{
"cells": [
{
"cell_type": "markdown",
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"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"exclude"
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},
"outputs": [
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"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datetime import timedelta\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"\n",
"import cufflinks\n",
"cufflinks.go_offline()\n",
"import numpy as np\n",
"from numpy.polynomial import Polynomial\n",
"import pandas as pd\n",
"from tqdm.notebook import tqdm\n",
"\n",
"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"
]
},
{
"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"
]
},
"source": [
"#### Meeting Notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"##### Meeting 25-11-2020"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR can help optimisation case\n",
"* SDE++ should be included\n",
"* Tsource sensitivity really gives interesting insights\n",
"* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
"* AP TNO: Update/verify COP curve\n",
"* AP TNO: Update CAPEX\n",
"* AP Mark: \n",
" * Create graphs on COP curve with different Tsource, Tsink\n",
" * Generate table and output in .csv (send it to Andrew)\n",
"* Investigate opportunity to lower the COP and negative electricity prices\n",
" * Technically feasible, but not really needed/possible to do it in this project\n",
"* Could be interesting to run this model on a usecase with higher delta T\n",
"* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
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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": [
"exclude"
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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 = storage_volume * storage_cap_per_volume * 0.05\n",
" storage_capex_per_MW = 7_000\n",
" storage_capex_per_MWh = 5_000\n",
" storage_opex_perc_of_capex = 0.02\n",
" # storage_initial_level = 5\n",
" threshold = 20\n",
" \n",
" tax_bracket_g = 4 \n",
" tax_bracket_e = 4\n",
" \n",
" include_transport_costs = False\n",
" grid_operator = 'Liander'\n",
" connection_type = 'TS/MS'\n",
" \n",
" discount_rate = 0.1\n",
" project_duration = 12\n",
"\n",
" forecast = 'ForeNeg'\n",
" gas_price_multiplier = 1\n",
" e_price_multiplier = 1\n",
" e_price_volatility_multiplier = 1\n",
" co2_price_multiplier = 1\n",
" tsource_delta = 0\n",
" tsink_delta = 0\n",
" energy_tax_multiplier = 1\n",
" \n",
" # Review the SDE implementation\n",
" sde_base_amount = 81\n",
" longterm_gas_price = 24.00\n",
" longterm_co2_price = 37.90\n",
" sde_switch_price_correction = 40\n",
" \n",
" day_ahead_buying_perc = 0.3\n",
" \n",
" afrr_capacity_fee = 25_000\n",
"\n",
"c = Config()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"exclude"
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},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"## Model set-up"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [
"exclude"
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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": {
"tags": [
"exclude"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\shahla huseynova\\recoy\\recoy - documents\\03 - libraries\\29 - pyrecoy modelling framework\\v1.2\\pyrecoy\\pyrecoy\\framework.py:40: UserWarning:\n",
"\n",
"The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: Entries missing from dataset 'imbalanceRT_nl.csv'.\n",
"Issue solved: Dataset was reindexed automatically.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_8620/332434720.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",
" s.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\"\n",
" ).data\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": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": true
},
"data": [
{
"line": {
"color": "rgba(255, 153, 51, 1.0)",
"dash": "solid",
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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": [],
"source": [
"# s.demand.iplot()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \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",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \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",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \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",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \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",
