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Build biomass potential

Build biomass potential data for energy system modeling.

This module processes biomass resource data and calculates biomass potential for different regions and technologies in the PyPSA-China model.

build_biomass_potential_xing(biomass_potentials_path)

Build potential from Xing et al. https://doi.org/10.1038/s41467-021-23282-x

Parameters:

Name Type Description Default
biomass_potentials_path PathLike

the path to the Xing SI data (xlsx).

 required
Source code in workflow/scripts/build_biomass_potential.py 
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def build_biomass_potential_xing(biomass_potentials_path: PathLike):
    """Build potential from Xing et al. https://doi.org/10.1038/s41467-021-23282-x

    Args:
        biomass_potentials_path (PathLike, optional): the path to the Xing SI data (xlsx).
    """

    df = read_xing_si_data(biomass_potentials_path)

    # select only relevant part of potential
    df = df[df.columns[df.columns.str.contains("Agricultural residues burnt as waste")]].sum(axis=1)

    # convert t biomass yr-1 to MWh, heat content is from paper reference 92
    heat_content = 19  # GJ (t biomass−1)
    heat_content *= 1000 / 3600  # GJ/t -> MWh
    df = df * heat_content

    return df

estimate_co2_intensity_xing()

Estimate the biomass Co2 intensity from the Xing paper

Returns:

Name Type Description
float float

the biomass co2 intensity in t/MWhth
Source code in workflow/scripts/build_biomass_potential.py 
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def estimate_co2_intensity_xing() -> float:
    """Estimate the biomass Co2 intensity from the Xing paper

    Returns:
        float: the biomass co2 intensity in t/MWhth
    """

    biomass_potential_tot = 3.04  # Gt
    embodied_co2_tot = 5.24  # Gt
    heat_content = 19 * 1000 / 3600  # GJ/t -> MWh_th/t
    unit_co2 = embodied_co2_tot / biomass_potential_tot  # t CO2/t biomass
    co2_intens = unit_co2 / heat_content  # t CO2/MWh_th

    return co2_intens

read_xing_si_data(biomass_potentials_path)

Read and prepare the xing SI data

Parameters:

Name Type Description Default
biomass_potentials_path PathLike

the path to the Xing SI data (xlsx).

 required
Source code in workflow/scripts/build_biomass_potential.py 
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def read_xing_si_data(biomass_potentials_path: PathLike):
    """Read and prepare the xing SI data

    Args:
        biomass_potentials_path (PathLike): the path to the Xing SI data (xlsx).
    """
    # data is indexed by province and county
    df = pd.read_excel(biomass_potentials_path, sheet_name="supplementary data 1")
    df = df.groupby("Province name").sum()

    df = df.rename(index={"Inner-Monglia": "InnerMongolia", "Anhui ": "Anhui"})
    df = df.add_suffix(" biomass")

    return df