光储系统优化调度问题遗传算法求解python代码

光储系统优化调度问题通常涉及到如何有效利用太阳能光伏板和电池储能设备来平衡电网供需。遗传算法是一种启发式搜索方法，常用于这类复杂的优化问题，因为它能够模拟自然选择和遗传机制，寻找全局最优解。

以下是使用Python实现光储系统优化调度问题遗传算法的一个简化示例：

import numpy as np
from deap import base, creator, tools

# 定义遗传算法所需组件
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def evaluate(individual):
    # 这里是一个假设的计算函数，需要根据实际问题调整
    energy_production = calculate_energy_production(individual)
    storage_capacity = individual[-1]  # 假设最后一个元素是电池容量
    cost = calculate_cost(energy_production, storage_capacity)
    return cost,

toolbox = base.Toolbox()
toolbox.register("individual", tools.initRepeat, creator.Individual,
                lambda x: random.randint(0, 1) for _ in range(num_solar_panels + 1))
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evaluate)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.1)
toolbox.register("select", tools.selTournament, tournsize=3)

def genetic_algorithm(population_size, num_generations):
    pop = toolbox.population(n=population_size)
    hof = tools.HallOfFame(1)
    stats = tools.Statistics(lambda ind: ind.fitness.values)
    stats.register("avg", np.mean)
    stats.register("min", np.min)
    stats.register("max", np.max)

    for g in range(num_generations):
        offspring = toolbox.select(pop, len(pop))
        offspring = [toolbox.clone(ind) for ind in offspring]

        # 变异和交叉操作
        for i in range(len(offspring)):
            if random.random() < mutation_probability:
                toolbox.mutate(offspring[i])
            offspring[i].fitness.values = toolbox.evaluate(offspring[i])

        # 更新种群
        pop[:] = offspring
        fits = [ind.fitness.values[0] for ind in pop]
        length = len(pop)
        mean = sum(fits) / length
        min_fit = min(fits)
        max_fit = max(fits)
        hof.update(pop)

        print(f"Generation {g+1}: Min={min_fit:.4f}, Max={max_fit:.4f}, Avg={mean:.4f}")

    return hof[0], hof

# 调用遗传算法
solution, best_fitness = genetic_algorithm(population_size, num_iterations)
print(f"Best solution found: {solution}")

注意，这只是一个基础示例，实际应用中你需要根据具体的光储系统模型、能量生产计算函数`calculate_energy_production()`、成本计算函数`calculate_cost()`以及系统的约束条件来调整上述代码。