Grid Maintenance Optimization Based on Particle Swarm Optimization

In order to alleviate the impact pressure on the power network when the electric automobile enters the power grid indirectly during the peak period of electric energy loss, the incentive mechanism of time-of-use price is adopted to lead users to choose orderly charging mode for electric vehicle charging. According to the survey report of user travel rule of NHTS in the United States, Monte Carlo algorithm is used to simulate the load curve of electric automobiles when they are connected to the grid, and this is analyzed. The main goal of this paper is to reduce the access of electric vehicles during the peak load period of the power grid and reduce the peak valley difference of the load. Particle swarm optimization algorithm is used to simulate charging load of different numbers of electric automobiles, and the original load, orderly charging load and disorderly charging load of electric vehicles are compared when they enter the network. It is concluded that compared with disorderly charging mode, orderly charging mode can reduce the load difference between peak and valley of electric vehicles. The weighted method is used to convert multiple targets into single targets, improve particle swarm speed, and obtain that the orderly charging mode of electric vehicles can greatly decrease the pressure of the power grid during the peak period of electric energy loss, and come true the orderly scheduling goal of electric vehicles entering the network.