Merge pull request #352 from openego/feature/csv_import_ding0_no_chan…

…ged_test_grids

Feature/csv import ding0 no changed test grids

edisgo/io/ding0_import.py

@@ -15,7 +15,7 @@
 logger = logging.getLogger(__name__)
 
 
-def import_ding0_grid(path, edisgo_obj):
+def import_ding0_grid(path, edisgo_obj, legacy_ding0_grids=True):
     """
     Import an eDisGo network topology from
     `Ding0 data <https://github.com/openego/ding0>`_.
@@ -30,7 +30,8 @@ def import_ding0_grid(path, edisgo_obj):
         Path to ding0 network csv files.
     edisgo_obj: :class:`~.EDisGo`
         The eDisGo data container object.
-
+    legacy_ding0_grids: `bool`
+        Allow import of old ding0 grids. Default: True
     """
 
     def sort_transformer_buses(transformers_df):
@@ -73,9 +74,13 @@ def sort_hvmv_transformer_buses(transformers_df):
     # write dataframes to edisgo_obj
     edisgo_obj.topology.buses_df = grid.buses[edisgo_obj.topology.buses_df.columns]
     edisgo_obj.topology.lines_df = grid.lines[edisgo_obj.topology.lines_df.columns]
-
-    grid.loads = grid.loads.drop(columns="p_set").rename(columns={"peak_load": "p_set"})
-
+    if legacy_ding0_grids:
+        logger.debug("Use ding0 legacy grid import.")
+        grid.loads = grid.loads.drop(columns="p_set").rename(
+            columns={"peak_load": "p_set"}
+        )
+    else:
+        edisgo_obj.topology.buses_df["in_building"] = False
     edisgo_obj.topology.loads_df = grid.loads[edisgo_obj.topology.loads_df.columns]
     # set loads without type information to be conventional loads
     # this is done, as ding0 currently does not provide information on the type of load
@@ -85,7 +90,13 @@ def sort_hvmv_transformer_buses(transformers_df):
         (edisgo_obj.topology.loads_df.type.isnull())
         | (edisgo_obj.topology.loads_df.type == "")
     ].index
-    edisgo_obj.topology.loads_df.loc[loads_without_type, "type"] = "conventional_load"
+    if legacy_ding0_grids:
+        edisgo_obj.topology.loads_df.loc[
+            loads_without_type, "type"
+        ] = "conventional_load"
+        edisgo_obj.topology.loads_df.replace(
+            to_replace=["retail"], value="cts", inplace=True
+        )
     # drop slack generator from generators
     slack = grid.generators.loc[grid.generators.control == "Slack"].index
     grid.generators.drop(slack, inplace=True)

edisgo/io/timeseries_import.py

@@ -174,7 +174,7 @@ def load_time_series_demandlib(config_data, timeindex):
 
     elec_demand.rename(
         columns={
-            "g0": "retail",
+            "g0": "cts",
             "h0": "residential",
             "l0": "agricultural",
             "i0": "industrial",

tests/io/test_timeseries_import.py

@@ -40,9 +40,9 @@ def test_import_load_timeseries(self):
         timeindex = pd.date_range("1/1/2018", periods=8760, freq="H")
         load = timeseries_import.load_time_series_demandlib(self.config, timeindex)
         assert (
-            load.columns == ["retail", "residential", "agricultural", "industrial"]
+            load.columns == ["cts", "residential", "agricultural", "industrial"]
         ).all()
-        assert np.isclose(load.loc[timeindex[453], "retail"], 8.33507e-05)
+        assert np.isclose(load.loc[timeindex[453], "cts"], 8.33507e-05)
         assert np.isclose(load.loc[timeindex[13], "residential"], 1.73151e-04)
         assert np.isclose(load.loc[timeindex[6328], "agricultural"], 1.01346e-04)
         assert np.isclose(load.loc[timeindex[4325], "industrial"], 9.91768e-05)

tests/network/test_timeseries.py

@@ -1526,7 +1526,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
         # define expected profiles
         profiles = pd.DataFrame(
             index=index,
-            columns=["retail", "residential", "agricultural", "industrial"],
+            columns=["cts", "residential", "agricultural", "industrial"],
             data=[
                 [0.0000597, 0.0000782, 0.0000654, 0.0000992],
                 [0.0000526, 0.0000563, 0.0000611, 0.0000992],
@@ -1589,7 +1589,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
                 self.edisgo.topology.loads_df.loc[
                     "Load_retail_LVGrid_9_14", "annual_consumption"
                 ]
-                * profiles["retail"]
+                * profiles["cts"]
             ).values,
             atol=1e-4,
         ).all()
@@ -1645,7 +1645,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
         # test manual - all
         profiles = pd.DataFrame(
             index=index,
-            columns=["retail", "residential", "agricultural", "industrial"],
+            columns=["cts", "residential", "agricultural", "industrial"],
             data=[
                 [0.003, 0.02, 0.00, 0.1],
                 [0.004, 0.01, 0.10, 0.2],
@@ -1683,7 +1683,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
                 self.edisgo.topology.loads_df.loc[
                     "Load_retail_LVGrid_9_14", "annual_consumption"
                 ]
-                * profiles["retail"]
+                * profiles["cts"]
             ).values,
         ).all()
         assert np.isclose(
@@ -1701,7 +1701,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
         profiles_new = (
             pd.DataFrame(
                 index=index,
-                columns=["retail", "residential", "agricultural", "industrial"],
+                columns=["cts", "residential", "agricultural", "industrial"],
                 data=[
                     [0.003, 0.02, 0.00, 0.1],
                     [0.004, 0.01, 0.10, 0.2],
@@ -1742,7 +1742,7 @@ def test_predefined_conventional_loads_by_sector(self, caplog):
                 self.edisgo.topology.loads_df.loc[
                     "Load_retail_LVGrid_9_14", "annual_consumption"
                 ]
-                * profiles["retail"]
+                * profiles["cts"]
             ).values,
         ).all()
         assert np.isclose(