Update app.py

app.py

@@ -97,9 +97,9 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
     # Battery storage constraints
     for t in time_steps:
         if t == 0:
-            model += battery_storage[t] == battery_capacity * 0.5 + battery_charge[t] * battery_efficiency - battery_discharge[t] / battery_efficiency, f"StorageBalance_{t}"
+            model += battery_storage[t] == battery_capacity * 0.5 + battery_charge[t] * battery_efficiency - battery_discharge[t] * (1 / battery_efficiency), f"StorageBalance_{t}"
         else:
-            model += battery_storage[t] == battery_storage[t - 1] + battery_charge[t] * battery_efficiency - battery_discharge[t] / battery_efficiency, f"StorageBalance_{t}"
+            model += battery_storage[t] == battery_storage[t - 1] + battery_charge[t] * battery_efficiency - battery_discharge[t] * (1 / battery_efficiency), f"StorageBalance_{t}"
         model += battery_storage[t] <= battery_capacity, f"StorageCapacity_{t}"
         model += battery_storage[t] >= 0, f"StorageNonNegative_{t}"
 
@@ -130,19 +130,19 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
             alt_model = pulp.LpProblem(f"AlternativeModel_{tech}_{threshold}", pulp.LpMinimize)
 
             # Variables (need to create new variables for the new model)
-            alt_renewable_capacity = pulp.LpVariable.dicts("renewable_capacity",
+            alt_renewable_capacity = pulp.LpVariable.dicts("alt_renewable_capacity",
                                                            [(r, g) for r in regions for g in technologies],
                                                            lowBound=0, cat='Continuous')
-            alt_battery_capacity = pulp.LpVariable("battery_capacity", lowBound=0, cat='Continuous')
+            alt_battery_capacity = pulp.LpVariable("alt_battery_capacity", lowBound=0, cat='Continuous')
 
-            alt_renewable_generation = pulp.LpVariable.dicts("renewable_generation",
+            alt_renewable_generation = pulp.LpVariable.dicts("alt_renewable_generation",
                                                              [(t, g) for t in time_steps for g in technologies],
                                                              lowBound=0, cat='Continuous')
-            alt_battery_charge = pulp.LpVariable.dicts("battery_charge",
+            alt_battery_charge = pulp.LpVariable.dicts("alt_battery_charge",
                                                        time_steps, lowBound=0, cat='Continuous')
-            alt_battery_discharge = pulp.LpVariable.dicts("battery_discharge",
+            alt_battery_discharge = pulp.LpVariable.dicts("alt_battery_discharge",
                                                           time_steps, lowBound=0, cat='Continuous')
-            alt_battery_storage = pulp.LpVariable.dicts("battery_storage",
+            alt_battery_storage = pulp.LpVariable.dicts("alt_battery_storage",
                                                         time_steps, lowBound=0, cat='Continuous')
 
             # Objective: minimize or maximize the capacity of the selected technology
@@ -170,9 +170,9 @@ def optimize_energy_system(city_code, solar_cost, onshore_wind_cost, offshore_wi
             # Battery storage constraints
             for t in time_steps:
                 if t == 0:
-                    alt_model += alt_battery_storage[t] == alt_battery_capacity * 0.5 + alt_battery_charge[t] * battery_efficiency - alt_battery_discharge[t] / battery_efficiency, f"StorageBalance_{t}"
+                    alt_model += alt_battery_storage[t] == alt_battery_capacity * 0.5 + alt_battery_charge[t] * battery_efficiency - alt_battery_discharge[t] * (1 / battery_efficiency), f"StorageBalance_{t}"
                 else:
-                    alt_model += alt_battery_storage[t] == alt_battery_storage[t - 1] + alt_battery_charge[t] * battery_efficiency - alt_battery_discharge[t] / battery_efficiency, f"StorageBalance_{t}"
+                    alt_model += alt_battery_storage[t] == alt_battery_storage[t - 1] + alt_battery_charge[t] * battery_efficiency - alt_battery_discharge[t] * (1 / battery_efficiency), f"StorageBalance_{t}"
                 alt_model += alt_battery_storage[t] <= alt_battery_capacity, f"StorageCapacity_{t}"
                 alt_model += alt_battery_storage[t] >= 0, f"StorageNonNegative_{t}"