Reactive power optimization via deep transfer reinforcement learning for efficient adaptation to multiple scenarios

Fast reactive power optimization policy-making for various operating scenarios is an important part of power system dispatch. Existing reinforcement learning algorithms alleviate the computational complexity in optimization but suffer from the inefficiency of model retraining for different operating...

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Bibliographic Details

Published in:	International Journal of Electrical Power & Energy Systems
Main Authors:	Congbo Bi, Di Liu, Lipeng Zhu, Chao Lu, Shiyang Li, Yingqi Tang
Format:	Article
Language:	English
Published:	Elsevier 2025-03-01
Subjects:	Reactive power optimization Transfer reinforcement learning Data efficiency Power system
Online Access:	http://www.sciencedirect.com/science/article/pii/S0142061524005994