openego /
eGon-data
| @@ 684-695 (lines=12) @@ | ||
| 681 | ) |
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| 682 | return |
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| 683 | ||
| 684 | if scenario == "eGon100RE": |
|
| 685 | ec_cts_ind2 = pd.read_csv( |
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| 686 | "data_bundle_powerd_data/egon_demandregio_cts_ind.csv" |
|
| 687 | ) |
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| 688 | ec_cts_ind2.to_sql( |
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| 689 | targets["cts_ind_demand"]["table"], |
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| 690 | engine, |
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| 691 | targets["cts_ind_demand"]["schema"], |
|
| 692 | if_exists="append", |
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| 693 | index=False, |
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| 694 | ) |
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| 695 | return |
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| 696 | ||
| 697 | for sector in ["CTS", "industry"]: |
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| 698 | # get demands per nuts3 and wz of demandregio |
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| @@ 671-682 (lines=12) @@ | ||
| 668 | ||
| 669 | # Workaround: Since the disaggregator does not work anymore, data from |
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| 670 | # previous runs is used for eGon2035 and eGon100RE |
|
| 671 | if scenario == "eGon2035": |
|
| 672 | ec_cts_ind2 = pd.read_csv( |
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| 673 | "data_bundle_powerd_data/egon_demandregio_cts_ind_egon2035.csv" |
|
| 674 | ) |
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| 675 | ec_cts_ind2.to_sql( |
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| 676 | targets["cts_ind_demand"]["table"], |
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| 677 | engine, |
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| 678 | targets["cts_ind_demand"]["schema"], |
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| 679 | if_exists="append", |
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| 680 | index=False, |
|
| 681 | ) |
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| 682 | return |
|
| 683 | ||
| 684 | if scenario == "eGon100RE": |
|
| 685 | ec_cts_ind2 = pd.read_csv( |
|
