openego /
eGon-data
| @@ 231-266 (lines=36) @@ | ||
| 228 | return dsm |
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| 229 | ||
| 230 | ||
| 231 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 232 | """ |
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| 233 | Import industry sites necessary to identify DSM-potential. |
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| 234 | ---------- |
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| 235 | ind_vent_share: float |
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| 236 | Share of considered application in industry demand |
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| 237 | wz: int |
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| 238 | Wirtschaftszweig to be considered within industry sites |
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| 239 | """ |
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| 240 | ||
| 241 | # import load data |
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| 242 | ||
| 243 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 244 | "ind_sites_loadcurves_individual" |
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| 245 | ] |
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| 246 | ||
| 247 | dsm = db.select_dataframe( |
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| 248 | f""" |
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| 249 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 250 | {sources['schema']}.{sources['table']} |
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| 251 | WHERE scn_name IN ('eGon2035', 'eGon100RE') |
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| 252 | AND wz = '{wz}' |
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| 253 | """ |
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| 254 | ) |
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| 255 | ||
| 256 | # calculate share of timeseries for ventilation |
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| 257 | ||
| 258 | timeseries = dsm["p_set"].copy() |
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| 259 | ||
| 260 | for index, liste in timeseries.iteritems(): |
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| 261 | share = [float(item) * ind_vent_share for item in liste] |
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| 262 | timeseries.loc[index] = share |
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| 263 | ||
| 264 | dsm["p_set"] = timeseries.copy() |
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| 265 | ||
| 266 | return dsm |
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| 267 | ||
| 268 | ||
| 269 | def calc_ind_site_timeseries(scenario): |
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| @@ 194-228 (lines=35) @@ | ||
| 191 | return dsm |
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| 192 | ||
| 193 | ||
| 194 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 195 | """ |
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| 196 | Import industry sites necessary to identify DSM-potential. |
|
| 197 | ---------- |
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| 198 | ind_vent_share: float |
|
| 199 | Share of considered application in industry demand |
|
| 200 | wz: int |
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| 201 | Wirtschaftszweig to be considered within industry sites |
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| 202 | """ |
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| 203 | ||
| 204 | # import load data |
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| 205 | ||
| 206 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 207 | "ind_sites_loadcurves" |
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| 208 | ] |
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| 209 | ||
| 210 | dsm = db.select_dataframe( |
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| 211 | f""" |
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| 212 | SELECT bus, scn_name, p_set FROM |
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| 213 | {sources['schema']}.{sources['table']} |
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| 214 | WHERE wz = '{wz}' |
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| 215 | """ |
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| 216 | ) |
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| 217 | ||
| 218 | # calculate share of timeseries for ventilation |
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| 219 | ||
| 220 | timeseries = dsm["p_set"].copy() |
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| 221 | ||
| 222 | for index, liste in timeseries.iteritems(): |
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| 223 | share = [float(item) * ind_vent_share for item in liste] |
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| 224 | timeseries.loc[index] = share |
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| 225 | ||
| 226 | dsm["p_set"] = timeseries.copy() |
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| 227 | ||
| 228 | return dsm |
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| 229 | ||
| 230 | ||
| 231 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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