docs: def aggregate_cap_val(self, conn, **kwargs):
          '''
          Returns the normalised feedin profile and installed capacity for
          a given region.
  
          Parameters
          ----------
          region : Region instance
          region.geom : shapely.geometry object
              Geo-spatial data with information for location/region-shape. The
              geometry can be a polygon/multi-polygon or a point.
  
          Returns
          -------
          feedin_df : pandas dataframe
              Dataframe containing the normalised feedin profile of the given
              region. Index of the dataframe is the hour of the year; columns
              are 'pv_pwr' and 'wind_pwr'.
          cap : pandas series
              Series containing the installed capacity (in W) of PV and wind
              turbines of the given region.
          '''
          region = kwargs['region']
          [pv_df, wind_df, cap] = self.get_timeseries(
              conn,
              geometry=region.geom,
              **kwargs)
  
          if kwargs.get('store', False):
              self.store_full_df(pv_df, wind_df, **kwargs)
  
          # Summerize the results to one column for pv and one for wind
          cap = cap.sum()
          df = pd.concat([pv_df.sum(axis=1) / cap['pv_pwr'],
              wind_df.sum(axis=1) / cap['wind_pwr']], axis=1)
          feedin_df = df.rename(columns={0: 'pv_pwr', 1: 'wind_pwr'})
  
          return feedin_df, cap