Model-Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach

Model-Data Hybrid-Driven Real-Time Optimal Power Flow: A Physics-Informed Reinforcement Learning Approach

With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory beh...

Full description

Saved in:
Bibliographic Details
Main Authors: Ximing Zhang, Xiyuan Ma, Yun Yu, Duotong Yang, Zhida Lin, Changcheng Zhou, Huan Xu, Zhuohuan Li
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Energies
Subjects:
deep reinforcement learning (DRL)
real-time optimal power flow (RT-OPF)
physics-informed neural network (PINN)
physics-informed reinforcement learning (PIRL)
Online Access:https://www.mdpi.com/1996-1073/18/13/3483
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/1996-1073/18/13/3483

Similar Items