pypsa-eur/scripts/build_monthly_prices.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 16 10:37:35 2023.

This script extracts monthly fuel prices of oil, gas, coal and lignite,
as well as CO2 prices


Inputs
------
- ``data/energy-price-trends-xlsx-5619002.xlsx``: energy price index of fossil fuels
- ``emission-spot-primary-market-auction-report-2019-data.xls``: CO2 Prices spot primary auction


Outputs
-------

- ``data/validation/monthly_fuel_price.csv``
- ``data/validation/CO2_price_2019.csv``


Description
-----------

The rule :mod:`build_monthly_prices` collects monthly fuel prices and CO2 prices
and translates them from different input sources to pypsa syntax

Data sources:
    [1] Fuel price index. Destatis
    https://www.destatis.de/EN/Home/_node.html
    [2] average annual import price (coal, gas, oil) Agora, slide 24
    https://static.agora-energiewende.de/fileadmin/Projekte/2019/Jahresauswertung_2019/A-EW_German-Power-Market-2019_Summary_EN.pdf
    [3] average annual fuel price lignite, ENTSO-E
    https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
    [4] CO2 Prices, Emission spot primary auction, EEX
    https://www.eex.com/en/market-data/environmental-markets/eua-primary-auction-spot-download


Data was accessed at 16.5.2023
"""

import logging

import pandas as pd
from _helpers import configure_logging

logger = logging.getLogger(__name__)

validation_year = 2019

# sheet names to pypsa syntax
sheet_name_map = {
    "5.1 Hard coal and lignite": "coal",
    "5.2 Mineral oil": "oil",
    "5.3.1 Natural gas - indices": "gas",
}

# keywords in datasheet
keywords = {
    "coal": " GP09-051 Hard coal",
    "lignite": " GP09-052 Lignite and lignite briquettes",
    "oil": " GP09-0610 10 Mineral oil, crude",
    "gas": "GP09-062 Natural gas",
}

# import fuel price 2015 in Eur/MWh
# source for coal, oil, gas, Agora, slide 24 [2]
# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
price_2015 = {"coal": 8.3, "oil": 30.6, "gas": 20.6, "lignite": 3.8}  # 2020 3.96/1.04


def get_fuel_price():
    fuel_price = pd.read_excel(
        snakemake.input.fuel_price_raw, sheet_name=list(sheet_name_map.keys())
    )
    fuel_price = {
        sheet_name_map[key]: value
        for key, value in fuel_price.items()
        if key in sheet_name_map
    }
    # lignite and hard coal are on the same sheet
    fuel_price["lignite"] = fuel_price["coal"]

    def extract_df(sheet, keyword):
        # Create a DatetimeIndex for the first day of each month of a given year
        month_list = pd.date_range(
            start=f"{validation_year}-01-01", end=f"{validation_year}-12-01", freq="MS"
        ).month
        start = fuel_price[sheet].index[(fuel_price[sheet] == keyword).any(axis=1)]
        df = fuel_price[sheet].loc[start[0] : start[0] + 18]
        df = df.dropna(axis=0)
        df.set_index(df.columns[0], inplace=True)
        df.index = df.index.map(lambda x: int(x.replace(" ...", "")))
        df = df.iloc[:, :12]
        df.columns = month_list
        return df

    m_price = {}
    for carrier, keyword in keywords.items():
        df = extract_df(carrier, keyword).loc[validation_year]
        m_price[carrier] = df.mul(price_2015[carrier] / 100)

    pd.concat(m_price, axis=1).to_csv(snakemake.output.fuel_price)


def get_co2_price():
    # emission price
    CO2_price = pd.read_excel(snakemake.input.co2_price_raw, index_col=1, header=5)
    CO2_price["Auction Price €/tCO2"].to_csv(snakemake.output.co2_price)


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake("build_monthly_prices")

    configure_logging(snakemake)

    get_fuel_price()
    get_co2_price()