openego /
eGon-data
| @@ 315-350 (lines=36) @@ | ||
| 312 | return dsm |
|
| 313 | ||
| 314 | ||
| 315 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
|
| 316 | """ |
|
| 317 | Import industry sites necessary to identify DSM-potential. |
|
| 318 | ---------- |
|
| 319 | ind_vent_share: float |
|
| 320 | Share of considered application in industry demand |
|
| 321 | wz: int |
|
| 322 | Wirtschaftszweig to be considered within industry sites |
|
| 323 | """ |
|
| 324 | ||
| 325 | # import load data |
|
| 326 | ||
| 327 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 328 | "ind_sites_loadcurves_individual" |
|
| 329 | ] |
|
| 330 | ||
| 331 | dsm = db.select_dataframe( |
|
| 332 | f""" |
|
| 333 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
|
| 334 | {sources['schema']}.{sources['table']} |
|
| 335 | WHERE wz = {wz} |
|
| 336 | """ |
|
| 337 | ) |
|
| 338 | ||
| 339 | # calculate share of timeseries for ventilation |
|
| 340 | ||
| 341 | timeseries = dsm["p_set"].copy() |
|
| 342 | ||
| 343 | for index, liste in timeseries.items(): |
|
| 344 | share = [float(item) * ind_vent_share for item in liste] |
|
| 345 | timeseries.loc[index] = share |
|
| 346 | ||
| 347 | dsm["p_set"] = timeseries.copy() |
|
| 348 | ||
| 349 | return dsm |
|
| 350 | ||
| 351 | ||
| 352 | def calc_ind_site_timeseries(scenario): |
|
| 353 | # calculate timeseries per site |
|
| @@ 278-312 (lines=35) @@ | ||
| 275 | return dsm |
|
| 276 | ||
| 277 | ||
| 278 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 279 | """ |
|
| 280 | Import industry sites necessary to identify DSM-potential. |
|
| 281 | ---------- |
|
| 282 | ind_vent_share: float |
|
| 283 | Share of considered application in industry demand |
|
| 284 | wz: int |
|
| 285 | Wirtschaftszweig to be considered within industry sites |
|
| 286 | """ |
|
| 287 | ||
| 288 | # import load data |
|
| 289 | ||
| 290 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
|
| 291 | "ind_sites_loadcurves" |
|
| 292 | ] |
|
| 293 | ||
| 294 | dsm = db.select_dataframe( |
|
| 295 | f""" |
|
| 296 | SELECT bus, scn_name, p_set FROM |
|
| 297 | {sources['schema']}.{sources['table']} |
|
| 298 | WHERE wz = {wz} |
|
| 299 | """ |
|
| 300 | ) |
|
| 301 | ||
| 302 | # calculate share of timeseries for ventilation |
|
| 303 | ||
| 304 | timeseries = dsm["p_set"].copy() |
|
| 305 | ||
| 306 | for index, liste in timeseries.items(): |
|
| 307 | share = [float(item) * ind_vent_share for item in liste] |
|
| 308 | timeseries.loc[index] = share |
|
| 309 | ||
| 310 | dsm["p_set"] = timeseries.copy() |
|
| 311 | ||
| 312 | return dsm |
|
| 313 | ||
| 314 | ||
| 315 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
|
