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!

Be the first to leave a comment!

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

Similar Items