" 20.83 | \n",
" 34.24 | \n",
" 6.6 | \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",
" | 2019-01-24 11:16:00+01:00 | \n",
" 103.69 | \n",
" 531.18 | \n",
" 531.18 | \n",
" 1.0 | \n",
" 244.87 | \n",
" 243.68 | \n",
" NaN | \n",
" NaN | \n",
" 21.09 | \n",
" 33.02 | \n",
" ... | \n",
" 0.0 | \n",
" 2.500 | \n",
" 0.46 | \n",
" -0.086 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-09 02:36:00+01:00 | \n",
" 35.00 | \n",
" 46.31 | \n",
" 46.31 | \n",
" 1.0 | \n",
" 48.43 | \n",
" 46.42 | \n",
" 50.98 | \n",
" 42.93 | \n",
" 16.88 | \n",
" 26.41 | \n",
" ... | \n",
" 0.0 | \n",
" 4.185 | \n",
" 0.46 | \n",
" -0.087 | \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",
" 24.354573 | \n",
" 0.894037 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 7.151965 | \n",
" 2.633368 | \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",
" 22.760000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 26.980000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 28.010000 | \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",
" from_storage_MW | \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",
" 0.0 | \n",
" 0.0 | \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",
" 0.0 | \n",
" 0.0 | \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",
" 0.0 | \n",
" 0.0 | \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",
" 0.0 | \n",
" 0.0 | \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",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" Total demand | \n",
" hp_input_MW | \n",
" hp_output_MW | \n",
" cop | \n",
" to_storage_MW | \n",
" from_storage_MW | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-11-10 20:39:00+01:00 | \n",
" 51.01 | \n",
" 38.02 | \n",
" 38.02 | \n",
" 27.935 | \n",
" -5.153 | \n",
" 27.935 | \n",
" 5.421 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-05 06:59:00+02:00 | \n",
" 36.66 | \n",
" 32.08 | \n",
" 32.08 | \n",
" 26.636 | \n",
" -4.973 | \n",
" 26.636 | \n",
" 5.356 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-10 12:04:00+02:00 | \n",
" 28.69 | \n",
" 26.08 | \n",
" 26.08 | \n",
" 22.902 | \n",
" -4.015 | \n",
" 22.902 | \n",
" 5.704 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-10-07 08:38:00+02:00 | \n",
" 59.86 | \n",
" 32.35 | \n",
" 32.35 | \n",
" 27.034 | \n",
" -5.206 | \n",
" 31.000 | \n",
" 5.955 | \n",
" 3.966 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-01 06:20:00+02:00 | \n",
" 38.30 | \n",
" 190.02 | \n",
" 190.02 | \n",
" 27.620 | \n",
" -5.243 | \n",
" 27.620 | \n",
" 5.268 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-08 14:38:00+02:00 | \n",
" 35.78 | \n",
" 33.00 | \n",
" 33.00 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-20 16:28:00+02:00 | \n",
" 31.83 | \n",
" 57.95 | \n",
" 57.95 | \n",
" 0.640 | \n",
" -0.776 | \n",
" 0.640 | \n",
" 0.825 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-28 07:42:00+02:00 | \n",
" 28.10 | \n",
" 47.45 | \n",
" 47.45 | \n",
" 29.750 | \n",
" -5.361 | \n",
" 29.750 | \n",
" 5.549 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-05 02:07:00+01:00 | \n",
" 38.42 | \n",
" 37.07 | \n",
" 37.07 | \n",
" 22.815 | \n",
" -4.087 | \n",
" 22.815 | \n",
" 5.582 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-11 05:29:00+01:00 | \n",
" 45.03 | \n",
" 39.94 | \n",
" 39.94 | \n",
" 18.855 | \n",
" -3.468 | \n",
" 18.855 | \n",
" 5.437 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-08-15 18:55:00+02:00 | \n",
" 35.76 | \n",
" 26.43 | \n",
" 26.43 | \n",
" 23.050 | \n",
" -4.048 | \n",
" 23.050 | \n",
" 5.695 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 13:28:00+01:00 | \n",
" 50.76 | \n",
" 40.97 | \n",
" 47.38 | \n",
" 26.036 | \n",
" -5.032 | \n",
" 26.036 | \n",
" 5.174 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-09-18 08:12:00+02:00 | \n",
" 51.22 | \n",
" 31.04 | \n",
" 31.04 | \n",
" 22.876 | \n",
" -4.572 | \n",
" 31.000 | \n",
" 6.781 | \n",
" 8.124 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-24 19:55:00+02:00 | \n",
" 44.88 | \n",
" 142.31 | \n",
" 142.31 | \n",
" 28.165 | \n",
" -4.938 | \n",
" 28.165 | \n",
" 5.703 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-25 22:13:00+01:00 | \n",
" 34.94 | \n",
" 26.70 | \n",
" 37.03 | \n",
" 27.642 | \n",
" -5.176 | \n",
" 27.642 | \n",
