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"cell_type": "markdown",
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"source": [
"#### Imports"
]
},
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"execution_count": 1,
"metadata": {
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"data": {
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" \n",
" "
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"output_type": "display_data"
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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"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
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},
"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"
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},
"source": [
"##### Meeting 25-11-2020"
]
},
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"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
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},
"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"
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},
"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 = 10\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"
]
},
"source": [
"## Model set-up"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
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"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"
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},
"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_25832/332434720.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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"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": true
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"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",
" | 2019-10-02 06:19:00+02:00 | \n",
" 43.75 | \n",
" 11.76 | \n",
" 11.76 | \n",
" -1.0 | \n",
" 18.95 | \n",
" 19.55 | \n",
" 62.79 | \n",
" 62.79 | \n",
" 10.0 | \n",
" 24.20 | \n",
" ... | \n",
" 0.0 | \n",
" 33.440 | \n",
" 0.511 | \n",
" -0.074 | \n",
" 0.037 | \n",
" 0.0 | \n",
" 0.130 | \n",
" 0.0 | \n",
" 0.167 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-11 11:16:00+01:00 | \n",
" 71.33 | \n",
" 52.55 | \n",
" 52.55 | \n",
" 1.0 | \n",
" 28.71 | \n",
" 38.46 | \n",
" NaN | \n",
" NaN | \n",
" 21.6 | \n",
" 22.59 | \n",
" ... | \n",
" 0.0 | \n",
" 29.702 | \n",
" 0.517 | \n",
" -0.078 | \n",
" 0.034 | \n",
" 0.0 | \n",
" 0.103 | \n",
" 0.0 | \n",
" 0.137 | \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",
" tsource_ndg | \n",
" tsource_vdg | \n",
" cop | \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",
" 525600.000000 | \n",
" 501212.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 41.195801 | \n",
" 40.391060 | \n",
" 42.554082 | \n",
" 25.034942 | \n",
" 2.039792 | \n",
" 23.460537 | \n",
" 65.004023 | \n",
" 65.999198 | \n",
" 5.691525 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 7.209481 | \n",
" 3.294336 | \n",
" 6.814125 | \n",
" 14.946889 | \n",
" 10.209534 | \n",
" 0.618899 | \n",
"
\n",
" \n",
" | min | \n",
" -9.020000 | \n",
" -487.650000 | \n",
" -487.650000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.687000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 34.100000 | \n",
" 24.470000 | \n",
" 25.530000 | \n",
" 22.810000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
" 64.040000 | \n",
" 64.946000 | \n",
" 5.452000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 27.576000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
" 65.040000 | \n",
" 65.024000 | \n",
" 5.651000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 30.130000 | \n",
" 3.330000 | \n",
" 27.690000 | \n",
" 67.694000 | \n",
" 65.970000 | \n",
" 5.851000 | \n",
"
\n",
" \n",
" | max | \n",
" 121.460000 | \n",
" 936.120000 | \n",
" 936.120000 | \n",
" 31.000000 | \n",
" 10.000000 | \n",
" 30.900000 | \n",
" 95.000000 | \n",
" 95.000000 | \n",
" 7.442000 | \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",
" tsource_vdg | \n",
