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"cell_type": "markdown",
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"source": [
"#### Imports"
]
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"execution_count": 1,
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" \n",
" "
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],
"source": [
"from datetime import timedelta\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"\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"
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},
"source": [
"#### Meeting Notes"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
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},
"source": [
"##### Meeting 25-11-2020"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"* aFRR can help optimisation case\n",
"* SDE++ should be included\n",
"* Tsource sensitivity really gives interesting insights\n",
"* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
"* AP TNO: Update/verify COP curve\n",
"* AP TNO: Update CAPEX\n",
"* AP Mark: \n",
" * Create graphs on COP curve with different Tsource, Tsink\n",
" * Generate table and output in .csv (send it to Andrew)\n",
"* Investigate opportunity to lower the COP and negative electricity prices\n",
" * Technically feasible, but not really needed/possible to do it in this project\n",
"* Could be interesting to run this model on a usecase with higher delta T\n",
"* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
]
},
{
"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 = 200\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 * 0.01\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"
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},
"source": [
"## Model set-up"
]
},
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"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": {
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"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",
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_2428/332434720.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": [
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",
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
" 525600.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 63.658798 | \n",
" 138.637674 | \n",
" 17.636712 | \n",
" 62.176305 | \n",
" 145.351645 | \n",
" 5.823824 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 6.422773 | \n",
" 14.328580 | \n",
" 5.123252 | \n",
" 10.252555 | \n",
" 14.896056 | \n",
" 1.691697 | \n",
" 6.814125 | \n",
"
\n",
" \n",
" | min | \n",
" 25.310000 | \n",
" 82.480000 | \n",
" 0.000000 | \n",
" 13.540000 | \n",
" 106.330000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 64.892000 | \n",
" 126.074000 | \n",
" 17.014000 | \n",
" 63.516000 | \n",
" 131.551500 | \n",
" 5.612000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 64.991000 | \n",
" 142.450000 | \n",
" 19.892000 | \n",
" 64.313000 | \n",
" 148.490000 | \n",
" 6.540000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 65.076000 | \n",
" 151.730000 | \n",
" 20.819000 | \n",
" 65.546000 | \n",
" 158.100000 | \n",
" 6.880000 | \n",
" 27.690000 | \n",
"
\n",
" \n",
" | max | \n",
" 70.070000 | \n",
" 166.020000 | \n",
" 23.250000 | \n",
" 73.470000 | \n",
" 172.050000 | \n",
" 7.650000 | \n",
