openego /
eGon-data
| @@ 985-1026 (lines=42) @@ | ||
| 982 | return df_demand_share |
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| 983 | ||
| 984 | ||
| 985 | def get_peta_demand(mvgd, scenario): |
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| 986 | """ |
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| 987 | Retrieve annual peta heat demand for CTS for either |
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| 988 | eGon2035 or eGon100RE scenario. |
|
| 989 | ||
| 990 | Parameters |
|
| 991 | ---------- |
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| 992 | mvgd : int |
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| 993 | ID of substation for which to get CTS demand. |
|
| 994 | scenario : str |
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| 995 | Possible options are eGon2035 or eGon100RE |
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| 996 | ||
| 997 | Returns |
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| 998 | ------- |
|
| 999 | df_peta_demand : pd.DataFrame |
|
| 1000 | Annual residential heat demand per building and scenario. Columns of |
|
| 1001 | the dataframe are zensus_population_id and demand. |
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| 1002 | ||
| 1003 | """ |
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| 1004 | ||
| 1005 | with db.session_scope() as session: |
|
| 1006 | query = ( |
|
| 1007 | session.query( |
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| 1008 | MapZensusGridDistricts.zensus_population_id, |
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| 1009 | EgonPetaHeat.demand, |
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| 1010 | ) |
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| 1011 | .filter(MapZensusGridDistricts.bus_id == int(mvgd)) |
|
| 1012 | .filter( |
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| 1013 | MapZensusGridDistricts.zensus_population_id |
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| 1014 | == EgonPetaHeat.zensus_population_id |
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| 1015 | ) |
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| 1016 | .filter( |
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| 1017 | EgonPetaHeat.sector == "service", |
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| 1018 | EgonPetaHeat.scenario == scenario, |
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| 1019 | ) |
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| 1020 | ) |
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| 1021 | ||
| 1022 | df_peta_demand = pd.read_sql( |
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| 1023 | query.statement, query.session.bind, index_col=None |
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| 1024 | ) |
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| 1025 | ||
| 1026 | return df_peta_demand |
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| 1027 | ||
| 1028 | ||
| 1029 | def calc_cts_building_profiles( |
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| @@ 737-778 (lines=42) @@ | ||
| 734 | ) |
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| 735 | ||
| 736 | ||
| 737 | def get_peta_demand(mvgd, scenario): |
|
| 738 | """ |
|
| 739 | Retrieve annual peta heat demand for residential buildings for either |
|
| 740 | eGon2035 or eGon100RE scenario. |
|
| 741 | ||
| 742 | Parameters |
|
| 743 | ---------- |
|
| 744 | mvgd : int |
|
| 745 | MV grid ID. |
|
| 746 | scenario : str |
|
| 747 | Possible options are eGon2035 or eGon100RE |
|
| 748 | ||
| 749 | Returns |
|
| 750 | ------- |
|
| 751 | df_peta_demand : pd.DataFrame |
|
| 752 | Annual residential heat demand per building and scenario. Columns of |
|
| 753 | the dataframe are zensus_population_id and demand. |
|
| 754 | ||
| 755 | """ |
|
| 756 | ||
| 757 | with db.session_scope() as session: |
|
| 758 | query = ( |
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| 759 | session.query( |
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| 760 | MapZensusGridDistricts.zensus_population_id, |
|
| 761 | EgonPetaHeat.demand, |
|
| 762 | ) |
|
| 763 | .filter(MapZensusGridDistricts.bus_id == mvgd) |
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| 764 | .filter( |
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| 765 | MapZensusGridDistricts.zensus_population_id |
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| 766 | == EgonPetaHeat.zensus_population_id |
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| 767 | ) |
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| 768 | .filter( |
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| 769 | EgonPetaHeat.sector == "residential", |
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| 770 | EgonPetaHeat.scenario == scenario, |
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| 771 | ) |
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| 772 | ) |
|
| 773 | ||
| 774 | df_peta_demand = pd.read_sql( |
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| 775 | query.statement, query.session.bind, index_col=None |
|
| 776 | ) |
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| 777 | ||
| 778 | return df_peta_demand |
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| 779 | ||
| 780 | ||
| 781 | def get_residential_heat_profile_ids(mvgd): |
|
