openego /
eGon-data
| @@ 571-606 (lines=36) @@ | ||
| 568 | return dsm |
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| 569 | ||
| 570 | ||
| 571 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
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| 572 | """ |
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| 573 | Import industry sites necessary to identify DSM-potential. |
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| 574 | ||
| 575 | Parameters |
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| 576 | ---------- |
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| 577 | ind_vent_share: float |
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| 578 | Share of considered application in industry demand |
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| 579 | wz: int |
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| 580 | Wirtschaftszweig to be considered within industry sites |
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| 581 | """ |
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| 582 | ||
| 583 | # import load data |
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| 584 | ||
| 585 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 586 | "ind_sites_loadcurves_individual" |
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| 587 | ] |
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| 588 | ||
| 589 | dsm = db.select_dataframe( |
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| 590 | f""" |
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| 591 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 592 | {sources['schema']}.{sources['table']} |
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| 593 | WHERE wz = {wz} |
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| 594 | """ |
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| 595 | ) |
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| 596 | ||
| 597 | # calculate share of timeseries for ventilation |
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| 598 | ||
| 599 | timeseries = dsm["p_set"].copy() |
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| 600 | ||
| 601 | for index, liste in timeseries.items(): |
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| 602 | share = [float(item) * ind_vent_share for item in liste] |
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| 603 | timeseries.loc[index] = share |
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| 604 | ||
| 605 | dsm["p_set"] = timeseries.copy() |
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| 606 | ||
| 607 | return dsm |
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| 608 | ||
| 609 | ||
| @@ 532-566 (lines=35) @@ | ||
| 529 | return dsm |
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| 530 | ||
| 531 | ||
| 532 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 533 | """ |
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| 534 | Import industry sites necessary to identify DSM-potential. |
|
| 535 | ||
| 536 | Parameters |
|
| 537 | ---------- |
|
| 538 | ind_vent_share: float |
|
| 539 | Share of considered application in industry demand |
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| 540 | wz: int |
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| 541 | Wirtschaftszweig to be considered within industry sites |
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| 542 | """ |
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| 543 | ||
| 544 | # import load data |
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| 545 | ||
| 546 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 547 | "ind_sites_loadcurves" |
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| 548 | ] |
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| 549 | ||
| 550 | dsm = db.select_dataframe( |
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| 551 | f""" |
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| 552 | SELECT bus, scn_name, p_set FROM |
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| 553 | {sources['schema']}.{sources['table']} |
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| 554 | WHERE wz = {wz} |
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| 555 | """ |
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| 556 | ) |
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| 557 | ||
| 558 | # calculate share of timeseries for ventilation |
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| 559 | ||
| 560 | timeseries = dsm["p_set"].copy() |
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| 561 | ||
| 562 | for index, liste in timeseries.items(): |
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| 563 | share = [float(item) * ind_vent_share for item in liste] |
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| 564 | timeseries.loc[index] = share |
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| 565 | ||
| 566 | dsm["p_set"] = timeseries.copy() |
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| 567 | ||
| 568 | return dsm |
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| 569 | ||
