy_(case, decimals, s):\n", " gb = case.assets['Gasboiler']\n", " hp_vdg = case.assets['Heatpump VDG']\n", " hp_ndg = case.assets['Heatpump NDG']\n", " demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n", " demand_ndg = case.data['MW (NDG)'].round(decimals).to_list()\n", " Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n", " Tsink_ndg = case.data['Tsink (NDG)'].round(decimals).to_list()\n", " Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n", " Tsource_ndg = case.data['Tsource (NDG)'].round(decimals).to_list()\n", " fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n", " gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n", " co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n", " eb_ode_g = s.eb_ode_g\n", " eb_ode_e = s.eb_ode_e\n", " \n", " gb_input = [0] * len(case.data)\n", " gb_output = [0] * len(case.data)\n", "\n", " minutes = range(len(case.data))\n", " hp_output = [0] * len(case.data)\n", " hp_input = [0] * len(case.data)\n", "\n", " for m in tqdm(minutes):\n", " dem_vgd = demand_vdg[m]\n", " if dem_vgd != 0:\n", " max_load = min(hp_vdg.max_th_power, dem_vgd)\n", " min_load = hp_vdg.min_th_power\n", " Tsink = Tsink_vdg[m]\n", " Tsource = Tsource_vdg[m]\n", " cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", " cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n", " \n", " cost_full_load = cost_function(\n", " th_load=max_load,\n", " cop=cop_max_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_vgd\n", " )\n", " \n", " cost_min_load = cost_function(\n", " th_load=min_load,\n", " cop=cop_min_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_vgd\n", " )\n", " \n", " if cost_full_load < cost_min_load:\n", " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n", " else:\n", " hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n", " else:\n", " hp_vdg_input, hp_vdg_output = (0, 0)\n", "\n", " dem_ngd = demand_ndg[m]\n", " if dem_ngd != 0:\n", " max_load = min(hp_ndg.max_th_power, dem_ngd)\n", " min_load = hp_ndg.min_th_power\n", " Tsink = Tsink_ndg[m]\n", " Tsource = Tsource_ndg[m]\n", " cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n", " cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n", " \n", " cost_full_load = cost_function(\n", " th_load=max_load,\n", " cop=cop_max_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_ngd\n", " )\n", " \n", " cost_min_load = cost_function(\n", " th_load=min_load,\n", " cop=cop_min_load,\n", " electricity_cost=fore_neg[m] + eb_ode_e,\n", " alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n", " demand=dem_vgd\n", " )\n", " \n", " if cost_full_load <= cost_min_load:\n", " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n", " else:\n", " hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n", " else:\n", " hp_ndg_input, hp_ndg_output = (0, 0)\n", "\n", " hp_out = hp_vdg_output + hp_ndg_output\n", " hp_output[m] = hp_out\n", " hp_input[m] = hp_vdg_input + hp_ndg_input\n", " remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n", " gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n", "\n", " case.data['hp_output_MW'] = np.array(hp_output)\n", " case.data['hp_input_MW'] = np.array(hp_input)\n", " case.data['gb_output_MW'] = np.array(gb_output)\n", " case.data['gb_input_MW'] = np.array(gb_input)\n", "\n", " for col in case.data.columns:\n", " if col.endswith('MW'):\n", " case.data[col + 'h'] = case.data[col] / 60\n", "\n", " case.data = case.data.round(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run optimisation" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 677 ms\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\assets.py:254: UserWarning:\n", "\n", "Chosen heat output is out of range [6.99 - 23.3]. Heat output is being limited to the closest boundary.\n", "\n", "c:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\assets.py:254: UserWarning:\n", "\n", "Chosen heat output is out of range [2.31 - 7.7]. Heat output is being limited to the closest boundary.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 4.18 s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8da8fff565db4f21ab658f450c26949e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=525600.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Wall time: 1min 32s\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "259da37035154248baeb948ecb0ae768", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=525600.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Wall time: 13.4 s\n" ] } ], "source": [ "def run_optimisation(c, s):\n", " s.baseline.assign_algorithm(baseline_sim)\n", " %time s.baseline.run()\n", " \n", " s.hpcase.assign_algorithm(hponly)\n", " %time s.hpcase.run()\n", " \n", " s.optcase1.assign_algorithm(strategy)\n", " %time s.optcase1.run(decimals=0, tolerance=1, s=s)\n", "\n", " s.optcase2.assign_algorithm(strategy_)\n", " %time s.optcase2.run(decimals=2, s=s)\n", " return s\n", "\n", "s = run_optimisation(c, s)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'RS', 'ForePos', 'ForeNeg', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Tsource (NDG)', 'Tsink (NDG)', 'MW (NDG)',\n", " 'Total demand', 'hp_output_MW', 'hp_input_MW', 'gb_output_MW',\n", " 