docs: def _pypsa_generator_timeseries_aggregated_at_lv_station(network, timesteps):
      """
      Aggregates generator time series per generator subtype and LV grid.
  
      Parameters
      ----------
      network : Network
          The eDisGo grid topology model overall container
      timesteps : array_like
          Timesteps is an array-like object with entries of type
          :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
          to export to pypsa representation and use in power flow analysis.
  
      Returns
      -------
      tuple of :pandas:`pandas.DataFrame<dataframe>`
          Tuple of size two containing DataFrames that represent
  
              1. 'p_set' of aggregated Generation per subtype at each LV station
              2. 'q_set' of aggregated Generation per subtype at each LV station
  
      """
  
      generation_p = []
      generation_q = []
  
      for lv_grid in network.mv_grid.lv_grids:
          # Determine aggregated generation at LV stations
          generation = {}
          for gen in lv_grid.generators:
              # for type in gen.type:
              #     for subtype in gen.subtype:
              gen_name = '_'.join([gen.type,
                                   gen.subtype,
                                   'aggregated',
                                   'LV_grid',
                                   str(lv_grid.id)])
  
              generation.setdefault(gen.type, {})
              generation[gen.type].setdefault(gen.subtype, {})
              generation[gen.type][gen.subtype].setdefault('timeseries_p', [])
              generation[gen.type][gen.subtype].setdefault('timeseries_q', [])
              generation[gen.type][gen.subtype]['timeseries_p'].append(
                  gen.pypsa_timeseries('p').rename(gen_name).to_frame().loc[
                      timesteps])
              generation[gen.type][gen.subtype]['timeseries_q'].append(
                  gen.pypsa_timeseries('q').rename(gen_name).to_frame().loc[
                      timesteps])
  
          for k_type, v_type in generation.items():
              for k_type, v_subtype in v_type.items():
                  col_name = v_subtype['timeseries_p'][0].columns[0]
                  generation_p.append(
                      pd.concat(v_subtype['timeseries_p'],
                                axis=1).sum(axis=1).rename(col_name).to_frame())
                  generation_q.append(
                      pd.concat(v_subtype['timeseries_q'], axis=1).sum(
                          axis=1).rename(col_name).to_frame())
  
      return generation_p, generation_q