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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Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
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Series: | International Journal of Electrical Power & Energy Systems |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061524005994 |
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