You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
3186 lines
132 KiB
Plaintext
3186 lines
132 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": "true"
|
|
},
|
|
"source": [
|
|
"#### Imports"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
" <script type=\"text/javascript\">\n",
|
|
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
|
" if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
|
" if (typeof require !== 'undefined') {\n",
|
|
" require.undef(\"plotly\");\n",
|
|
" requirejs.config({\n",
|
|
" paths: {\n",
|
|
" 'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
|
|
" }\n",
|
|
" });\n",
|
|
" require(['plotly'], function(Plotly) {\n",
|
|
" window._Plotly = Plotly;\n",
|
|
" });\n",
|
|
" }\n",
|
|
" </script>\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": {
|
|
"collapsed": "true"
|
|
},
|
|
"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": {},
|
|
"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,
|
|
0.5700000000000001,
|
|
0.58,
|
|
0.59,
|
|
0.6,
|
|
0.61,
|
|
0.62,
|
|
0.63,
|
|
0.64,
|
|
0.65,
|
|
0.66,
|
|
0.67,
|
|
0.68,
|
|
0.6900000000000001,
|
|
0.7000000000000001,
|
|
0.71,
|
|
0.72,
|
|
0.73,
|
|
0.74,
|
|
0.75,
|
|
0.76,
|
|
0.77,
|
|
0.78,
|
|
0.79,
|
|
0.8,
|
|
0.81,
|
|
0.8200000000000001,
|
|
0.8300000000000001,
|
|
0.84,
|
|
0.85,
|
|
0.86,
|
|
0.87,
|
|
0.88,
|
|
0.89,
|
|
0.9,
|
|
0.91,
|
|
0.92,
|
|
0.93,
|
|
0.9400000000000001,
|
|
0.9500000000000001,
|
|
0.96,
|
|
0.97,
|
|
0.98,
|
|
0.99
|
|
],
|
|
"y": [
|
|
1.786090909090909,
|
|
1.800411818181818,
|
|
1.8147327272727272,
|
|
1.8290536363636363,
|
|
1.8433745454545454,
|
|
1.8576954545454545,
|
|
1.8720163636363636,
|
|
1.8863372727272727,
|
|
1.9006581818181816,
|
|
1.9149790909090907,
|
|
1.9292999999999998,
|
|
1.943620909090909,
|
|
1.957941818181818,
|
|
1.9722627272727271,
|
|
1.9865836363636362,
|
|
2.0009045454545453,
|
|
2.015225454545454,
|
|
2.0295463636363635,
|
|
2.0438672727272724,
|
|
2.0581881818181818,
|
|
2.0725090909090906,
|
|
2.08683,
|
|
2.101150909090909,
|
|
2.115471818181818,
|
|
2.129792727272727,
|
|
2.1441136363636364,
|
|
2.1584345454545453,
|
|
2.1727554545454546,
|
|
2.1870763636363635,
|
|
2.2013972727272724,
|
|
2.2157181818181817,
|
|
2.2300390909090906,
|
|
2.24436,
|
|
2.258680909090909,
|
|
2.273001818181818,
|
|
2.287322727272727,
|
|
2.3016436363636363,
|
|
2.315964545454545,
|
|
2.3302854545454545,
|
|
2.3446063636363634,
|
|
2.3589272727272728,
|
|
2.3732481818181816,
|
|
2.387569090909091,
|
|
2.40189,
|
|
2.4162109090909087,
|
|
2.430531818181818,
|
|
2.4448527272727274,
|
|
2.4591736363636363,
|
|
2.473494545454545,
|
|
2.4878154545454545,
|
|
2.5021363636363634,
|
|
2.5164572727272727,
|
|
2.5307781818181816,
|
|
2.545099090909091,
|
|
2.55942,
|
|
2.573740909090909,
|
|
2.588061818181818,
|
|
2.6023827272727273,
|
|
2.6167036363636362,
|
|
2.631024545454545,
|
|
2.6453454545454544,
|
|
2.6596663636363633,
|
|
2.6739872727272727,
|
|
2.6883081818181815,
|
|
2.702629090909091,
|
|
2.7169499999999998,
|
|
2.731270909090909,
|
|
2.745591818181818,
|
|
2.7599127272727273,
|
|
2.774233636363636,
|
|
2.7885545454545455,
|
|
2.8028754545454544,
|
|
2.8171963636363637,
|
|
2.8315172727272726,
|
|
2.8458381818181815,
|
|
2.860159090909091,
|
|
2.87448,
|
|
2.888800909090909,
|
|
2.903121818181818,
|
|
2.9174427272727272,
|
|
2.9317636363636366,
|
|
2.9460845454545455,
|
|
2.9604054545454543,
|
|
2.9747263636363637,
|
|
2.9890472727272726,
|
|
3.003368181818182,
|
|
3.0176890909090908,