" tsource_ndg | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-09-07 01:06:00+02:00 | \n",
" 30.00 | \n",
" 22.98 | \n",
" 22.98 | \n",
" 26.900 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 26.900 | \n",
" 65.040 | \n",
" 63.614 | \n",
"
\n",
" \n",
" | 2019-03-29 21:54:00+01:00 | \n",
" 43.04 | \n",
" 31.18 | \n",
" 31.18 | \n",
" 30.790 | \n",
" 1.156 | \n",
" 1.000 | \n",
" 29.844 | \n",
" 64.024 | \n",
" 69.316 | \n",
"
\n",
" \n",
" | 2019-09-28 08:52:00+02:00 | \n",
" 33.20 | \n",
" 24.08 | \n",
" 31.70 | \n",
" 27.660 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 27.660 | \n",
" 65.002 | \n",
" 62.044 | \n",
"
\n",
" \n",
" | 2019-05-01 11:12:00+02:00 | \n",
" 32.89 | \n",
" 221.02 | \n",
" 221.02 | \n",
" 27.362 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 27.362 | \n",
" 65.030 | \n",
" 63.570 | \n",
"
\n",
" \n",
" | 2019-11-18 08:39:00+01:00 | \n",
" 45.87 | \n",
" 41.74 | \n",
" 41.74 | \n",
" 28.325 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 28.325 | \n",
" 66.071 | \n",
" 65.108 | \n",
"
\n",
" \n",
" | 2019-05-13 09:21:00+02:00 | \n",
" 47.26 | \n",
" 34.62 | \n",
" 34.62 | \n",
" 22.930 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 22.930 | \n",
" 65.086 | \n",
" 67.476 | \n",
"
\n",
" \n",
" | 2019-09-13 20:02:00+02:00 | \n",
" 52.43 | \n",
" 49.03 | \n",
" 49.03 | \n",
" 27.708 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 27.708 | \n",
" 64.940 | \n",
" 65.036 | \n",
"
\n",
" \n",
" | 2019-04-30 20:19:00+02:00 | \n",
" 51.76 | \n",
" 32.85 | \n",
" 32.85 | \n",
" 30.250 | \n",
" 3.120 | \n",
" 3.600 | \n",
" 27.880 | \n",
" 64.060 | \n",
" 77.574 | \n",
"
\n",
" \n",
" | 2019-02-27 10:31:00+01:00 | \n",
" 44.57 | \n",
" 24.98 | \n",
" 24.98 | \n",
" 30.716 | \n",
" 1.379 | \n",
" 1.779 | \n",
" 29.621 | \n",
" 64.010 | \n",
" 71.214 | \n",
"
\n",
" \n",
" | 2019-03-10 16:31:00+01:00 | \n",
" 33.95 | \n",
" 19.59 | \n",
" 19.59 | \n",
" 30.092 | \n",
" 3.505 | \n",
" 3.505 | \n",
" 27.495 | \n",
" 65.000 | \n",
" 77.649 | \n",
"
\n",
" \n",
" | 2019-01-11 02:11:00+01:00 | \n",
" 45.43 | \n",
" 142.21 | \n",
" 142.21 | \n",
" 22.902 | \n",
" 0.000 | \n",
" 10.000 | \n",
" 22.902 | \n",
" 78.001 | \n",
" 68.744 | \n",
"
\n",
" \n",
" | 2019-12-16 09:13:00+01:00 | \n",
" 46.10 | \n",
" 59.95 | \n",
" 59.95 | \n",
" 20.788 | \n",
" 0.000 | \n",
" 10.000 | \n",
" 20.788 | \n",
" 79.016 | \n",
" 65.880 | \n",
"
\n",
" \n",
" | 2019-06-24 16:33:00+02:00 | \n",
" 33.87 | \n",
" 27.95 | \n",
" 27.95 | \n",
" 27.058 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 27.058 | \n",
" 63.983 | \n",
" 64.069 | \n",
"
\n",
" \n",
" | 2019-11-03 01:58:00+01:00 | \n",
" 25.70 | \n",
" -3.93 | \n",
" 27.42 | \n",
" 23.816 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 23.816 | \n",
" 65.942 | \n",
" 58.290 | \n",
"
\n",
" \n",
" | 2019-11-18 16:33:00+01:00 | \n",
" 55.00 | \n",
" 25.71 | \n",
" 25.71 | \n",
" 0.000 | \n",
" 10.000 | \n",
" 10.000 | \n",
" 0.000 | \n",
" 95.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" | 2019-03-06 07:00:00+01:00 | \n",
" 49.64 | \n",
" 32.74 | \n",
" 32.74 | \n",
" 30.780 | \n",
" 10.000 | \n",
" 0.000 | \n",
" 20.780 | \n",
" 64.990 | \n",
" 65.450 | \n",
"
\n",
" \n",
" | 2019-05-21 07:37:00+02:00 | \n",
" 49.92 | \n",
" 40.73 | \n",
" 40.73 | \n",
" 22.870 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 22.870 | \n",
" 66.017 | \n",
" 68.964 | \n",
"
\n",
" \n",
" | 2019-07-24 02:37:00+02:00 | \n",
" 38.48 | \n",
" 23.80 | \n",
" 23.80 | \n",
" 30.246 | \n",
" 2.912 | \n",
" 2.912 | \n",
" 28.088 | \n",
" 65.959 | \n",
" 76.437 | \n",
"
\n",
" \n",
" | 2019-05-25 00:26:00+02:00 | \n",
" 41.97 | \n",
" 29.21 | \n",
" 29.21 | \n",
" 0.000 | \n",
" 10.000 | \n",
" 10.000 | \n",
" 0.000 | \n",
" 95.000 | \n",
" 0.000 | \n",
"
\n",
" \n",
" | 2019-06-14 20:36:00+02:00 | \n",
" 57.36 | \n",
" 32.65 | \n",
" 32.65 | \n",
" 0.000 | \n",
" 10.000 | \n",
" 10.000 | \n",
" 0.000 | \n",
" 95.000 | \n",
" 0.000 | \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",
"