Grouping control of electric vehicles based on improved golden eagle optimization for peaking

Abstract

To address the problem of high lifespan loss and poor state of charge (SOC) balance of electric vehicles (EVs) participating in grid peak shaving, an improved golden eagle optimizer (IGEO) algorithm for EV grouping control strategy is proposed for peak shaving scenarios. First, considering the difference between peak and valley loads and the operating costs of EVs, a peak shaving model for EVs is constructed. Second, the design of IGEO has improved the global exploration and local development capabilities of the golden eagle optimizer (GEO) algorithm. Subsequently, IGEO is used to solve the peak shaving model and obtain the overall EV grid connected charging and discharging instructions. Next, using the k-means algorithm, EVs are dynamically divided into priority charging groups, backup groups, and priority discharging groups based on SOC differences. Finally, a dual layer power distribution scheme for EVs is designed. The upper layer determines the charging and discharging sequences and instructions for the three groups of EVs, whereas the lower layer allocates the charging and discharging instructions for each group to each EV. The proposed strategy was simulated and verified, and the results showed that the designed IGEO had faster optimization speed and higher optimization accuracy. The proposed EV grouping control strategy effectively reduces the peak–valley difference in the power grid, reduces the operational life loss of EVs, and maintains a better SOC balance for EVs.