'gb_input_MW', 'hp_output_MWh', 'hp_input_MWh', 'gb_output_MWh',\n", " 'gb_input_MWh'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.optcase1.data.columns" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime\n", "2019-01-01 00:00:00+01:00 0.00000\n", "2019-01-01 00:01:00+01:00 0.00000\n", "2019-01-01 00:02:00+01:00 0.00000\n", "2019-01-01 00:03:00+01:00 0.00000\n", "2019-01-01 00:04:00+01:00 0.00000\n", " ... \n", "2019-12-31 23:55:00+01:00 -6.69262\n", "2019-12-31 23:56:00+01:00 -6.69262\n", "2019-12-31 23:57:00+01:00 -6.69262\n", "2019-12-31 23:58:00+01:00 -6.69262\n", "2019-12-31 23:59:00+01:00 -6.69262\n", "Freq: T, Name: hp_input_MW, Length: 525600, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.optcase1.data['hp_input_MW']" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "datetime\n", "2019-01-01 00:00:00+01:00 0.00000\n", "2019-01-01 00:01:00+01:00 0.00000\n", "2019-01-01 00:02:00+01:00 0.00000\n", "2019-01-01 00:03:00+01:00 0.00000\n", "2019-01-01 00:04:00+01:00 0.00000\n", " ... \n", "2019-12-31 23:55:00+01:00 -6.85751\n", "2019-12-31 23:56:00+01:00 -6.85751\n", "2019-12-31 23:57:00+01:00 -6.85751\n", "2019-12-31 23:58:00+01:00 -6.85751\n", "2019-12-31 23:59:00+01:00 -6.85751\n", "Freq: T, Name: hp_input_MW, Length: 525600, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.optcase2.data['hp_input_MW']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Financials" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# SDE++\n", "# ..." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def collect_cashflows(c, s):\n", " s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n", " s.optcase1.generate_electr_market_results(nom_col=None, real_col='hp_input_MWh')\n", " s.optcase2.generate_electr_market_results(nom_col=None, real_col='hp_input_MWh')\n", " \n", " for case in [s.baseline, s.optcase1, s.optcase2]:\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.optcase1, s.optcase2]:\n", " case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e)\n", " case.add_grid_costs(\n", " power_MW_col='hp_input_MW',\n", " grid_operator=c.grid_operator,\n", " year=2020, \n", " connection_type=c.connection_type\n", " )\n", " \n", " return s\n", "\n", "s = collect_cashflows(c, s)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def calculate_financials(c, s):\n", " for case in s.cases:\n", " case.calculate_ebitda()\n", "\n", " for case in [s.optcase1, s.optcase2]:\n", " case.calculate_business_case(\n", " project_duration=c.project_duration, \n", " discount_rate=c.discount_rate, \n", " baseline=s.baseline\n", " )\n", " \n", " return s\n", "\n", "s = calculate_financials(c, s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualisations" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "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": [ "2019-01-01 00:00:00+01:00", "2019-01-01 01:00:00+01:00", "2019-01-01 02:00:00+01:00", "2019-01-01 03:00:00+01:00", "2019-01-01 04:00:00+01:00", "2019-01-01 05:00:00+01:00", "2019-01-01 06:00:00+01:00", "2019-01-01 07:00:00+01:00", "2019-01-01 08:00:00+01:00", "2019-01-01 09:00:00+01:00", "2019-01-01 10:00:00+01:00", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s.demand['Total demand'].resample('H').mean().iplot(title='Heat demand by Hour in MW', yTitle='MW', colors=recoygreen)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['DAM', 'POS', 'NEG', 'RS', 'ForePos', 'ForeNeg', 'Gas prices (€/MWh)',\n", " 'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n", " 'Tsink (VDG)', 'MW (VDG)', 'Tsource (NDG)', 'Tsink (NDG)', 'MW (NDG)',\n", " 'Total demand', 'hp_output_MW', 'hp_input_MW', 'gb_output_MW',\n", " 'gb_input_MW', 'hp_output_MWh', 'hp_input_MWh', 'gb_output_MWh',\n", " 'gb_input_MWh', 'Nom. vol.', 'Prod. vol.', 'Cons. vol.', 'Imb. vol.',\n", " 'Day-Ahead Result', 'POS Result', 'NEG Result', 'Imbalance Result',\n", " 'Combined Result', 'Gas commodity costs (€)'],\n", " dtype='object')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.optcase1.data.columns" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "test_hp = Heatpump(\n", " name='Heatpump for Testing',\n", " max_th_power=1,\n", " min_th_power=0.3,\n", " cop_curve=cop_curve\n", ")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "Tsrc_vdg, Tsnk_vdg, Tsrc_ndg, Tsnk_ndg = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].mean().to_list()\n", "mean_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].mean() \n", " + s.eb_ode_g \n", " + s.optcase1.data['CO2 prices (€/MWh)'].mean()\n", " )\n", "\n", "max_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].max() \n", " + s.eb_ode_g \n", " + s.optcase1.data['CO2 prices (€/MWh)'].max()\n", " )\n", "\n", "min_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].min() \n", " + s.eb_ode_g \n", " + s.optcase1.data['CO2 prices (€/MWh)'].min()\n", " )\n", "\n", "Tsrc_vdg_min, Tsnk_vdg_min, Tsrc_ndg_min, Tsnk_ndg_min = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].min().to_list()\n", "Tsrc_vdg_max, Tsnk_vdg_max, Tsrc_ndg_max, Tsnk_ndg_max = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].max().to_list()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def create_load_trace(gas_price, Tsnk, Tsrc, name):\n", " loads = []\n", " eprices = list(range(500))\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": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\assets.py:254: UserWarning:\n", "\n", "Chosen heat output is out of range [0.3 - 1]. Heat output is being limited to the closest boundary.