|
@@ 157-189 (lines=33) @@
|
| 154 |
|
return dsm |
| 155 |
|
|
| 156 |
|
|
| 157 |
|
def ind_osm_data_import_individual(ind_vent_cool_share): |
| 158 |
|
""" |
| 159 |
|
Import industry data per osm-area necessary to identify DSM-potential. |
| 160 |
|
---------- |
| 161 |
|
ind_share: float |
| 162 |
|
Share of considered application in industry demand |
| 163 |
|
""" |
| 164 |
|
|
| 165 |
|
# import load data |
| 166 |
|
|
| 167 |
|
sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
| 168 |
|
"ind_osm_loadcurves_individual" |
| 169 |
|
] |
| 170 |
|
|
| 171 |
|
dsm = db.select_dataframe( |
| 172 |
|
f""" |
| 173 |
|
SELECT osm_id, bus_id as bus, scn_name, p_set FROM |
| 174 |
|
{sources['schema']}.{sources['table']} |
| 175 |
|
WHERE scn_name in ('eGon2035', 'eGon100RE') |
| 176 |
|
""" |
| 177 |
|
) |
| 178 |
|
|
| 179 |
|
# calculate share of timeseries for cooling and ventilation out of |
| 180 |
|
# industry-data |
| 181 |
|
|
| 182 |
|
timeseries = dsm["p_set"].copy() |
| 183 |
|
|
| 184 |
|
for index, liste in timeseries.iteritems(): |
| 185 |
|
share = [float(item) * ind_vent_cool_share for item in liste] |
| 186 |
|
|
| 187 |
|
timeseries.loc[index] = share |
| 188 |
|
|
| 189 |
|
dsm["p_set"] = timeseries.copy() |
| 190 |
|
|
| 191 |
|
return dsm |
| 192 |
|
|
|
@@ 123-155 (lines=33) @@
|
| 120 |
|
return dsm |
| 121 |
|
|
| 122 |
|
|
| 123 |
|
def ind_osm_data_import(ind_vent_cool_share): |
| 124 |
|
""" |
| 125 |
|
Import industry data per osm-area necessary to identify DSM-potential. |
| 126 |
|
---------- |
| 127 |
|
ind_share: float |
| 128 |
|
Share of considered application in industry demand |
| 129 |
|
""" |
| 130 |
|
|
| 131 |
|
# import load data |
| 132 |
|
|
| 133 |
|
sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
| 134 |
|
"ind_osm_loadcurves" |
| 135 |
|
] |
| 136 |
|
|
| 137 |
|
dsm = db.select_dataframe( |
| 138 |
|
f"""SELECT bus, scn_name, p_set FROM |
| 139 |
|
{sources['schema']}.{sources['table']}""" |
| 140 |
|
) |
| 141 |
|
|
| 142 |
|
# calculate share of timeseries for cooling and ventilation out of |
| 143 |
|
# industry-data |
| 144 |
|
|
| 145 |
|
timeseries = dsm["p_set"].copy() |
| 146 |
|
|
| 147 |
|
for index, liste in timeseries.iteritems(): |
| 148 |
|
share = [float(item) * ind_vent_cool_share for item in liste] |
| 149 |
|
|
| 150 |
|
timeseries.loc[index] = share |
| 151 |
|
|
| 152 |
|
dsm["p_set"] = timeseries.copy() |
| 153 |
|
|
| 154 |
|
return dsm |
| 155 |
|
|
| 156 |
|
|
| 157 |
|
def ind_osm_data_import_individual(ind_vent_cool_share): |
| 158 |
|
""" |
openego /
eGon-data
