openego /
eGon-data
| @@ 562-597 (lines=36) @@ | ||
| 559 | return dsm |
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| 560 | ||
| 561 | ||
| 562 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
|
| 563 | """ |
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| 564 | Import industry sites necessary to identify DSM-potential. |
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| 565 | ---------- |
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| 566 | ind_vent_share: float |
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| 567 | Share of considered application in industry demand |
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| 568 | wz: int |
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| 569 | Wirtschaftszweig to be considered within industry sites |
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| 570 | """ |
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| 571 | ||
| 572 | # import load data |
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| 573 | ||
| 574 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 575 | "ind_sites_loadcurves_individual" |
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| 576 | ] |
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| 577 | ||
| 578 | dsm = db.select_dataframe( |
|
| 579 | f""" |
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| 580 | SELECT site_id, bus_id as bus, scn_name, p_set FROM |
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| 581 | {sources['schema']}.{sources['table']} |
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| 582 | WHERE wz = {wz} |
|
| 583 | """ |
|
| 584 | ) |
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| 585 | ||
| 586 | # calculate share of timeseries for ventilation |
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| 587 | ||
| 588 | timeseries = dsm["p_set"].copy() |
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| 589 | ||
| 590 | for index, liste in timeseries.items(): |
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| 591 | share = [float(item) * ind_vent_share for item in liste] |
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| 592 | timeseries.loc[index] = share |
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| 593 | ||
| 594 | dsm["p_set"] = timeseries.copy() |
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| 595 | ||
| 596 | return dsm |
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| 597 | ||
| 598 | ||
| 599 | def calc_ind_site_timeseries(scenario): |
|
| 600 | # calculate timeseries per site |
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| @@ 525-559 (lines=35) @@ | ||
| 522 | return dsm |
|
| 523 | ||
| 524 | ||
| 525 | def ind_sites_vent_data_import(ind_vent_share, wz): |
|
| 526 | """ |
|
| 527 | Import industry sites necessary to identify DSM-potential. |
|
| 528 | ---------- |
|
| 529 | ind_vent_share: float |
|
| 530 | Share of considered application in industry demand |
|
| 531 | wz: int |
|
| 532 | Wirtschaftszweig to be considered within industry sites |
|
| 533 | """ |
|
| 534 | ||
| 535 | # import load data |
|
| 536 | ||
| 537 | sources = config.datasets()["DSM_CTS_industry"]["sources"][ |
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| 538 | "ind_sites_loadcurves" |
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| 539 | ] |
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| 540 | ||
| 541 | dsm = db.select_dataframe( |
|
| 542 | f""" |
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| 543 | SELECT bus, scn_name, p_set FROM |
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| 544 | {sources['schema']}.{sources['table']} |
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| 545 | WHERE wz = {wz} |
|
| 546 | """ |
|
| 547 | ) |
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| 548 | ||
| 549 | # calculate share of timeseries for ventilation |
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| 550 | ||
| 551 | timeseries = dsm["p_set"].copy() |
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| 552 | ||
| 553 | for index, liste in timeseries.items(): |
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| 554 | share = [float(item) * ind_vent_share for item in liste] |
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| 555 | timeseries.loc[index] = share |
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| 556 | ||
| 557 | dsm["p_set"] = timeseries.copy() |
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| 558 | ||
| 559 | return dsm |
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| 560 | ||
| 561 | ||
| 562 | def ind_sites_vent_data_import_individual(ind_vent_share, wz): |
|
