Optimal scheduling of electric vehicle ordered charging and discharging based on improved gravitational search and particle swarm optimization algorithm

With the rapid growth of the number of Electric Vehicles (EVs), access to large-scale EVs will bring serious safety hazards to the operation planning of the power system. It needs to be supported by an effective EV charging and discharging behavior control strategy to meet the operation demand of the power system. An optimization model with the objectives of minimizing grid load variance and minimizing user charging cost is established. An improved hybrid algorithm is proposed for the optimal allocation of charging and discharging power of EVs by combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA). The performance of variant algorithm is tested using CEC2005 benchmarking functions sets and applied to the solution of the ordered charge-discharge optimal scheduling model. The results show that the convergence accuracy of the algorithm is better than the traditional algorithm, and it can effectively balance exploration and exploitation ability of the particles. In addition, the scheduling analysis is performed for different charging strategies of EVs. The scheduling results show that with the same optimization weights, implementing the ordered charging and discharging strategy can significantly reduce the charging cost of users and the load variance of the grid. Thus, the operational stability of the grid and the economic benefits for users are improved.