Adding Power constraints to simulation

data/grid_capacity.txt

@@ -0,0 +1,9 @@
+                    |   Maximum     |   Minimum
+---------------------------------------------------
+Consumption (Winter)|   87 028 Mwh  |   46 847 Mwh
+            (Summer)|   52 374 Mwh  |   29 819 Mwh
+---------------------------------------------------
+Production  (Winter)|   91 341 Mwh  |   72 926 Mwh
+            (Summer)|   86 579 Mwh  |   49 127 Mwh
+
+Winter correspond to S2-S5 and Summer correspond to S29-S32 (same as prices)

main.py

@@ -90,44 +90,35 @@ def generate_capacities(csv_file:str, nb_vehicles:int, seed:int=42, sep:str=';')
     print(f'Capacities of vehicles (kwh): ${capacities}')
     return capacities
 
+def get_power_constants(nb_vehicles:int, nb_consumers:int=67000000):
+    mean_consumption = (87028 + 46847 + 52374 + 29819)/4 # Mean of consumption in France in 2025 (estimate according to data/grid_capacity.txt)
+    sim_ratio = nb_vehicles / nb_consumers # Ratio to reduce A_max of simulation to realistic restrictions
+
+    a_max = sim_ratio * mean_consumption
+    x_max = a_max / nb_vehicles # For init, uniform charging/discharging for every vehicle
+    x_min = -x_max
+    return a_max, x_max, x_min
+
 # --- EXECUTION FUNCTION ---
-def run_scenario(scenario_name, model_type=None):
-    elec_price_csv = 'data/elec_prices.csv'
-    capacity_csv = 'data/vehicle_capacity.csv'
-
-    # Simulation parameters
-    N = 20 # Number of vehicles
-    T = 30 # Number of iterations (for the particles)
-    W = 0.4 # Inertia (for exploration)
-    C1 = 0.3 # Individual trust
-    C2 = 0.2 # Social trust
-    ARC_SIZE = 10 # Archive size
-
-    P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
-    CAPACITIES = generate_capacities(capacity_csv, N)
-
-    NB_TICKS = 48
-    DELTA = 60
-
-    A_MAX = 0
-    X_MAX = 0
-    X_MIN = 0
+def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:float, model_type=None, n:int=20, t:int=30, w:float=0.4, c1:float=0.3, c2:float=0.2, archive_size:int=10, nb_vehicles:int=10, delta_t:int=60, nb_of_ticks:int=48):
+    A_MAX, X_MAX, X_MIN = get_power_constants(nb_vehicles=nb_vehicles)
 
 
     print(f"\n--- Launching Scenario: {scenario_name} ---")
-    start_time = time.time()
-    
     # Simulation parameters
     params = {
-        'A_max': 500, 'price_mean': 0.15, 'price_std': 0.05,
-        'capacities': [50]*10, 'n': 20, 't': 50, 
-        'w': 0.4, 'c1': 2.0, 'c2': 2.0,
-        'nb_vehicles': 10, 'delta_t': 60, 'nb_of_ticks': 72
+        'A_max': A_MAX, 'price_mean': price_mean, 'price_std': price_std,
+        'capacities': capacities, 'n': n, 't': t, 
+        'w': w, 'c1': c1, 'c2': c2,
+        'nb_vehicles': nb_vehicles, 'delta_t': delta_t, 'nb_of_ticks': nb_of_ticks,
+        'x_min':X_MIN, 'x_max':X_MAX
     }
     
     # Instantiate extended class
     optimizer = SmartMOPSO(model_type=model_type, **params)
     
+    start_time = time.time()
+    
     # Run simulation
     optimizer.iterate()
         
@@ -144,6 +135,23 @@ def run_scenario(scenario_name, model_type=None):
 
 # --- MAIN ---
 if __name__ == "__main__":
+    # CSV files
+    elec_price_csv = 'data/elec_prices.csv'
+    capacity_csv = 'data/vehicle_capacity.csv'
+
+    # Global Simulation parameters
+    T = 30 # Number of iterations (for the particles)
+    W = 0.4 # Inertia (for exploration)
+    C1 = 0.3 # Individual trust
+    C2 = 0.2 # Social trust
+    ARC_SIZE = 10 # Archive size
+
+    P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)
+    CAPACITIES = generate_capacities(capacity_csv, N)
+
+    NB_TICKS = 48
+    DELTA = 60
+
     results = {}
     
     # 1. Without Surrogate (Baseline)

particle.py

@@ -65,8 +65,6 @@ class Particle():
                         self.x[tick][i] = self.x[tick][i] * 0.9
                 current_power = self.get_current_grid_stress(tick)
 
-
-
     def generate_position(self):
         pos = []
         for _ in range(self.nb_of_ticks):