{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"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, WaterStorage\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"
]
},
"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": 32,
"metadata": {
"tags": [
"exclude"
]
},
"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 = 0.3\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",
" 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",
" Tref = 273 #K\n",
" Cp = 4190 #J/kgK\n",
" \n",
" tank_max_power=10,\n",
" tank_min_power=-10,\n",
" tank_roundtrip_eff=0.90,\n",
" tank_energy_density = 50 * 10e-3,\n",
" tank_volume = 500,\n",
" tank_lifetime = 25,\n",
" tank_temperature = 368, #K\n",
" min_storagelevel = 5,\n",
" max_storagelevel = 23,\n",
" tank_capex_per_MW = 3_000,\n",
" tancapex_per_MWh = 10_000\n",
"\n",
"c = Config()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"273"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.Tref"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"## Model set-up"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"tags": [
"exclude"
]
},
"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\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"c:\\users\\shahla huseynova\\python\\29 - pyrecoy modelling framework\\pyrecoy\\pyrecoy\\framework.py:35: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n",
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_17920/4233922362.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
},
{
"data": {
"text/plain": [
"[,\n",
" ,\n",
" ,\n",
" ,\n",
" ]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def setup_model(c):\n",
" s = Store()\n",
" \n",
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
" mipf = Mipf(\n",
" start=s.time_fw.start, \n",
" end=s.time_fw.end, \n",
" tidy=True, \n",
" 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.hp_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, s.hp_storage_case]\n",
" s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n",
" \n",
" s.demand = load_demand_data(c, s)\n",
" return s\n",
"\n",
"s = setup_model(c)\n",
"s.optcases"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_17920/4233922362.py:3: FutureWarning:\n",
"\n",
"Argument `closed` is deprecated in favor of `inclusive`.\n",
"\n"
]
},
{
"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",
" | 2019-01-01 00:00:00+01:00 | \n",
" 64.990 | \n",
" 143.360 | \n",
" 0.00 | \n",
" 20.510 | \n",
" 147.620 | \n",
" 0.00 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-01-01 00:01:00+01:00 | \n",
" 64.985 | \n",
" 143.089 | \n",
" 0.00 | \n",
" 20.387 | \n",
" 147.642 | \n",
" 0.00 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-01-01 00:02:00+01:00 | \n",
" 64.980 | \n",
" 142.818 | \n",
" 0.00 | \n",
" 20.264 | \n",
" 147.664 | \n",
" 0.00 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-01-01 00:03:00+01:00 | \n",
" 64.975 | \n",
" 142.547 | \n",
" 0.00 | \n",
" 20.141 | \n",
" 147.686 | \n",
" 0.00 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | 2019-01-01 00:04:00+01:00 | \n",
" 64.970 | \n",
" 142.276 | \n",
" 0.00 | \n",
" 20.018 | \n",
" 147.708 | \n",
" 0.00 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 2019-12-31 23:55:00+01:00 | \n",
" 65.050 | \n",
" 129.970 | \n",
" 17.04 | \n",
" 67.070 | \n",
" 129.350 | \n",
" 5.69 | \n",
" 22.73 | \n",
"
\n",
" \n",
" | 2019-12-31 23:56:00+01:00 | \n",
" 65.050 | \n",
" 129.970 | \n",
" 17.04 | \n",
" 67.070 | \n",
" 129.350 | \n",
" 5.69 | \n",
" 22.73 | \n",
"
\n",
" \n",
" | 2019-12-31 23:57:00+01:00 | \n",
" 65.050 | \n",
" 129.970 | \n",
" 17.04 | \n",
" 67.070 | \n",
" 129.350 | \n",
" 5.69 | \n",
" 22.73 | \n",
"
\n",
" \n",
" | 2019-12-31 23:58:00+01:00 | \n",
" 65.050 | \n",
" 129.970 | \n",
" 17.04 | \n",
" 67.070 | \n",
" 129.350 | \n",
" 5.69 | \n",
" 22.73 | \n",
"
\n",
" \n",
" | 2019-12-31 23:59:00+01:00 | \n",
" 65.050 | \n",
" 129.970 | \n",
" 17.04 | \n",
" 67.070 | \n",
" 129.350 | \n",
" 5.69 | \n",
" 22.73 | \n",
"
\n",
" \n",
"
\n",
"
525600 rows × 7 columns
\n",
"
\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",
"