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"cell_type": "markdown",
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"source": [
"#### Imports"
]
},
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"execution_count": 1,
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" \n",
" "
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],
"source": [
"from datetime import timedelta\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"from statistics import mean\n",
"\n",
"import cufflinks\n",
"cufflinks.go_offline()\n",
"import numpy as np\n",
"from numpy.polynomial import Polynomial\n",
"import pandas as pd\n",
"from tqdm.notebook import tqdm\n",
"import plotly.graph_objs as go\n",
"\n",
"from pyrecoy.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\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\n",
"from pyrecoy.casestudy import 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",
"from pyrecoy.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
"from pyrecoy.forecasts import Mipf\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Development backlog"
]
},
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"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR (can improve the optimisation case)\n",
"* Report\n",
"\n",
"--\n",
"* Create strategy on imbalance POS (buy 100% day-ahead, and respond to high prices)\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": {
"tags": [
"exclude"
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},
"source": [
"#### Meeting Notes"
]
},
{
"cell_type": "markdown",
"metadata": {
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"exclude"
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},
"source": [
"##### Meeting 25-11-2020"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR can help optimisation case\n",
"* SDE++ should be included\n",
"* Tsource sensitivity really gives interesting insights\n",
"* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
"* AP TNO: Update/verify COP curve\n",
"* AP TNO: Update CAPEX\n",
"* AP Mark: \n",
" * Create graphs on COP curve with different Tsource, Tsink\n",
" * Generate table and output in .csv (send it to Andrew)\n",
"* Investigate opportunity to lower the COP and negative electricity prices\n",
" * Technically feasible, but not really needed/possible to do it in this project\n",
"* Could be interesting to run this model on a usecase with higher delta T\n",
"* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
]
},
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"metadata": {
"tags": [
"exclude"
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"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": {
"tags": [
"exclude"
]
},
"source": [
"#### Loading config"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
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},
"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\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",
" storage_power = 25\n",
" storage_volume = 1000\n",
" storage_cap_per_volume = 50 * 1e-3\n",
" storage_lifetime = 25\n",
" storage_temperature = 95\n",
" storage_min_level = 5\n",
" storage_capex_per_MW = 8_000\n",
" storage_capex_per_MWh = 10_000\n",
" storage_opex_perc_of_capex = 0.02\n",
" storage_initial_level = 5\n",
" \n",
" tax_bracket_g = 4 \n",
" tax_bracket_e = 4\n",
" \n",
" include_transport_costs = False\n",
" grid_operator = 'Liander'\n",
" connection_type = 'TS/MS'\n",
" \n",
" discount_rate = 0.1\n",
" project_duration = 12\n",
"\n",
" forecast = 'ForeNeg'\n",
" gas_price_multiplier = 1\n",
" e_price_multiplier = 1\n",
" e_price_volatility_multiplier = 1\n",
" co2_price_multiplier = 1\n",
" tsource_delta = 0\n",
" tsink_delta = 0\n",
" energy_tax_multiplier = 1\n",
" \n",
