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...

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Egile Nagusiak:	Ximing Zhang, Xiyuan Ma, Yun Yu, Duotong Yang, Zhida Lin, Changcheng Zhou, Huan Xu, Zhuohuan Li
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-07-01
Saila:	Energies
Gaiak:	deep reinforcement learning (DRL) real-time optimal power flow (RT-OPF) physics-informed neural network (PINN) physics-informed reinforcement learning (PIRL)
Sarrera elektronikoa:	https://www.mdpi.com/1996-1073/18/13/3483
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