\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "name": "mean", "type": "scatter", "x": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 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O21MenL/d/rZkcllZ00Vwui7BiVoJTtRKcKJWCGZ0VZ00NbfYHoPgLBdsL+ao+sG5ZrIjwTntebk4ZpSce7vB+ixBHvaC8zv3pDs4K9rnSiaXldVdp6zPguESnKiV4EStBCdqhWAqKqulq/uC7TEIznLB9mKOasd6x4Jz+gtecM6yPkuQR7pMcN6W8uD8HS84VxGczktwolaCE7USnKg1zRTuUDY1t8joqrrrPu+fNpsfmPkf8wOv8LH5x5rRuExqauuHfP2a2vrIp+KGHZvgLBNsL+aodmwwwdlc71hwznnD+ixBHj1tgvPbKQ/O/739HcnksrLy9Enrs2C4BCdqJThRK8GJWpPk+JZ+2buo73P30+7i80QJzvx4mzS5YUg0VlRWS2tH55DPFx7f/1hX94UhwVr45+EIOzbBWSbYXsxR3bPhslvB+dpUE5yNr1ufJchj3g7nt+9Od3D+rhec758+YX0WDJfgRK0EJ2olOFFrkmx4/prMH9v7uXumY6DoPFF3OIM+XxichRTuaubvaDY1t1y34xmH/GMTnGWC7cUc1Y6NJjhXP+vIbVFee8lcNOitmdZnCfLoGROct96V7uD8P7zgXN7FDqfrEpyoleBErQQnak2SctzhjBuc/q6ob35U5v/9mtr62BccCjo2wVkm2F7MUd2zydvhdCQ4z73+sgnO2a9ZnyXI42e94Lwz3cH5e+3zJJPLSlMXO5yuS3CiVoITtRKcqDXNJB2co6vqhrwns9j7Nv2/EzcKw45NcJYJthdzVPds9nY4n3ElOLPmlNo3p1ufJcjj3SY4a8anOzh/v8ME59LTx63PguESnKiV4EStBCdqTTM1tfVD3hs5uqruhoKz8M81tfXXBeekyQ1SUVl93XsyhyPs2ARnmWB7MUd17xYvOJ92JDhnvWKCs8Hd4DxxzgTnLSkPzj/omC+ZXFaWEJzOS3CiVoITtRKcqDXN+Bfu8Z3RuJZve/8AACAASURBVOyGgrPwSrLFgtMPwrD3fhYj7NgEZ5lgezFHde9WE5yrnnIlOKeZ4Jw1zfosQZ70gvPm76U7OP/Pjnclk8vK4tPHrM+C4RKcqJXgRK0EJ2oFEBmhwVlTW1/0zbhBFL7JthDbizmq+1q84HzSkeBseNX54DzVY4Jz7Lh0B+cf7jHBuegUwem6BCdqJThRK8GJWqH0FN7Ts9Cot04pJSMuOAvvgVN4/nUhhZ8v/PsiZRSc20xwrnzCkeB8c7oJzjdesT5LmCMhOP9ozwLJ5LKy8NRR67NguAQnaiU4USvBiVoBREZgcI6uqhty3nNrR+eQ86mLPT7s/GuRMgrO7d4O5+NuBGf37BkmOF/PWp8lTD84T/Ukd0zXgvOP9yyUTC4rC04dsT4LhktwolaCE7USnKgVQGSEBaf/5t78reViH8tnRuOywTcCiwy96aqP7cUc1Q+94FzpSnC+NVMujhklPTNfsj5LmDd/zwTnyXPJHdO14BzlBed8gtN5CU7USnCiVoITtQKIEJzDBqf/+fz3cRZy9Vp/Wdi1r0/mj+2VtU9dsz7L1Wv98tmCBrk4ZpR8NvsV67OEect4E5yXriR3zL6+AenrH7D+vfn+2f7FksllZeXFE9ZnwXCv9fVLv0PPHSwfe/v6pX+A5w7Gt/davwwMlM/rHXRHABGCc9jgLLz0sL/jmU/Px1fKwkNtV8wO52O91mfp+fiKXJg7Sy6OGSUXZk61PkuYNV5wnrmQ3DE/u3xNLl/ts/69+VbuXWROqT1zxPosGO4nP7wmV671W58Dy8+PP+uVa30D1ufA8vOjT69KXz/PHYwvgMgIC06ReO/hjBqotk9XiOr+nDml9v1H3Til9tzbJjjPTX/B+ixh3nqnCc5jZ5I7pmun1N7kBefck4esz4Lhckotau35mFNqUSen1KJWAJERGJzDXaW28CarFZXVQz5feFNXkTIKzlYTnCsmuRKc5pTac69OsT5LmLfeZYLzaIqD80/3mlNq55w8aH0WDJfgRK0EJ2olOFErgMgIDE6R8PtwFganyND72xTbDbW9mKPqXHDOfdME57Tnrc8S5rfv9oKzK7ljuhacf+YFZyPB6bwEJ2olOFErwYla00xrR+dgHxReUNTGHIX/2yVGZHAmje3FHNUDbV5wTnQjOLvfecsLzueszxLmt+/xgvN0csd0LTj/fN8SyeSyMvtEp/VZMFyCE7USnKiV4EStaabw1om2IDhHCLYXc1QP7PKC82FHgnOeCc6eV9wOztu84Dyc6uBcKplcVt48ccD6LBguwYlaCU7USnCi1jQTdtHRzxOCc4RgezFHtXPXJZk/tleWT3AkOOfPMcGZfdb6LGF+xw/OU8kd07Xg/Iu9ZoezgeB0XoITtRKcqJXgRK1pJf/tdn7gNTW3BL4Nb0bjsqJv44t6Km7YsQnOEYLtxRzVzt1+cPZan+X0+UvS/e7bJjhffsb6LGHeXmeC8+DJ5I7pWnD+5V6zwznrJMHpugQnaiU4USvBiVoT5fBGkV3vfv5+crboOIW3Tsy/yKiIOeXW/1jhXS6Guy1jIWHHJjhHCLYXc1Q7201wNj3kSHAumGuC86Wnrc8S5h0jIDi/sG+ZZHJZef3EfuuzYLgEJ2olOFErwYlaE2X1UyINX/n8PbWr6DiFwVlI4a5m/o5mU3PLdTueccg/NsE5QrC9mKPa2eEF54OOBOfCd0xwTn3K+ixh3nGvF5wnkjuma8E52gvOmQSn8xKcqJXgRK0EJ2pNFMd3OEXMzmP+qa/5UdnU3DJ4KmxNbX3sCw4FHZvgHCHYXsxRPbjHC84HHAnO9+aZq9S++KT1WcL87vdNcHYeT+6YrgXnX+1rkkwuKzNOfGh9FgyX4EStBCdqJThRa5opDM7RVXVD3pNZ7H2b/t+JG4VhxyY4Rwi2F3NUD+71gvN+t4Kz54UnrM8S5rj7THAeOJbcMV0Lzv/5oQnO6ccJTtclOFErwYlaCU7UmmYKg7PwzzW19dcF56TJDVJRWX3dezLjfq38YxOcIwTbizmqh/aZ26Isu8+Rq9QuetcLzsetzxLm9+43wbn/aHLHdC04//rD5ZLJZWXa8X3WZ8FwCU7USnCiVoITtaaZwggsvJJsseD0gzDsvZ/FCDs2wTlCsL2Yo+pccC5eYIJzymPWZwlz/P1XZey4Xvnw6OXEjulacP7Nhyu84NxrfRYMl+BErQQnaiU4USuACMGZCLYXc1QPf2iCc+m9jgTnkoUmOJ9/1PosYfrBuS/Fwfm3XnBmjxGcrktwolaCE7USnKgVhqfwnp6FRr11issQnAlgezFH9ZBrwbn0PROckx+xPkuYdz7gBefh9AbnFz98XzK5rLx8bI/1WTBcghO1EpyoleBErQAiBGci2F7MUT28363gPLtskReck6zPEuadD5rg3Jvi4Py7D1dKJpeVl44TnK5LcKJWghO1EpyoFUCE4EwE24s5qocPeMFZ50hwNi02wVk/0fosYd71kAnOPYfSG5x/7wXni8c7rM+C4RKcqJXgRK0EJ2oFECE4E8H2Yo7qkU4vOGsdCc7lS+XimFFyvv5h67OEefcELzgPpjc4x+xfJZlcVl44RnC6LsGJWglO1EpwolYAEYIzEWwv5qgeOWiCc8k9jgTnCi84n5lgfZYw7/GCs70zvcH5JS84pxxrtz4LhktwolaCE7USnKgVQITgTATbizmqRw+5FZzdK5rMKbXPPGR9ljBrH/aC80B6g/PL+1dLJpeV54/ttj4LhktwolaCE7USnKgVQITgTATbizmqRw6b4Fx8lyPB+f5yE5xPP2B9ljDrJnrBuT+9wfmPB0xwPneU4HRdghO1EpyoleBErQAiBGci2F7MUT16xK3gPLtyhRec91ufJUw/OHeneIez6kCzZHJZqT+6y/osGC7BiVoJTtRKcKJWABGCMxFsL+aoHj1yyQTneEeCc9X7JjifvM/6LGHWTTLBuSvFO5xf3W+C85mjbdZnwXAJTtRKcKJWghO1AogQnIlgezFH9dhRx4Jz9Spz0aAnv299ljC//4gJzrYPkzuma8H5tQNrJJPLytMEp/MSnKiV4EStBCdqBRAhOBPB9mKOqh+ci8b3Wp/l9PlLcrbZC87H77U+S5h+cLbuS+6YrgXn173gfJLgdF6CE7USnKiV4EStACIEZyLYXsxRPX7cC87vORKca5q94KyzPkuY9z3WK2PH9crOvckd07Xg/MaBtQRnmUhwolaCE7USnKgVQITgTATbizmqfnC+N86R4Fy7xgTno/dYnyXM+73gzO1J7piuBec3veB8/Gir9VkwXIITtRKcqJXgRK0AIgRnIthezFE9fsILzu8SnHG8//H0B+fYzg8kk8vKY0d2Wp8FwyU4USvBiVoJTtQKIEJwJoLtxRzV4ye94LzDjeA888FaE5yT7rI+S5gPPGGCc0d7csd0LThv9oLzEYLTeQlO1EpwolaCE7UCiIzQ4KyprZeKymqpqKyWmtr6wMd1dV8YfFyh+dhezFE9ccoLztvdCM6z69aZ26I84nZwPvhk+oPzls51ksllZdKRnPVZMFyCE7USnKiV4EStACIjMDgnTW4YEpk1tfUyaXJD5L8/o3HZdY+3vZijeuK0Cc6F33EkONeb4Dw/8U7rs4T50JPmKrXb2tN7H86ag+slk8vKw4d