import json import os import time import numpy as np import pandas as pd import torch from concurrent.futures import ThreadPoolExecutor from deap import base, creator, tools, algorithms def fitness_torch(individuals, price, load, temperature, irradiance, wind_speed, device): individuals_torch = torch.tensor(individuals, device=device) num_individuals = individuals_torch.shape[0] Ac, Ag1, Ag2, Ag3, Av = [individuals_torch[:, i * period:(i + 1) * period] for i in range(5)] soc = torch.zeros((num_individuals, period), device=device) Pg1, Pg2, Pg3 = [torch.zeros((num_individuals, period), device=device) for _ in range(3)] Cb, Cg1, Cg2, Cg3, Cs, Cw, Rs, Cp, Pe, Ps, Ee, Es = [torch.zeros((num_individuals, period), device=device) for _ in range(12)] price_torch = torch.tensor(price, device=device) load_torch = torch.tensor(load, device=device) temperature_torch = torch.tensor(temperature, device=device) irradiance_torch = torch.tensor(irradiance, device=device) wind_speed_torch = torch.tensor(wind_speed, device=device) for t in range(period): if t > 0: soc[:, t] = torch.clamp(soc[:, t - 1] + 0.2 * Ac[:, t] * 0.9, 0.2, 0.8) Pg1[:, t] = torch.clamp(Pg1[:, t - 1] + 100 * Ag1[:, t], min=0, max=150) Pg2[:, t] = torch.clamp(Pg2[:, t - 1] + 100 * Ag2[:, t], min=0, max=375) Pg3[:, t] = torch.clamp(Pg3[:, t - 1] + 200 * Ag3[:, t], min=0, max=500) else: soc[:, t] = 0.4 Pg1[:, t] = torch.clamp(100 * Ag1[:, t], min=0, max=150) Pg2[:, t] = torch.clamp(100 * Ag2[:, t], min=0, max=375) Pg3[:, t] = torch.clamp(200 * Ag3[:, t], min=0, max=500) Pso = torch.clamp((0.2 * irradiance_torch[t] + 0.05 * temperature_torch[t] - 9.25) * (1 + Av[:, t]), min=0) Pw = torch.where( (wind_speed_torch[t] >= 3) & (wind_speed_torch[t] < 8), wind_speed_torch[t] ** 3 * 172.2625 / 1000, torch.where( (wind_speed_torch[t] >= 8) & (wind_speed_torch[t] < 12), 64 * 172.2625 / 125, torch.zeros_like(wind_speed_torch[t]) ) ) P = Ac[:, t] + Pg1[:, t] + Pg2[:, t] + Pg3[:, t] + Pso + Pw Ee[:, t] = torch.where(P >= load_torch[t], P - load_torch[t], torch.zeros_like(P)) Es[:, t] = torch.where(P < load_torch[t], load_torch[t] - P, torch.zeros_like(P)) Cb[:, t] = 0.01 * Ac[:, t] + 0.1 * soc[:, t] Cg1[:, t] = 0.0034 * Pg1[:, t] ** 2 + 3 * Pg1[:, t] + 30 Cg2[:, t] = 0.001 * Pg2[:, t] ** 2 + 10 * Pg2[:, t] + 40 Cg3[:, t] = 0.001 * Pg3[:, t] ** 2 + 15 * Pg3[:, t] + 70 Cs[:, t] = 0.01 * Pso Cw[:, t] = 0.01 * Pw Rs[:, t] = 0.5 * price_torch[t] * Ee[:, t] Cp[:, t] = price_torch[t] * Es[:, t] Pe[:, t] = torch.where(Ee[:, t] > 100, (Ee[:, t] - 100) * 50, torch.zeros_like(Ee[:, t])) Ps[:, t] = torch.where(Es[:, t] > 100, (Es[:, t] - 100) * 50, torch.zeros_like(Es[:, t])) total_cost = torch.sum(Cb + Cg1 + Cg2 + Cg3 + Cs + Cw + Pe + Ps - Rs + Cp, dim=1) reward = -total_cost / 1000 return reward.cpu().numpy() def check_bounds(func): def wrapper(*args, **kwargs): offspring = func(*args, **kwargs) for individual in offspring: for i in range(len(individual)): individual[i] = np.clip(individual[i], -1, 1) return offspring return wrapper def save_progress(population, gen, filename='ga_progress.json'): data = { 'population': [[list(ind), ind.fitness.values[0]] for ind in population], 'generation': gen } with open(filename, 'w') as f: json.dump(data, f) print(f"进度已保存到第 {gen} 代到 '{filename}'。") def load_progress(filename='ga_progress.json'): if os.path.exists(filename): with open(filename, 'r') as f: data = json.load(f) population = [] for ind_data in data['population']: ind = creator.Individual(ind_data[0]) ind.fitness.values = (ind_data[1],) population.append(ind) gen = data['generation'] print(f"从第 {gen} 代加载进度。") return population, gen else: return None, 0 def save_decision_values(best_ind, period, file_suffix): decision_values = [ {"x1": best_ind[i], "x2": best_ind[i + period], "x3": best_ind[i + 2 * period], "x4": best_ind[i + 3 * period], "x5": best_ind[i + 4 * period]} for i in range(period)] filename = f'./decision_values_{file_suffix}.json' with open(filename, 'w') as f: json.dump(decision_values, f) print(f"周期 {file_suffix}：决策值已保存到 '{filename}'。") def main(): data = pd.read_csv('./data.csv') # period = len(data) period = 2 price, load, temperature, irradiance, wind_speed = [data[col].values for col in ['price', 'load', 'temperature', 'irradiance', 'wind_speed']] creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", np.random.uniform, -1, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=5 * period) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mate", check_bounds(tools.cxBlend), alpha=0.5) toolbox.register("mutate", check_bounds(tools.mutGaussian), mu=0, sigma=0.1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", fitness_torch) population, start_gen = load_progress() if population is None: population = toolbox.population(n=500) start_gen = 0 NGEN, CXPB, MUTPB = 500, 0.7, 0.2 batch_size = 500 for gen in range(start_gen, NGEN): start_time = time.time() offspring = algorithms.varAnd(population, toolbox, cxpb=CXPB, mutpb=MUTPB) num_individuals = len(offspring) with ThreadPoolExecutor() as executor: futures = [] for i in range(0, num_individuals, batch_size): batch = offspring[i:i + batch_size] individuals = [ind[:] for ind in batch] futures.append( executor.submit(toolbox.evaluate, individuals, price, load, temperature, irradiance, wind_speed, 0)) for future in futures: fitnesses = future.result() for ind, fitness in zip(offspring, fitnesses): ind.fitness.values = (fitness,) population = toolbox.select(offspring, k=len(population)) end_time = time.time() elapsed_time = end_time - start_time print(f"第 {gen + 1} 代完成于 {elapsed_time:.2f} 秒") if (gen + 1) % 100 == 0: best_ind = tools.selBest(population, 1)[0] save_decision_values(best_ind, period, gen + 1) save_progress(population, gen + 1) best_ind = tools.selBest(population, 1)[0] print('最佳个体:', best_ind) print('适应度:', best_ind.fitness.values) save_decision_values(best_ind, period, NGEN) save_progress(population, NGEN) if __name__ == "__main__": main()