def import_lv_grid_districts(self, session, lv_stations):
        """Imports all lv grid districts within given load area

        Parameters
        ----------
        session : sqlalchemy.orm.session.Session
            Database session

        Returns
        -------
        lv_grid_districts: :pandas:`pandas.DataFrame<dataframe>`
            Table of lv_grid_districts
        """

        # get ding0s' standard CRS (SRID)
        srid = str(int(cfg_ding0.get('geo', 'srid')))
        # SET SRID 3035 to achieve correct area calculation of lv_grid_district
        # srid = '3035'

        gw2kw = 10 ** 6  # load in database is in GW -> scale to kW

        # 1. filter grid districts of relevant load area
        lv_grid_districs_sqla = session.query(
            self.orm['orm_lv_grid_district'].mvlv_subst_id,
            self.orm['orm_lv_grid_district'].la_id,
            self.orm['orm_lv_grid_district'].zensus_sum.label('population'),
            (self.orm[
                 'orm_lv_grid_district'].sector_peakload_residential * gw2kw).
                label('peak_load_residential'),
            (self.orm['orm_lv_grid_district'].sector_peakload_retail * gw2kw).
                label('peak_load_retail'),
            (self.orm[
                 'orm_lv_grid_district'].sector_peakload_industrial * gw2kw).
                label('peak_load_industrial'),
            (self.orm[
                 'orm_lv_grid_district'].sector_peakload_agricultural * gw2kw).
                label('peak_load_agricultural'),
            ((self.orm['orm_lv_grid_district'].sector_peakload_residential
              + self.orm['orm_lv_grid_district'].sector_peakload_retail
              + self.orm['orm_lv_grid_district'].sector_peakload_industrial
              + self.orm['orm_lv_grid_district'].sector_peakload_agricultural)
             * gw2kw).label('peak_load'),
            func.ST_AsText(func.ST_Transform(
                self.orm['orm_lv_grid_district'].geom, srid)).label('geom'),
            self.orm['orm_lv_grid_district'].sector_count_residential,
            self.orm['orm_lv_grid_district'].sector_count_retail,
            self.orm['orm_lv_grid_district'].sector_count_industrial,
            self.orm['orm_lv_grid_district'].sector_count_agricultural,
            (self.orm[
                 'orm_lv_grid_district'].sector_consumption_residential * gw2kw). \
                label('sector_consumption_residential'),
            (self.orm['orm_lv_grid_district'].sector_consumption_retail * gw2kw). \
                label('sector_consumption_retail'),
            (self.orm[
                'orm_lv_grid_district'].sector_consumption_industrial * gw2kw). \
                label('sector_consumption_industrial'),
            (self.orm[
                'orm_lv_grid_district'].sector_consumption_agricultural * gw2kw). \
                label('sector_consumption_agricultural'),
            self.orm['orm_lv_grid_district'].mvlv_subst_id). \
            filter(self.orm['orm_lv_grid_district'].mvlv_subst_id.in_(
            lv_stations.index.tolist())). \
            filter(self.orm['version_condition_lvgd'])

        # read data from db
        lv_grid_districts = pd.read_sql_query(lv_grid_districs_sqla.statement,
                                              session.bind,
                                              index_col='mvlv_subst_id')

        lv_grid_districts[
            ['sector_count_residential',
             'sector_count_retail',
             'sector_count_industrial',
             'sector_count_agricultural']] = lv_grid_districts[
            ['sector_count_residential',
             'sector_count_retail',
             'sector_count_industrial',
             'sector_count_agricultural']].fillna(0)

        return lv_grid_districts