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Coordination of V2G and distributed wind power using the storage-like aggregate PEV model

机译:使用类似存储的聚合PEV模型协调V2G和分布式风电

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A plug-in electric vehicle (PEV) fleet utilizing vehicle-to-grid (V2G) technology, i.e., a V2G fleet, can behave as a storage system, e.g., promoting integration of distributed wind power resources. However, because the PEVs' behaviors are stochastic and a V2G fleet's population is large, three technical difficulties hinder the utilization of V2G: charging demand forecasting; ahead-of-time charge and discharge scheduling; real-time charge and discharge power dispatching. This paper utilizes a storage-like aggregate model (SLAM) of a V2G fleet that employs aggregated parameters to represent energy and power constraints of the entire PEV population, and therefore reduces the difficulty of forecasting. Then, a stochastic joint power scheduling strategy for a distributed wind generation and a V2G fleet based on the SLAM is proposed aiming to promote the integration of distributed wind power by V2G technology, which has low computational burden. A real-time heuristic strategy is designed to efficiently dispatch the power schedules to PEVs and guarantee the modeling accuracy of SLAM and effectiveness of the proposed power scheduling strategy.
机译:利用车辆到电网(V2G)技术的插电式电动汽车(PEV)车队,即V2G车队,可以充当存储系统,例如,促进分布式风能资源的整合。但是,由于PEV的行为是随机的,并且V2G车队的人口众多,因此三个技术难题阻碍了V2G的使用:充电需求预测;充电需求预测。提前充电和放电时间表;实时充放电功率调度。本文利用V2G车队的类似存储的聚合模型(SLAM),该模型使用聚合参数来表示整个PEV人群的能量和功率约束,因此降低了预测难度。然后,提出了一种基于SLAM的分布式风电与V2G机队的随机联合电力调度策略,旨在促进计算量低的V2G技术对分布式风电的整合。设计了一种实时启发式策略,以有效地将功率调度分配给PEV,并保证SLAM的建模精度和所提出的功率调度策略的有效性。

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