3WJ8FwyU4USvBiVoJTtQKIDICg3N0VZ20dnQO/rm1o1NGV9VF/vsVldXS1X1hyMdsL+aonvSD8zZHgnPDBi84x1ufJcwJT3nBuTu9wfntzg0EZ5lIcKJWghO1EpyoFUBkhAWnf4psfjAW+1gQxXY3RcooOLtMcC5wJTg3bjTBOWGc9VnCfNgLzpZd6Q3O27zgfOjIduuzYLgEJ2olOFErwYlaAUQIzljBGfS43mv9ZeGVS/2DO5y2Z+m91i+X27bJxTGj5JNHxlufJczH6q/J2HG9sv9gX2LH7OsfkP7+Aevfm2/tiS3mPpxdrdZnwXD7+gakf8Cd545z9mGQ1/r6ZWBgwPocJdX28y+lDj53HJgFy0sAEYIzcnBOmtwQ+F7P7ouXy8IzPZfNDuetvdZn6b54Wc5t3SIXx4ySCw/dYX2WMCc+a3Y4t++6ktgxP/lhr/zwyjXr35vvuEMbJZPLyoNHtlufBcP9+LOrcvlqn/U5nPUjDPKjT67K1Wv91ucoqbaffyn1/A+uyLW+AetzYPkJIDLCglNE9x7O4aLU9ukKUT3VfWkwOG3Pcvr8JTmzxQTn+Qdvtz5LmJO84NzSmt5Tascd3CSZXFbuO9xifRYMl1NqUSun1KLW7oucUos6AURGYHAOd5Xamtr6626VEra7KVJ+wflujSvBudXscN7/HeuzhDmp3gTn5p3pD857CU7nJThRK8GJWglO1AogMgKDUyT8PpyFwdnU3DLsKbe2F3Nke7zgvMWR4Nxq3sN54YHbrM8S5qPPmeDclEtvcI4/tFkyuazUHdpqfRYMl+BErQQnaiU4USuAyAgNzqSxvZgj6wXn/JsdCc6W7SY477vV+ixh+sG5McXBeechc9Gg2kNbrM+C4RKcqJXgRK0EJ2oFECE4E8H2Yo7j/LG9Mn+sG8F5dtsO8x5Ox4PzsckmODfsSG9w3u0F5z0Ep/MSnKiV4EStBCdqBRAhOBPB9mKOo1PBuT1ngvPeW6zPEuZgcG5Pb3De4wXnXQSn8xKcqJXgRK0EJ2oFECE4E8H2Yo7j/Ju94OyxP8uZHTu94LzZ+ixhPv58+oOz7tBWyeSycifB6bwEJ2olOFErwYlaAUQIzkSwvZjj+O4tJjhPnbM/y5lcq3kPZ+1Y67OE+cQUE5zrWtIfnN87tNn6LBguwYlaCU7USnCiVgARgjMRbC/mOL5b4wVnt/1Zzuzc5QXnt6zPEuaTL6Q/OL9/uEUyuayMO7jJ+iwYLsGJWglO1EpwolYAEYIzEWwv5jguuNUE58mz9mc5s7PNBOfd37Q+S5hPvWiC84Ot6Q3O+7zg/C7B6bwEJ2olOFErwYlaAUQIzkSwvZjj+O63HQrO1t1ecH7D+ixhPj3VBOfaLekNzgcOb5NMLit3HNxofRYMl+BErQQnaiU4USuACMGZCLYXcxwX3GaC80SX/VnOtLWb4Lyr2vosYT41GJxXEjuma8H54JHtksll5TudG6zPguESnKiV4EStBCdqBRAhOBPB9mKOox+cJx0Izq5dHV5wft36LGE+M/WKjB3XK2s2pzc4Jxw2wXkbwem8BCdqJThRK8GJWgFECM5EsL2Y47jQ3+E8bX+Wrt175OKYUfLR+K9ZnyXMZ14ywdm8Kb3B+fDhHZLJZeXWzvXWZ8FwCU7USnCiVoITtQKIEJyJYHsxx3Hh7e4E55n2fWaHc/xXrc8SZv3LJjhXpzg4Jx7JSSaXlZqDBKfrEpyoleBErQQnagUQITgTwfZijuN7XnAeP2V/ljMdXnCOq7I+S5j1WROcqzamNzgnecF5S+c667NguAQnaiU4USvBiVoBRAjORLC9mOO48LtecJ60P8uZPfu94PyK9VnCfO4VE5wrN6Q3OB89slMyuazc3PmB9VkwXIITtRKcqJXgRK0AIgRnIthezHF8zwvOYyfsz3Jm7wETnHd82fosYU72g3N9em+L8pgXnN8iOJ2X4EStBCdqJThRK4AIwZkIthdzHN8b5+1wHrc/S9fezvIIzlfNbVFWrktvcD5xtFUyuax888Ba67NguAQnaiU4USvBiVoBRAjORLC9mOO46HveDucx+7N07TtogvP2L1mfJcznveBc8UF6g/PJo22SyWWl+sAa67NguAQnaiU4USvBiVoBRAjORLC9mOO4aLwXnEftz9L14WFzW5Tbx1ifJcwpfnCmeIfzKS84v05wOi/BiVoJTtRKcKJWABGCMxFsL+Y4Lh5/VeaP7ZWjR+zP0rXfC87b/t76LGG+MN0EZ9Pa9Abn015wfm1/s/VZMFyCE7USnKiV4EStACIEZyLYXsxxXHynQ8F54KgXnH9nfZYwX5xhgnPZmvQG57NHd0kml5WvEpzOS3CiVoITtRKcqBVAhOBMBNuLOY6L7/KDM7l40trVecwE57f/P+uzhDn1NW+HM8XBWe8FZ9UBgtN1CU7USnCiVoITtQKIEJyJYHsxx3Hx3SY4jxx2IDgPHjcXDbr1i9ZnCfNFLziXNqc3OCcf2y2ZXFb+8cBq67NguAQnaiU4USvBiVoBRAjORLC9mOO45B5vh/OQA8F56KQJzpq/tT5LmFNnesG5Or3BOeVYu2RyWfnyfoLTdQlO1EpwolaCE7UCiBCciWB7McdxSa23w3nQheA8YU6pveVvrM8S5kuvm+Bcsir9wfml/ausz4LhEpyoleBErQQnagUQITgTwfZijuPSOi84Ox0IzsOnTHDe7HZwvvyGCc7FK9MbnC8e75BMLitjCE7nJThRK8GJWglO1AogQnAmgu3FHMel95rgPHzAgeA8ctoLzr+2PkuY2VkmOBetvJLYMV0LzqlecP79hyutz4LhEpyoleBErQQnagUQGaHBWVNbLxWV1VJRWS01tfWR/o7/+IrKamlqbhnyOduLOY6DwbnfgeA82mWCc+z/tD5LmK/MuiJjx/XKeykOzpeP7ZFMLit/R3A6L8GJWglO1EpwolYAkREYnJMmNwyJzJraepk0uSHw8V3dF4pGZj62F3Mc/eA89KEDwXnsjAnOb/2V9VnCnOYH54r0Bmf22F7J5LLyxQ/ftz4LhktwolaCE7USnKgVQGQEBufoqjpp7egc/HNrR6eMrqoLfPykyQ0yo3FZ6DFtL+Y4LrvP2+F0IDhPH+82wfnN/8f+LCFOazDBuTDFwTntuAnOv/1whfVZMFyCE7USnKiV4EStACIjLDj93cqu7guhH8unorJaRlfVDTmltvCxthdzHP3gPLTPgeA84QXnN75gf5YQX33TBOeC5WkOzn2SyWXlbwhO5yU4USvBiVoJTtQKIEJwhgan/7n8HdEZjcukorJ6yOP6+gfKxpUPXZP5Y3vl4in7s/R9+qlcHDNKPv7GF+zPEuJb8/pk7LheWflBX2LH7B8YkIEB+9+b77zzh8xVag+ttD4LhtvfPyADA+X17w66I88d1DpQZq930A0BRAjOSMFZ+LnCCD178XLZ2PRAr7ktyof2Z+nuumB2OL/+l9ZnCXNG49XBHc6kjvnJpWty6Uqf9e/Nd+apDyWTy8pf719ufRYM9+PPeuVyb7/1ObD8/OiTq9Lbx3MH43v+B1ekr3/A+hxYfgKIjLDgFIn/Hs7CuCz2MdunK8Sx6UETnAf32J+l65QXnF/7C+uzhPlaozmldv7S9J5SO/PEfsnksvJX+5qsz4LhckotauWUWtTKKbWoFUBkBAbncFepramtH/L5SZMbhgTpjMZl1wWq7cUcx6aHTHB2dtifpev0RyY4v/o/rM8Sph+c85akNzhf94Jz9L5l1mfBcAlO1EpwolaCE7UCiIzA4BQJvw9nYXCKmOj0H19sN9T2Yo7j8glecLbbn+V018decP65/VlCfO1tLzhTvMM56+QByeSy8gWC03kJTtRKcKJWghO1AoiM0OBMGtuLOY7LJ5ir1Hbutj/L6TNecFb9mf1ZQpw5xwTnOyne4Ww4YYLzL/cutT4LhktwolaCE7USnKgVQITgTATbizmOKx72gnOX/VlOn/1ELo4ZJRe/4nZwvu7tcM5dnN7gfNMLzr/Yu8T6LBguwYlaCU7USnCiVgARgjMRbC/mOK6YaILzwC4H7sPZbW6LcvEfK+3PEuIbc01wvr0ovcE5+0SnZHJZ+fN97HC6LsGJWglO1EpwolYAEYIzEWwv5jiumOQFZ5sDwXnuMxOcX77J/iwhznrHBOec99IbnI0nD3rByQ6n6xKcqJXgRK0EJ2oFECE4E8H2Yo6jH5z7Wx0Izp4fmuD8hz+xP0uIDfO84FyY3uCc4wXnn+1dbH0WDJfgRK0EJ2olOFErgAjBmQi2F3Mc33/UoeA8f8kE55f+L+tzhNkw3wRnY4qDc+7JQ5LJZeVPCU7nJThRK8GJWglO1AogQnAmgu3FHMfB4Mw5FJxjRlmfI8w3veB8a0F6g/OdUyY4b9q7yPosGC7BiVoJTtRKcKJWABGCMxFsL+Y4rnzcseD8hz9xPjhnv2uCc3aKg3PeqcOSyWXlT/YQnK5LcKJWghO1EpyoFUCE4EwE24s5jn5wfrjdleC8yQTnuc+szxLk7AUmON98N73BOf/UEcnksjJqz3vWZ8FwCU7USnCiVoITtQKIEJyJYHsxx3GVa8H5j5VecH5qfZYgG/3gnJ/e4FwwGJwLrc+C4RKcqJXgRK0EJ2oFECE4E8H2Yo7jyidMcO5zJjj/1ATn2U+szxJk40ITnA3z0hucC08dlUwuK39McDovwYlaCU7USnCiVgARgjMRbC/mOK560gvObW4E50dVf+YF5w+szxJk43vpD85Fp45JJpeVP9qzwPosGC7BiVoJTtRKcKJWABGCMxFsL+Y4rnrKC84WV4Lzv5vg7PrY+ixBzvGC84256Q3OxadNcP7hnnetz4LhEpyoleBErQQnagUQITgTwfZijuPqp73g3OpIcH71f3jBedH6LEG+vSj9wbnk9HHJ5LLy3zoITtclOFErwYlaCU7UCiBCcCaC7cUcx9XPmODcu8WV4PwLuThmlHSdumB9liDnLjbB+frb6Q3OpV5w/kHHfOuzYLgEJ2olOFErwYlaAUQIzkSwvZjj2PysF5ybHQnOr/+lF5znrc8S5DtLTHDOnJPe4GzqOiGZXFZ+n+B0XoITtRKcqJXgRK0AIgRnIthezHFc7QXnnk1uBOfFr//fJjhP9lifJch5S01wvpbi4FzedVIyuaz8Xvs867NguAQnaiU4USvBiVoBRAjORLC9mOPYXH/FqeD86BtfMO/hPHnO+ixBzveDszG9wfn+abPD+V/b37E+C4ZLcKJWghO1EpyoFUCE4EwE24s5joPBucGV4BxtgvN4t/VZgvSDc8Zb6Q3OlafNDufvEpzOS3CiVoITtRKcqBVAhOBMBNuLOY5rJnvBuTG5eLoRP/rWX3nBedb6LEG+22SCc3qKg3NV1ynJ5LLyXzoITtclOFErwYlaCU7UCiBCcCaC7cUcRz84O9a7FZxdR7uszxKkH5yvzk5vcK72gvN32udanwXDJThRK8GJWglO1AogQnAmgu3FHMe1zzsWnDf/tfPBuXC5F5xvpjc413SdlkwuKxUEp/MSnKiV4EStBCdqBRAhOBPB9mKO49opJjjb1zkSnGO94Dx8yvosQS5cYYLzlYb0BufaMyY4f7v9beuzYLgEJ2olOFErwYlaAUQIzkSwvZjjuPYFc1uU9g8cCc5b/sYLzpPWZwnyvfdNcE6bld7gXHemSzK5rPzn3QSn6xKcqJXgRK0EJ2oFECE4E8H2Yo7j2hdNcO5e68ZVai/c8v+a4DzkcHCuNMGZnXU1sWO6Fpzrz5yRTC4rv7V7jvVZMFyCE7USnKiV4EStACIEZyLYXsxx/MALzvY1jgTnrV/0gvOE9VmCXLTycuqDc6MXnP9pd6P1WTBcghO1EpyoleBErQAiIzQ4a2rrpaKyWioqq6Wmtj70sTMalw0+Nt98bC/mOH4w1dvhdCQ4P/KD8+Bx67MEudgLzpdfT3NwnpVMLiv/keB0XoITtRKcqJXgRK0AIiMwOCdNbhgSmTW19TJpckPg42c0Lhs2Sm0v5jiue8kLztWOBOdtf2eCs/OY9VmCXLLKBOdLKQ7OzWe6JZPLyr/f9Zb1WTBcghO1EpyoleBErQAiIzA4R1fVSWtH5+CfWzs6ZXRVXeDjUxecL5vg3NXsSnD+vQnOA0etzxLk0tUmOKfOTG9wbvGC8zd3E5yuS3CiVoITtRKcqBVAZIQFZ1f3BamorJau7guhH8un8JTaYnFqezHHcV3WC85VjgTn7WNMcO4/bH2WIJc2m+B88bX0BmfL2XOSyWXl3+2ebX0WDJfgRK0EJ2olOFErgAjBOWxwFlJTW3/djmd//0DZuO21Ppk/tleObu6zPkt//4D84Lv/IBfHjJJrXaeszxLkppY+GTuuV6Y3JPgzGxiQgQH735vvkcs/MKfUtr9lfRYcRseeO1hG8tzBG5DnDmoEECE4Ywdna0fndRcN6rpwqWxcP+2fdjhtz9J14ZJc+O6X5eKYUXLmw4PWZwmyaY3Z4XxhxtXEjvkDb4fT9vfmu6O7RzK5rPzGrjetz4LhXvR2OG3PgeXneW+H0/YcWH6e+9jscNqeA8tPAJERFpwi8d/DWUix4LR9ukIc/eBse9+NU2ov3GGCs2tvp/VZgmxaa4JzyqvpPaXWD85f39VgfRYMl1NqUSun1KJWTqlFrQAiIzA4h7tKbeEps4UxOrqq7rqr2tpezHHc8KoXnCscCc5xXzE7nHsPWJ8lyOUfmOB8fnp6g3Nn93nJ5LLya20Ep+sSnKiV4EStBCdqBRAZgcEpEn4fzsLgzH9sRWV10Vuo2F7Mcdzw6hWZP7ZXWpe7EpxVJjj37Lc+S5ArvOCcnOIdztazJjh/tW2W9VkwXIITtRKcqJXgRK0AIiM0OJPG9mKO44bpXnA2ORKc479qgrNjn/VZgnx/nRec064kdkzXgnNX9wXJ5LLyK21vWJ8FwyU4USvBiVoJTtQKIEJwJoLtxRzHDTNMcO5c5kZwfjT+ayY42/dZnyXIletNcD73SnqDc7cXnL9McDovwYlaCU7USnCiVgARgjMRbC/mOG58zQ/O5OLpRrxw19fNRYN277E+S5CDwZlNb3B2dH8kmVxWfongdF6CE7USnKiV4EStACIEZyLYXsxx3DjTC86lrgRntQnOXR3WZwly1cYrMnZcr9S/nN7g3OMF5y8SnM5LcKJWghO1EpyoFUCE4EwE24s5jpte94JziSPBefc3zCm1UMmXnwAAFPBJREFUbe3WZwlytRecz6Y4OPeeuyiZXFZ+oe1167NguAQnaiU4USvBiVoBRAjORLC9mOO46Q0TnDlngvObJjhbd1ufJcjmTSY4n5ma3uDc5wXnz7fOtD4LhktwolaCE7USnKgVQITgTATbizmOm2Z5wbnIkeCs/ZYJzp1t1mcJsnmzCc6nUxyc+899LJlcVn6O4HReghO1EpyoleBErQAiBGci2F7McfSDc4czwTnWC85d1mcJcu0WPzjTex/OAz0/kEwuKz/b+pr1WTBcghO1EpyoleBErQAiBGci2F7Mcdzc4AXne24E5/l7bzbBmWu1PkuQa7eYq9Q+9WJ6g/PguU8kk8vKz+ycYX0WDJfgRK0EJ2olOFErgAjBmQi2F3McB4NzoSvBeYsJzh07rc8S5AdbTXA++UJ6g/OQF5z/kuB0XoITtRKcqJXgRK0AIgRnIthezHHc8qZjwXnfrXJxzCg5uz1nfZYg/eB8Ykp6g/Nwz6eSyWXlp1unW58FwyU4USvBiVoJTtQKIEJwJoLtxRzHLbNNcG5f4EZwXvCDc9sO67MEua7FBOfjz6c3OI94wflTBKfzEpyoleBErQQnagUQITgTwfZijuPWt7zgfNeR4HzgNnNKbct267MEuX5b+oPzWM9nksll5Sd3vmp9FgyX4EStBCdqJThRK4AIwZkIthdzHLc0muDcNt+R4Lz/OyY4t26zPkuQG7ab4HxscnqD87gXnD+xc5r1WTBcghO1EpyoleBErQAiBGci2F7Mcdw6xwvOeW4E5/kHbzfBuWWr9VmC3LjDBOejz6U3OE+e/6Fkcln5cYLTeQlO1EpwolaCE7UCiBCciWB7Mcex5W0TnC3vOBKcD33XC84t1mcJcmMu/cF5+vwlyeSy8mO5V6zPgeESnKiV4EStBCdqBRAhOBPB9mKOox+c2+Y6EpwTxpng3LzZ+ixBbvKCc1J9+oPzR3NZ63NguAQnaiU4USvBiVoBRAjORLC9mOO4ba63w+lKcD78PXOV2k2brM8S5OadXnA+m+7g/NFcVn6E4HReghO1EpyoleBErQAiBGci2F7Mcdz2jhecbzsSnBPHm+DcuNH6LEH6wfnwM+kOzn+We0UyBKfzEpyoleBErQQnagUQITgTwfZijuO2eSY4t7oSnJPuNMG5YYP1WYLc2uoF59PpDs4f3zlNMrmsnHJgFgyW4EStBCdqJThRK4AIwZkIthdzHLfN94JzjhvB2fPIXSY416+zPkuQW9u84Hwq3cH5z73gPHH+h9ZnwWAJTtRKcKJWghO1AogQnIlgezHHcfu7Jji3NLoRnOcfudsE5zp3g7NllwnOCSkPzp/c+apkclk53vOZ9VkwWIITtRKcqJXgRK0AIgRnIthezHHcvsDb4XzLkeB89B5zldoP1lqfJciW3SY4H3pyZATnUYLTaQlO1EpwolaCE7UCiBCciWB7Mcdxx0Jvh3O2I8H5WK3Z4Vy7xvosQW5rN8H54JO9iR3TxeD86dbpksll5UjPp9ZnwWAJTtRKcKJWghO1AogQnIlgezHHcYe3w7nlTUeC8/E654NzuxecDzyR7uD8FztNcB4694n1WTBYghO1EpyoleBErQAiBGci2F7McdzxngnOzQ2OBOcT95rgXNNsfZbAn1n7JRk7rlfufzzdwfkzO2cQnGUgwYlaCU7USnCiVgCRERqcNbX1UlFZLRWV1VJTWx/57zU1t0hFZbW0dnQO+bjtxRzHweCc5UhwPvl9E5zNq6zPEmSuwwvOx9IdnP+q9TXJ5LLSee4H1mfBYAlO1EpwolaCE7UCiIzA4Jw0uWFIZNbU1sukyQ3D/r2m5hYZXVVX9sGZW2SCc5Mjwdnz1P0mOFc7HJx7THDel/Lg/LnWmZLJZeVAD8HpsgQnaiU4USvBiVoBREZgcI6uqhsSjK0dnTK6qi707/ixKSLlH5yLvR3ONxwJzqe94Fz1vvVZgty51wTn9x9J91Vq/eD88NzH1mfBYAlO1EpwolaCE7UCiIyw4OzqviAVldXS1X0h9GP55MemSPkH584l3g6nM8H5oAnOlSuszxJk676REZy/0Pa6ZHJZ2XfuovVZMFiCE7USnKiV4EStACIEZ2hwFsamSPHg7B8YKBsPrumT+WN7pbWxz/os/QMD8ukLE+XimFFyddNq67MEefJ0/+B7OJM65oCn7e8t31/aZYKz59pl67OUiwOIiOiMtv8/Aa8XQITgDA3OSZMbBi8uVGhTc8s/HeP8pbKxdZnZ4dw484r1WbrOX5Lzz06Qi2NGSfeKJuuzBLl7v7ktyr0TryZ2TH+H0/b3lu8vtb0hmVxW9nR/ZH2WctHGfy1mhxO1ssOJWstlh9P2/yfg9QKIjLDgFNG9hzOfsj+ltskLztfcOKX2fP3D5pTaFUutzxLkLi846yam+5Taf+0FZ3v3BeuzYLAEJ2olOFFruQQnuieAyAgMzuGuUltTWx96q5RyD87Wpssyf2yvbJjhRnD2PDfJBOdyd4Oz3QvO2ofTHZy/4gXnboLTaQlO1EpwolaCE7UCiIzA4BQJvw9n6oNzuRec0x0JzslecDYtsT5LkLsPmOC8Z0K6g/NX22ZJJpeVXQSn0xKcqJXgRK0EJ2oFEBmhwZk0thdzHNtWeMH5qiPB+fyjJjiXLbI+S5DtXnDenfLg/LW2BsnkstJ69rz1WTBYghO1EpyoleBErQAiBGci2F7Mcfyn4Ewunm7EnimPmYsGLXU3ODsOmuC866F0B+e/2WWCc2c3wemyBCdqJThRK8GJWgFECM5EsL2Y47hrpQnO9dMcCc4XHjfBuWSh9VmC3OMF550Ppjs4f2PXm5LJZWVHd4/1WTBYghO1EpyoleBErQAiBGci2F7McWxzNTgXL7A+S5B7D3nB+UC6g/Pf+sF5luB0WYITtRKcqJXgRK0AIgRnIthezHHctcoE57qsG8F57sUnTXAuetf6LEHuPWyCc/z96Q7Of7d7tmRyWdl29pz1WTBYghO1EpyoleBErQAiBGci2F7Mcdy12rHgnOoF53vzrc8S5L4jJji/d39vYsd0MTh/0wvOFoL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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = s.optcase1.data[['Total demand', 'hp_output_MW', 'hp_input_MW', 'gb_output_MW', 'gb_input_MW', 'NEG', 'Gas prices (€/MWh)']].loc[date, :].iplot(\n", " subplots=True, \n", " title=f'Hybrid case on {date}',\n", " subplot_titles=['Total demand (MW)', 'Heatpump output (MW)', 'Heatpump input (MW)', 'Steam boiler output (MW)', 'Steam boiler input (MW)', 'Imbalance price (€/MWh)', 'Gas price (€/MWh)'],\n", " legend=False,\n", " dimensions=(1000, 600)\n", ")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Heatpump onlyOptimisationOptimisation 2
