以下是一個簡單的多時間尺度的配電網深度強化學習無功優化策略的Python示例代碼框架，用于幫助你理解如何使用深度強化學習（以深度Q網絡 DQN 為例）來處理配電網的無功優化問題。在實際應用中，你可能需要根據具體的配電網模型和需求進行大量的修改和擴展。

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt# 定義深度Q網絡模型
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# 定義環境類，這里簡化為配電網無功優化的環境抽象
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,))  # 示例狀態，可根據實際定義def step(self, action):# 這里需要根據實際的配電網模型計算獎勵、下一個狀態和是否結束reward = 0next_state = self.state  # 示例，需實際計算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# 訓練函數
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:# 選擇動作q_values = dqn(state)action = torch.argmax(q_values, dim=1).item()# 執行動作，獲取下一個狀態、獎勵和是否結束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)# 存儲經驗memory.append((state, action, reward, next_state, done_tensor))if len(memory) > batch_size:# 從記憶中采樣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)# 計算目標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# 計算當前Q值current_q = dqn(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze(1)# 計算損失并更新網絡loss = loss_fn(current_q, target_q)optimizer.zero_grad()loss.backward()optimizer.step()# 更新目標網絡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# 主函數
def main():num_buses = 10  # 示例配電網的節點數time_steps = 100  # 示例時間尺度state_size = num_busesaction_size = 5  # 示例動作數量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)# 這里可以添加對訓練好的模型進行評估和可視化的代碼if __name__ == "__main__":main()

上述代碼的主要功能和結構如下：

1. 定義深度Q網絡模型：DQN類定義了一個簡單的多層感知機，用于估計狀態的Q值。
2. 定義環境類：DistributionNetworkEnv類是配電網無功優化環境的一個簡單抽象，包含了狀態、動作、獎勵等相關的操作。
3. 訓練函數：train_dqn函數實現了深度Q網絡的訓練過程，包括選擇動作、執行動作、存儲經驗、采樣經驗、計算Q值和更新網絡等步驟。
4. 主函數：main函數設置了環境和模型的參數，并調用訓練函數進行訓練。

請注意，這只是一個非常基礎的示例，實際的配電網無功優化問題需要更復雜的環境建模、狀態表示和獎勵設計。你可能需要結合電力系統的專業知識和實際數據進行進一步的開發和優化。