{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"#### Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n"
]
}
],
"source": [
"from datetime import timedelta, datetime\n",
"import json\n",
"import pprint\n",
"from copy import deepcopy\n",
"import pytz\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",
"import sys\n",
"\n",
"import os\n",
" \n",
"# Path to the folder containing the alternate version of PyRecoy\n",
"\n",
"alternate_pyrecoy_path = 'C:\\\\Users\\\\shahla.huseynova\\\\source\\\\repos\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n",
" \n",
"# Add the path to sys.path\n",
"\n",
"if alternate_pyrecoy_path not in sys.path:\n",
"\n",
" sys.path.insert(0, alternate_pyrecoy_path)\n",
" \n",
"# Now import PyRecoy\n",
"\n",
"import pyrecoy2\n",
"\n",
"# Check the version or path to confirm\n",
"\n",
"print(pyrecoy2.__file__)\n",
"\n",
"\n",
"from pyrecoy2.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
"from pyrecoy2.colors import *\n",
"from pyrecoy2.converters import *\n",
"from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
"from pyrecoy2.framework import TimeFramework\n",
"from pyrecoy2.casestudy import CaseStudy\n",
"from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n",
"from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
"from pyrecoy2.sensitivity import SensitivityAnalysis\n",
"from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
"from pyrecoy2.forecasts import Mipf, Forecast\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": 2,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [],
"source": [
"class Config():\n",
" start = '2023-01-01'\n",
" end = '2023-12-31'\n",
" # year = '2019'\n",
" # startdate = year + '-01-01'\n",
" # enddate = str(year) + '-12-31'\n",
" # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
" # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
" # start = start.astimezone(pytz.UTC)\n",
" # end = end.astimezone(pytz.UTC)\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"
]
},
"outputs": [],
"source": [
"class Store():\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"exclude"
]
},
"source": [
"## Model set-up"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Define the file paths\n",
"excel_file_path = r'C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed2.xlsx'\n",
"output_directory = r'C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\Kickstart\\V2\\data'\n",
"output_file_name = 'smurfit_demand_preprocessed2.csv'\n",
"csv_file_path = os.path.join(output_directory, output_file_name)\n",
"\n",
"df = pd.read_excel(excel_file_path)\n",
"# df = df.fillna(0) \n",
"\n",
"# # Save the DataFrame to CSV\n",
"df.to_csv(csv_file_path, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [],
"source": [
"def load_demand_data(c, s):\n",
" demand = pd.read_csv('data/smurfit_demand_preprocessed2.csv', delimiter=',', decimal=';')\n",
" dt_index = pd.date_range(\n",
" start=s.time_fw.start,\n",
" end=s.time_fw.start + timedelta(days=365), \n",
" freq='1T',\n",
" tz='Europe/Amsterdam')\n",
" dt_index = dt_index[:len(demand)]\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": 6,
"metadata": {},
"outputs": [],
"source": [
"s = Store()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": [
"exclude"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"testets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: 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.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_31900\\3886619535.py:3: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n"
]
}
],
"source": [
"def setup_model(c):\n",
" s = Store()\n",
" \n",
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
" # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
" mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n",
" s.mipf = mipf\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_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
" # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
" # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\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_PCM]\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": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_31900\\2657160476.py:1: FutureWarning:\n",
"\n",
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
"\n",
"C:\\Users\\shahla.huseynova\\AppData\\Roaming\\Python\\Python310\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
"\n",
"DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
"\n"
]
},
{
"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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"\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",
" 52560.0 | \n",
" 52560.0 | \n",
" 52560.0 | \n",
" 52560.0 | \n",
" 52560.0 | \n",
" 52560.0 | \n",
" 52560.0 | \n",
"
\n",
" \n",
" | mean | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | std | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
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\n",
" \n",
" | min | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | 25% | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
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\n",
" \n",
" | 50% | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | 75% | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
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\n",
" \n",
" | max | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 75.0 | \n",
" 144.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
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" \n",
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"\n",
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\n",
" \n",
" \n",
" | \n",
" DAM | \n",
" POS | \n",
" NEG | \n",
" ForeNeg | \n",
" ForePos | \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",
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\n",
" \n",
" \n",
" \n",
" | 2023-01-01 00:00:00+01:00 | \n",
" -3.61 | \n",
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" 144.0 | \n",
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