微电网 强化学习环境python代码

微电网的强化学习环境通常涉及到多个因素，如能源管理、负载预测、电池充放电等。以下是一个简单的微电网强化学习环境的Python代码示例，供参考：

import numpy as np

class MicrogridEnvironment:
    def __init__(self, initial_state, battery_capacity, energy_demand):
        self.state = initial_state
        self.battery_capacity = battery_capacity
        self.energy_demand = energy_demand
        self.time_step = 0
        self.total_reward = 0
        self.done = False
    
    def reset(self):
        self.state = np.array([0, 0, 0])
        self.time_step = 0
        self.total_reward = 0
        self.done = False
    
    def step(self, action):
        # action: 0表示不放电、1表示放电
        battery_soc = self.state[0]
        energy_load = self.state[1]
        energy_generation = self.state[2]
        
        # 更新电池SOC
        if action == 0:
            battery_soc += 0.1
        else:
            battery_soc -= 0.1
        
        # 约束电池SOC在[0,1]之间
        battery_soc = max(0, min(1, battery_soc))
        
        # 计算负载
        energy_load = self.energy_demand[self.time_step]
        
        # 计算能量生成量
        if self.time_step % 24 in range(6, 18):
            energy_generation = 0.5
        else:
            energy_generation = 0
        
        # 更新时间步
        self.time_step += 1
        
        # 计算奖励
        if battery_soc == 0 and action == 1:
            reward = -10
        elif battery_soc == 1 and action == 0:
            reward = -10
        else:
            reward = energy_generation - abs(energy_load - energy_generation) - abs(action - 0.5)
        
        # 更新总奖励
        self.total_reward += reward
        
        # 判断是否结束
        if self.time_step == len(self.energy_demand):
            self.done = True
        
        # 更新状态
        self.state = np.array([battery_soc, energy_load, energy_generation])
        
        return self.state, reward, self.done, {}

在此简单的环境中，我们只考虑了电池的充放电以及能量的生成和负载。在实际应用中，还需要考虑更多的因素，比如风速、太阳辐射等。此外，该环境中的奖励函数是根据一些经验规则定义的，实际应用中可能需要更复杂的奖励函数。