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"cells": [
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"cell_type": "markdown",
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"source": [
"#### Imports"
]
},
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"cell_type": "code",
"execution_count": 1,
"metadata": {
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"outputs": [
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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",
"\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"
]
},
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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 = 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"
]
},
{
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"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_36740/332434720.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\"\n",
" ).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": [
{
"data": {
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"linkText": "Export to plot.ly",
"plotlyServerURL": "https://plot.ly",
"showLink": true
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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",
" 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",
" 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",
" 24.493768 | \n",
" 1.033232 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 6.843474 | \n",
" 3.268826 | \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",
" 22.768000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 26.960000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 27.970000 | \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",
" | 2019-08-25 05:20:00+02:00 | \n",
" 28.03 | \n",
" 27.40 | \n",
" 27.40 | \n",
" 20.630 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 20.630 | \n",
"
\n",
" \n",
" | 2019-10-26 20:53:00+02:00 | \n",
" 35.77 | \n",
" 21.65 | \n",
" 21.65 | \n",
" 27.714 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.714 | \n",
"
\n",
" \n",
" | 2019-01-31 03:39:00+01:00 | \n",
" 46.82 | \n",
" 43.84 | \n",
" 43.84 | \n",
" 26.408 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 26.408 | \n",
"
\n",
" \n",
" | 2019-04-07 07:22:00+02:00 | \n",
" 39.43 | \n",
" 32.22 | \n",
" 32.22 | \n",
" 27.406 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.406 | \n",
"
\n",
" \n",
" | 2019-09-09 19:27:00+02:00 | \n",
" 60.64 | \n",
" 19.02 | \n",
" 19.02 | \n",
" 22.983 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.983 | \n",
"
\n",
" \n",
" | 2019-11-09 08:35:00+01:00 | \n",
" 42.02 | \n",
" 32.21 | \n",
" 32.21 | \n",
" 22.930 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.930 | \n",
"
\n",
" \n",
" | 2019-01-20 10:33:00+01:00 | \n",
" 57.00 | \n",
" 44.67 | \n",
" 44.67 | \n",
" 21.121 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 21.121 | \n",
"
\n",
" \n",
" | 2019-11-11 13:55:00+01:00 | \n",
" 45.60 | \n",
" 46.37 | \n",
" 46.37 | \n",
" 27.570 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.570 | \n",
"
\n",
" \n",
" | 2019-11-25 03:18:00+01:00 | \n",
" 35.40 | \n",
" 33.35 | \n",
" 176.35 | \n",
" 29.860 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 29.860 | \n",
"
\n",
" \n",
" | 2019-03-13 03:20:00+01:00 | \n",
" 37.40 | \n",
" 31.54 | \n",
" 31.54 | \n",
" 20.690 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 20.690 | \n",
"
\n",
" \n",
" | 2019-11-10 12:45:00+01:00 | \n",
" 40.10 | \n",
" 11.37 | \n",
" 11.37 | \n",
" 31.000 | \n",
" 2.875 | \n",
" 0.0 | \n",
" 28.125 | \n",
"
\n",
" \n",
" | 2019-12-19 15:14:00+01:00 | \n",
" 38.72 | \n",
" 26.37 | \n",
" 35.02 | \n",
" 27.450 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.450 | \n",
"
\n",
" \n",
" | 2019-04-16 04:08:00+02:00 | \n",
" 35.06 | \n",
" 34.73 | \n",
" 34.73 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 0.000 | \n",
"
\n",
" \n",
" | 2019-09-16 09:36:00+02:00 | \n",
" 55.31 | \n",
" 28.61 | \n",
" 28.61 | \n",
" 31.000 | \n",
" 10.302 | \n",
" 0.0 | \n",
" 20.698 | \n",
"
\n",
" \n",
" | 2019-01-15 01:28:00+01:00 | \n",
" 42.00 | \n",
" 342.00 | \n",
" 342.00 | \n",
" 27.868 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.868 | \n",
"
\n",
" \n",
" | 2019-11-05 03:40:00+01:00 | \n",
" 24.99 | \n",
" 25.61 | \n",
" 25.61 | \n",
" 22.430 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.430 | \n",
"
\n",
" \n",
