def METHOD_NAME(
    ind_curves_sites,
    ind_curves_osm,
    cts_curves,
    hh_curves,
    scenario,
):
    """
    Store electrical load timeseries aggregated for national level as an
    input for pypsa-eur-sec
    Parameters
    ----------
    ind_curves_sites : pd.DataFrame
        Industrial load timeseries for industrial sites per bus
    ind_curves_osm : pd.DataFrame
        Industrial load timeseries for industrial osm areas per bus
    cts_curves : pd.DataFrame
        CTS load curves per bus
    hh_curves : pd.DataFrame
        Household load curves per bus
    scenario : str
        Scenario name
    Returns
    -------
    None.
    """
    folder = Path(".") / "input-pypsa-eur-sec"
    # Create the folder, if it does not exists already
    if not os.path.exists(folder):
        os.mkdir(folder)
    national_demand = pd.DataFrame(
        columns=["residential_and_service", "industry"],
        index=pd.date_range(datetime(2011, 1, 1, 0), periods=8760, freq="H"),
    )
    national_demand["industry"] = (
        pd.DataFrame(ind_curves_sites.p_set.tolist()).sum()
        + pd.DataFrame(ind_curves_osm.p_set.tolist()).sum()
    ).values
    national_demand["residential_and_service"] = (
        pd.DataFrame(cts_curves.p_set.tolist()).sum()
        + pd.DataFrame(hh_curves.p_set.tolist()).sum()
    ).values
    national_demand.to_csv(
        folder / f"electrical_demand_timeseries_DE_{scenario}.csv"
    )