|
|
3.0320099999999996,
|
|
3.046330909090909,
|
|
3.0606518181818183,
|
|
3.074972727272727,
|
|
3.089293636363636,
|
|
3.1036145454545454,
|
|
3.1179354545454547,
|
|
3.1322563636363636,
|
|
3.1465772727272725,
|
|
3.1608981818181814,
|
|
3.1752190909090907,
|
|
3.18954,
|
|
3.203860909090909
|
|
]
|
|
}
|
|
],
|
|
"layout": {
|
|
"autosize": true,
|
|
"legend": {
|
|
"bgcolor": "#F5F6F9",
|
|
"font": {
|
|
"color": "#4D5663"
|
|
}
|
|
},
|
|
"paper_bgcolor": "#F5F6F9",
|
|
"plot_bgcolor": "#F5F6F9",
|
|
"template": {
|
|
"data": {
|
|
"bar": [
|
|
{
|
|
"error_x": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"error_y": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
|
|
}
|
|
},
|
|
"type": "bar"
|
|
}
|
|
],
|
|
"barpolar": [
|
|
{
|
|
"marker": {
|
|
"line": {
|
|
"color": "#E5ECF6",
|
|
"width": 0.5
|
|
}
|
|
},
|
|
"type": "barpolar"
|
|
}
|
|
],
|
|
"carpet": [
|
|
{
|
|
"aaxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
|
"startlinecolor": "#2a3f5f"
|
|
},
|
|
"baxis": {
|
|
"endlinecolor": "#2a3f5f",
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"minorgridcolor": "white",
|
|
"startlinecolor": "#2a3f5f"
|
|
},
|
|
"type": "carpet"
|
|
}
|
|
],
|
|
"choropleth": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "choropleth"
|
|
}
|
|
],
|
|
"contour": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "contour"
|
|
}
|
|
],
|
|
"contourcarpet": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "contourcarpet"
|
|
}
|
|
],
|
|
"heatmap": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "heatmap"
|
|
}
|
|
],
|
|
"heatmapgl": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "heatmapgl"
|
|
}
|
|
],
|
|
"histogram": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "histogram"
|
|
}
|
|
],
|
|
"histogram2d": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "histogram2d"
|
|
}
|
|
],
|
|
"histogram2dcontour": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "histogram2dcontour"
|
|
}
|
|
],
|
|
"mesh3d": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"type": "mesh3d"
|
|
}
|
|
],
|
|
"parcoords": [
|
|
{
|
|
"line": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "parcoords"
|
|
}
|
|
],
|
|
"pie": [
|
|
{
|
|
"automargin": true,
|
|
"type": "pie"
|
|
}
|
|
],
|
|
"scatter": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatter"
|
|
}
|
|
],
|
|
"scatter3d": [
|
|
{
|
|
"line": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatter3d"
|
|
}
|
|
],
|
|
"scattercarpet": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattercarpet"
|
|
}
|
|
],
|
|
"scattergeo": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattergeo"
|
|
}
|
|
],
|
|
"scattergl": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattergl"
|
|
}
|
|
],
|
|
"scattermapbox": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scattermapbox"
|
|
}
|
|
],
|
|
"scatterpolar": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterpolar"
|
|
}
|
|
],
|
|
"scatterpolargl": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterpolargl"
|
|
}
|
|
],
|
|
"scatterternary": [
|
|
{
|
|
"marker": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"type": "scatterternary"
|
|
}
|
|
],
|
|
"surface": [
|
|
{
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
},
|
|
"colorscale": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"type": "surface"
|
|
}
|
|
],
|
|
"table": [
|
|
{
|
|
"cells": {
|
|
"fill": {
|
|
"color": "#EBF0F8"
|
|
},
|
|
"line": {
|
|
"color": "white"
|
|
}
|
|
},
|
|
"header": {
|
|
"fill": {
|
|
