Features/edisgo run edisgo ego interface

edisgo/io/powermodels_io.py

@@ -365,13 +365,22 @@ def from_powermodels(
         # calculate relative error
         df2 = deepcopy(df)
         for flex in df2.columns:
-            abs_error = abs(df2[flex].values - hv_flex_dict[flex].values)
-            rel_error = [
-                abs_error[i] / hv_flex_dict[flex].iloc[i]
-                if ((abs_error > 0.01)[i] & (hv_flex_dict[flex].iloc[i] != 0))
-                else 0
-                for i in range(len(abs_error))
-            ]
+            if type(hv_flex_dict[flex]) == pd.Series:
+                abs_error = abs(df2[flex].values - hv_flex_dict[flex].values)
+                rel_error = [
+                    abs_error[i] / hv_flex_dict[flex].iloc[i]
+                    if ((abs_error > 0.01)[i] & (hv_flex_dict[flex].iloc[i] != 0))
+                    else 0
+                    for i in range(len(abs_error))
+                ]
+            else:
+                abs_error = abs(df2[flex].values - hv_flex_dict[flex].sum(axis=1).values)
+                rel_error = [
+                    abs_error[i] / hv_flex_dict[flex].sum(axis=1).iloc[i]
+                    if ((abs_error > 0.01)[i] & (hv_flex_dict[flex].sum(axis=1).iloc[i] != 0))
+                    else 0
+                    for i in range(len(abs_error))
+                ]
             df2[flex] = rel_error
         # write results to edisgo object
         edisgo_object.opf_results.overlying_grid = pd.DataFrame(
@@ -1015,8 +1024,22 @@ def _build_battery_storage(
     """
     branches = pd.concat([psa_net.lines, psa_net.transformers])
     if not edisgo_obj.overlying_grid.storage_units_soc.empty:
+        # Select relevant timesteps
+        timesteps = edisgo_obj.timeseries.timeindex.union(
+            [
+                edisgo_obj.timeseries.timeindex[-1]
+                + edisgo_obj.timeseries.timeindex.freq
+            ]
+        )
+
+        # If the overlying grid data uses another year in the timeindex then
+        # edisgo.timindex, unify them
+        og_year = edisgo_obj.overlying_grid.storage_units_soc.index[0].year
+        if og_year != edisgo_obj.timeseries.timeindex[0].year:
+            timesteps = timesteps.map(lambda t: t.replace(year=og_year))
+
         data = pd.concat(
-            [edisgo_obj.overlying_grid.storage_units_soc]
+            [edisgo_obj.overlying_grid.storage_units_soc.loc[timesteps]]
             * len(edisgo_obj.topology.storage_units_df),
             axis=1,
         ).values
@@ -1593,11 +1616,18 @@ def _build_hv_requirements(
     )
 
     for i in np.arange(len(opf_flex)):
-        pm["HV_requirements"][str(i + 1)] = {
-            "P": hv_flex_dict[opf_flex[i]].iloc[0],
-            "name": opf_flex[i],
-            "count": count,
-        }
+        if type(hv_flex_dict[opf_flex[i]]) == pd.DataFrame:
+            pm["HV_requirements"][str(i + 1)] = {
+                "P": hv_flex_dict[opf_flex[i]].sum(axis=1).iloc[0],
+                "name": opf_flex[i],
+                "count": count,
+            }
+        else:
+            pm["HV_requirements"][str(i + 1)] = {
+                "P": hv_flex_dict[opf_flex[i]].iloc[0],
+                "name": opf_flex[i],
+                "count": count,
+            }
 
 
 def _build_timeseries(
@@ -1923,9 +1953,14 @@ def _build_component_timeseries(
 
     if (kind == "HV_requirements") & (pm["opf_version"] in [3, 4]):
         for i in np.arange(len(opf_flex)):
-            pm_comp[(str(i + 1))] = {
-                "P": hv_flex_dict[opf_flex[i]].round(20).tolist(),
-            }
+            if type(hv_flex_dict[opf_flex[i]])==pd.DataFrame:
+                pm_comp[(str(i + 1))] = {
+                    "P": hv_flex_dict[opf_flex[i]].sum(axis=1).round(20).tolist(),
+                }
+            else:
+                pm_comp[(str(i + 1))] = {
+                    "P": hv_flex_dict[opf_flex[i]].round(20).tolist(),
+                }
 
     pm["time_series"][kind] = pm_comp
 

edisgo/network/overlying_grid.py

@@ -84,6 +84,17 @@ def __init__(self, **kwargs):
             "feedin_district_heating", pd.DataFrame(dtype="float64")
         )
 
+        self.dispatchable_generators_active_power = kwargs.get(
+            "dispatchable_generators_active_power", pd.DataFrame(dtype="float64")
+        )
+
+        self.dispatchable_generators_reactive_power = kwargs.get(
+            "dispatchable_generators_reactive_power", pd.DataFrame(dtype="float64")
+        )
+
+        self.renewables_potential = kwargs.get(
+            "renewables_potential", pd.Series(dtype="float64")
+        )
     @property
     def _attributes(self):
         return [
@@ -97,6 +108,9 @@ def _attributes(self):
             "heat_pump_central_active_power",
             "thermal_storage_units_central_soc",
             "feedin_district_heating",
+            "dispatchable_generators_active_power",
+            "dispatchable_generators_reactive_power",
+            "renewables_potential",
         ]
 
     def reduce_memory(self, attr_to_reduce=None, to_type="float32"):

