Use scenario table in demandregio import

src/egon/data/airflow/dags/pipeline.py

@@ -155,6 +155,18 @@
     ] >> map_zensus_vg250 >> zensus_inside_ger >> zensus_inside_ger_metadata
     zensus_inside_ger >> vg250_population >> vg250_population_metadata
 
+    # Scenario table
+    scenario_input_tables = PythonOperator(
+        task_id="create-scenario-parameters-table",
+        python_callable=import_scenarios.create_table
+    )
+
+    scenario_input_import = PythonOperator(
+        task_id="import-scenario-parameters",
+        python_callable=import_scenarios.insert_scenarios
+    )
+    setup >> scenario_input_tables >> scenario_input_import
+
     # DemandRegio data import
     demandregio_tables = PythonOperator(
         task_id="demandregio-tables",
@@ -169,20 +181,23 @@
     )
     vg250_clean_and_prepare >> demandregio_society
     demandregio_tables >> demandregio_society
+    scenario_input_import >> demandregio_society
 
     demandregio_demand_households = PythonOperator(
         task_id="demandregio-household-demands",
         python_callable=import_dr.insert_household_demand,
     )
     vg250_clean_and_prepare >> demandregio_demand_households
     demandregio_tables >> demandregio_demand_households
+    scenario_input_import >> demandregio_demand_households
 
     demandregio_demand_cts_ind = PythonOperator(
         task_id="demandregio-cts-industry-demands",
         python_callable=import_dr.insert_cts_ind_demands,
     )
     vg250_clean_and_prepare >> demandregio_demand_cts_ind
     demandregio_tables >> demandregio_demand_cts_ind
+    scenario_input_import >> demandregio_demand_cts_ind
 
     # Society prognosis
     prognosis_tables = PythonOperator(
@@ -354,15 +369,5 @@
     nep_insert_data >> power_plant_import
     retrieve_mastr_data >> power_plant_import
 
-    # Scenario table
-    scenario_input_tables = PythonOperator(
-        task_id="create-scenario-parameters-table",
-        python_callable=import_scenarios.create_table
-    )
 
-    scenario_input_import = PythonOperator(
-        task_id="import-scenario-parameters",
-        python_callable=import_scenarios.insert_scenarios
-    )
-    setup >> scenario_input_tables >> scenario_input_import
 

src/egon/data/datasets.yml

@@ -76,15 +76,17 @@ demandregio_society:
     vg250_krs:
       schema: 'boundaries'
       table: 'vg250_krs'
+    scenarios:
+      schema: 'grid'
+      table: 'egon_scenarios'
   targets:
     population:
       schema: 'society'
       table: 'egon_demandregio_population'
-      target_years: [2018, 2035, 2050]
     household:
       schema: 'society'
       table: 'egon_demandregio_household'
-      target_years: [2018, 2035, 2050]
+
 
 demandregio_household_demand:
   sources:
@@ -93,11 +95,13 @@ demandregio_household_demand:
     vg250_krs:
       schema: 'boundaries'
       table: 'vg250_krs'
+    scenarios:
+      schema: 'grid'
+      table: 'egon_scenarios'
   targets:
     household_demand:
       schema: 'demand'
       table: 'egon_demandregio_hh'
-      scenarios: ["eGon2035", "eGon100RE"]
 
 demandregio_cts_ind_demand:
   sources:
@@ -110,11 +114,13 @@ demandregio_cts_ind_demand:
     wz_definitions:
       "CTS": 'CTS_WZ_definition.csv'
       "industry": 'ind_WZ_definition.csv'
+    scenarios:
+      schema: 'grid'
+      table: 'egon_scenarios'
   targets:
     cts_ind_demand:
       schema: 'demand'
       table: 'egon_demandregio_cts_ind'
-      scenarios: ["eGon2035", "eGon100RE"]
     wz_definitions:
       schema: 'demand'
       table: 'egon_demandregio_wz'

src/egon/data/importing/demandregio/__init__.py

@@ -4,11 +4,13 @@
 """
 import os
 import pandas as pd
+import numpy as np
 import egon.data.config
 from egon.data import db
 from sqlalchemy import Column, String, Float, Integer
 from sqlalchemy.ext.declarative import declarative_base
 from disaggregator import data, spatial
+import egon.data.importing.scenarios.parameters as scenario_parameters
 # will be later imported from another file ###
 Base = declarative_base()
 
@@ -385,14 +387,9 @@ def insert_household_demand():
         db.execute_sql(
                 f"DELETE FROM {targets[t]['schema']}.{targets[t]['table']};")
 
-    for scn in targets['household_demand']['scenarios']:
+    for scn in ['eGon2035', 'eGon100RE']:
 
-        if scn == 'eGon2035':
-            year = 2035
-        elif scn == 'eGon100RE':
-            year = 2050
-        else:
-            print(f"Warning: Scenario {scn} can not be imported.")
+        year = scenario_parameters.global_settings(scn)['population_year']
 
         # Insert demands of private households
         insert_hh_demand(scn, year, engine)
@@ -417,14 +414,12 @@ def insert_cts_ind_demands():
 
     insert_cts_ind_wz_definitions()
 
-    for scn in targets['cts_ind_demand']['scenarios']:
+    for scn in ['eGon2035', 'eGon100RE']:
 
-        if scn == 'eGon2035':
-            year = 2035
-        elif scn == 'eGon100RE':
+        year = scenario_parameters.global_settings(scn)['population_year']
+
+        if year > 2035:
             year = 2035
-        else:
-            print(f"Warning: Scenario {scn} can not be imported.")
 
         # target values per scenario in MWh
         target_values = {
@@ -458,8 +453,11 @@ def insert_society_data():
         db.execute_sql(
                 f"DELETE FROM {targets[t]['schema']}.{targets[t]['table']};")
 
+    target_years = np.append(db.select_dataframe(
+        "SELECT global_parameters -> 'population_year' as years "
+        "FROM grid.egon_scenarios").years.values, 2018)
 
-    for year in targets['population']['target_years']:
+    for year in target_years:
         df_pop = pd.DataFrame(data.population(year=year))
         df_pop['year'] = year
         df_pop = df_pop.rename({'value': 'population'}, axis='columns')
@@ -471,7 +469,7 @@ def insert_society_data():
                       if_exists='append')
 
 
-    for year in targets['household']['target_years']:
+    for year in target_years:
         df_hh = pd.DataFrame(data.households_per_size(year=year))
         # Select data for nuts3-regions in boundaries (needed for testmode)
         df_hh = data_in_boundaries(df_hh)