"color": "#C8D4E3"
|
|
},
|
|
"line": {
|
|
"color": "white"
|
|
}
|
|
},
|
|
"type": "table"
|
|
}
|
|
]
|
|
},
|
|
"layout": {
|
|
"annotationdefaults": {
|
|
"arrowcolor": "#2a3f5f",
|
|
"arrowhead": 0,
|
|
"arrowwidth": 1
|
|
},
|
|
"coloraxis": {
|
|
"colorbar": {
|
|
"outlinewidth": 0,
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"colorscale": {
|
|
"diverging": [
|
|
[
|
|
0,
|
|
"#8e0152"
|
|
],
|
|
[
|
|
0.1,
|
|
"#c51b7d"
|
|
],
|
|
[
|
|
0.2,
|
|
"#de77ae"
|
|
],
|
|
[
|
|
0.3,
|
|
"#f1b6da"
|
|
],
|
|
[
|
|
0.4,
|
|
"#fde0ef"
|
|
],
|
|
[
|
|
0.5,
|
|
"#f7f7f7"
|
|
],
|
|
[
|
|
0.6,
|
|
"#e6f5d0"
|
|
],
|
|
[
|
|
0.7,
|
|
"#b8e186"
|
|
],
|
|
[
|
|
0.8,
|
|
"#7fbc41"
|
|
],
|
|
[
|
|
0.9,
|
|
"#4d9221"
|
|
],
|
|
[
|
|
1,
|
|
"#276419"
|
|
]
|
|
],
|
|
"sequential": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
],
|
|
"sequentialminus": [
|
|
[
|
|
0,
|
|
"#0d0887"
|
|
],
|
|
[
|
|
0.1111111111111111,
|
|
"#46039f"
|
|
],
|
|
[
|
|
0.2222222222222222,
|
|
"#7201a8"
|
|
],
|
|
[
|
|
0.3333333333333333,
|
|
"#9c179e"
|
|
],
|
|
[
|
|
0.4444444444444444,
|
|
"#bd3786"
|
|
],
|
|
[
|
|
0.5555555555555556,
|
|
"#d8576b"
|
|
],
|
|
[
|
|
0.6666666666666666,
|
|
"#ed7953"
|
|
],
|
|
[
|
|
0.7777777777777778,
|
|
"#fb9f3a"
|
|
],
|
|
[
|
|
0.8888888888888888,
|
|
"#fdca26"
|
|
],
|
|
[
|
|
1,
|
|
"#f0f921"
|
|
]
|
|
]
|
|
},
|
|
"colorway": [
|
|
"#636efa",
|
|
"#EF553B",
|
|
"#00cc96",
|
|
"#ab63fa",
|
|
"#FFA15A",
|
|
"#19d3f3",
|
|
"#FF6692",
|
|
"#B6E880",
|
|
"#FF97FF",
|
|
"#FECB52"
|
|
],
|
|
"font": {
|
|
"color": "#2a3f5f"
|
|
},
|
|
"geo": {
|
|
"bgcolor": "white",
|
|
"lakecolor": "white",
|
|
"landcolor": "#E5ECF6",
|
|
"showlakes": true,
|
|
"showland": true,
|
|
"subunitcolor": "white"
|
|
},
|
|
"hoverlabel": {
|
|
"align": "left"
|
|
},
|
|
"hovermode": "closest",
|
|
"mapbox": {
|
|
"style": "light"
|
|
},
|
|
"paper_bgcolor": "white",
|
|
"plot_bgcolor": "#E5ECF6",
|
|
"polar": {
|
|
"angularaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"bgcolor": "#E5ECF6",
|
|
"radialaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"scene": {
|
|
"xaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
},
|
|
"yaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
},
|
|
"zaxis": {
|
|
"backgroundcolor": "#E5ECF6",
|
|
"gridcolor": "white",
|
|
"gridwidth": 2,
|
|
"linecolor": "white",
|
|
"showbackground": true,
|
|
"ticks": "",
|
|
"zerolinecolor": "white"
|
|
}
|
|
},
|
|
"shapedefaults": {
|
|
"line": {
|
|
"color": "#2a3f5f"
|
|
}
|
|
},
|
|
"ternary": {
|
|
"aaxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"baxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
},
|
|
"bgcolor": "#E5ECF6",
|
|
"caxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"title": {
|
|
"x": 0.05
|
|
},
|
|
"xaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
},
|
|
"yaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "Heatpump COP curve"
|
|
},
|
|
"xaxis": {
|
|
"autorange": true,
|
|
"gridcolor": "#E1E5ED",
|
|
"range": [
|
|
0,
|
|
0.99
|
|
],
|
|
"showgrid": true,
|
|
"tickfont": {
|
|
"color": "#4D5663"
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "Thermal load in %"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
|
|
},
|
|
"yaxis": {
|
|
"autorange": true,
|
|
"gridcolor": "#E1E5ED",
|
|
"range": [
|
|
1.707325909090909,
|
|
3.282625909090909
|
|
],
|
|
"showgrid": true,
|
|
"tickfont": {
|
|
"color": "#4D5663"
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "COP"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