" # Review the SDE implementation\n",
" sde_base_amount = 81\n",
" longterm_gas_price = 24.00\n",
" longterm_co2_price = 37.90\n",
" sde_switch_price_correction = 40\n",
" \n",
" day_ahead_buying_perc = 0.3\n",
" \n",
" afrr_capacity_fee = 25_000\n",
"\n",
"c = Config()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"exclude"
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},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
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},
"source": [
"## Model set-up"
]
},
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"execution_count": 4,
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"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": [
"s = Store()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
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"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_9828/332434720.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
}
],
"source": [
"def setup_model(c):\n",
" s = Store()\n",
" \n",
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
" mipf = Mipf(start=s.time_fw.start, end=s.time_fw.end, tidy=True, include_nextQ=True,\n",
" folder_path = r\"C:\\Users\\Shahla Huseynova\\Recoy\\Recoy - Documents\\03 - Libraries\\12 - Data Management\\Forecast Data\").data\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', data=mipf)\n",
" #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
" #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
" #s.afrr_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation + aFRR', data=mipf)\n",
" s.storage_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage', data=mipf)\n",
" \n",
" s.cases = list(CaseStudy.instances.values())\n",
" #s.optcases = [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]\n",
" s.optcases = [s.hpcase, s.storage_case]\n",
" #s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n",
" s.sde_cases = []\n",
" \n",
" s.demand = load_demand_data(c, s)\n",
" return s\n",
"\n",
"s = setup_model(c)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 63.658798 | \n",
" 138.637674 | \n",
" 17.636712 | \n",
" 62.176305 | \n",
" 145.351645 | \n",
" 5.823824 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 6.422773 | \n",
" 14.328580 | \n",
" 5.123252 | \n",
" 10.252555 | \n",
" 14.896056 | \n",
" 1.691697 | \n",
" 6.814125 | \n",
"
\n",
" \n",
" | min | \n",
" 25.310000 | \n",
" 82.480000 | \n",
" 0.000000 | \n",
" 13.540000 | \n",
" 106.330000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 64.892000 | \n",
" 126.074000 | \n",
" 17.014000 | \n",
" 63.516000 | \n",
" 131.551500 | \n",
" 5.612000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 64.991000 | \n",
" 142.450000 | \n",
" 19.892000 | \n",
" 64.313000 | \n",
" 148.490000 | \n",
" 6.540000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 65.076000 | \n",
" 151.730000 | \n",
" 20.819000 | \n",
" 65.546000 | \n",
" 158.100000 | \n",
" 6.880000 | \n",
" 27.690000 | \n",
"
\n",
" \n",
" | max | \n",
" 70.070000 | \n",
" 166.020000 | \n",
" 23.250000 | \n",
" 73.470000 | \n",
" 172.050000 | \n",
" 7.650000 | \n",
" 30.900000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" CO2 prices (€/MWh) | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 46.83 | \n",
" 52.70 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.990 | \n",
" 143.360 | \n",
" 0.0 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 32.12 | \n",
" 54.45 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.985 | \n",
" 143.089 | \n",
" 0.0 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 30.97 | \n",
" 48.64 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.980 | \n",
" 142.818 | \n",
" 0.0 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 51.13 | \n",
