Small adjustments in console output

Author: paulhoefer

edisgo/io/electromobility_import.py

@@ -1440,13 +1440,35 @@ def integrate_charging_parks_14a(edisgo_obj):
     from collections import defaultdict
 
     charging_parks = list(edisgo_obj.electromobility.potential_charging_parks)
-        
+    
+    #temp1
+    total_parks = len(charging_parks)
+    
+    parks_demand_gt_0 = [
+        cp for cp in charging_parks
+        if cp.designated_charging_point_capacity > 0
+    ]
+    parks_within_grid = [
+        cp for cp in charging_parks
+        if cp.within_grid
+    ]
+    #temp2
+    
     designated_charging_parks = [
         cp
         for cp in charging_parks
         if (cp.designated_charging_point_capacity > 0) and cp.within_grid
     ]
 
+    #temp1    
+    print("\nCharging park filter check (14a)")
+    print("-" * 44)
+    print(f"{'Total parks in all mv_grid_ids':<30}{total_parks:>6}")
+    print(f"{'CP with Demand > 0':<30}{len(parks_demand_gt_0):>6}")
+    print(f"{'CP within grid':<30}{len(parks_within_grid):>6}")
+    print(f"{'Designated CP (both true)':<30}{len(designated_charging_parks):>6}")
+    #temp2
+    
     charging_park_ids = []
     edisgo_ids = []
     comp_type = "charging_point"
@@ -1457,10 +1479,9 @@ def integrate_charging_parks_14a(edisgo_obj):
     }
     voltage_level_counter = defaultdict(int)
 
-    print("\n\n=========== START INTEGRATING CHARGING PARKS (_14a) ===========\n")
-    print(f"Designated charging parks: {len(designated_charging_parks)}\n")
-
-    total_start = time.perf_counter()
+    print("\nIntegrating charging parks (14a)")
+    print("-" * 44)
+    print(f"{'Designated parks':<30}{len(designated_charging_parks):>6}\n")
 
     for i, cp in enumerate(designated_charging_parks, start=1):
         park_start = time.perf_counter()
@@ -1490,7 +1511,7 @@ def integrate_charging_parks_14a(edisgo_obj):
             charging_park_ids.append(cp.id)
             edisgo_ids.append(edisgo_id)
 
-            if i % 1000 == 0:
+            if i % 500 == 0:
                 avg_park = stats["parks"]["total_time"] / stats["parks"]["count"]
                 print(f"[{i:>5}/{len(designated_charging_parks)}] last={park_dt:8.4f}s | avg={avg_park:8.4f}s")
 
@@ -1502,20 +1523,16 @@ def integrate_charging_parks_14a(edisgo_obj):
             print("Exception:                      ", e)
             print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
             raise
-
-    total_dt = time.perf_counter() - total_start
-
-    print("\n=========== FINISHED INTEGRATING CHARGING PARKS (_14a) ===========\n")
+    
+    print("\nIntegration finished (14a)")
+    print("-" * 44)
+    print(f"{'Integrated parks':<30}{len(charging_park_ids):>6}")
 
     edisgo_obj.electromobility.integrated_charging_parks_df = pd.DataFrame(
         columns=COLUMNS["integrated_charging_parks_df"],
         data=edisgo_ids,
         index=charging_park_ids,
     )
-    
-    if stats["parks"]["count"] > 0:
-        print(f"Total parks: {stats['parks']['count']}")
-        print(f"Total time: {total_dt:.4f}s")
 
 def import_electromobility_from_oedb(
     edisgo_obj: EDisGo,
@@ -1694,10 +1711,12 @@ def potential_charging_parks_from_oedb_14a(
     """
     14a variant of potential_charging_parks_from_oedb.
 
-    Test variant:
-    Do NOT filter charging parks directly by shapefile geometry.
+    Does not filter charging parks directly by shapefile geometry.
     Instead, determine intersecting mv_grid_ids from the shapefile and then
     load all charging parks belonging to those mv_grid_ids.
+    
+    Later on in integrate_charging_parks_14a only the charging parks that are 
+    within_grid are kept.
     """
     if shapefile_path is None:
         raise ValueError("shapefile_path must be provided.")

examples/loma-14a.py

@@ -238,9 +238,9 @@ def run_optimization_14a(edisgo):
 edisgo.topology.grid_district["geom"] = mv_grid_geom.loc[0, "geometry"]
 edisgo.topology.grid_district["srid"] = 4326
 
-# edisgo.topology.check_integrity()
-# pypsa_n = edisgo.to_pypsa()
-# edisgo.analyze()
+edisgo.topology.check_integrity()
+pypsa_n = edisgo.to_pypsa()
+edisgo.analyze()
 
 ############################# MANUAL FIXES ####################################
 edisgo.topology.generators_df = edisgo.topology.generators_df[
@@ -262,13 +262,27 @@ def run_optimization_14a(edisgo):
     buses_with_existing_loads,
 )
 
+#Temporary Check: Amount of CPs before importing eDisGo CPs
+names = edisgo.topology.loads_df.query("type == 'charging_point'").index.astype(str)
+print({
+    "existing": names.str.contains("Existing", case=False).sum(),
+    "additional": names.str.contains("Additional", case=False).sum(),
+    "rest": (~(names.str.contains("Existing", case=False) |
+               names.str.contains("Additional", case=False))).sum(),
+    "total": len(names)
+})
+
 # -------------------------
 # Import + distribute + integrate EV data (creates new charging points) 
 # -------------------------
 '''
 After this function there are no time series yet. Only charging points and 
 a overall demand which is then transferred into a time series in 
 apply_charging_strategy.
+
+Note: Afterwards there should be the Existing CP (411) and Additional CP (589) 
+from the LoMa side for the 2035 scenario and all new eDisGo CP (for whole Husum 
+there should be 2337). So the total should be 3337. 
 '''
 edisgo.import_electromobility_14a( 
     scenario="eGon2035",
@@ -277,13 +291,25 @@ def run_optimization_14a(edisgo):
     },
 )
 