|
|
}
|
|
}
|
|
},
|
|
"image/png": "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",
|
|
"text/html": [
|
|
"<div>\n",
|
|
" \n",
|
|
" \n",
|
|
" <div id=\"637f8593-e5c0-4a7f-b17d-5d339800aeab\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
|
|
" <script type=\"text/javascript\">\n",
|
|
" require([\"plotly\"], function(Plotly) {\n",
|
|
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
|
|
" window.PLOTLYENV.BASE_URL='https://plot.ly';\n",
|
|
" \n",
|
|
" if (document.getElementById(\"637f8593-e5c0-4a7f-b17d-5d339800aeab\")) {\n",
|
|
" Plotly.newPlot(\n",
|
|
" '637f8593-e5c0-4a7f-b17d-5d339800aeab',\n",
|
|
" [{\"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, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.5700000000000001, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.6900000000000001, 0.7000000000000001, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.8200000000000001, 0.8300000000000001, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.9400000000000001, 0.9500000000000001, 0.96, 0.97, 0.98, 0.99], \"y\": [1.786090909090909, 1.800411818181818, 1.8147327272727272, 1.8290536363636363, 1.8433745454545454, 1.8576954545454545, 1.8720163636363636, 1.8863372727272727, 1.9006581818181816, 1.9149790909090907, 1.9292999999999998, 1.943620909090909, 1.957941818181818, 1.9722627272727271, 1.9865836363636362, 2.0009045454545453, 2.015225454545454, 2.0295463636363635, 2.0438672727272724, 2.0581881818181818, 2.0725090909090906, 2.08683, 2.101150909090909, 2.115471818181818, 2.129792727272727, 2.1441136363636364, 2.1584345454545453, 2.1727554545454546, 2.1870763636363635, 2.2013972727272724, 2.2157181818181817, 2.2300390909090906, 2.24436, 2.258680909090909, 2.273001818181818, 2.287322727272727, 2.3016436363636363, 2.315964545454545, 2.3302854545454545, 2.3446063636363634, 2.3589272727272728, 2.3732481818181816, 2.387569090909091, 2.40189, 2.4162109090909087, 2.430531818181818, 2.4448527272727274, 2.4591736363636363, 2.473494545454545, 2.4878154545454545, 2.5021363636363634, 2.5164572727272727, 2.5307781818181816, 2.545099090909091, 2.55942, 2.573740909090909, 2.588061818181818, 2.6023827272727273, 2.6167036363636362, 2.631024545454545, 2.6453454545454544, 2.6596663636363633, 2.6739872727272727, 2.6883081818181815, 2.702629090909091, 2.7169499999999998, 2.731270909090909, 2.745591818181818, 2.7599127272727273, 2.774233636363636, 2.7885545454545455, 2.8028754545454544, 2.8171963636363637, 2.8315172727272726, 2.8458381818181815, 2.860159090909091, 2.87448, 2.888800909090909, 2.903121818181818, 2.9174427272727272, 2.9317636363636366, 2.9460845454545455, 2.9604054545454543, 2.9747263636363637, 2.9890472727272726, 3.003368181818182, 3.0176890909090908, 3.0320099999999996, 3.046330909090909, 3.0606518181818183, 3.074972727272727, 3.089293636363636, 3.1036145454545454, 3.1179354545454547, 3.1322563636363636, 3.1465772727272725, 3.1608981818181814, 3.1752190909090907, 3.18954, 3.203860909090909]}],\n",
|
|
" {\"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}}, \"paper_bgcolor\": \"#F5F6F9\", \"plot_bgcolor\": \"#F5F6F9\", \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Heatpump COP curve\"}, \"xaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"Thermal load in %\"}, \"zerolinecolor\": \"#E1E5ED\"}, \"yaxis\": {\"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"tickfont\": {\"color\": \"#4D5663\"}, \"title\": {\"font\": {\"color\": \"#4D5663\"}, \"text\": \"COP\"}, \"zerolinecolor\": \"#E1E5ED\"}},\n",
