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An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Based on Cloud-Edge Collaboration

机译:基于云协作的电动汽车充电和放电的最佳调度策略

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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.
机译:本文提出了一种基于云协作的电动汽车充电和放电管理的分散调度方法,以保护用户的隐私。 首先,作为云计算中心,分配系统运营商基于二阶锥编程解决了最佳功率流模型,以便最小化功率成本。 其次,作为边缘计算单元,充电站基于混合整数线性编程解决了能量管理模型,以便跟踪分发系统操作员的调度指令。 最后,充电站返回弯道将限制削减到分销系统运营商以修改能源计划。 并迭代地更新调度指令以确保能量计划的可行性和最优性。 模拟在IEEE 33总线测试系统中进行。 结果表明,建议的云边缘协作策略可以减少内存使用,保护用户的隐私以及降低电力成本。

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