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Joint Rate Control and Demand Balancing for Electric Vehicle Charging

机译:电动汽车充电的联合费率控制和需求平衡

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Charging stations have become indispensable infrastructure to support the rapid proliferation of electric vehicles (EVs). The operational scheme of charging stations is crucial to satisfy the stability of the power grid and the quality of service (QoS) to EV users. Most existing schemes target either of the two major operations: charging rate control and demand balancing. This partial focus overlooks the coupling relation between the two operations and thus causes the degradation on the grid stability or customer QoS. A thoughtful scheme should manage both operations together. A big challenge to design such a scheme is the aggregated uncertainty caused by their coupling relation. This uncertainty accumulates from three aspects: the renewable generators co-located with charging stations, the power load of other (or non-EV) consumers, and the charging demand arriving in the future. To handle this aggregated uncertainty, we propose a stochastic optimization based operational scheme. The scheme jointly manages charging rate control and demand balancing to satisfy both the grid stability and user QoS. Further, our scheme consists of two algorithms that we design for managing the two operations respectively. An appealing feature of our algorithms is that they have robust performance guarantees in terms of the prediction errors on these three aspects. Simulation results demonstrate the efficacy of the proposed operational scheme and also validate our theoretical results.
机译:充电站已成为支持电动汽车(EV)迅速增长的必不可少的基础设施。充电站的运行方案对于满足电网的稳定性和为EV用户提供服务质量(QoS)至关重要。现有的大多数方案都针对以下两个主要操作之一:收费率控制和需求平衡。该部分焦点忽略了两个操作之间的耦合关系,因此导致网格稳定性或客户QoS下降。一个周到的方案应该同时管理这两个操作。设计这样一种方案的最大挑战是由它们的耦合关系引起的总不确定性。这种不确定性从三个方面累积:与充电站并置的可再生发电机,其他(或非EV)用户的电力负荷以及未来出现的充电需求。为了处理这种汇总的不确定性,我们提出了一种基于随机优化的操作方案。该方案共同管理充电速率控制和需求平衡,以同时满足电网稳定性和用户QoS。此外,我们的方案包含两个算法,分别设计用于管理两个操作。我们算法的一个吸引人的特征是,就这三个方面的预测误差而言,它们具有可靠的性能保证。仿真结果证明了所提方案的有效性,并验证了我们的理论结果。

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