" | 2019-07-13 15:58:00+02:00 | \n",
" 33.51 | \n",
" 26.57 | \n",
" 26.57 | \n",
" 28.328 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 28.328 | \n",
"
\n",
" \n",
" | 2019-05-22 14:06:00+02:00 | \n",
" 36.99 | \n",
" 48.65 | \n",
" 48.65 | \n",
" 27.722 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.722 | \n",
"
\n",
" \n",
" | 2019-07-21 06:48:00+02:00 | \n",
" 29.88 | \n",
" 27.07 | \n",
" 27.07 | \n",
" 28.686 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 28.686 | \n",
"
\n",
" \n",
" | 2019-12-31 17:53:00+01:00 | \n",
" 46.06 | \n",
" 20.75 | \n",
" 20.75 | \n",
" 31.000 | \n",
" 8.227 | \n",
" 0.0 | \n",
" 22.773 | \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",
" | 2019-12-04 09:46:00+01:00 | \n",
" 58.01 | \n",
" 18.48 | \n",
" 18.48 | \n",
" 27.534 | \n",
" -5.525 | \n",
" 31.000 | \n",
" 5.610 | \n",
" 3.466 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-09-17 06:21:00+02:00 | \n",
" 45.90 | \n",
" 31.50 | \n",
" 31.50 | \n",
" 20.806 | \n",
" -3.524 | \n",
" 20.806 | \n",
" 5.904 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-29 21:58:00+02:00 | \n",
" 47.13 | \n",
" 37.11 | \n",
" 37.11 | \n",
" 27.780 | \n",
" -5.185 | \n",
" 27.780 | \n",
" 5.358 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-05 00:52:00+02:00 | \n",
" 29.80 | \n",
" 22.89 | \n",
" 22.89 | \n",
" 22.738 | \n",
" -4.070 | \n",
" 22.738 | \n",
" 5.586 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-29 08:22:00+01:00 | \n",
" 47.62 | \n",
" 31.03 | \n",
" 31.03 | \n",
" 29.712 | \n",
" -5.401 | \n",
" 29.712 | \n",
" 5.501 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-12 06:40:00+02:00 | \n",
" 42.97 | \n",
" 44.83 | \n",
" 44.83 | \n",
" 22.940 | \n",
" -3.998 | \n",
" 22.940 | \n",
" 5.738 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-18 20:26:00+02:00 | \n",
" 45.17 | \n",
" 49.42 | \n",
" 49.42 | \n",
" 0.000 | \n",
" -0.000 | \n",
" 0.000 | \n",
" NaN | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-05-28 22:01:00+02:00 | \n",
" 38.77 | \n",
" 25.92 | \n",
" 25.92 | \n",
" 29.816 | \n",
" -5.353 | \n",
" 29.816 | \n",
" 5.570 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-22 12:28:00+01:00 | \n",
" 35.00 | \n",
" 53.20 | \n",
" 53.20 | \n",
" 20.706 | \n",
" -3.731 | \n",
" 20.706 | \n",
" 5.549 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-11-05 11:51:00+01:00 | \n",
" 48.43 | \n",
" 28.71 | \n",
" 28.71 | \n",
" 27.582 | \n",
" -5.211 | \n",
" 27.582 | \n",
" 5.294 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-02-15 07:28:00+01:00 | \n",
" 59.98 | \n",
" 39.43 | \n",
" 39.43 | \n",
" 27.678 | \n",
" -5.436 | \n",
" 31.000 | \n",
" 5.702 | \n",
" 3.322 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-05 02:06:00+01:00 | \n",
" 45.68 | \n",
" 57.14 | \n",
" 57.14 | \n",
" 20.670 | \n",
" -3.644 | \n",
" 20.670 | \n",
" 5.673 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-16 22:45:00+02:00 | \n",
" 47.80 | \n",
" 24.11 | \n",
" 24.11 | \n",
" 22.735 | \n",
" -4.714 | \n",
" 31.000 | \n",
" 6.576 | \n",
" 8.265 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-05 12:37:00+01:00 | \n",
" 42.59 | \n",
" -2.16 | \n",
" -2.16 | \n",
" 22.783 | \n",
" -4.868 | \n",
" 31.000 | \n",
" 6.368 | \n",
" 8.217 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-15 17:41:00+01:00 | \n",
" 39.10 | \n",
" 26.43 | \n",
" 109.95 | \n",
" 20.667 | \n",
" -3.527 | \n",
" 20.667 | \n",
" 5.860 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-26 20:50:00+01:00 | \n",
" 37.24 | \n",
" 15.86 | \n",
" 15.86 | \n",
" 27.780 | \n",
" -5.408 | \n",
" 31.000 | \n",
" 5.732 | \n",
" 3.220 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-06-19 23:19:00+02:00 | \n",
" 34.99 | \n",
" 52.11 | \n",
" 52.11 | \n",
" 23.017 | \n",
" -3.944 | \n",
" 23.017 | \n",
" 5.836 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-12-19 15:53:00+01:00 | \n",
" 38.72 | \n",
" 21.35 | \n",
" 21.35 | \n",
" 27.814 | \n",
" -5.117 | \n",
" 27.814 | \n",
" 5.436 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-03-11 11:01:00+01:00 | \n",
" 42.66 | \n",
" 39.17 | \n",
" 39.17 | \n",
" 26.731 | \n",
" -4.866 | \n",
" 26.731 | \n",
" 5.493 | \n",
" 0.000 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-07-12 08:44:00+02:00 | \n",
" 58.30 | \n",
" 63.30 | \n",
" 63.30 | \n",
" 22.880 | \n",
" -3.994 | \n",
" 22.880 | \n",
" 5.728 | \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",
"