Research on EV Scheduling Optimization Strategy Based on Monte Carlo and AntLion Optimizer - Peng, Daogang; Li, Chenxi; Zhao, Huirong; Yin, Shen (Frontiers in Energy Research, 2022)
As environmental problems become more serious, the trend of “carbon peaking and carbon neutral” has become necessary. However, the disorderly entry of large-scale EVs into the grid has threatened the security of the grid. The purpose of this paper is to study the optimization strategy of EVs to improve the economy and stability of distributed energy. Firstly, the EV user behavior model is constructed to study the charging and discharging behavior influencing factors, and the EV charging and discharging loads are simulated using Monte Carlo simulation. Secondly, we build a hierarchical scheduling optimization strategy based on EV user satisfaction using an improved AntLion optimizer, finally, the load peaks of the distributed energy system are suppressed and the satisfaction of EV customers is significantly improved; in the process of EV scheduling optimization at the source storage layer, EVs fully consume renewable energy output and the comprehensive operating costs of the distributed energy system are reduced. The conclusions are verified and the system is optimized, resulting in improved user satisfaction and optimized system economy and stability.