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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Vydáno v:IEEE transactions on power systems Ročník 38; číslo 1; s. 204 - 217
Hlavní autoři: Ding, Lifu, Lin, Zhiyun, Shi, Xiasheng, Yan, Gangfeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 reinforcement learning algorithms and update their local Q-functions with a newly defined joint reward. The double network structure is adopted to approximate the Q-function so that the offline trained model can be used online to provide recommended power outputs for time-varying demands in real-time. By introducing the reward network, the competition mechanism between the reward network and the target network is established to determine a progressively stable target value, which achieves coordination among agents and pledges the losses of the Q-networks to converge well. Theoretical analysis is given and case studies are conducted to prove the advantages compared with existing approaches.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2022.3159825