" 30.900000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" CO2 prices (€/MWh) | \n",
" Tsource (VDG) | \n",
" Tsink (VDG) | \n",
" MW (VDG) | \n",
" Tsource (NDG) | \n",
" Tsink (NDG) | \n",
" MW (NDG) | \n",
" Total demand | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-01-01 00:00:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 46.83 | \n",
" 52.70 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.990 | \n",
" 143.360 | \n",
" 0.0 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 32.12 | \n",
" 54.45 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.985 | \n",
" 143.089 | \n",
" 0.0 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 30.97 | \n",
" 48.64 | \n",
" NaN | \n",
" NaN | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.980 | \n",
" 142.818 | \n",
" 0.0 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 51.13 | \n",
" 48.35 | \n",
" 57.71 | \n",
" 73.05 | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.975 | \n",
" 142.547 | \n",
" 0.0 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 68.92 | \n",
" 34.85 | \n",
" 58.01 | \n",
" 2.0 | \n",
" 48.04 | \n",
" 52.01 | \n",
" 63.60 | \n",
" 79.07 | \n",
" 22.38 | \n",
" 24.98 | \n",
" 4.82 | \n",
" 64.970 | \n",
" 142.276 | \n",
" 0.0 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" RS | \n",
" ForePos | \n",
" ForeNeg | \n",
" ForePos_nextQ | \n",
" ForeNeg_nextQ | \n",
" Gas prices (€/MWh) | \n",
" CO2 prices (€/ton) | \n",
" ... | \n",
" from_storage_MW | \n",
" storage_level_MWh | \n",
" hp_output_MWh | \n",
" hp_input_MWh | \n",
" to_storage_ndg_MWh | \n",
" from_storage_ndg_MWh | \n",
" to_storage_vdg_MWh | \n",
" from_storage_vdg_MWh | \n",
" to_storage_MWh | \n",
" from_storage_MWh | \n",
"
\n",
" \n",
" \n",
" \n",
" | 2019-04-10 20:13:00+02:00 | \n",
" 46.97 | \n",
" 33.14 | \n",
" 33.14 | \n",
" -1.0 | \n",
" 35.97 | \n",
" 37.34 | \n",
" 25.11 | \n",
" 22.96 | \n",
" 16.125 | \n",
" 26.04 | \n",
" ... | \n",
" 0.0 | \n",
" 9.5 | \n",
" 0.383 | \n",
" -0.067 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2019-10-25 19:09:00+02:00 | \n",
" 46.69 | \n",
" 21.98 | \n",
" 21.98 | \n",
" -1.0 | \n",
" 22.88 | \n",
" 25.41 | \n",
" 56.41 | \n",
" 56.61 | \n",
" 10.900 | \n",
" 24.96 | \n",
" ... | \n",
" 0.0 | \n",
" 9.5 | \n",
" 0.494 | \n",
" -0.089 | \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",
" 24.568078 | \n",
" 1.107542 | \n",
" 23.460537 | \n",
"
\n",
" \n",
" | std | \n",
" 11.278173 | \n",
" 51.226931 | \n",
" 52.003252 | \n",
" 7.070876 | \n",
" 3.017005 | \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",
" 22.764000 | \n",
" 0.000000 | \n",
" 22.676000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 39.700000 | \n",
" 31.110000 | \n",
" 32.160000 | \n",
" 27.015000 | \n",
" 0.000000 | \n",
" 26.441000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 47.392500 | \n",
" 40.900000 | \n",
" 42.540000 | \n",
" 28.334000 | \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",
" | 2019-05-25 02:18:00+02:00 | \n",
" 35.07 | \n",
" 27.14 | \n",
" 34.33 | \n",
" 28.190 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 28.190 | \n",
"
\n",
" \n",
" | 2019-07-19 21:55:00+02:00 | \n",
" 49.16 | \n",
" 21.54 | \n",
" 29.13 | \n",
" 22.870 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.870 | \n",
"
\n",
" \n",
" | 2019-12-11 14:52:00+01:00 | \n",
" 51.29 | \n",
" 32.22 | \n",
" 32.22 | \n",
