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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_ac_using_ref(data, reference_data, reference_year)

Spatially Disaggregate the load using regional/nodal reference data (e.g. the projections from Hu2013 as in the Zhou et al PyPSA-China version)

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

Returns: pd.DataFrame: Disaggregated load data (Region x Year)

Source code in workflow/scripts/remind_coupling/disaggregate_data.py 
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@register_etl("disagg_acload_ref")
def disagg_ac_using_ref(
    data: pd.DataFrame,
    reference_data: pd.DataFrame,
    reference_year: int | str,
) -> pd.DataFrame:
    """Spatially Disaggregate the load using regional/nodal reference data
        (e.g. the projections from Hu2013 as in the Zhou et al PyPSA-China version)

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

    regional_reference = reference_data[int(reference_year)]
    regional_reference /= regional_reference.sum()
    electricity_demand = data["loads"].query("load == 'ac'")
    electricity_demand.set_index("year", inplace=True)
    logger.info("Disaggregating load according to Hu et al. demand projections")
    disagg_load = SpatialDisaggregator().use_static_reference(
        electricity_demand.value, regional_reference
    )

    return disagg_load