openego /
eGon-data
| @@ 523-558 (lines=36) @@ | ||
| 520 | return dsm |
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| 521 | ||
| 522 | ||
| 523 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
|
| 524 | """ |
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| 525 | Import industry sites necessary to identify DSM-potential. |
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| 526 | ---------- |
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| 527 | ind_vent_share: float |
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| 528 | Share of considered application in industry demand |
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| 529 | wz: int |
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| 530 | Wirtschaftszweig to be considered within industry sites |
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| 531 | """ |
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| 532 | ||
| 533 | # import load data |
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| 534 | ||
| 535 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 536 | "ind_sites_loadcurves_individual" |
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| 537 | ] |
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| 538 | ||
| 539 | dsm = db.select_dataframe( |
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| 540 | f""" |
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| 541 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 542 | {sources['schema']}.{sources['table']} |
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| 543 | WHERE wz = {wz} |
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| 544 | """ |
|
| 545 | ) |
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| 546 | ||
| 547 | # calculate share of timeseries for ventilation |
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| 548 | ||
| 549 | timeseries = dsm["p_set"].copy() |
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| 550 | ||
| 551 | for index, liste in timeseries.items(): |
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| 552 | share = [float(item) * ind_vent_share for item in liste] |
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| 553 | timeseries.loc[index] = share |
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| 554 | ||
| 555 | dsm["p_set"] = timeseries.copy() |
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| 556 | ||
| 557 | return dsm |
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| 558 | ||
| 559 | ||
| 560 | def calc_ind_site_timeseries(scenario): |
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| 561 | # calculate timeseries per site |
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| @@ 486-520 (lines=35) @@ | ||
| 483 | return dsm |
|
| 484 | ||
| 485 | ||
| 486 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 487 | """ |
|
| 488 | Import industry sites necessary to identify DSM-potential. |
|
| 489 | ---------- |
|
| 490 | ind_vent_share: float |
|
| 491 | Share of considered application in industry demand |
|
| 492 | wz: int |
|
| 493 | Wirtschaftszweig to be considered within industry sites |
|
| 494 | """ |
|
| 495 | ||
| 496 | # import load data |
|
| 497 | ||
| 498 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 499 | "ind_sites_loadcurves" |
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| 500 | ] |
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| 501 | ||
| 502 | dsm = db.select_dataframe( |
|
| 503 | f""" |
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| 504 | SELECT bus, scn_name, p_set FROM |
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| 505 | {sources['schema']}.{sources['table']} |
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| 506 | WHERE wz = {wz} |
|
| 507 | """ |
|
| 508 | ) |
|
| 509 | ||
| 510 | # calculate share of timeseries for ventilation |
|
| 511 | ||
| 512 | timeseries = dsm["p_set"].copy() |
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| 513 | ||
| 514 | for index, liste in timeseries.items(): |
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| 515 | share = [float(item) * ind_vent_share for item in liste] |
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| 516 | timeseries.loc[index] = share |
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| 517 | ||
| 518 | dsm["p_set"] = timeseries.copy() |
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| 519 | ||
| 520 | return dsm |
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| 521 | ||
| 522 | ||
| 523 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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