add rule to build monthly prices
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lisazeyen
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399907c1f4
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@ -233,6 +233,22 @@ rule build_renewable_profiles:
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script:
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"../scripts/build_renewable_profiles.py"
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rule build_monthly_prices:
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input:
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co2_price_raw="data/validation/emission-spot-primary-market-auction-report-2019-data.xls",
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fuel_price_raw="/home/lisa/Documents/pypsa-eur/data/validation/energy-price-trends-xlsx-5619002.xlsx"
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output:
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co2_price="data/validation/CO2_price_2019.csv",
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fuel_price="data/validation/onthly_fuel_price.csv"
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log:
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LOGS + "build_monthly_prices.log",
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threads: 1
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resources:
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mem_mb=5000,
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conda:
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"../envs/environment.yaml"
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script:
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"../scripts/build_monthly_prices.py"
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rule build_hydro_profile:
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input:
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@ -269,6 +285,7 @@ rule add_electricity:
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powerplants=RESOURCES + "powerplants.csv",
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hydro_capacities=ancient("data/bundle/hydro_capacities.csv"),
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geth_hydro_capacities="data/geth2015_hydro_capacities.csv",
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monthly_fuel_price="data/monthly_fuel_price.csv",
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load=RESOURCES + "load.csv",
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nuts3_shapes=RESOURCES + "nuts3_shapes.geojson",
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output:
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117
scripts/build_monthly_prices.py
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117
scripts/build_monthly_prices.py
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@ -0,0 +1,117 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue May 16 10:37:35 2023
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This script extracts monthly fuel prices of oil, gas, coal and lignite,
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as well as CO2 prices
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Inputs
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------
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- ``data/energy-price-trends-xlsx-5619002.xlsx``: energy price index of fossil fuels
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- ``emission-spot-primary-market-auction-report-2019-data.xls``: CO2 Prices spot primary auction
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Outputs
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-------
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- ``data/validation/monthly_fuel_price.csv``
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- ``data/validation/CO2_price_2019.csv``
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Description
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-----------
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The rule :mod:`build_monthly_prices` collects monthly fuel prices and CO2 prices
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and translates them from different input sources to pypsa syntax
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Data sources:
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[1] Fuel price index. Destatis
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https://www.destatis.de/EN/Home/_node.html
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[2] average annual import price (coal, gas, oil) Agora, slide 22
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https://static.agora-energiewende.de/fileadmin/Projekte/2022/2022-10_DE_JAW2022/2023-02-20_Praesentation_Agora_Jahresauswertung.pdf
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[3] average annual fuel price lignite, ENTSO-E
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https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
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[4] CO2 Prices, Emission spot primary auction, EEX
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https://www.eex.com/en/market-data/environmental-markets/eua-primary-auction-spot-download
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Data was accessed at 16.5.2023
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"""
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import pandas as pd
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import logging
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from _helpers import configure_logging
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logger = logging.getLogger(__name__)
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validation_year = 2019
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# sheet names to pypsa syntax
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sheet_name_map = {"5.1 Hard coal and lignite": "coal",
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"5.2 Mineral oil" : "oil",
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"5.3.1 Natural gas - indices":"gas"}
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# keywords in datasheet
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keywords = {"coal": " GP09-051 Hard coal",
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"lignite": " GP09-052 Lignite and lignite briquettes",
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"oil": " GP09-0610 10 Mineral oil, crude",
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"gas": "GP09-3522 24 Natural gas, when supplied to power plants"
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}
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# import fuel price 2015 in Eur/MWh
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# source for coal, oil, gas, Agora, slide 22 [2]
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# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
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price_2015 = {"coal": 8,
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"oil": 31,
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"gas": 21,
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"lignite": 3.8} # 2020 3.96/1.04
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def get_fuel_price():
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fuel_price = pd.read_excel(snakemake.input.fuel_price_raw,
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sheet_name=list(sheet_name_map.keys()))
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fuel_price = {sheet_name_map[key]: value for key, value in fuel_price.items()
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if key in sheet_name_map}
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# lignite and hard coal are on the same sheet
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fuel_price["lignite"] = fuel_price["coal"]
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def extract_df(sheet, keyword):
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# Create a DatetimeIndex for the first day of each month of a given year
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dti = pd.date_range(start=f'{validation_year}-01-01',
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end=f'{validation_year}-12-01', freq='MS')
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# Extract month names
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month_list = dti.month
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start = fuel_price[sheet].index[(fuel_price[sheet] == keyword).any(axis=1)]
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df = fuel_price[sheet].loc[start[0]:start[0]+18,:]
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df.dropna(axis=0, inplace=True)
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df.iloc[:,0] = df.iloc[:,0].apply(lambda x: int(x.replace(" ...", "")))
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df.set_index(df.columns[0], inplace=True)
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df = df.iloc[:, :12]
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df.columns = month_list
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return df
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m_price = {}
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for carrier, keyword in keywords.items():
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df = extract_df(carrier, keyword).loc[validation_year]
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m_price[carrier] = df.mul(price_2015[carrier]/100)
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pd.concat(m_price, axis=1).to_csv(snakemake.output.fuel_price)
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def get_co2_price():
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# emission price
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CO2_price = pd.read_excel(snakemake.input.co2_price_raw, index_col=1,
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header=5)
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CO2_price["Auction Price €/tCO2"].to_csv(snakemake.output.co2_price)
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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snakemake = mock_snakemake("build_monthly_prices")
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configure_logging(snakemake)
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get_fuel_price()
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get_co2_price()
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