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Disaggregate data

generic disaggregation development Split steps into:

  • ETL
  • disagg (also an ETL op)

to be rebalanced with the remind_coupling package

add_possible_techs_to_paidoff(paidoff, tech_groups)

Add possible PyPSA technologies to the paid off capacities DataFrame. The paidoff capacities are grouped in case the Remind-PyPSA tecg mapping is not 1:1 but the network needs to add PyPSA techs. A constraint is added so the paid off caps per group are not exceeded.

Parameters:

Name Type Description Default
paidoff DataFrame

DataFrame with paid off capacities

 required

Returns: pd.DataFrame: paid off techs with list of PyPSA technologies Example: >> tech_groups PyPSA_tech, group coal CHP, coal coal, coal >> add_possible_techs_to_paidoff(paidoff, tech_groups) >> paidoff tech_group, paid_off_capacity, techs coal, 1000, ['coal CHP', 'coal']

Source code in workflow/scripts/remind_coupling/disaggregate_data.py 
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def add_possible_techs_to_paidoff(paidoff: pd.DataFrame, tech_groups: pd.Series) -> pd.DataFrame:
    """Add possible PyPSA technologies to the paid off capacities DataFrame.
    The paidoff capacities are grouped in case the Remind-PyPSA tecg mapping is not 1:1
    but the network needs to add PyPSA techs.
    A constraint is added so the paid off caps per group are not exceeded.

    Args:
        paidoff (pd.DataFrame): DataFrame with paid off capacities
    Returns:
        pd.DataFrame: paid off techs with list of PyPSA technologies
    Example:
        >> tech_groups
            PyPSA_tech, group
            coal CHP, coal
            coal, coal
        >> add_possible_techs_to_paidoff(paidoff, tech_groups)
        >> paidoff
            tech_group, paid_off_capacity, techs
            coal, 1000, ['coal CHP', 'coal']
    """
    df = tech_groups.reset_index()
    possibilities = df.groupby("group").PyPSA_tech.apply(lambda x: list(x.unique()))
    paidoff["techs"] = paidoff.tech_group.map(possibilities)
    return paidoff

disagg_load_using_ref(data, reference_data, reference_year, sector_coupling_enabled=False)

Spatially disaggregate the load using regional/nodal reference data.

Automatically chooses between single-sector (electric-only) and multi-sector disaggregation based on sector_coupling_enabled parameter.

Parameters:

Name Type Description Default
data DataFrame

DataFrame containing the load data

 required
reference_data DataFrame

DataFrame containing the reference data

 required
reference_year int | str

Year to use for disaggregation

 required
sector_coupling_enabled bool

Whether to use multi-sector disaggregation

 False

Returns:

Type Description
DataFrame

pd.DataFrame: Disaggregated load data (Region x Year) or (Province x Sector x Year)
Source code in workflow/scripts/remind_coupling/disaggregate_data.py 
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@register_etl("disagg_load_ref")
def disagg_load_using_ref(
    data: pd.DataFrame,
    reference_data: pd.DataFrame,
    reference_year: int | str,
    sector_coupling_enabled: bool = False,
) -> pd.DataFrame:
    """Spatially disaggregate the load using regional/nodal reference data.

    Automatically chooses between single-sector (electric-only) and multi-sector disaggregation
    based on sector_coupling_enabled parameter.

    Args:
        data (pd.DataFrame): DataFrame containing the load data
        reference_data (pd.DataFrame): DataFrame containing the reference data
        reference_year (int | str): Year to use for disaggregation
        sector_coupling_enabled (bool): Whether to use multi-sector disaggregation

    Returns:
        pd.DataFrame: Disaggregated load data (Region x Year) or (Province x Sector x Year)
    """

    if sector_coupling_enabled:
        logger.info("Sector coupling enabled - using multi-sector disaggregation")
        return _disagg_multisector_load(data, reference_data, reference_year)
    else:
        logger.info("Sector coupling disabled - using total electricity load disaggregation")
        return _disagg_total_load(data, reference_data, reference_year)