多时间尺度的配电网深度强化学习无功优化策略的Python示例代码框架

以下是一个简单的多时间尺度的配电网深度强化学习无功优化策略的Python示例代码框架，用于帮助你理解如何使用深度强化学习（以深度Q网络 DQN 为例）来处理配电网的无功优化问题。在实际应用中，你可能需要根据具体的配电网模型和需求进行大量的修改和扩展。

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt# 定义深度Q网络模型
class DQN(nn.Module):def __init__(self, state_size, action_size):super(DQN, self).__init__()self.fc1 = nn.Linear(state_size, 128)self.fc2 = nn.Linear(128, 128)self.fc3 = nn.Linear(128, action_size)def forward(self, x):x = torch.relu(self.fc1(x))x = torch.relu(self.fc2(x))x = self.fc3(x)return x# 定义环境类，这里简化为配电网无功优化的环境抽象
class DistributionNetworkEnv:def __init__(self, num_buses, time_steps):self.num_buses = num_busesself.time_steps = time_stepsself.current_time_step = 0self.state = np.zeros((self.num_buses,))  # 示例状态，可根据实际定义def step(self, action):# 这里需要根据实际的配电网模型计算奖励、下一个状态和是否结束reward = 0next_state = self.state  # 示例，需实际计算done = self.current_time_step >= self.time_steps - 1self.current_time_step += 1return next_state, reward, donedef reset(self):self.current_time_step = 0self.state = np.zeros((self.num_buses,))return self.state# 训练函数
def train_dqn(env, dqn, target_dqn, memory, batch_size, gamma, tau, episodes):optimizer = optim.Adam(dqn.parameters(), lr=0.001)loss_fn = nn.MSELoss()for episode in range(episodes):state = env.reset()state = torch.FloatTensor(state).unsqueeze(0)done = Falsewhile not done:# 选择动作q_values = dqn(state)action = torch.argmax(q_values, dim=1).item()# 执行动作，获取下一个状态、奖励和是否结束next_state, reward, done = env.step(action)next_state = torch.FloatTensor(next_state).unsqueeze(0)reward = torch.tensor([reward], dtype=torch.float32).unsqueeze(0)done_tensor = torch.tensor([done], dtype=torch.float32).unsqueeze(0)# 存储经验memory.append((state, action, reward, next_state, done_tensor))if len(memory) > batch_size:# 从记忆中采样batch = np.random.choice(len(memory), batch_size, replace=False)state_batch, action_batch, reward_batch, next_state_batch, done_batch = zip(*[memory[i] for i in batch])state_batch = torch.cat(state_batch)action_batch = torch.tensor(action_batch, dtype=torch.long)reward_batch = torch.cat(reward_batch)next_state_batch = torch.cat(next_state_batch)done_batch = torch.cat(done_batch)# 计算目标Q值with torch.no_grad():target_q_values = target_dqn(next_state_batch)max_target_q = torch.max(target_q_values, dim=1)[0].unsqueeze(1)target_q = reward_batch + (1 - done_batch) * gamma * max_target_q# 计算当前Q值current_q = dqn(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze(1)# 计算损失并更新网络loss = loss_fn(current_q, target_q)optimizer.zero_grad()loss.backward()optimizer.step()# 更新目标网络for target_param, param in zip(target_dqn.parameters(), dqn.parameters()):target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)state = next_stateprint(f"Episode {episode + 1} completed")return dqn# 主函数
def main():num_buses = 10  # 示例配电网的节点数time_steps = 100  # 示例时间尺度state_size = num_busesaction_size = 5  # 示例动作数量batch_size = 32gamma = 0.99tau = 0.001episodes = 100env = DistributionNetworkEnv(num_buses, time_steps)dqn = DQN(state_size, action_size)target_dqn = DQN(state_size, action_size)memory = []trained_dqn = train_dqn(env, dqn, target_dqn, memory, batch_size, gamma, tau, episodes)# 这里可以添加对训练好的模型进行评估和可视化的代码if __name__ == "__main__":main()