" 48.35 | \n",
" 57.71 | \n",
" 73.05 | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.975 | \n",
" 142.547 | \n",
" 0.0 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 48.04 | \n",
" 52.01 | \n",
" 63.60 | \n",
" 79.07 | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.970 | \n",
" 142.276 | \n",
" 0.0 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" ... | \n",
" from_storage_MW | \n",
" storage_level_MWh | \n",
" hp_output_MWh | \n",
" hp_input_MWh | \n",
" to_storage_ndg_MWh | \n",
" from_storage_ndg_MWh | \n",
" to_storage_vdg_MWh | \n",
" from_storage_vdg_MWh | \n",
" to_storage_MWh | \n",
" from_storage_MWh | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-08-15 09:37:00+02:00 | \n",
" 43.47 | \n",
" 47.02 | \n",
" 47.02 | \n",
" 1.0 | \n",
" 49.90 | \n",
" 51.80 | \n",
" 86.44 | \n",
" 86.59 | \n",
" 9.55 | \n",
" 25.96 | \n",
" ... | \n",
" 0.0 | \n",
" 5.0 | \n",
" 0.473 | \n",
" -0.085 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-10-05 15:08:00+02:00 | \n",
" 35.89 | \n",
" 29.07 | \n",
" 29.07 | \n",
" -1.0 | \n",
" 28.41 | \n",
" 28.44 | \n",
" 35.99 | \n",
" 35.99 | \n",
" 11.82 | \n",
" 22.94 | \n",
" ... | \n",
" 0.0 | \n",
" 5.0 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
2 rows × 38 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" hp_output_MW | \n",
" to_storage_MW | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 41.195801 | \n",
" 40.391060 | \n",
" 42.554082 | \n",
" 24.322043 | \n",
" 1.221385 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 7.029645 | \n",
" 3.620719 | \n",
" 6.814125 | \n",
"
\n",
" \n",
" | min | \n",
" -9.020000 | \n",
" -487.650000 | \n",
" -487.650000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 34.100000 | \n",
" 24.470000 | \n",
" 25.530000 | \n",
" 22.766000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 26.972000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 27.987000 | \n",
" 0.000000 | \n",
" 27.690000 | \n",
"
\n",
" \n",
" | max | \n",
" 121.460000 | \n",
" 936.120000 | \n",
" 936.120000 | \n",
" 31.000000 | \n",
" 25.000000 | \n",
" 30.900000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" hp_output_MW | \n",
" to_storage_MW | \n",
" Total demand | \n",
" hp_input_MW | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-07-25 03:01:00+02:00 | \n",
" 39.09 | \n",
" 36.70 | \n",
" 36.70 | \n",
" 22.957 | \n",
" 0.000 | \n",
" 22.957 | \n",
" -3.969 | \n",
"
\n",
" \n",
" | 2019-11-27 00:22:00+01:00 | \n",
" 36.20 | \n",
" -22.61 | \n",
" -22.61 | \n",
" 0.000 | \n",
" 25.000 | \n",
" 0.000 | \n",
" -0.000 | \n",
"
\n",
" \n",
" | 2019-09-26 14:47:00+02:00 | \n",
" 36.80 | \n",
" 26.07 | \n",
" 26.07 | \n",
" 27.752 | \n",
" 0.000 | \n",
" 27.752 | \n",
" -5.130 | \n",
"
\n",
" \n",
" | 2019-10-19 21:31:00+02:00 | \n",
" 35.06 | \n",
" 23.80 | \n",
" 23.80 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.000 | \n",
" -0.000 | \n",
"
\n",
" \n",
" | 2019-08-05 23:46:00+02:00 | \n",
" 39.87 | \n",
" 16.32 | \n",
" 16.32 | \n",
" 30.186 | \n",
" 3.442 | \n",
" 27.558 | \n",
" -5.351 | \n",
"
\n",
" \n",
" | 2019-04-16 06:00:00+02:00 | \n",
" 46.22 | \n",
" 36.40 | \n",
" 36.40 | \n",
" 27.570 | \n",
" 0.000 | \n",
" 27.570 | \n",
" -5.188 | \n",
"
\n",
" \n",
" | 2019-06-18 01:32:00+02:00 | \n",
" 32.85 | \n",
" 24.39 | \n",
" 24.39 | \n",
" 20.820 | \n",
" 0.000 | \n",
" 20.820 | \n",
" -3.590 | \n",
"
\n",
" \n",
" | 2019-01-07 10:21:00+01:00 | \n",
" 73.59 | \n",
" 46.35 | \n",
" 46.35 | \n",
" 30.150 | \n",
" 3.332 | \n",
" 27.668 | \n",
" -5.282 | \n",
"
\n",
" \n",
" | 2019-08-15 15:47:00+02:00 | \n",
" 28.30 | \n",
" 18.18 | \n",
" 55.91 | \n",
" 22.890 | \n",
" 0.000 | \n",
" 22.890 | \n",
" -4.083 | \n",
"
\n",
" \n",
" | 2019-02-25 04:40:00+01:00 | \n",
