openego /
eGon-data
| @@ 319-354 (lines=36) @@ | ||
| 316 | return dsm |
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| 317 | ||
| 318 | ||
| 319 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 320 | """ |
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| 321 | Import industry sites necessary to identify DSM-potential. |
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| 322 | ---------- |
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| 323 | ind_vent_share: float |
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| 324 | Share of considered application in industry demand |
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| 325 | wz: int |
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| 326 | Wirtschaftszweig to be considered within industry sites |
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| 327 | """ |
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| 328 | ||
| 329 | # import load data |
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| 330 | ||
| 331 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 332 | "ind_sites_loadcurves_individual" |
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| 333 | ] |
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| 334 | ||
| 335 | dsm = db.select_dataframe( |
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| 336 | f""" |
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| 337 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 338 | {sources['schema']}.{sources['table']} |
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| 339 | WHERE wz = {wz} |
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| 340 | """ |
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| 341 | ) |
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| 342 | ||
| 343 | # calculate share of timeseries for ventilation |
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| 344 | ||
| 345 | timeseries = dsm["p_set"].copy() |
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| 346 | ||
| 347 | for index, liste in timeseries.items(): |
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| 348 | share = [float(item) * ind_vent_share for item in liste] |
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| 349 | timeseries.loc[index] = share |
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| 350 | ||
| 351 | dsm["p_set"] = timeseries.copy() |
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| 352 | ||
| 353 | return dsm |
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| 354 | ||
| 355 | ||
| 356 | def calc_ind_site_timeseries(scenario): |
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| 357 | # calculate timeseries per site |
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| @@ 282-316 (lines=35) @@ | ||
| 279 | return dsm |
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| 280 | ||
| 281 | ||
| 282 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 283 | """ |
|
| 284 | Import industry sites necessary to identify DSM-potential. |
|
| 285 | ---------- |
|
| 286 | ind_vent_share: float |
|
| 287 | Share of considered application in industry demand |
|
| 288 | wz: int |
|
| 289 | Wirtschaftszweig to be considered within industry sites |
|
| 290 | """ |
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| 291 | ||
| 292 | # import load data |
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| 293 | ||
| 294 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 295 | "ind_sites_loadcurves" |
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| 296 | ] |
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| 297 | ||
| 298 | dsm = db.select_dataframe( |
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| 299 | f""" |
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| 300 | SELECT bus, scn_name, p_set FROM |
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| 301 | {sources['schema']}.{sources['table']} |
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| 302 | WHERE wz = {wz} |
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| 303 | """ |
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| 304 | ) |
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| 305 | ||
| 306 | # calculate share of timeseries for ventilation |
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| 307 | ||
| 308 | timeseries = dsm["p_set"].copy() |
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| 309 | ||
| 310 | for index, liste in timeseries.items(): |
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| 311 | share = [float(item) * ind_vent_share for item in liste] |
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| 312 | timeseries.loc[index] = share |
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| 313 | ||
| 314 | dsm["p_set"] = timeseries.copy() |
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| 315 | ||
| 316 | return dsm |
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| 317 | ||
| 318 | ||
| 319 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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