edisgo/run/tasks/io.py

@@ -235,14 +235,30 @@ def task_import_overlying_grid_data(edisgo, ctx, *, overlying_grid_path=None):
         for attr in edisgo.overlying_grid._attributes:
             if attr in overlying_grid_data:
                 setattr(edisgo.overlying_grid, attr, overlying_grid_data[attr])
+
+        if not overlying_grid_data.get(
+            "dispatchable_generators_active_power"
+        ).empty:
         # set generator time series
-        edisgo.set_time_series_active_power_predefined(
-            dispatchable_generators_ts=overlying_grid_data.get(
-                "dispatchable_generators_active_power"
-            ),
-            fluctuating_generators_ts=overlying_grid_data.get("renewables_potential"),
-        )
-        return edisgo
+            edisgo.set_time_series_active_power_predefined(
+                dispatchable_generators_ts=overlying_grid_data.get(
+                    "dispatchable_generators_active_power"
+                ),
+            )
+        if not overlying_grid_data.get("renewables_potential").empty:
+            pot_ts = overlying_grid_data.get("renewables_potential")
+            edisgo_ti = edisgo.timeseries.timeindex
+            csv_year = pot_ts.index[0].year
+            edisgo_year = edisgo_ti[0].year
+            if csv_year != edisgo_year:
+                pot_ts.index = pot_ts.index + pd.DateOffset(
+                    years=edisgo_year - csv_year
+                )
+            pot_ts = pot_ts.reindex(edisgo_ti)
+
+            edisgo.set_time_series_active_power_predefined(
+                fluctuating_generators_ts=pot_ts,
+            )
 
     # resolve path: explicit arg → runner config overlying_grid.path → skip
     if overlying_grid_path is None:
@@ -255,8 +271,9 @@ def task_import_overlying_grid_data(edisgo, ctx, *, overlying_grid_path=None):
         )
         return edisgo
 
-    # load overlying-grid attributes from CSV directory
-    edisgo.overlying_grid.from_csv(overlying_grid_path)
+    if overlying_grid_data is None:
+        # load overlying-grid attributes from CSV directory
+        edisgo.overlying_grid.from_csv(overlying_grid_path)
 
     # reindex overlying-grid attributes to match edisgo timeindex
     # CSVs may use a different year — shift year then reindex
@@ -281,41 +298,43 @@ def task_import_overlying_grid_data(edisgo, ctx, *, overlying_grid_path=None):
             target_ti = edisgo_ti_plus1 if attr in soc_attrs else edisgo_ti
             setattr(edisgo.overlying_grid, attr, ts.reindex(target_ti))
 
-    # load dispatchable generator and renewables time series from the same dir
-    disp_path = os.path.join(
-        overlying_grid_path, "dispatchable_generators_active_power.csv"
-    )
-    if os.path.isfile(disp_path):
-        disp_ts = pd.read_csv(disp_path, index_col=0, parse_dates=True)
-        if not edisgo_ti.empty:
-            csv_year = disp_ts.index[0].year
-            edisgo_year = edisgo_ti[0].year
-            if csv_year != edisgo_year:
-                disp_ts.index = disp_ts.index + pd.DateOffset(
-                    years=edisgo_year - csv_year
-                )
-            disp_ts = disp_ts.reindex(edisgo_ti)
-    else:
-        disp_ts = None
-
-    pot_path = os.path.join(overlying_grid_path, "renewables_potential.csv")
-    if os.path.isfile(pot_path):
-        pot_ts = pd.read_csv(pot_path, index_col=0, parse_dates=True)
-        if not edisgo_ti.empty:
-            csv_year = pot_ts.index[0].year
-            edisgo_year = edisgo_ti[0].year
-            if csv_year != edisgo_year:
-                pot_ts.index = pot_ts.index + pd.DateOffset(
-                    years=edisgo_year - csv_year
-                )
-            pot_ts = pot_ts.reindex(edisgo_ti)
-    else:
-        pot_ts = None
-
-    if disp_ts is not None or pot_ts is not None:
-        edisgo.set_time_series_active_power_predefined(
-            dispatchable_generators_ts=disp_ts,
-            fluctuating_generators_ts=pot_ts,
+    if overlying_grid_data is None:
+        # load dispatchable generator and renewables time series from the same dir
+        disp_path = os.path.join(
+            overlying_grid_path, "dispatchable_generators_active_power.csv"
         )
+        if os.path.isfile(disp_path):
+            disp_ts = pd.read_csv(disp_path, index_col=0, parse_dates=True)
+            if not edisgo_ti.empty:
+                csv_year = disp_ts.index[0].year
+                edisgo_year = edisgo_ti[0].year
+                if csv_year != edisgo_year:
+                    disp_ts.index = disp_ts.index + pd.DateOffset(
+                        years=edisgo_year - csv_year
+                    )
+                disp_ts = disp_ts.reindex(edisgo_ti)
+        else:
+            disp_ts = None
+
+        pot_path = os.path.join(overlying_grid_path, "renewables_potential.csv")
+        if os.path.isfile(pot_path):
+            pot_ts = pd.read_csv(pot_path, index_col=0, parse_dates=True)
+            if not edisgo_ti.empty:
+                csv_year = pot_ts.index[0].year
+                edisgo_year = edisgo_ti[0].year
+                if csv_year != edisgo_year:
+                    pot_ts.index = pot_ts.index + pd.DateOffset(
+                        years=edisgo_year - csv_year
+                    )
+                pot_ts = pot_ts.reindex(edisgo_ti)
+        else:
+            pot_ts = None
+
+
+        if disp_ts is not None or pot_ts is not None:
+            edisgo.set_time_series_active_power_predefined(
+                dispatchable_generators_ts=disp_ts,
+                fluctuating_generators_ts=pot_ts,
+            )
 
     return edisgo