" 27.672 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.672 | \n",
"
\n",
" \n",
" | 2019-10-06 02:31:00+02:00 | \n",
" 27.43 | \n",
" 28.46 | \n",
" 28.46 | \n",
" 22.718 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.718 | \n",
"
\n",
" \n",
" | 2019-03-24 07:40:00+01:00 | \n",
" 34.98 | \n",
" 32.66 | \n",
" 32.66 | \n",
" 26.590 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 26.590 | \n",
"
\n",
" \n",
" | 2019-08-12 13:32:00+02:00 | \n",
" 35.79 | \n",
" 24.30 | \n",
" 25.69 | \n",
" 28.348 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 28.348 | \n",
"
\n",
" \n",
" | 2019-02-11 05:48:00+01:00 | \n",
" 45.03 | \n",
" 39.80 | \n",
" 39.80 | \n",
" 20.880 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 20.880 | \n",
"
\n",
" \n",
" | 2019-09-19 21:30:00+02:00 | \n",
" 51.24 | \n",
" 19.68 | \n",
" 19.68 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 0.000 | \n",
"
\n",
" \n",
" | 2019-08-23 19:12:00+02:00 | \n",
" 57.33 | \n",
" 57.33 | \n",
" 57.33 | \n",
" 22.978 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.978 | \n",
"
\n",
" \n",
" | 2019-04-21 10:27:00+02:00 | \n",
" 29.10 | \n",
" 32.65 | \n",
" 32.65 | \n",
" 22.876 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.876 | \n",
"
\n",
" \n",
" | 2019-07-19 21:50:00+02:00 | \n",
" 49.16 | \n",
" 21.54 | \n",
" 29.13 | \n",
" 22.870 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.870 | \n",
"
\n",
" \n",
" | 2019-06-21 17:40:00+02:00 | \n",
" 34.98 | \n",
" 35.15 | \n",
" 35.15 | \n",
" 0.000 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 0.000 | \n",
"
\n",
" \n",
" | 2019-07-15 08:49:00+02:00 | \n",
" 50.06 | \n",
" 25.79 | \n",
" 25.79 | \n",
" 31.000 | \n",
" 3.367 | \n",
" 0.0 | \n",
" 27.633 | \n",
"
\n",
" \n",
" | 2019-05-31 14:31:00+02:00 | \n",
" 30.00 | \n",
" 22.03 | \n",
" 22.03 | \n",
" 26.060 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 26.060 | \n",
"
\n",
" \n",
" | 2019-04-23 16:00:00+02:00 | \n",
" 37.67 | \n",
" 34.31 | \n",
" 34.31 | \n",
" 27.550 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 27.550 | \n",
"
\n",
" \n",
" | 2019-01-14 08:07:00+01:00 | \n",
" 63.14 | \n",
" 188.96 | \n",
" 188.96 | \n",
" 2.142 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 2.142 | \n",
"
\n",
" \n",
" | 2019-06-07 23:19:00+02:00 | \n",
" 28.80 | \n",
" 209.93 | \n",
" 209.93 | \n",
" 22.632 | \n",
" 0.000 | \n",
" 25.0 | \n",
" 22.632 | \n",
"
\n",
" \n",
" | 2019-12-30 08:47:00+01:00 | \n",
" 43.54 | \n",
" 28.78 | \n",
" 28.78 | \n",
" 22.754 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 22.754 | \n",
"
\n",
" \n",
" | 2019-02-22 03:54:00+01:00 | \n",
" 39.59 | \n",
" 36.72 | \n",
" 36.72 | \n",
" 26.614 | \n",
" 0.000 | \n",
" 0.0 | \n",
" 26.614 | \n",
"
\n",
" \n",
" | 2019-01-06 16:22:00+01:00 | \n",
" 54.71 | \n",
" 44.68 | \n",
" 44.68 | \n",
" 31.000 | \n",
" 1.414 | \n",
" 0.0 | \n",
" 29.586 | \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",
" | 2019-07-25 05:35:00+02:00 | \n",
" 40.67 | \n",
" 27.40 | \n",
" 27.40 | \n",
" 22.915 | \n",
" -4.035 | \n",
" 22.915 | \n",
" 5.678 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-04-25 17:53:00+02:00 | \n",
" 40.52 | \n",
" 40.98 | \n",
" 40.98 | \n",
" 26.973 | \n",
" -4.889 | \n",
" 26.973 | \n",
" 5.518 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-06-29 20:21:00+02:00 | \n",
" 56.23 | \n",
