A Multi-Agent Cooperative Voltage Control Method of Distribution System Based on Deep Reinforcement Learning
With the promotion of the market-orientated reform of power distribution,numerous controllable resources(such as distributed generators,distributed energy storage systems,etc.)in the distribution network are presented in the form of multi-stakeholders,which makes it it impossible for the distribution network to forcibly dispatch and control them.To make full use of controllable resources,this paper proposes a voltage optimization control method for distribution networks based on deep reinforced learning and considering multi-agent participation.Firstly,a Stackelberg Game voltage control model of the distribution network operator and the multi-stakeholder is established.Second,to ensure the fairness and privacy among multi-agents,a multi-agent dynamic decision-making method based on incomplete information and deep reinforcement learning Actor-Critic algorithm is proposed to maximize its own interests and provide auxiliary voltage control service for the distribution network.Finally,the feasibility and effectiveness of the proposed method are verified in the improved IEEE33 and IEEE 123 examples.
voltage control of distribution networkelectricity marketmulti-stakeholderdeep reinforcement learningStackelberg Game
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