Multi-Agent Deep Reinforcement Learning for Voltage Control With Coordinated Active and Reactive Power Optimization

The increasing penetration of distributed renewable energy resources causes voltage fluctuations in distribution networks. The controllable active and reactive power resources such as energy storage (ES) systems and electric vehicles (EVs) in active distribution networks play an important role in mi...

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Veröffentlicht in:	IEEE transactions on smart grid Jg. 13; H. 6; S. 4873 - 4886
Hauptverfasser:	Hu, Daner, Ye, Zhenhui, Gao, Yuanqi, Ye, Zuzhao, Peng, Yonggang, Yu, Nanpeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.11.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Active control Algorithms Capacitor banks Controllability Costs Deep learning Deep reinforcement learning Electric potential Electric vehicles Energy policy Energy sources Energy storage experience augmentation Inverters Machine learning Mixed integer multi-agent Multiagent systems Optimization Quality control Reactive power Reinforcement learning Robustness (mathematics) soft actor-critic Tap changers Time Voltage Voltage control Voltage fluctuations
ISSN:	1949-3053, 1949-3061
Online-Zugang:	Volltext
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