面向主动配电网的安全多智能体深度强化学习电压优化控制

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面向主动配电网的安全多智能体深度强化学习电压优化控制

更新时间：2025-07-09

- 面向主动配电网的安全多智能体深度强化学习电压优化控制
- Issue 12, Pages: 157-167(2023)
- 作者机构：
  
  1. 西安交通大学电气工程学院
  2. 西安交通大学陕西省智能电网重点实验室
  3. 西安交通大学电力设备电气绝缘国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18;TM714.2
- Published：2023
- 稿件说明：
移动端阅览
[J]. 2023, (12): 157-167.
DOI：

[J]. 2023, (12): 157-167. DOI：

摘要

针对主动配电网电压优化控制中模型不确定性和通信代价大的问题，提出了一种基于灵敏度矩阵安全的多智能体深度强化学习(SMS-MADRL)算法。该算法利用安全深度强化学习，应对主动配电网的固有不确定性，并采用多智能体结构实现通信代价较小的分布式控制。首先，将电压优化控制问题描述为受约束的马尔可夫博弈(CMG);然后，对无功功率进行适当修改，通过分析节点电压的变化得到灵敏度矩阵，进而与主动配电网环境进行交互，训练出若干可以独立给出最优无功功率指令的智能体。与现有多智能体深度强化学习算法相比，该算法的优点在于给智能体的动作网络增添了基于灵敏度矩阵的安全层，在智能体的训练和执行阶段保证了主动配电网的电压安全性。在IEEE 33节点系统上的仿真结果表明：所提出的算法不仅能够满足电压约束，而且相较于多智能体深度确定性策略梯度(MADDPG)算法，网络损耗减少了4.18%

控制代价减少了70.5%。该研究可为主动配电网的电压优化控制提供理论基础。

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