ac606c1f67
* Removed tabula dependency for building biomass transport costs. Instead use pre-extracted .csvs. Updated rule and script accordingly. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Updated script to optionally allow running the script with tabula-py dependency to recreate csv, if needed. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Reads biomass transport costs for different countries of the JRC report.
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"The JRC-EU-TIMES model.
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Bioenergy potentials
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for EU and neighbouring countries."
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(2015)
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converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
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assuming as an approximation energy content of wood pellets
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@author: bw0928
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"""
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import pandas as pd
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ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
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def get_cost_per_tkm(pdf, datapage, countrypage):
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"""
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Extracts the cost tables from the JRC report PDF.
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https://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass%20potentials%20in%20europe_web%20rev.pdf
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- pdf (str): The filepath of the PDF file containing the data.
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- datapage (int): The page number of the data table in the PDF.
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- countrypage (int): The page number of the table containing the country indices in the PDF.
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Returns:
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- pandas.DataFrame: The data table with the cost per tkm for biomass transport, indexed by country.
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Raises:
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- ImportError: If tabula-py and platform are not installed.
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"""
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try:
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import platform
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import tabula as tbl
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except:
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ImportError("Please install tabula-py and platform")
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system = platform.system()
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encoding = "cp1252" if system == "Windows" else "utf-8"
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# Obtain countries:
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pandas_options_country = dict(
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skiprows=range(6), header=None, index_col=0, encoding=encoding
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)
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countries = tbl.read_pdf(
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pdf,
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pages=countrypage,
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multiple_tables=False,
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pandas_options=pandas_options_country,
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encoding=encoding,
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)[0].index
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# Obtain data tables
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pandas_options_data = dict(
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skiprows=range(6),
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header=0,
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sep=" |,",
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engine="python",
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index_col=False,
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encoding=encoding,
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)
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sc = tbl.read_pdf(
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pdf,
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pages=datapage,
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multiple_tables=False,
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pandas_options=pandas_options_data,
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encoding=encoding,
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)[0]
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sc.index = countries
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sc.columns = sc.columns.str.replace("€", "EUR")
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return sc
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def build_biomass_transport_costs():
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# Optional build from JRC report pdf, requires tabula and java dependencies.
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# Update `pdf` path to the JRC report if needed.
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# sc1 = get_cost_per_tkm(pdf = "report.pdf", datapage=146, countrypage=145)
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# sc2 = get_cost_per_tkm(pdf = "report.pdf", datapage=147, countrypage=145)
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# Use extracted csv from JRC report
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# https://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass%20potentials%20in%20europe_web%20rev.pdf
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# Pages 146 (144) for supply chain 1 and 147 (145) for supply chain 2
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sc1 = pd.read_csv(snakemake.input.sc1, index_col=0, skiprows=2)
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sc2 = pd.read_csv(snakemake.input.sc2, index_col=0, skiprows=2)
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# take mean of both supply chains
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to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
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transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
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# convert tonnes to MWh
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transport_costs /= ENERGY_CONTENT
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transport_costs.name = "EUR/km/MWh"
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# rename country names
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to_rename = {"UK": "GB", "EL": "GR"}
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transport_costs.rename(to_rename, inplace=True)
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# add missing Norway with data from Sweden
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transport_costs["NO"] = transport_costs["SE"]
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transport_costs.to_csv(snakemake.output[0])
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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_biomass_transport_costs")
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build_biomass_transport_costs()
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