|
|
" {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}\n",
|
|
" ).then(function(){\n",
|
|
" \n",
|
|
"var gd = document.getElementById('637f8593-e5c0-4a7f-b17d-5d339800aeab');\n",
|
|
"var x = new MutationObserver(function (mutations, observer) {{\n",
|
|
" var display = window.getComputedStyle(gd).display;\n",
|
|
" if (!display || display === 'none') {{\n",
|
|
" console.log([gd, 'removed!']);\n",
|
|
" Plotly.purge(gd);\n",
|
|
" observer.disconnect();\n",
|
|
" }}\n",
|
|
"}});\n",
|
|
"\n",
|
|
"// Listen for the removal of the full notebook cells\n",
|
|
"var notebookContainer = gd.closest('#notebook-container');\n",
|
|
"if (notebookContainer) {{\n",
|
|
" x.observe(notebookContainer, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
"// Listen for the clearing of the current output cell\n",
|
|
"var outputEl = gd.closest('.output');\n",
|
|
"if (outputEl) {{\n",
|
|
" x.observe(outputEl, {childList: true});\n",
|
|
"}}\n",
|
|
"\n",
|
|
" })\n",
|
|
" };\n",
|
|
" });\n",
|
|
" </script>\n",
|
|
" </div>"
|
|
]
|
|
},
|
|
"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": {},
|
|
"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": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Gas prices (€/MWh)</th>\n",
|
|
" <th>CO2 prices (€/ton)</th>\n",
|
|
" <th>CO2 prices (€/MWh)</th>\n",
|
|
" <th>Tsource (VDG)</th>\n",
|
|
" <th>Tsink (VDG)</th>\n",
|
|
" <th>MW (VDG)</th>\n",
|
|
" <th>Tsource (NDG)</th>\n",
|
|
" <th>Tsink (NDG)</th>\n",
|
|
" <th>MW (NDG)</th>\n",
|
|
" <th>Total demand</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" <td>525600.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>13.538745</td>\n",
|
|
" <td>24.864018</td>\n",
|
|
" <td>4.683144</td>\n",
|
|
" <td>63.658798</td>\n",
|
|
" <td>138.637674</td>\n",
|
|
" <td>17.636712</td>\n",
|
|
" <td>62.176305</td>\n",
|
|
" <td>145.351645</td>\n",
|
|
" <td>5.823824</td>\n",
|
|
" <td>23.460537</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>3.644779</td>\n",
|
|
" <td>2.142574</td>\n",
|
|
" <td>0.403827</td>\n",
|
|
" <td>6.422773</td>\n",
|
|
" <td>14.328580</td>\n",
|
|
" <td>5.123252</td>\n",
|
|
" <td>10.252555</td>\n",
|
|
" <td>14.896056</td>\n",
|
|
" <td>1.691697</td>\n",
|
|
" <td>6.814125</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>7.530000</td>\n",
|
|
" <td>18.700000</td>\n",
|
|
" <td>3.520000</td>\n",
|
|
" <td>25.310000</td>\n",
|
|
" <td>82.480000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>13.540000</td>\n",
|
|
" <td>106.330000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>10.380000</td>\n",
|
|
" <td>23.670000</td>\n",
|
|
" <td>4.460000</td>\n",
|
|
" <td>64.892000</td>\n",
|
|
" <td>126.074000</td>\n",
|
|
" <td>17.014000</td>\n",
|
|
" <td>63.516000</td>\n",
|
|
" <td>131.551500</td>\n",
|
|
" <td>5.612000</td>\n",
|
|
" <td>22.676000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>12.850000</td>\n",
|
|
" <td>25.080000</td>\n",
|
|
" <td>4.720000</td>\n",
|
|
" <td>64.991000</td>\n",
|
|
" <td>142.450000</td>\n",
|
|
" <td>19.892000</td>\n",
|
|
" <td>64.313000</td>\n",
|
|
" <td>148.490000</td>\n",
|
|
" <td>6.540000</td>\n",
|
|
" <td>26.441000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>15.600000</td>\n",
|
|
" <td>26.290000</td>\n",
|
|
" <td>4.950000</td>\n",
|
|
" <td>65.076000</td>\n",
|
|
" <td>151.730000</td>\n",
|
|
" <td>20.819000</td>\n",
|
|
" <td>65.546000</td>\n",
|
|
" <td>158.100000</td>\n",
|
|
" <td>6.880000</td>\n",
|
|
