Snakefile

# SPDX-FileCopyrightText: Contributors to technology-data <https://github.com/pypsa/technology-data>
#
# SPDX-License-Identifier: GPL-3.0-only


configfile: "config.yaml"


rule compile_cost_assumptions:
    input:
        inflation_rate="inputs/prc_hicp_aind__custom_9928419_spreadsheet.xlsx",
        pypsa_costs="inputs/costs_PyPSA.csv",
        fraunhofer_costs="inputs/Fraunhofer_ISE_costs.csv",
        fraunhofer_energy_prices="inputs/Fraunhofer_ISE_energy_prices.csv",
        fraunhofer_vehicles_costs="inputs/Fraunhofer_ISE_vehicles_costs.csv",
        EWG_costs="inputs/EWG_costs.csv",
        dea_transport="inputs/energy_transport_data_sheet_dec_2017.xlsx",
        dea_vehicles="inputs/data_sheets_for_commercial_freight_and_passenger_transport_0.xlsx",
        dea_renewable_fuels="inputs/data_sheets_for_renewable_fuels.xlsx",
        dea_storage="inputs/technology_data_catalogue_for_energy_storage.xlsx",
        dea_generation="inputs/technology_data_for_el_and_dh.xlsx",
        dea_heating="inputs/technologydatafor_heating_installations_marts_2018.xlsx",
        dea_industrial="inputs/technology_data_for_industrial_process_heat.xlsx",
        dea_ship="inputs/data_sheets_for_maritime_commercial_freight_and_passenger_transport.xlsx",
        dea_ccts="inputs/technology_data_for_carbon_capture_transport_storage.xlsx",
        pnnl_energy_storage="inputs/pnnl-energy-storage-database.xlsx",
        manual_input="inputs/manual_input.csv",
    output:
        expand("outputs/costs_{year}.csv", year=config["years"]),
    threads: 1
    resources:
        mem=500,
    conda:
        "environment.yaml"
    script:
        "scripts/compile_cost_assumptions.py"


# rule convert_fraunhofer:
#     input:
#         fraunhofer = "docu/Anhang-Studie-Wege-zu-einem-klimaneutralen-Energiesystem.pdf"
#     output:
#         costs = "inputs/Fraunhofer_ISE_costs.csv",
#         energy_prices = "inputs/Fraunhofer_ISE_energy_prices.csv"
#     threads: 1
#     resources: mem=500
#     conda: "environment.yaml"
#     script: "scripts/convert_pdf_fraunhofer_to_dataframe.py"


rule convert_EWG:
    input:
        EWG="docu/EWG_LUT_100RE_All_Sectors_Global_Report_2019.pdf",
    output:
        costs="inputs/EWG_costs.csv",
    threads: 1
    resources:
        mem=500,
    conda:
        "environment.yaml"
    script:
        "scripts/convert_pdf_EWG_to_dataframe.py"