Research on the Control of Multi-Agent Microgrid with Dual Neural Network Based on Priority Experience Storage Policy

Abstract

In this paper, an improved dual neural network control method based on multi-agent system is proposed to solve the problem of rating the frequency deviation and voltage deviation of the microgrid system due to the uneven impedance distribution of the circuit. The microgrid multi-agent system control model is constructed; the microgrid operation problem is transformed into Markov decision-making process, and the frequency error model of distributed secondary control adjusting system is established. In the course of training, the priority experience replay mechanism is introduced to accelerate the training reward return by using the experience of high feedback reward, and the frequency and voltage bias of the microgrid system are reduced. The model of isolated island microgrid of distributed power supply communication topology is established, and the control strategy of double neural network is simulated. Compared with the traditional sagging control method, the double neural network algorithm proposed in this paper stabilizes the frequency of the grid at rated frequency and improves the convergence speed. Simulation results show that the proposed method is helpful to provide stable and high-quality power resources for enterprises.