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Target-Value-Competition-Based Multi-Agent Deep Reinforcement Learning Algorithm for Distributed Nonconvex Economic Dispatch
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DOI：10.1109/TPWRS.2022.3159825.png)
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摘要
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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.
Keyword:
Deep neural network
distributed economic dispatch
multi-agent deep reinforcement learning
nonconvex optimization
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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