From d1c2475d1b3189a35103d04da28f690c88cf31d3 Mon Sep 17 00:00:00 2001 From: KuMiShi Date: Sun, 18 Jan 2026 13:43:44 +0100 Subject: [PATCH] Adding Power constraints to simulation --- data/grid_capacity.txt | 9 ++++++ main.py | 62 ++++++++++++++++++++++++------------------ particle.py | 2 -- 3 files changed, 44 insertions(+), 29 deletions(-) create mode 100644 data/grid_capacity.txt diff --git a/data/grid_capacity.txt b/data/grid_capacity.txt new file mode 100644 index 0000000..e38a224 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/main.py b/main.py index 09e2965..71e8277 100644 --- a/main.py +++ b/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) diff --git a/particle.py b/particle.py index 4b00ff1..7f70df8 100644 --- a/particle.py +++ b/particle.py @@ -64,8 +64,6 @@ class Particle(): if self.x[tick][i] > 0: self.x[tick][i] = self.x[tick][i] * 0.9 current_power = self.get_current_grid_stress(tick) - - def generate_position(self): pos = []