" 36.06 | \n",
" 26.91 | \n",
" 26.91 | \n",
" 22.940 | \n",
" 0.000 | \n",
" 22.940 | \n",
" -4.038 | \n",
"
\n",
" \n",
" | 2019-02-17 22:40:00+01:00 | \n",
" 42.83 | \n",
" 29.08 | \n",
" 29.08 | \n",
" 20.510 | \n",
" 0.000 | \n",
" 20.510 | \n",
" -3.569 | \n",
"
\n",
" \n",
" | 2019-07-13 17:07:00+02:00 | \n",
" 34.71 | \n",
" 34.57 | \n",
" 34.57 | \n",
" 28.306 | \n",
" 0.000 | \n",
" 28.306 | \n",
" -5.219 | \n",
"
\n",
" \n",
" | 2019-09-21 19:56:00+02:00 | \n",
" 50.00 | \n",
" 33.21 | \n",
" 33.21 | \n",
" 28.152 | \n",
" 0.000 | \n",
" 28.152 | \n",
" -4.937 | \n",
"
\n",
" \n",
" | 2019-10-18 00:28:00+02:00 | \n",
" 33.15 | \n",
" 63.43 | \n",
" 63.43 | \n",
" 22.864 | \n",
" 0.000 | \n",
" 22.864 | \n",
" -3.421 | \n",
"
\n",
" \n",
" | 2019-03-25 15:35:00+01:00 | \n",
" 31.52 | \n",
" 35.57 | \n",
" 35.57 | \n",
" 28.330 | \n",
" 0.000 | \n",
" 28.330 | \n",
" -4.926 | \n",
"
\n",
" \n",
" | 2019-02-05 07:12:00+01:00 | \n",
" 62.22 | \n",
" 21.72 | \n",
" 21.72 | \n",
" 31.000 | \n",
" 4.000 | \n",
" 27.000 | \n",
" -5.402 | \n",
"
\n",
" \n",
" | 2019-02-18 17:38:00+01:00 | \n",
" 52.94 | \n",
" 39.38 | \n",
" 39.38 | \n",
" 20.652 | \n",
" 0.000 | \n",
" 20.652 | \n",
" -3.525 | \n",
"
\n",
" \n",
" | 2019-03-05 10:02:00+01:00 | \n",
" 49.90 | \n",
" 36.74 | \n",
" 53.02 | \n",
" 23.778 | \n",
" 0.000 | \n",
" 23.778 | \n",
" -4.272 | \n",
"
\n",
" \n",
" | 2019-07-03 01:23:00+02:00 | \n",
" 28.07 | \n",
" 26.22 | \n",
" 26.22 | \n",
" 22.701 | \n",
" 0.000 | \n",
" 22.701 | \n",
" -4.176 | \n",
"
\n",
" \n",
" | 2019-08-28 13:50:00+02:00 | \n",
" 44.73 | \n",
" 26.05 | \n",
" 26.05 | \n",
" 27.860 | \n",
" 0.000 | \n",
" 27.860 | \n",
" -5.153 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" ... | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
" hp_output_MW | \n",
" hp_input_MW | \n",
" cop | \n",
" hp_output_MWh | \n",
" hp_input_MWh | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 46.83 | \n",
" 52.70 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" ... | \n",
" 0.0 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 32.12 | \n",
" 54.45 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" ... | \n",
" 0.0 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 30.97 | \n",
" 48.64 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" ... | \n",
" 0.0 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 51.13 | \n",
" 48.35 | \n",
" 57.71 | \n",
" 73.05 | \n",
" 22.38 | \n",
" 24.98 | \n",
" ... | \n",
" 0.0 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 48.04 | \n",
" 52.01 | \n",
" 63.60 | \n",
" 79.07 | \n",
" 22.38 | \n",
" 24.98 | \n",
" ... | \n",
" 0.0 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" NaN | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 23 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Heatpump only | \n",
" Heatpump + Storage | \n",
"
\n",
" \n",
" \n",
" \n",
" | Total net volume (MWh) | \n",
" -37,696 | \n",
" -38,172 | \n",
"
\n",
" \n",
" | Total exported to grid (MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Total consumed from grid (MWh) | \n",
" 37,696 | \n",
" 38,172 | \n",
"
\n",
" \n",
" | Total nominated volume (MWh) | \n",
" -37,696 | \n",
" -37,696 | \n",
"
\n",
" \n",
" | Absolute imbalance volume (MWh) | \n",
" - | \n",
" 689 | \n",
"
\n",
" \n",
" | Mean selling price (€/MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Mean buying price (€/MWh) | \n",
" 41 | \n",
" 41 | \n",
"
\n",
" \n",
" | Total day-ahead result (€) | \n",
" -1,553,699 | \n",
" -1,553,699 | \n",
"
\n",
" \n",
" | Total POS result (€) | \n",
" - | \n",
" 8,521 | \n",
"
\n",
" \n",
" | Total NEG result (€) | \n",
" - | \n",
" -4,811 | \n",
"
\n",
" \n",
" | Total imbalance result (€) | \n",
" - | \n",
