Merge load curves and export data to db

src/egon/data/datasets.yml

@@ -634,10 +634,10 @@ etrago_electricity:
       table: 'egon_etrago_electricity_cts'
   targets:
     etrago_load:
-      schema: 'demand'
+      schema: 'grid'
       table: 'egon_etrago_load'
     etrago_load_curves:
-      schema: 'demand'
+      schema: 'grid'
       table: 'egon_etrago_load_timeseries'
 
 

src/egon/data/datasets/electricity_demand_etrago.py

@@ -5,35 +5,34 @@
 
 
 import egon.data.config
-import geopandas as gpd
-import numpy as np
 import pandas as pd
 from egon.data import db
 from egon.data.datasets import Dataset
-from egon.data.datasets.industry.temporal import (
-    insert_osm_ind_load,
-    insert_sites_ind_load,
-)
-from sqlalchemy import Column, String, Float, Integer, ARRAY
 from sqlalchemy.ext.declarative import declarative_base
 
 
 Base = declarative_base()
 
 
-def merge_electricity_demand():
+def demands_per_bus(scenario):
     """Sum all electricity demand curves up per bus
+
+    Parameters
+    ----------
+    scenario : str
+        Scenario name.
+
     Returns
     -------
-    None.
+    pandas.DataFrame
+        Aggregated electrical demand timeseries per bus
     """
 
     # Read information from configuration file
     sources = egon.data.config.datasets()["etrago_electricity"][
         "sources"
     ]
 
-    scenario = 'eGon2035'
 
     # Select data on CTS electricity demands per bus
     cts_curves = db.select_dataframe(
@@ -72,7 +71,111 @@ def merge_electricity_demand():
 
     demand_curves = cts_curves.append([ind_curves_osm, ind_curves_sites])
 
+    # Split array to single columns in the dataframe
+    demand_curves_split = demand_curves
+
+    demand_curves_split = pd.DataFrame(demand_curves.p_set.tolist(), index=demand_curves_split.index)
+
     # Group all rows with the same bus
+    demand_curves_bus = demand_curves_split.groupby(demand_curves_split.index).sum()
+
+    # Initzialize and fill resulsting dataframe
+    curves = pd.DataFrame(columns=["bus", "p_set"])
+    curves["bus"] = demand_curves_bus.index
+    curves["p_set"] = demand_curves_bus.values.tolist()
+
+    return curves
+
+
+def export_to_db():
+    """Prepare and export eTraGo-ready information of loads per bus and their
+    time series to the database
+
+    Returns
+    -------
+    None.
+
+    """
+
+    targets = egon.data.config.datasets()["etrago_electricity"][
+        "targets"
+    ]
+
+    for scenario in ["eGon2035", "eGon100RE"]:
 
-    demand_curves_bus = demand_curves.groupby(demand_curves.index).p_set.sum()
+        # Delete existing data from database
+        db.execute_sql(
+            f"""
+            DELETE FROM
+            {targets['etrago_load']['schema']}.{targets['etrago_load']['table']}
+            WHERE scn_name = '{scenario}'
+            """
+        )
+
+        db.execute_sql(
+            f"""
+            DELETE FROM
+            {targets['etrago_load_curves']['schema']}.{targets['etrago_load_curves']['table']}
+            WHERE scn_name = '{scenario}'
+            """
+        )
+
+        curves = demands_per_bus(scenario)
+
+        # Initialize dataframes equivalent to database tables
+
+        load = pd.DataFrame(columns=["version", "scn_name", "load_id", "bus", "type", "carrier", "p_set_fixed", "q_set_fixed", "sign", "p_set"])
+        load_timeseries = pd.DataFrame(columns=["version", "scn_name", "load_id", "temp_id", "p_set", "q_set"])
+
+        # Choose next unused load_id
+        next_load_id= db.next_etrago_id('load')
+
+        # Insert values into load df
+        load.bus = curves.bus
+        load.version = '0.0.0'
+        load.scn_name = scenario
+        load.sign = -1
+        load.carrier = 'AC'
+        load.load_id = range(next_load_id, next_load_id+len(load))
+        load.p_set = curves.p_set
+
+        # Insert values into load timeseries df
+        load_timeseries[['load_id', 'p_set']] = load[['load_id', 'p_set']]
+        load_timeseries.version = '0.0.0'
+        load_timeseries.scn_name = scenario
+        load_timeseries.temp_id = 1
 
+        # Delete p_set column from load df
+        load.drop(columns=['p_set'], inplace=True)
+
+        # Set index
+
+        load = load.set_index(["version", "scn_name", "load_id"])
+        load_timeseries = load_timeseries.set_index(["version", "scn_name", "load_id", "temp_id"])
+
+        # Insert data into database
+        load.to_sql(
+            targets["etrago_load"]["table"],
+            schema=targets["etrago_load"]["schema"],
+            con=db.engine(),
+            if_exists="append",
+        )
+
+        load_timeseries.to_sql(
+            targets["etrago_load_curves"]["table"],
+            schema=targets["etrago_load_curves"]["schema"],
+            con=db.engine(),
+            if_exists="append",
+        )
+
+
+class ElectricalLoadEtrago(Dataset):
+    def __init__(self, dependencies):
+        super().__init__(
+            name="Electrical_load_etrago",
+            version="0.0.0",
+            dependencies=dependencies,
+            tasks=(
+                export_to_db,
+            ),
+        )