" 5.340 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-14 21:21:00+01:00 | \n",
" 35.02 | \n",
" 60.97 | \n",
" 60.97 | \n",
" 20.647 | \n",
" -3.097 | \n",
" 20.647 | \n",
" 6.666 | \n",
" 0.000 | \n",
" 25.0 | \n",
"
\n",
" \n",
" | 2019-05-22 01:46:00+02:00 | \n",
" 32.50 | \n",
" 24.57 | \n",
" 24.57 | \n",
" 26.870 | \n",
" -4.768 | \n",
" 26.870 | \n",
" 5.635 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-18 23:17:00+02:00 | \n",
" 41.65 | \n",
" 34.37 | \n",
" 34.37 | \n",
" 17.930 | \n",
" -3.325 | \n",
" 17.930 | \n",
" 5.392 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-11-16 10:15:00+01:00 | \n",
" 41.51 | \n",
" 28.77 | \n",
" 28.77 | \n",
" 2.765 | \n",
" -1.150 | \n",
" 2.765 | \n",
" 2.404 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-06 03:03:00+02:00 | \n",
" 39.56 | \n",
" 33.16 | \n",
" 33.16 | \n",
" 22.835 | \n",
" -4.090 | \n",
" 22.835 | \n",
" 5.583 | \n",
" 0.000 | \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",
" 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",
" 20.83 | \n",
" 34.24 | \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",
" 20.83 | \n",
" 34.24 | \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",
" 20.83 | \n",
" 34.24 | \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",
" 20.83 | \n",
" 34.24 | \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",
" 20.83 | \n",
" 34.24 | \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",
" -38,187 | \n",
"
\n",
" \n",
" | Total exported to grid (MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Total consumed from grid (MWh) | \n",
" 37,696 | \n",
" 38,187 | \n",
"
\n",
" \n",
" | Total nominated volume (MWh) | \n",
" -37,696 | \n",
" -37,696 | \n",
"
\n",
" \n",
" | Absolute imbalance volume (MWh) | \n",
" - | \n",
" 805 | \n",
"
\n",
" \n",
" | Mean selling price (€/MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Mean buying price (€/MWh) | \n",
" 41 | \n",
" 40 | \n",
"
\n",
" \n",
" | Total day-ahead result (€) | \n",
" -1,553,699 | \n",
" -1,553,699 | \n",
"
\n",
" \n",
" | Total POS result (€) | \n",
" - | \n",
" 23,481 | \n",
"
\n",
" \n",
" | Total NEG result (€) | \n",
" - | \n",
" -5,581 | \n",
"
\n",
" \n",
" | Total imbalance result (€) | \n",
" - | \n",
" 17,901 | \n",
"
\n",
" \n",
" | Total combined result (€) | \n",
" -1,553,699 | \n",
" -1,535,798 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Heatpump only | \n",
" Heatpump + Storage | \n",
"
\n",
" \n",
" \n",
" \n",
" | Gasboiler OPEX (€) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Gas consumption costs (€) | \n",
" 3,090,164 | \n",
" 3,090,164 | \n",
"
\n",
" \n",
" | CO2 emission costs (€) | \n",
" 1,124,572 | \n",
" 1,124,572 | \n",
"
\n",
" \n",
" | Gas taxes (€) | \n",
" 790,305 | \n",
" 790,305 | \n",
"
\n",
" \n",
" | Heatpump VDG OPEX (€) | \n",
" -46,600 | \n",
" -46,600 | \n",
"
\n",
" \n",
" | Heatpump NDG OPEX (€) | \n",
" -15,400 | \n",
" -15,400 | \n",
"
\n",
" \n",
" | Result on electricity market (€) | \n",
" -1,553,699 | \n",
" -1,535,798 | \n",
"
\n",
" \n",
" | Electricity taxes (€) | \n",
" -35,811 | \n",
" -36,277 | \n",
"
\n",
" \n",
" | Grid transport costs (€) | \n",
" -273,944 | \n",
" -275,718 | \n",
"
\n",
" \n",
" | HotWaterStorage OPEX (€) | \n",
" - | \n",
" -8,500 | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" 3,079,586 | \n",
" 3,086,747 | \n",
"
\n",
" \n",
" | Depreciation (€) | \n",
" -248,000 | \n",
" -265,000 | \n",
"
\n",
" \n",
" | EBIT (€) | \n",
" 2,831,586 | \n",
" 2,821,747 | \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",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
"
\n",
" \n",
" | Irregular Cashflows (€) | \n",
" | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \n",
" 3,079,592 | \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",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
" 2,831,592 | \n",
"
\n",
" \n",
" | Income tax (Vpb.) (€) | \n",
" | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
" -694,648 | \n",
"
\n",
" \n",
" | NOPLAT (€) | \n",
" | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \n",
" 2,136,944 | \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",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \n",
" 2,384,944 | \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",
" 9,108,429 | \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",
" 9,108,429 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
"