code: def _worst_case_load(self, worst_case_scale_factors,
                           peakload_consumption_ratio, modes):
          
  
          sectors = ['residential', 'retail', 'industrial', 'agricultural']
          lv_power_scaling = np.array(
              [worst_case_scale_factors['lv_{}_load'.format(mode)]
               for mode in modes])
          mv_power_scaling = np.array(
              [worst_case_scale_factors['mv_{}_load'.format(mode)]
               for mode in modes])
  
          lv = {(sector, 'lv'): peakload_consumption_ratio[sector] *
                                lv_power_scaling
                for sector in sectors}
          mv = {(sector, 'mv'): peakload_consumption_ratio[sector] *
                                mv_power_scaling
                for sector in sectors}
          self.timeseries.load = pd.DataFrame({**lv, **mv},
                                              index=self.timeseries.timeindex)
docs: Define worst case load time series for each sector.
  
          Parameters
          ----------
          worst_case_scale_factors : dict
              Scale factors defined in config file 'config_timeseries.cfg'.
              Scale factors describe actual power to nominal power ratio of in
              worst-case scenarios.
          peakload_consumption_ratio : dict
              Ratios of peak load to annual consumption per sector, defined in
              config file 'config_timeseries.cfg'
          modes : list
              List with worst-cases to generate time series for. Can be
              'feedin_case', 'load_case' or both.