Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic Dispatch

With the increasing expansion of the power grid, economic dispatch problems have received considerable attention. A multi-agent coordinated deep reinforcement learning algorithm is proposed to deal with distributed nonconvex economic dispatch problems. In the algorithm, agents run independent reinfo...

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Veröffentlicht in:	IEEE transactions on power systems Jg. 38; H. 1; S. 204 - 217
Hauptverfasser:	Ding, Lifu, Lin, Zhiyun, Shi, Xiasheng, Yan, Gangfeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation algorithms Competition Deep learning Deep neural network distributed economic dispatch Estimation Heuristic algorithms Inference algorithms Machine learning multi-agent deep reinforcement learning Multiagent systems nonconvex optimization Observability Power dispatch Reinforcement learning Training
ISSN:	0885-8950, 1558-0679
Online-Zugang:	Volltext
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