+#Temporary Check: Amount of CPs after importing eDisGo CPs
+names = edisgo.topology.loads_df.query("type == 'charging_point'").index.astype(str)
+print({
+    "existing": names.str.contains("Existing", case=False).sum(),
+    "additional": names.str.contains("Additional", case=False).sum(),
+    "rest": (~(names.str.contains("Existing", case=False) |
+               names.str.contains("Additional", case=False))).sum(),
+    "total": len(names)
+})
+
 # -------------------------
-# Apply charging strategy (writes time series for the new integrated charging points from eDisGo)
+# Apply charging strategy 
 # -------------------------
 '''
+This step created the time series for the new eDisGo charging points.
 Without the preparation of Q before charging strategy I got an error while 
-apply_charging_strategy which was caused by deviating time index.
-After this step only the charging point from eDisGo have a time series.
+apply_charging_strategy which was caused by a deviating time index.
+
+Note: After this step ONLY the charging points from eDisGo have a time series.
 '''
 # Prepare Q before charging strategy
 ti = edisgo.timeseries.timeindex
@@ -304,6 +330,10 @@ def run_optimization_14a(edisgo):
 This step then finally transfers the time series from suitable eDisGo 
 charging_points to Existing_ und Additional_ charging points which are 
 created on the LoMa side.
+
+Note: After this step there should be 411 Existing CP and 589 Additional for
+the 2035 scenario and 1337 eDisGo CP as 1000 of those were used for matching
+and transferring the time series and deleted afterwards.
 '''
 ev_match_results = transfer_ts_from_new_to_existing_cp(
     edisgo,
@@ -314,6 +344,16 @@ def run_optimization_14a(edisgo):
     tol_2=0.9,   
 )
 
+#Temporary Check: Amount of CPs fter transferring time series 
+names = edisgo.topology.loads_df.query("type == 'charging_point'").index.astype(str)
+print({
+    "existing": names.str.contains("Existing", case=False).sum(),
+    "additional": names.str.contains("Additional", case=False).sum(),
+    "rest": (~(names.str.contains("Existing", case=False) |
+               names.str.contains("Additional", case=False))).sum(),
+    "total": len(names)
+})
+
 # ============================================================
 # Optional Utilities for sensitivity analysis/chaning the amount of cp/hp
 # - target by absolute value or relative percentage
@@ -324,23 +364,27 @@ def run_optimization_14a(edisgo):
 Either by percentage or by a total amount including the infrastructure from
 LoMa. When deleting CP/HP there is an option to export the deleted ones.
 New CP/HP will have 'dup' in their name.
+
+Note: for the 2035 scenario the target total would need to be set to 1000.
+CPs with the marker Additional and Existing in their name will be removed last.
+This way only the remaining 1337 eDisGo CP would be deleted.
 '''
 output_dir = "/home/paul/LoMa/test/shapes"
 
 cp_eligible_buses = buses_with_existing_loads(edisgo)
 hp_eligible_buses = buses_with_existing_loads(edisgo)
 
-# change_cp_amount = set_charging_points_to_target(
-#     edisgo,
-#     target_total=500, # sets total amount of CP to 1000
-#     #percentage=0.10, # increases total amount of CP by 10%
-#     #percentage=-0.10, # decreases total amount of CP by 10%
-#     eligible_buses=cp_eligible_buses,
-#     removal_priority=["Additional", "Existing"],
-#     add_tracking_columns=False,
-#     export_removed=True, # only applies when negative percentage for debugging
-#     export_dir=output_dir, # only applies when negative percentage for debugging
-# )
+change_cp_amount = set_charging_points_to_target(
+    edisgo,
+    target_total=1000, # sets total amount of CP to 1000
+    #percentage=0.10, # increases total amount of CP by 10%
+    #percentage=-0.10, # decreases total amount of CP by 10%
+    eligible_buses=cp_eligible_buses,
+    removal_priority=["Additional", "Existing"],
+    add_tracking_columns=False,
+    export_removed=True, # only applies when there are deleted CP
+    export_dir=output_dir, # only applies when there are deleted CP
+)
 
 # change_hp_amount = set_heat_pumps_to_target(
 #     edisgo,
@@ -349,10 +393,20 @@ def run_optimization_14a(edisgo):
 #     #percentage=-0.10, # decreases total amount of CP by 10%
 #     eligible_buses=hp_eligible_buses,
 #     add_tracking_columns=False,
-#     export_removed=True, # only applies when negative percentage for debugging
-#     export_dir=output_dir, # only applies when negative percentage for debugging
+#     export_removed=True, # only applies when there are deleted HP
+#     export_dir=output_dir, # only applies when there are deleted HP
 # )
 
+#Temporary Check: Amount of CPs after total amount changed
+names = edisgo.topology.loads_df.query("type == 'charging_point'").index.astype(str)
+print({
+    "existing": names.str.contains("Existing", case=False).sum(),
+    "additional": names.str.contains("Additional", case=False).sum(),
+    "rest": (~(names.str.contains("Existing", case=False) |
+               names.str.contains("Additional", case=False))).sum(),
+    "total": len(names)
+})
+
 ############################ EV INTEGRATION PART ##############################
 
 # Set Zero active power for batteries (let the OPF optimize dispatch freely)