Updating optimiser to only optimise solar recommendations that include the battery

backend/app/plan/router.py

@@ -75,7 +75,7 @@ async def trigger_plan(body: PlanTriggerRequest):
     logger.info("Connecting to db")
     session = sessionmaker(bind=db_engine)()
     created_at = datetime.now().isoformat()
-    
+
     # TODO: We should store the trigger file path in the database with the plan so we can track the file that
     #       triggered the plan
 
@@ -242,7 +242,7 @@ async def trigger_plan(body: PlanTriggerRequest):
                 expected_adjusted_energy=expected_adjusted_energy
             )
 
-            input_measures = prepare_input_measures(recommendations_with_impact, body.goal, body.housing_type)
+            input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
 
             current_sap_points = int(property_instance.data["current-energy-efficiency"])
             target_sap_points = epc_to_sap_lower_bound(body.goal_value)
@@ -279,9 +279,6 @@ async def trigger_plan(body: PlanTriggerRequest):
                 if ventilation_rec:
                     selected_recommendations.add(ventilation_rec["recommendation_id"])
 
-            # We check if the selected recommendation is wall ventilation and if so, we make sure
-            # mechanical ventilation is selected
-
             # We'll use the set of selected recommendations to filter the recommendations to upload
             final_recommendations = [
                 [

recommendations/SolarPvRecommendations.py

@@ -122,6 +122,7 @@ class SolarPvRecommendations:
                     **cost_result,
                     # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
                     # back up here
-                    "photo_supply": 100 * roof_coverage
+                    "photo_supply": 100 * roof_coverage,
+                    "has_battery": has_battery
                 }
             )

recommendations/optimiser/optimiser_functions.py

@@ -1,17 +1,13 @@
-def prepare_input_measures(property_recommendations, goal, housing_type):
+def prepare_input_measures(property_recommendations, goal):
     """
     Basic function to convert recommendations_to_upload to a format that is
     suitable for the optimiser - large
     :param property_recommendations:   object containing the recommendations, created in the plan trigger api
     :param goal:    goal to be optimised for, should be one of the keys in gain_map. E.g. if the gain is SAP points,
                     the goal should reflect that desired gain
-    :param housing_type:    type of housing the recommendations are for - should be one of "Social" or "Private"
     :return:    Nested list of input measures
     """
 
-    if housing_type not in ["Social", "Private"]:
-        raise ValueError("Invalid housing type - investigate me")
-
     goal_map = {
         "Increase EPC": "sap_points"
     }
@@ -22,6 +18,12 @@ def prepare_input_measures(property_recommendations, goal, housing_type):
 
     input_measures = []
     for recs in property_recommendations:
+        if recs[0]["type"] == "solar_pv":
+            # if the recommendation is a solar recommendation without a battery, we exclude it from the optimisation.
+            # That will ensure that the optimiser only considers solar recommendations with batteries, so we don't
+            # under-report the potential cost
+            recs = [r for r in recs if recs["has_battery"]]
+
         input_measures.append(
             [
                 {