" 32.99 | \n",
" 32.99 | \n",
" 27.879 | \n",
" -5.396 | \n",
" 31.000 | \n",
" 5.745 | \n",
" 3.121 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-04-13 23:12:00+02:00 | \n",
" 44.64 | \n",
" 237.02 | \n",
" 237.02 | \n",
" 28.502 | \n",
" -4.820 | \n",
" 28.502 | \n",
" 5.913 | \n",
" 0.000 | \n",
" 25.00 | \n",
"
\n",
" \n",
" | 2019-02-20 05:21:00+01:00 | \n",
" 38.06 | \n",
" 10.43 | \n",
" 10.43 | \n",
" 22.938 | \n",
" -4.582 | \n",
" 31.000 | \n",
" 6.766 | \n",
" 8.062 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-02-24 12:17:00+01:00 | \n",
" 37.14 | \n",
" 36.46 | \n",
" 36.46 | \n",
" 26.956 | \n",
" -4.761 | \n",
" 26.956 | \n",
" 5.662 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-02-09 14:41:00+01:00 | \n",
" 38.90 | \n",
" 41.51 | \n",
" 41.51 | \n",
" 26.791 | \n",
" -4.827 | \n",
" 26.791 | \n",
" 5.550 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-06-21 12:36:00+02:00 | \n",
" 36.21 | \n",
" 26.23 | \n",
" 26.23 | \n",
" 28.170 | \n",
" -5.230 | \n",
" 28.170 | \n",
" 5.386 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-10-11 05:04:00+02:00 | \n",
" 20.17 | \n",
" -5.42 | \n",
" -5.42 | \n",
" 28.946 | \n",
" -5.469 | \n",
" 31.000 | \n",
" 5.668 | \n",
" 2.054 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-02-21 02:24:00+01:00 | \n",
" 35.49 | \n",
" 32.82 | \n",
" 32.82 | \n",
" 26.880 | \n",
" -4.789 | \n",
" 26.880 | \n",
" 5.613 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-04-30 12:16:00+02:00 | \n",
" 40.85 | \n",
" 49.63 | \n",
" 49.63 | \n",
" 27.866 | \n",
" -5.092 | \n",
" 27.866 | \n",
" 5.472 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-08-09 13:14:00+02:00 | \n",
" 39.23 | \n",
" 25.51 | \n",
" 25.51 | \n",
" 21.056 | \n",
" -4.406 | \n",
" 31.000 | \n",
" 7.036 | \n",
" 9.944 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-02-03 23:12:00+01:00 | \n",
" 41.76 | \n",
" 10.03 | \n",
" 10.03 | \n",
" 20.718 | \n",
" -3.670 | \n",
" 20.718 | \n",
" 5.645 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-12-16 01:28:00+01:00 | \n",
" 33.68 | \n",
" 26.43 | \n",
" 26.43 | \n",
" 20.784 | \n",
" -3.544 | \n",
" 20.784 | \n",
" 5.864 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-06-22 06:55:00+02:00 | \n",
" 29.48 | \n",
" 27.46 | \n",
" 27.46 | \n",
" 26.645 | \n",
" -4.872 | \n",
" 26.645 | \n",
" 5.469 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-07-02 17:14:00+02:00 | \n",
" 33.05 | \n",
" 24.30 | \n",
" 24.30 | \n",
" 22.776 | \n",
" -4.118 | \n",
" 22.776 | \n",
" 5.531 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-11-18 14:24:00+01:00 | \n",
" 49.80 | \n",
" 48.06 | \n",
" 48.06 | \n",
" 27.582 | \n",
" -5.141 | \n",
" 27.582 | \n",
" 5.365 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-12-05 10:02:00+01:00 | \n",
" 57.15 | \n",
" 209.92 | \n",
" 209.92 | \n",
" 27.560 | \n",
" -5.069 | \n",
" 27.560 | \n",
" 5.437 | \n",
" 0.000 | \n",
" 4.24 | \n",
"
\n",
" \n",
" | 2019-07-11 05:21:00+02:00 | \n",
" 35.90 | \n",
" 28.20 | \n",
" 28.20 | \n",
" 20.811 | \n",
" -3.600 | \n",
" 20.811 | \n",
" 5.780 | \n",
" 0.000 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-10-06 20:21:00+02:00 | \n",
" 38.41 | \n",
" 32.06 | \n",
" 32.06 | \n",
" 27.598 | \n",
" -5.216 | \n",
" 27.598 | \n",
" 5.291 | \n",
" 0.000 | \n",
" 0.00 | \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",
"