Total net volume (MWh)206,178-63,006-66,080
Total exported to grid (MWh)206,178--
Total consumed from grid (MWh)-63,00666,080
Total nominated volume (MWh)206,178--
Absolute imbalance volume (MWh)-63,00666,080
Mean selling price (€/MWh)41--
Mean buying price (€/MWh)--37-39
Total day-ahead result (€)8,498,909--
Total POS result (€)---
Total NEG result (€)--2,357,716-2,577,150
Total imbalance result (€)--2,357,716-2,577,150
Total combined result (€)8,498,909-2,357,716-2,577,150
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" ], "text/plain": [ " Heatpump only Optimisation Optimisation 2\n", "Total net volume (MWh) 206,178 -63,006 -66,080\n", "Total exported to grid (MWh) 206,178 - -\n", "Total consumed from grid (MWh) - 63,006 66,080\n", "Total nominated volume (MWh) 206,178 - -\n", "Absolute imbalance volume (MWh) - 63,006 66,080\n", "Mean selling price (€/MWh) 41 - -\n", "Mean buying price (€/MWh) - -37 -39\n", "Total day-ahead result (€) 8,498,909 - -\n", "Total POS result (€) - - -\n", "Total NEG result (€) - -2,357,716 -2,577,150\n", "Total imbalance result (€) - -2,357,716 -2,577,150\n", "Total combined result (€) 8,498,909 -2,357,716 -2,577,150" ] }, "execution_count": 41, "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": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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BaselineHeatpump onlyOptimisationOptimisation 2
Gasboiler OPEX (€)----
Gas consumption costs (€)-3,089,622--361,063-159,867
CO2 emission costs (€)-1,095,004--130,381-58,031
Gas taxes (€)-790,305--94,112-42,021
Heatpump VDG OPEX (€)--46,600-46,600-46,600
Heatpump NDG OPEX (€)--15,400-15,400-15,400
Result on electricity market (€)-8,498,909-2,357,716-2,577,150
Electricity taxes (€)---59,856-62,776
Grid transport costs (€)--1,513,431-561,328-572,998
EBITDA (€)-4,974,9306,923,479-3,626,457-3,534,843
\n", "
" ], "text/plain": [ " Baseline Heatpump only Optimisation \\\n", "Gasboiler OPEX (€) - - - \n", "Gas consumption costs (€) -3,089,622 - -361,063 \n", "CO2 emission costs (€) -1,095,004 - -130,381 \n", "Gas taxes (€) -790,305 - -94,112 \n", "Heatpump VDG OPEX (€) - -46,600 -46,600 \n", "Heatpump NDG OPEX (€) - -15,400 -15,400 \n", "Result on electricity market (€) - 8,498,909 -2,357,716 \n", "Electricity taxes (€) - - -59,856 \n", "Grid transport costs (€) - -1,513,431 -561,328 \n", "EBITDA (€) -4,974,930 6,923,479 -3,626,457 \n", "\n", " Optimisation 2 \n", "Gasboiler OPEX (€) - \n", "Gas consumption costs (€) -159,867 \n", "CO2 emission costs (€) -58,031 \n", "Gas taxes (€) -42,021 \n", "Heatpump VDG OPEX (€) -46,600 \n", "Heatpump NDG OPEX (€) -15,400 \n", "Result on electricity market (€) -2,577,150 \n", "Electricity taxes (€) -62,776 \n", "Grid transport costs (€) -572,998 \n", "EBITDA (€) -3,534,843 " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc')\n", "casereport.show(presentation_format=True)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Year 0Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10Year 11Year 12Year 13Year 14Year 15
CAPEX (€)-6,231,000
Regular Earnings (€)1,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,474
Irregular Earnings (€)---------------
EBITDA (€)1,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,4741,348,474
Depreciations (€) -/--248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000-248,000
EBIT (€)1,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,4741,100,474
Income tax (Vpb.) (€)-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118-258,118
NOPLAT (€)842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355842,355
Depreciations (€) +/+248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000248,000
Free Cash Flow (€)-6,231,0001,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,3551,090,355
IRR (%)15%
WACC (%)10%
NPV of explicit period (€)1,874,844
Discounted residual value (€)601,113
NPV (€)2,475,957
