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Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow:A Near-optimal Solution

Bin Feng Jiayue Zhao Gang Huang Yijie Hu Huating Xu Changxin Guo Zhe Chen

中国电机工程学会电力与能源系统学报（英文版）2026，Vol.12Issue(1)：99-111,13.
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中国电机工程学会电力与能源系统学报（英文版）2026，Vol.12Issue(1)：99-111,13.DOI:10.17775/CSEEJPES.2023.02070

Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow:A Near-optimal Solution

Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow:A Near-optimal Solution

Bin Feng 1Jiayue Zhao 1Gang Huang 1Yijie Hu 1Huating Xu 1Changxin Guo 1Zhe Chen2

作者信息

  • 1. College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China
  • 2. Aalborg University,Aalborg 9220,Denmark
  • 折叠

摘要

关键词

Behavior cloning/deep reinforcement learning/multiprocessing training/optimal power flow/primal-dual optimization/proximal policy optimization

Key words

Behavior cloning/deep reinforcement learning/multiprocessing training/optimal power flow/primal-dual optimization/proximal policy optimization

引用本文复制引用

Bin Feng,Jiayue Zhao,Gang Huang,Yijie Hu,Huating Xu,Changxin Guo,Zhe Chen..Safe Deep Reinforcement Learning for Real-time AC Optimal Power Flow:A Near-optimal Solution[J].中国电机工程学会电力与能源系统学报（英文版）,2026,12(1):99-111,13.

基金项目

This work was supported by the National Natural Science Foundation of China(52007173 and U22B2098). （52007173 and U22B2098）

中国电机工程学会电力与能源系统学报（英文版）

2096-0042

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