基于可调度时长预测的电动汽车优化调度研究

doi:10.3969/j.issn.2097-0706.2026.04.007

摘要/Abstract

摘要：

随着电动汽车保有量的快速增长，电动汽车的规模化无序充电加剧了配网负荷峰谷差并降低了电能质量，有序充电控制至关重要。然而，现有调度方法未充分考虑电动汽车可充电时长的不确定性，缺乏针对性的预测机制。以美国ACM实验室提供的充电站实际数据为例，提出了一种基于可调度时长预测的电动汽车分层分群有序充电控制方法：首先，对电动汽车可充电时长进行预测，并以此为基础建立电动汽车分群模型；其次，在满足电力平衡与储能约束的条件下进行经济优化调度；随后，配网进行潮流校核并反馈越限信息，驱动充电站进行闭环重优化；最后，通过功率分配算法实现有序充放电控制，确保系统运行的安全性与经济性。案例仿真结果表明，所提优化调度方法可较好地挖掘电动汽车停留规律，提高预测准确度，充电站负荷方差减少了26.55%，有效降低了电动汽车充放电成本并提高了系统的经济性。

关键词: 电动汽车, 优化调度, 有序充电控制, 可充电时长预测, 分层分群, 充放电成本

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

中图分类号:

TK02：TM73

彭文河, 蔡瑞天, 张华英, 王化龙, 黄欢, 吴宜聪. 基于可调度时长预测的电动汽车优化调度研究[J]. 综合智慧能源, 2026, 48(4): 60-71.

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