Optimal scheduling of electric vehicles based on schedulable duration prediction

doi:10.3969/j.issn.2097-0706.2026.04.007

Abstract

Abstract:

With the rapid growth of the number of electric vehicles （EVs）， large-scale uncoordinated charging has exacerbated the peak-valley load differences in distribution networks and degraded power quality， making coordinated charging control critically important. However， existing scheduling methods fail to fully consider the uncertainty in EV charging duration and lack targeted prediction mechanisms. Taking the actual charging data provided by ACM Laboratory in the United States as an example， a hierarchical and clustered coordinated charging control method for EVs based on schedulable duration prediction was proposed. The EV charging duration was predicted， serving as the basis for constructing an EV clustering model. Economic optimal scheduling was performed under power balance and energy storage constraints. Then， power flow verification was conducted in the distribution network， with limit violation information fed back to drive closed-loop re-optimization of the charging station. A power allocation algorithm was employed to achieve coordinated charging and discharging control， ensuring both the security and economic efficiency of system operation. Case simulation results demonstrated that the proposed optimal scheduling method effectively captured EV parking patterns and improved prediction accuracy. The load variance of the charging station decreased by 26.55%， effectively lowering EV charging and discharging costs and enhancing system economic performance.

Key words: electric vehicles, optimal scheduling, coordinated charging control, charging duration prediction, hierarchical clustering, charging and discharging cost

CLC Number:

TK02：TM73

PENG Wenhe, CAI Ruitian, ZHANG Huaying, WANG Hualong, HUANG Huan, WU Yicong. Optimal scheduling of electric vehicles based on schedulable duration prediction[J]. Integrated Intelligent Energy, 2026, 48(4): 60-71.

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相关系数范围	相关程度	相关方向
0.5<r<1.0	强相关	正向
0.3≤r≤0.5	中度相关	正向
0<r<0.3	弱相关	正向
0	不相关	无
-0.3<r<0	弱相关	负向
-0.5≤r≤-0.3	中度相关	负向
-1.0<r<-0.5	强相关	负向

成本	案例1	案例2
电动汽车集群充放电成本	64.528 6	195.702 6
充电站调度成本	3 128.0	3 424.3