" <td>27.690000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>22.670000</td>\n",
|
|
" <td>29.770000</td>\n",
|
|
" <td>5.610000</td>\n",
|
|
" <td>70.070000</td>\n",
|
|
" <td>166.020000</td>\n",
|
|
" <td>23.250000</td>\n",
|
|
" <td>73.470000</td>\n",
|
|
" <td>172.050000</td>\n",
|
|
" <td>7.650000</td>\n",
|
|
" <td>30.900000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Wall time: 1.93 s\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"c:\\users\\mekre\\onedrive - recoy\\work\\code\\python\\_packages\\pyrecoy\\pyrecoy\\assets.py:253: 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:253: 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: 13.6 s\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "24cab81d4f1e493f86a78d3287d8e049",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"HBox(children=(FloatProgress(value=0.0, max=525600.0), HTML(value='')))"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.optcase1.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.optcase1.data['hp_input_MW']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.optcase2.data['hp_input_MW']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Financials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# SDE++\n",
|
|
"# ..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": null,
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].plot(kind='box')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.optcase1.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": null,
|
|
"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": null,
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import plotly.graph_objects as go\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"fig = go.Figure()\n",
|
|
"\n",
|
|
"configs = {\n",
|
|
" 'mean': [mean_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
" 'unfav_gas': [min_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
" 'fav_gas': [max_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
" 'unfav_all': [min_gas_price, Tsnk_vdg_max, Tsrc_vdg_min],\n",
|
|
" 'fav_all': [max_gas_price, Tsnk_vdg_min, Tsrc_vdg_max],\n",
|
|
"}\n",
|
|
"\n",
|
|
"for name, config in configs.items():\n",
|
|
" trace = create_load_trace(*config, name)\n",
|
|
" fig.add_trace(trace)\n",
|
|
"\n",
|
|
"fig.update_layout(title='Switch prices for different configurations')\n",
|
|
"fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"date = '2019-01-01'\n",
|
|
"s.baseline.data[['Total demand', 'output_MW_th', 'gb_input_MW']].abs().loc[date, :].iplot(\n",
|
|
" subplots=True, \n",
|
|
" title=f'Baseline case on {date}',\n",
|
|
" subplot_titles=['Total demand', 'Steam boiler output (MW)', 'Steam boiler input (MW)'],\n",
|
|
" legend=False,\n",
|
|
" dimensions=(1000, 500)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.hpcase.data[['Total demand', 'hp_output_MW', 'hp_input_MW']].abs().loc[date, :].iplot(\n",
|
|
" subplots=True, \n",
|
|
" title=f'Heatpump case on {date}', \n",
|
|
" subplot_titles=['Total demand', 'Heatpump output(MW)', 'Heatpump input (MW)'],\n",
|
|
" legend=False,\n",
|
|
" dimensions=(1000, 500)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"report = ComparisonReport(\n",
|
|
" cases=s.optcases, \n",
|
|
" kind='electr_market_results',\n",
|
|
")\n",
|
|
"report.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc')\n",
|
|
"casereport.show(presentation_format=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"BusinessCaseReport(s.optcase1).show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#price chart\n",
