openego /
eGon-data
| @@ 321-356 (lines=36) @@ | ||
| 318 | return dsm |
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| 319 | ||
| 320 | ||
| 321 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 322 | """ |
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| 323 | Import industry sites necessary to identify DSM-potential. |
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| 324 | ---------- |
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| 325 | ind_vent_share: float |
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| 326 | Share of considered application in industry demand |
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| 327 | wz: int |
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| 328 | Wirtschaftszweig to be considered within industry sites |
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| 329 | """ |
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| 330 | ||
| 331 | # import load data |
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| 332 | ||
| 333 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 334 | "ind_sites_loadcurves_individual" |
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| 335 | ] |
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| 336 | ||
| 337 | dsm = db.select_dataframe( |
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| 338 | f""" |
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| 339 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 340 | {sources['schema']}.{sources['table']} |
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| 341 | WHERE wz = {wz} |
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| 342 | """ |
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| 343 | ) |
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| 344 | ||
| 345 | # calculate share of timeseries for ventilation |
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| 346 | ||
| 347 | timeseries = dsm["p_set"].copy() |
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| 348 | ||
| 349 | for index, liste in timeseries.items(): |
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| 350 | share = [float(item) * ind_vent_share for item in liste] |
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| 351 | timeseries.loc[index] = share |
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| 352 | ||
| 353 | dsm["p_set"] = timeseries.copy() |
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| 354 | ||
| 355 | return dsm |
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| 356 | ||
| 357 | ||
| 358 | def calc_ind_site_timeseries(scenario): |
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| 359 | # calculate timeseries per site |
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| @@ 284-318 (lines=35) @@ | ||
| 281 | return dsm |
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| 282 | ||
| 283 | ||
| 284 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 285 | """ |
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| 286 | Import industry sites necessary to identify DSM-potential. |
|
| 287 | ---------- |
|
| 288 | ind_vent_share: float |
|
| 289 | Share of considered application in industry demand |
|
| 290 | wz: int |
|
| 291 | Wirtschaftszweig to be considered within industry sites |
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| 292 | """ |
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| 293 | ||
| 294 | # import load data |
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| 295 | ||
| 296 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 297 | "ind_sites_loadcurves" |
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| 298 | ] |
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| 299 | ||
| 300 | dsm = db.select_dataframe( |
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| 301 | f""" |
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| 302 | SELECT bus, scn_name, p_set FROM |
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| 303 | {sources['schema']}.{sources['table']} |
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| 304 | WHERE wz = {wz} |
|
| 305 | """ |
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| 306 | ) |
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| 307 | ||
| 308 | # calculate share of timeseries for ventilation |
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| 309 | ||
| 310 | timeseries = dsm["p_set"].copy() |
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| 311 | ||
| 312 | for index, liste in timeseries.items(): |
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| 313 | share = [float(item) * ind_vent_share for item in liste] |
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| 314 | timeseries.loc[index] = share |
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| 315 | ||
| 316 | dsm["p_set"] = timeseries.copy() |
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| 317 | ||
| 318 | return dsm |
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| 319 | ||
| 320 | ||
| 321 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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