黄小庆, 段建焱, 李隆意, 张琪媛, 朱彬, 黄会, 于慎仟. 基于负荷弹性的充电桩共享时间窗冲突控制策略[J]. 中国电机工程学报, 2025, 45(8): 2981-2991. DOI: 10.13334/j.0258-8013.pcsee.231278
引用本文: 黄小庆, 段建焱, 李隆意, 张琪媛, 朱彬, 黄会, 于慎仟. 基于负荷弹性的充电桩共享时间窗冲突控制策略[J]. 中国电机工程学报, 2025, 45(8): 2981-2991. DOI: 10.13334/j.0258-8013.pcsee.231278
HUANG Xiaoqing, DUAN Jianyan, LI Longyi, ZHANG Qiyuan, ZHU Bin, HUANG Hui, YU Shenqian. Conflict Control Strategy of Charging Pile Sharing Time Window Based on Load Elasticity[J]. Proceedings of the CSEE, 2025, 45(8): 2981-2991. DOI: 10.13334/j.0258-8013.pcsee.231278
Citation: HUANG Xiaoqing, DUAN Jianyan, LI Longyi, ZHANG Qiyuan, ZHU Bin, HUANG Hui, YU Shenqian. Conflict Control Strategy of Charging Pile Sharing Time Window Based on Load Elasticity[J]. Proceedings of the CSEE, 2025, 45(8): 2981-2991. DOI: 10.13334/j.0258-8013.pcsee.231278

基于负荷弹性的充电桩共享时间窗冲突控制策略

Conflict Control Strategy of Charging Pile Sharing Time Window Based on Load Elasticity

  • 摘要: 电动汽车(electric vehicle,EV)充电桩共享是缓解当前充电难问题的有效途径。针对充电桩共享中存在的EV与EV、EV与共享桩之间的时间冲突问题,该文基于EV充电负荷弹性可调节,提出一种充电桩共享时间窗冲突问题的求解策略。首先,分析共享充电桩时间窗冲突问题,提出基于EV充电时间弹性的共享充电思路;接着,建立计及时间冲突的共享车桩匹配模型,利用差分思想,将非凸非线性约束转化为线性约束;在满足EV充电需求的前提下,收缩EV充电时段区间,以解决EV与EV、EV与共享充电桩之间的时间冲突问题;引入二进制辅助变量,使原非线性模型转化为混合整数模型。进一步,针对模型中约束随决策量激增问题,提出基于充电时间调节的改进车桩匹配方法。结果表明,论文所提方法能有效提高共享车桩匹配成功数、共享桩使用率,算法求解效率高,能解决大规模充电桩共享中多重时间冲突导致车桩匹配成功率低的问题。

     

    Abstract: Electric vehicle (EV) charging pile sharing is an effective solution to alleviate current charging challenges. This paper addresses time conflicts between EVs and shared piles by proposing a conflict resolution strategy based on elastic EV charging load adjustment. First, the time window conflicts in shared charging piles are analyzed, and a shared charging approach leveraging EV charging time flexibility is introduced. Next, a vehicle-pile matching model considering time conflicts is established, where non-convex nonlinear constraints are linearized using a difference-based approach. While ensuring EV charging demand is met, the charging interval is compressed to resolve conflicts between EVs and between EVs and shared piles. By introducing binary auxiliary variables, the original nonlinear model is transformed into a mixed-integer model. Furthermore, an improved vehicle-pile matching method based on charging time adjustment is proposed to address the issue of surging constraints with increasing decision variables. Case studies demonstrate that the proposed method significantly increases successful vehicle-pile matching and shared pile utilization while maintaining high computational efficiency. This approach effectively mitigates the low success rate of matching in large-scale charging pile sharing caused by time conflicts.

     

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