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" ], "text/plain": [ " Year 0 Year 1 Year 2 Year 3 \\\n", "CAPEX (€) -6,231,000 \n", "Regular Earnings (€) 1,348,474 1,348,474 1,348,474 \n", "Irregular Earnings (€) - - - \n", "EBITDA (€) 1,348,474 1,348,474 1,348,474 \n", "Depreciations (€) -/- -248,000 -248,000 -248,000 \n", "EBIT (€) 1,100,474 1,100,474 1,100,474 \n", "Income tax (Vpb.) (€) -258,118 -258,118 -258,118 \n", "NOPLAT (€) 842,355 842,355 842,355 \n", "Depreciations (€) +/+ 248,000 248,000 248,000 \n", "Free Cash Flow (€) -6,231,000 1,090,355 1,090,355 1,090,355 \n", "IRR (%) 15% \n", "WACC (%) 10% \n", "NPV of explicit period (€) 1,874,844 \n", "Discounted residual value (€) 601,113 \n", "NPV (€) 2,475,957 \n", "\n", " Year 4 Year 5 Year 6 Year 7 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Irregular Earnings (€) - - - - \n", "EBITDA (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Depreciations (€) -/- -248,000 -248,000 -248,000 -248,000 \n", "EBIT (€) 1,100,474 1,100,474 1,100,474 1,100,474 \n", "Income tax (Vpb.) (€) -258,118 -258,118 -258,118 -258,118 \n", "NOPLAT (€) 842,355 842,355 842,355 842,355 \n", "Depreciations (€) +/+ 248,000 248,000 248,000 248,000 \n", "Free Cash Flow (€) 1,090,355 1,090,355 1,090,355 1,090,355 \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) \n", "\n", " Year 8 Year 9 Year 10 Year 11 \\\n", "CAPEX (€) \n", "Regular Earnings (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Irregular Earnings (€) - - - - \n", "EBITDA (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Depreciations (€) -/- -248,000 -248,000 -248,000 -248,000 \n", "EBIT (€) 1,100,474 1,100,474 1,100,474 1,100,474 \n", "Income tax (Vpb.) (€) -258,118 -258,118 -258,118 -258,118 \n", "NOPLAT (€) 842,355 842,355 842,355 842,355 \n", "Depreciations (€) +/+ 248,000 248,000 248,000 248,000 \n", "Free Cash Flow (€) 1,090,355 1,090,355 1,090,355 1,090,355 \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) \n", "\n", " Year 12 Year 13 Year 14 Year 15 \n", "CAPEX (€) \n", "Regular Earnings (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Irregular Earnings (€) - - - - \n", "EBITDA (€) 1,348,474 1,348,474 1,348,474 1,348,474 \n", "Depreciations (€) -/- -248,000 -248,000 -248,000 -248,000 \n", "EBIT (€) 1,100,474 1,100,474 1,100,474 1,100,474 \n", "Income tax (Vpb.) (€) -258,118 -258,118 -258,118 -258,118 \n", "NOPLAT (€) 842,355 842,355 842,355 842,355 \n", "Depreciations (€) +/+ 248,000 248,000 248,000 248,000 \n", "Free Cash Flow (€) 1,090,355 1,090,355 1,090,355 1,090,355 \n", "IRR (%) \n", "WACC (%) \n", "NPV of explicit period (€) \n", "Discounted residual value (€) \n", "NPV (€) " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BusinessCaseReport(s.optcase1).show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "line": { "color": "rgba(255, 153, 51, 1.0)", "dash": "solid", "shape": "linear", "width": 1.3 }, "mode": "lines", "name": "Gas prices (€/MWh)", "text": "", "type": "scatter", "x": [ "2019-01-01 00:00:00+01:00", "2019-01-02 00:00:00+01:00", "2019-01-03 00:00:00+01:00", "2019-01-04 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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=(600, 400)\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": 48, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "Attribute 'npv' is not available for 'Heatpump only' case. You should first generate it using the appropriate CaseStudy method.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m SingleFigureComparison(s.optcases, 'npv', label='NPV').report.iplot(\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bar'\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[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'NPV by Casestudy in €'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrecoygreen\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m400\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\reports.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, cases, kpi, label, presentation_format)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[0mfigure_dict\u001b[0m \u001b[1;33m=\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 121\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcase\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_if_attr_exists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m \u001b[0mfigure_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\reports.py\u001b[0m in \u001b[0;36m_check_if_attr_exists\u001b[1;34m(self, case, kind)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\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 27\u001b[0m raise AttributeError(\n\u001b[1;32m---> 28\u001b[1;33m \u001b[1;34mf\"Attribute '{kind}' is not available for '{case.name}' case. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 29\u001b[0m \u001b[1;34m\"You should first generate it using \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;34m\"the appropriate CaseStudy method.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: Attribute 'npv' is not available for 'Heatpump only' case. You should first generate it using the appropriate CaseStudy method." ] } ], "source": [ "SingleFigureComparison(s.optcases, 'npv', label='NPV').report.iplot(\n", " kind='bar',\n", " title='NPV by Casestudy in €',\n", " colors=recoygreen,\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100).iplot(\n", " kind='bar',\n", " title='IRR by Casestudy in %',\n", " colors=recoygreen,\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sensitivity analysis" ] }, { "cell_type": "markdown", "metadata": {}, "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 and two simulated years based on the prices in 2019 +30% and 2019 -30% . __Check 2018 results__\n", "2. __aFRR prices +/- 30%__\n", "3. CAPEX __(add +100% scenario's, CAPEX is w/out integration costs - integration costs might be very high.)