基于云边缘协同的电动汽车充放电优化调度策略

Jing Zhang, Q. Jiang, Aiqiang Pan, Taoyong Li, Zhe Liu, Yuanxing Zhang, Linru Jiang, Xiangpeng Zhan
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引用次数: 9

摘要

本文提出了一种基于云边缘协作的电动汽车充放电管理分散调度方法,以保护用户隐私。首先,配电系统运营商作为云计算中心,以电力成本最小化为目标,基于二阶锥规划求解最优潮流模型;其次,充电站作为边缘计算单元,求解基于混合整数线性规划的能量管理模型,以跟踪配电系统运营商的调度指令;最后,充电站返回弯管减少了配电系统运营商修改能源计划的约束。并对调度指令进行迭代更新,以保证能源计划的可行性和最优性。仿真在IEEE 33总线测试系统中进行。结果表明,提出的云边缘协同策略可以减少内存使用,保护用户隐私,降低功耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Based on Cloud-Edge Collaboration
This paper proposes a decentralized scheduling method for electric vehicles charge and discharge management based on cloud-edge collaboration so as to protect users' privacy. Firstly, as a cloud computing center, distribution system operator solves an optimal power flow model based on second-order cone programming in order to minimize power costs. Secondly, as an edge computing unit, charging station solves an energy management model based on mixed-integer linear programming in order to track scheduling instructions of the distribution system operator. Finally, charging stations return benders cut constraints to distribution system operator to revise energy plan. And the scheduling instructions are updated iteratively to ensure the feasibility and optimality of the energy plan. The simulation is carried out in IEEE 33-bus test system. And the results show that the proposed cloud-edge collaborative strategy can reduce memory use, protect users' privacy as well as reducing power costs.
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