Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model

Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model

The transition to decarbonized energy systems presents significant operational challenges due to increased uncertainties and complex dynamics. Deep reinforcement learning (DRL) has emerged as a powerful tool for optimizing power system operations. However, most existing DRL approaches rely on approx...

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Bibliographic Details
Main Authors: Ahmed Sayed, Khaled Al Jaafari, Xian Zhang, Hatem Zeineldin, Ahmed Al-Durra, Guibin Wang, Ehab Elsaadany
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:International Journal of Electrical Power & Energy Systems
Subjects:
Deep reinforcement learning
Operable power flow
Real-time economic control
Holomorphic embedding
Physics-driven policy gradient
Online Access:http://www.sciencedirect.com/science/article/pii/S0142061525001723
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