__\n", "4. CO2 price __(larger impacts expected, check calculations, try higher levels of 50-100-150€/ton average)__\n", "5. Tsource +/- 10 degrees Celsius __Verify results__\n", "6. __Tsink__ \n", " * +/- 10 degrees celcius\n", " * Roam off the peak / lower pressure\n", " * Stabalize / running average per hour/ 2 hours\n", "7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n", "8. Energy Tax and ODE +/- 30%" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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 = 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": {}, "outputs": [], "source": [ "#%time _s = setup()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%time result = routine(c, _s)\n", "#npv = result.hpcase.npv\n", "#npv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Heat Pump CAPEX" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [c.hp_capex * mult for mult in [0.7, 1, 1.3]]\n", "for value in values:\n", " _c = Config()\n", " _c.hp_capex = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "sens1 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output1 = sens1.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\n", "output1.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CAPEX (€)',\n", " yTitle='NPV in €',\n", " yrange=[0, output1.max().max()*1.1],\n", " title='Sensitivity: Heat Pump CAPEX (€)',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: CO2 prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [0.7, 1, 1.3]\n", "for value in values:\n", " _c = Config()\n", " _c.co2_price_multiplier = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "sens4 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens4.single_kpi_overview('irr', case_names=['Heatpump only', 'Optimisation'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='CO2 price factor',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: CO2 prices',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Gas prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "sens3 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens3.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\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=[0, output.max().max()*1.1],\n", " title='Sensitivity: Gas prices',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Electricity prices" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "sens5 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens5.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Electricity price factor',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Electricity prices',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Electricity price volatility" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": {}, "outputs": [], "source": [ "%%time\n", "sens6 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens6.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Volatility factor',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: E-price volatility',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Energy tax" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": {}, "outputs": [], "source": [ "%%time\n", "sens8 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens8.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\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],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Tsource" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_s = setup()\n", "configs = {}\n", "values = [-10, 0, 10]\n", "for value in values:\n", " _c = Config()\n", " _c.tsource_delta = value\n", " configs[value] = _c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%time\n", "sens7 = SensitivityAnalysis(_s, routine, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "output = sens7.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\n", "output.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Tsource delta',\n", " yTitle='NPV in €',\n", " yrange=[0, output.max().max()*1.1],\n", " title='Sensitivity: Tsource',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sensitivity: Time period" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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": {}, "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": {}, "outputs": [], "source": [ "%%time\n", "sens2 = SensitivityAnalysis(_s, routine2, configs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "result2 = sens2.single_kpi_overview('npv', case_names=['Heatpump only', 'Optimisation'])\n", "result2.iplot(\n", " mode='lines+markers',\n", " symbol='circle-dot',\n", " size=10,\n", " xTitle='Start date',\n", " yTitle='NPV in €',\n", " yrange=[0, result2.max().max()*1.1],\n", " title='Sensitivity: Modelled time period',\n", " colors=[recoygreen, recoyyellow],\n", " dimensions=(600, 400)\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.6 64-bit ('py38': conda)", "language": "python", "name": "python37664bitpy38condad5f9d6fa326845b3840837eaa94b771b" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc-autonumbering": true, "toc-showmarkdowntxt": false, "widgets": { 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