docs: def _pypsa_load_timeseries_aggregated_at_lv_station(network, timesteps):
      """
      Aggregates load time series per sector 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 Load per sector at each LV station
              2. 'q_set' of aggregated Load per sector at each LV station
  
      """
      # ToDo: Load.pypsa_timeseries is not differentiated by sector so this
      # function will not work (either change here and in
      # add_aggregated_lv_components or in Load class)
      
      load_p = []
      load_q = []
  
      for lv_grid in network.mv_grid.lv_grids:
          # Determine aggregated load at LV stations
          load = {}
          for lo in lv_grid.graph.nodes_by_attribute('load'):
              for sector, val in lo.consumption.items():
                  load.setdefault(sector, {})
                  load[sector].setdefault('timeseries_p', [])
                  load[sector].setdefault('timeseries_q', [])
  
                  load[sector]['timeseries_p'].append(
                      lo.pypsa_timeseries('p').rename(repr(lo)).to_frame().loc[
                          timesteps])
                  load[sector]['timeseries_q'].append(
                      lo.pypsa_timeseries('q').rename(repr(lo)).to_frame().loc[
                          timesteps])
  
          for sector, val in load.items():
              load_p.append(
                  pd.concat(val['timeseries_p'], axis=1).sum(axis=1).rename(
                      '_'.join(['Load', sector, repr(lv_grid)])).to_frame())
              load_q.append(
                  pd.concat(val['timeseries_q'], axis=1).sum(axis=1).rename(
                      '_'.join(['Load', sector, repr(lv_grid)])).to_frame())
  
      return load_p, load_q