Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

The change of electric vehicle (EV) cluster schedulable capacity in each period under multi-time scales has strong randomness and volatility. To ensure the trip demand and the charge and discharge cost of users, and increase the reliability of load demand curve prediction, this paper proposes a dynamic charge and discharge optimization strategy that takes the charge and discharge control coefficient in each response period as the control object. According to the trip chain, the proposed method models the trip time-space distribution of EV users, sorts each response period from small to large according to the starting time, comprehensively considers the interests of users and the peak shaving demand of the power grid, and uses Quantum Particle Swarm Optimization (QPSO) to solve the multi-objective optimization of charge and discharge control coefficient for the sorted response period. The charge and discharge control coefficient is modified by introducing virtual charge time and the virtual state of charge. Compared with the traditional method, the proposed method considers the mismatch between the expected parking time of users and the actual parking time of users and can update the load demand curve in real-time due to the dynamic changes in users' trip behavior, which is more practical. To verify the effectiveness of the method proposed in this paper, according to the simulation results of the time-space distribution of electric private car users, the load demand curves under different charge strategies, different optimization weights, and different vehicle-to-grid (V2G) responsiveness are simulated and analyzed. The results show that the proposed method can effectively reduce the peak valley difference and variance of the load demand curve under the condition of ensuring the trip demand and economic benefits for EV owners.

Automated summary

This paper proposes a dynamic charge and discharge optimization strategy for electric vehicle (EV) clusters, aiming to improve the scheduling capacity and reliability of load demand curve prediction. The strategy comprehensively considers user interests and grid demands, utilizing Quantum Particle Swarm Optimization (QPSO) to achieve accurate control of load demand through multi-objective optimization.