Multiobjective Optimization Scheduling of Sequential Charging Software for Networked Electric Vehicles

Abstract

In order to reduce the adverse effects of disordered charging of electric vehicles on the safe and stable operation of the distribution network, a multiobjective optimal scheduling method for the sequential charging software of networked electric vehicles is proposed. Aimed at minimizing charging costs and peak-to-valley differences in distribution network loads, its scheduling strategy will continuously roll and update EV charging schemes over time. The results show that the actual response data collected by the Internet of Vehicles app has corrected the probability distribution of the user’s choice of charging mode and response behavior. On the fifth day, the user’s actual charging response curve is close to the theoretical curve obtained by the optimization algorithm, and the expected charging is basically achieved. Calculations showed that the variance of the total load curve after charging decreased by 24.8 from 169.35 to 127.39. The proposed orderly charging strategy can effectively reduce the charging cost of electric vehicle users and the peak-to-valley difference of the distribution network load, play a good role in peak-valley filling, improve the convergence accuracy of the algorithm, and obtain the optimal solution of the problem.