This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.

中文翻译：

---

考虑快速充电的大型电动汽车聚合的扩展状态空间模型

本文介绍了一种扩展状态空间模型，用于聚合大型电动汽车 (EV)，以实现电力系统中的频率调节和调峰。所提出的模型系统地处理电动汽车的快速充电，作为即时充电需求的有效解决方案。此外，所提出的扩展状态空间模型通过使电动汽车能够以常规和快速充电/放电率参与辅助服务，增加了电动汽车聚合器 (EVA) 的灵活性。这将有助于 EVA 对严重的发电-消费失衡做出迅速有效的反应。开发了一种概率控制方法，减少了 EVA 的通信负担。此外，与电动汽车用户行为相关的不确定性是实时建模的。模拟是针对典型的电力系统进行的，包括大量电动汽车、传统发电机 (CG) 和风力发电系统。结果表明，所提出的聚合模型可以准确描述大量电动汽车的聚合行为，使它们能够有效地参与频率调节和调峰服务。最后，针对 EV 群体的不同驾驶行为和充电状态 (SOC) 水平评估 EVA 的性能。