def _worst_case_load(self, worst_case_scale_factors,
                         peakload_consumption_ratio, modes):
        """
        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.

        """

        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)
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 )
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.