{
"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 = '2019-09-16'\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 259.9576388888889 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_23264/332434720.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",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 63.650653 | \n",
" 138.587149 | \n",
" 17.621668 | \n",
" 62.173799 | \n",
" 145.364800 | \n",
" 5.818420 | \n",
" 23.440088 | \n",
"
\n",
" \n",
" | std | \n",
" 6.438823 | \n",
" 14.400208 | \n",
" 5.137628 | \n",
" 10.289513 | \n",
" 14.937009 | \n",
" 1.696358 | \n",
" 6.833167 | \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",
" 64.908000 | \n",
" 125.984000 | \n",
" 17.010000 | \n",
" 63.524000 | \n",
" 131.522000 | \n",
" 5.610000 | \n",
" 22.660000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 64.991000 | \n",
" 142.435000 | \n",
" 19.862000 | \n",
" 64.390000 | \n",
" 148.402000 | \n",
" 6.523000 | \n",
" 26.410000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 65.071000 | \n",
" 151.738000 | \n",
" 20.817000 | \n",
" 65.582000 | \n",
" 158.201000 | \n",
" 6.879000 | \n",
" 27.689000 | \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",
" | 2019-04-03 03:19:00+02:00 | \n",
" 31.19 | \n",
" 31.22 | \n",
" 31.22 | \n",
" 0.0 | \n",
" 27.79 | \n",
" 29.51 | \n",
" 23.31 | \n",
" 19.40 | \n",
" 14.25 | \n",
" 23.08 | \n",
" ... | \n",
" 0.0 | \n",
" 10.409 | \n",
" 0.465 | \n",
" -0.086 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-12 13:36:00+02:00 | \n",
" 42.91 | \n",
" 52.25 | \n",
" 52.25 | \n",
" 1.0 | \n",
" 46.14 | \n",
" 47.76 | \n",
" 114.04 | \n",
" 114.04 | \n",
" 11.05 | \n",
" 24.75 | \n",
" ... | \n",
" 0.0 | \n",
" 2.500 | \n",
" 0.384 | \n",
" -0.067 | \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",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
" 372900.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 42.101747 | \n",
" 41.402355 | \n",
" 43.118521 | \n",
" 24.238078 | \n",
" 0.797990 | \n",
" 23.440088 | \n",
"
\n",
" \n",
" | std | \n",
" 11.477942 | \n",
" 49.212332 | \n",
" 49.881532 | \n",
" 7.142229 | \n",
" 2.502055 | \n",
" 6.833167 | \n",
"
\n",
" \n",
" | min | \n",
" -9.020000 | \n",
" -429.950000 | \n",
" -429.950000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 34.900000 | \n",
" 25.690000 | \n",
" 26.240000 | \n",
" 22.748000 | \n",
" 0.000000 | \n",
" 22.660000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 40.200000 | \n",
" 32.450000 | \n",
" 33.200000 | \n",
" 26.922000 | \n",
" 0.000000 | \n",
" 26.410000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.840000 | \n",
" 42.130000 | \n",
" 43.680000 | \n",
" 27.930000 | \n",
" 0.000000 | \n",
" 27.689000 | \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",
" | 2019-03-29 23:04:00+01:00 | \n",
" 39.32 | \n",
" 32.31 | \n",
" 32.31 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-04 03:17:00+02:00 | \n",
" 32.59 | \n",
" 20.12 | \n",
" 20.12 | \n",
" 22.751 | \n",
" -4.157 | \n",
" 22.751 | \n",
" 5.472 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-09 03:27:00+02:00 | \n",
" 34.96 | \n",
" 47.70 | \n",
" 47.70 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-24 00:52:00+02:00 | \n",
" 38.00 | \n",
" 28.78 | \n",
" 32.85 | \n",
" 18.892 | \n",
" -4.357 | \n",
" 18.892 | \n",
" 4.336 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-23 20:53:00+02:00 | \n",
" 50.10 | \n",
" 41.29 | \n",
" 41.29 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-09 13:30:00+01:00 | \n",
" 43.60 | \n",
" 26.28 | \n",
" 26.28 | \n",
" 26.820 | \n",
" -4.847 | \n",
" 26.820 | \n",
" 5.533 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-22 20:16:00+01:00 | \n",
" 46.00 | \n",
" 38.59 | \n",
" 38.59 | \n",
" 22.872 | \n",
" -3.962 | \n",
" 22.872 | \n",
" 5.772 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-13 12:25:00+02:00 | \n",
" 37.87 | \n",
" 138.02 | \n",
" 138.02 | \n",
" 28.395 | \n",
" -5.216 | \n",
" 28.395 | \n",
" 5.443 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-04 04:46:00+01:00 | \n",
" 47.00 | \n",
" 44.95 | \n",
" 44.95 | \n",
" 22.786 | \n",
" -4.127 | \n",
" 22.786 | \n",
" 5.521 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-28 00:48:00+02:00 | \n",
" 30.98 | \n",
" 26.34 | \n",
" 26.34 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-05 06:01:00+01:00 | \n",
" 40.97 | \n",
" 15.23 | \n",
" 15.23 | \n",
" 22.751 | \n",
" -4.792 | \n",
" 31.000 | \n",
