Research on Reactive Power Optimization Strategy under the Intelligent Improvement Model of the Distribution Network

Abstract

In order to improve the reactive power optimization effect of the distribution network, this paper combines the multiagent deep reinforcement learning algorithm to analyze the reactive power optimization strategy of the distribution network and constructs an intelligent optimization model. Moreover, the simulation models of power conversion elements, power transmission elements, control elements, and measurement elements in the platform are described, and the program structure and interactive functions are analyzed. In addition, this paper proposes a reactive power optimization method for distribution networks based on data-driven thinking. Finally, by using historical data and an artificial neural network, this paper extracts electrical quantity data such as load power and distributed power output and environmental data such as temperature and wind speed to perform multiagent analysis. The experimental verification shows that the reactive power optimization effect of the distribution network based on multiagent and multiagent deep reinforcement learning proposed in this paper is very good.