DQN算法的能量管理

DQN算法是一种基于深度强化学习的智能算法，可以用于微能源网的能量管理。该算法通过学习环境信息和预测负荷、风光等可再生能源功率输出以及分时电价等因素，来制定能量管理策略。

以下是DQN算法在能量管理中的应用示例：

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

# 定义DQN模型
def build_dqn_model(state_size, action_size):
    model = Sequential()
    model.add(Dense(24, input_dim=state_size, activation='relu'))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(action_size, activation='linear'))
    return model

# 定义DQN算法
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95  # 折扣因子
        self.epsilon = 1.0  # 探索率
        self.epsilon_decay = 0.995  # 探索率衰减率
        self.epsilon_min = 0.01  # 最小探索率
        self.learning_rate = 0.001
        self.model = build_dqn_model(state_size, action_size)
        self.target_model = build_dqn_model(state_size, action_size)
        self.update_target_model()

    def update_target_model(self):
        self.target_model.set_weights(self.model.get_weights())

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

    def replay(self, batch_size):
        minibatch = np.random.choice(len(self.memory), batch_size, replace=False)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.target_model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 定义环境和参数
state_size = 4  # 状态空间维度
action_size = 2  # 动作空间维度
batch_size = 32
episodes = 100

# 创建DQN代理
agent = DQNAgent(state_size, action_size)

# 训练DQN代理
for episode in range(episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    for time in range(500):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        if done:
            agent.update_target_model()
            break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)

# 使用训练好的DQN代理进行能量管理
state = env.reset()
state = np.reshape(state, [1, state_size])
for time in range(500):
    action = agent.act(state)
    next_state, reward, done, _ = env.step(action)
    next_state = np.reshape(next_state, [1, state_size])
    state = next_state
    if done:
        break