def METHOD_NAME(scenario):
    """Sum all electricity demand curves up per bus
    Parameters
    ----------
    scenario : str
        Scenario name.
    Returns
    -------
    pandas.DataFrame
        Aggregated electrical demand timeseries per bus
    """
    # Read information from configuration file
    sources = egon.data.config.datasets()["etrago_electricity"]["sources"]
    # Select data on CTS electricity demands per bus
    cts_curves = db.select_dataframe(
        f"""SELECT bus_id, p_set FROM
                {sources['cts_curves']['schema']}.
                {sources['cts_curves']['table']}
                WHERE scn_name = '{scenario}'""",
        index_col="bus_id",
    )
    # Rename index
    cts_curves.index.rename("bus", inplace=True)
    # Select data on industrial demands assigned to osm landuse areas
    ind_curves_osm = db.select_dataframe(
        f"""SELECT bus, p_set FROM
                {sources['osm_curves']['schema']}.
                {sources['osm_curves']['table']}
                WHERE scn_name = '{scenario}'""",
        index_col="bus",
    )
    # Select data on industrial demands assigned to industrial sites
    ind_curves_sites = db.select_dataframe(
        f"""SELECT bus, p_set FROM
                {sources['sites_curves']['schema']}.
                {sources['sites_curves']['table']}
                WHERE scn_name = '{scenario}'""",
        index_col="bus",
    )
    # Select data on household electricity demands per bus
    hh_curves = db.select_dataframe(
        f"""SELECT bus_id, p_set FROM
                {sources['household_curves']['schema']}.
                {sources['household_curves']['table']}
                WHERE scn_name = '{scenario}'""",
        index_col="bus_id",
    )
    # Create one df by appending all imported dataframes
    demand_curves = cts_curves.append(
        [ind_curves_osm, ind_curves_sites, hh_curves]
    )
    # Split array to single columns in the dataframe
    demand_curves_split = demand_curves
    demand_curves_split = pd.DataFrame(
        demand_curves.p_set.tolist(), index=demand_curves_split.index
    )
    # Group all rows with the same bus
    demand_curves_bus = demand_curves_split.groupby(
        demand_curves_split.index
    ).sum()
    # Initzialize and fill resulsting dataframe
    curves = pd.DataFrame(columns=["bus", "p_set"])
    curves["bus"] = demand_curves_bus.index
    curves["p_set"] = demand_curves_bus.values.tolist()
    # Store national demand time series for pypsa-eur-sec
    store_national_profiles(
        ind_curves_sites,
        ind_curves_osm,
        cts_curves,
        hh_curves,
        scenario,
    )
    return curves