120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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This script extracts monthly fuel prices of oil, gas, coal and lignite, as well
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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 fuel price lignite, ENTSO-E
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https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
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[3] 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 logging
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import pandas as pd
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from _helpers import configure_logging
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logger = logging.getLogger(__name__)
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# keywords in datasheet
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keywords = {
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"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": "GP09-062 Natural gas",
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}
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# sheet names to pypsa syntax
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sheet_name_map = {
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"coal": "5.1 Hard coal and lignite",
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"lignite": "5.1 Hard coal and lignite",
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"oil": "5.2 Mineral oil",
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"gas": "5.3.1 Natural gas - indices",
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}
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# import fuel price 2015 in Eur/MWh
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# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
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price_2020 = (
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pd.Series({"coal": 3.0, "oil": 10.6, "gas": 5.6, "lignite": 1.1}) * 3.6
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) # Eur/MWh
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# manual adjustment of coal price
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price_2020["coal"] = 2.4 * 3.6
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price_2020["lignite"] = 1.6 * 3.6
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def get_fuel_price():
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price = {}
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for carrier, keyword in keywords.items():
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sheet_name = sheet_name_map[carrier]
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df = pd.read_excel(
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snakemake.input.fuel_price_raw,
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sheet_name=sheet_name,
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index_col=0,
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skiprows=6,
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nrows=18,
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)
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df = df.dropna(axis=0).iloc[:, :12]
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start, end = df.index[0], str(int(df.index[-1][:4]) + 1)
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df = df.stack()
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df.index = pd.date_range(start=start, end=end, freq="MS", inclusive="left")
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scale = price_2020[carrier] / df["2020"].mean() # scale to 2020 price
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df = df.mul(scale)
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price[carrier] = df
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return pd.concat(price, axis=1)
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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, header=5)
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return co2_price["Auction Price €/tCO2"]
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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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fuel_price = get_fuel_price()
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fuel_price.to_csv(snakemake.output.fuel_price)
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co2_price = get_co2_price()
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co2_price.to_csv(snakemake.output.co2_price)
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