|
|
"from copy import deepcopy\n",
|
|
"\n",
|
|
"data = deepcopy(s.baseline.data)\n",
|
|
"data = data[data.columns[:2]]\n",
|
|
"data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n",
|
|
"data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n",
|
|
"\n",
|
|
"data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"_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",
|
|
"_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",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"report = ComparisonReport(s.cases, kind='capex').report\n",
|
|
"report = report[report.index.str.contains('CAPEX')].T\n",
|
|
"report.iplot(\n",
|
|
" kind='bar', \n",
|
|
" barmode='relative',\n",
|
|
" colors=recoycolors,\n",
|
|
" title='CAPEX by Casestudy',\n",
|
|
" yTitle='CAPEX in €',\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n",
|
|
"\n",
|
|
"fig = cashflow_report.report.T.iplot(\n",
|
|
" kind='bar',\n",
|
|
" barmode='relative',\n",
|
|
" colors=recoycolors,\n",
|
|
" title='OPEX breakdown',\n",
|
|
" asFigure=True,\n",
|
|
" yTitle='€',\n",
|
|
" dimensions=(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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"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": {
|
|
"application/vnd.jupyter.widget-state+json": {
|
|
"state": {
|
|
"02721ae635a34afe94d7735f7cf72c56": {
|
|
"model_module": "@jupyter-widgets/base",
|
|
"model_module_version": "1.2.0",
|
|
"model_name": "LayoutModel",
|
|
"state": {}
|
|
},
|
|
"24cab81d4f1e493f86a78d3287d8e049": {
|
|
"model_module": "@jupyter-widgets/controls",
|
|
"model_module_version": "1.5.0",
|
|
"model_name": "HBoxModel",
|
|
"state": {
|
|
"children": [
|
|
"IPY_MODEL_57209fe141214d8ba670b292c49ae966",
|
|
"IPY_MODEL_e8600ad8df9e4fdfb3b0307b05f12a9e"
|
|
],
|
|
"layout": "IPY_MODEL_02721ae635a34afe94d7735f7cf72c56"
|
|
}
|
|
},
|
|
"3fe1649dd3c946409b2e3d4c7c7b6447": {
|
|
"model_module": "@jupyter-widgets/base",
|
|
"model_module_version": "1.2.0",
|
|
"model_name": "LayoutModel",
|
|
"state": {}
|
|
},
|
|
"54ab12a25f774ddb871e923a825f2fb8": {
|
|
"model_module": "@jupyter-widgets/controls",
|
|
"model_module_version": "1.5.0",
|
|
"model_name": "ProgressStyleModel",
|
|
"state": {
|
|
"description_width": "initial"
|
|
}
|
|
},
|
|
"57209fe141214d8ba670b292c49ae966": {
|
|
"model_module": "@jupyter-widgets/controls",
|
|
"model_module_version": "1.5.0",
|
|
"model_name": "FloatProgressModel",
|
|
"state": {
|
|
"description": " 8%",
|
|
"layout": "IPY_MODEL_7b5fa1be63f341e4ada3303f3d1e687c",
|
|
"max": 525600,
|
|
"style": "IPY_MODEL_54ab12a25f774ddb871e923a825f2fb8",
|
|
"value": 44504
|
|
}
|
|
},
|
|
"7b5fa1be63f341e4ada3303f3d1e687c": {
|
|
"model_module": "@jupyter-widgets/base",
|
|
"model_module_version": "1.2.0",
|
|
"model_name": "LayoutModel",
|
|
"state": {}
|
|
},
|
|
"94f430f7ac2a43d2ba688b156744f3aa": {
|
|
"model_module": "@jupyter-widgets/controls",
|
|
"model_module_version": "1.5.0",
|
|
"model_name": "DescriptionStyleModel",
|
|
"state": {
|
|
"description_width": ""
|
|
}
|
|
},
|
|
"e8600ad8df9e4fdfb3b0307b05f12a9e": {
|
|
"model_module": "@jupyter-widgets/controls",
|
|
"model_module_version": "1.5.0",
|
|
"model_name": "HTMLModel",
|
|
"state": {
|
|
"layout": "IPY_MODEL_3fe1649dd3c946409b2e3d4c7c7b6447",
|
|
"style": "IPY_MODEL_94f430f7ac2a43d2ba688b156744f3aa",
|
|
"value": " 44504/525600 [00:38<11:48, 678.91it/s]"
|
|
}
|
|
}
|
|
},
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|