" 3,710 | \n",
"
\n",
" \n",
" | Total combined result (€) | \n",
" -1,553,699 | \n",
" -1,549,989 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Year 0 | \n",
" Year 1 | \n",
" Year 2 | \n",
" Year 3 | \n",
" Year 4 | \n",
" Year 5 | \n",
" Year 6 | \n",
" Year 7 | \n",
" Year 8 | \n",
" Year 9 | \n",
" Year 10 | \n",
" Year 11 | \n",
" Year 12 | \n",
"
\n",
" \n",
" \n",
" \n",
" | CAPEX (€) | \n",
" -6,231,000 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | Regular Earnings (€) | \n",
" | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
"
\n",
" \n",
" | Irregular Cashflows (€) | \n",
" | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
" 3,049,475 | \n",
"
\n",
" \n",
" | Depreciations (€) -/- | \n",
" | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
" -248,000 | \n",
"
\n",
" \n",
" | EBIT (€) | \n",
" | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
" 2,801,475 | \n",
"
\n",
" \n",
" | Income tax (Vpb.) (€) | \n",
" | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
" -687,119 | \n",
"
\n",
" \n",
" | NOPLAT (€) | \n",
" | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
" 2,114,356 | \n",
"
\n",
" \n",
" | Depreciations (€) +/+ | \n",
" | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
" 248,000 | \n",
"
\n",
" \n",
" | Free Cash Flow (€) | \n",
" -6,231,000 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
" 2,362,356 | \n",
"
\n",
" \n",
" | Simple Payback Period | \n",
" 2.6 years | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | IRR (%) | \n",
" 37% | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | WACC (%) | \n",
" 10% | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | NPV of explicit period (€) | \n",
" 8,968,516 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | Discounted residual value (€) | \n",
" 1,037,143 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | NPV (€) | \n",
" 10,005,659 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Year 0 | \n",
" Year 1 | \n",
" Year 2 | \n",
" Year 3 | \n",
" Year 4 | \n",
" Year 5 | \n",
" Year 6 | \n",
" Year 7 | \n",
" Year 8 | \n",
" Year 9 | \n",
" Year 10 | \n",
" Year 11 | \n",
" Year 12 | \n",
"
\n",
" \n",
" \n",
" \n",
" | CAPEX (€) | \n",
" -6,931,000 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | Regular Earnings (€) | \n",
" | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
"
\n",
" \n",
" | Irregular Cashflows (€) | \n",
" | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
" 3,037,723 | \n",
"
\n",
" \n",
" | Depreciations (€) -/- | \n",
" | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
" -276,000 | \n",
"
\n",
" \n",
" | EBIT (€) | \n",
" | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
" 2,761,723 | \n",
"
\n",
" \n",
" | Income tax (Vpb.) (€) | \n",
" | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
" -677,181 | \n",
"
\n",
" \n",
" | NOPLAT (€) | \n",
" | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
" 2,084,542 | \n",
"
\n",
" \n",
" | Depreciations (€) +/+ | \n",
" | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
" 276,000 | \n",
"
\n",
" \n",
" | Free Cash Flow (€) | \n",
" -6,931,000 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
" 2,360,542 | \n",
"
\n",
" \n",
" | Simple Payback Period | \n",
" 2.9 years | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | IRR (%) | \n",
" 33% | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | WACC (%) | \n",
" 10% | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | NPV of explicit period (€) | \n",
" 8,320,915 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | Discounted residual value (€) | \n",
" 1,153,125 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" | NPV (€) | \n",
" 9,474,040 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
"