" 6.469 | \n",
" 8.249 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-02 02:36:00+01:00 | \n",
" 45.70 | \n",
" 34.84 | \n",
" 34.84 | \n",
" 27.856 | \n",
" -5.141 | \n",
" 27.856 | \n",
" 5.418 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-13 02:15:00+02:00 | \n",
" 39.90 | \n",
" 51.37 | \n",
" 51.37 | \n",
" 22.940 | \n",
" -4.041 | \n",
" 22.940 | \n",
" 5.677 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-09 23:29:00+02:00 | \n",
" 35.60 | \n",
" 26.12 | \n",
" 26.12 | \n",
" 22.902 | \n",
" -4.034 | \n",
" 22.902 | \n",
" 5.678 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-09-12 00:09:00+02:00 | \n",
" 27.88 | \n",
" 18.12 | \n",
" 18.12 | \n",
" 27.881 | \n",
" -5.175 | \n",
" 27.881 | \n",
" 5.387 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-19 05:05:00+02:00 | \n",
" 33.91 | \n",
" 45.42 | \n",
" 45.42 | \n",
" 10.385 | \n",
" -3.117 | \n",
" 10.385 | \n",
" 3.332 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-04-01 23:05:00+02:00 | \n",
" 36.81 | \n",
" 221.02 | \n",
" 221.02 | \n",
" 27.585 | \n",
" -5.277 | \n",
" 27.585 | \n",
" 5.227 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-09 07:11:00+01:00 | \n",
" 41.12 | \n",
" 43.63 | \n",
" 43.63 | \n",
" 26.951 | \n",
" -4.887 | \n",
" 26.951 | \n",
" 5.515 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-10 04:52:00+02:00 | \n",
" 27.01 | \n",
" 25.00 | \n",
" 25.00 | \n",
" 22.884 | \n",
" -4.037 | \n",
" 22.884 | \n",
" 5.669 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-22 16:39:00+02:00 | \n",
" 38.91 | \n",
" 31.44 | \n",
" 31.44 | \n",
" 27.716 | \n",
" -5.107 | \n",
" 27.716 | \n",
" 5.428 | \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",
" 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",
" -26,724 | \n",
" -27,037 | \n",
"
\n",
" \n",
" | Total exported to grid (MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Total consumed from grid (MWh) | \n",
" 26,724 | \n",
" 27,037 | \n",
"
\n",
" \n",
" | Total nominated volume (MWh) | \n",
" -26,724 | \n",
" -26,724 | \n",
"
\n",
" \n",
" | Absolute imbalance volume (MWh) | \n",
" - | \n",
" 508 | \n",
"
\n",
" \n",
" | Mean selling price (€/MWh) | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | Mean buying price (€/MWh) | \n",
" 42 | \n",
" 41 | \n",
"
\n",
" \n",
" | Total day-ahead result (€) | \n",
" -1,126,079 | \n",
" -1,126,079 | \n",
"
\n",
" \n",
" | Total POS result (€) | \n",
" - | \n",
" 14,310 | \n",
"
\n",
" \n",
" | Total NEG result (€) | \n",
" - | \n",
" -4,077 | \n",
"
\n",
" \n",
" | Total imbalance result (€) | \n",
" - | \n",
" 10,233 | \n",
"
\n",
" \n",
" | Total combined result (€) | \n",
" -1,126,079 | \n",
" -1,115,846 | \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",
" 2,276,598 | \n",
" 2,276,598 | \n",
"
\n",
" \n",
" | CO2 emission costs (€) | \n",
" 774,453 | \n",
" 774,453 | \n",
"
\n",
" \n",
" | Gas taxes (€) | \n",
" 560,213 | \n",
" 560,213 | \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",
" -1,126,079 | \n",
" -1,115,846 | \n",
"
\n",
" \n",
" | Electricity taxes (€) | \n",
" -25,388 | \n",
" -25,685 | \n",
"
\n",
" \n",
" | Grid transport costs (€) | \n",
" -194,357 | \n",
" -194,805 | \n",
"
\n",
" \n",
" | HotWaterStorage OPEX (€) | \n",
" - | \n",
" -8,500 | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" 2,203,441 | \n",
" 2,204,428 | \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",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
"
\n",
" \n",
" | Irregular Cashflows (€) | \n",
" | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
" - | \n",
"
\n",
" \n",
" | EBITDA (€) | \n",
" | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \n",
" 3,105,743 | \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",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
" 2,857,743 | \n",
"
\n",
" \n",
" | Income tax (Vpb.) (€) | \n",
" | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
" -701,186 | \n",
"
\n",
" \n",
" | NOPLAT (€) | \n",
" | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \n",
" 2,156,557 | \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",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \n",
" 2,404,557 | \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",
" 38% | \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",
" 9,229,920 | \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",
" 10,267,063 | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
"
\n",
"