Patch back parent cool tech params

Add growth up for extract surface water

Author: Wegatriespython

message_ix_models/model/water/data/water_for_ppl.py

@@ -692,26 +692,26 @@ def _expand_parent_parameters(
     param_names = [
         "historical_activity",
         "historical_new_capacity",
-        #     "initial_activity_up",
-        #     "initial_activity_lo",
-        #     "initial_new_capacity_up",
+        "initial_activity_up",
+        "initial_activity_lo",
+        "initial_new_capacity_up",
         #     "soft_activity_up",
         #     # "soft_activity_lo", #causes infeasibilty.
         #     "soft_new_capacity_up",
         #     "level_cost_activity_soft_up",
         #     "level_cost_activity_soft_lo",
         #     # "growth_activity_lo", #cause infeasibility
-        #     "growth_activity_up",
-        #     "growth_new_capacity_up",
+        "growth_activity_up",
+        "growth_new_capacity_up",
     ]
 
     # Parameters that need multiplier application
     multip_list = [
         "historical_activity",
         "historical_new_capacity",
-        # "initial_activity_up",
-        # "initial_activity_lo",
-        # "initial_new_capacity_up",
+        "initial_activity_up",
+        "initial_activity_lo",
+        "initial_new_capacity_up",
     ]
 
     # Extract parameters from parent technologies

message_ix_models/model/water/data/water_supply.py

@@ -886,6 +886,16 @@ def add_water_supply(context: "Context") -> dict[str, pd.DataFrame]:
         else:
             results["var_cost"] = var_cost_fossil
 
+        # Add growth constraint for extract_surfacewater (10% annual growth limit)
+        growth_activity_up = make_df(
+            "growth_activity_up",
+            technology="extract_surfacewater",
+            value=0.1,
+            unit="-",
+        ).pipe(broadcast, year_act=year_wat, node_loc=df_node["node"])
+
+        results["growth_activity_up"] = growth_activity_up
+
         # Remove duplicates and NaN values from all DataFrames in results
         for key, df in results.items():
             results[key] = df.dropna().drop_duplicates().reset_index(drop=True)