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arXiv 2301.03342math.OCcs.SYeess.SY
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基于实时反馈的在线聚合电动汽车功率灵活性表征

Real-time Feedback Based Online Aggregate EV Power Flexibility Characterization

Dongxiang Yan, Shihan Huang, Yue Chen

AI总结:

本文针对聚合电动汽车功率灵活性表征问题,提出离线模型界定可行区域,并开发基于李雅普诺夫优化的在线算法,集成实时反馈以提升灵活性,通过性能比较验证优势。

摘要:

作为应对全球变暖的重要措施,电动汽车(EV)经历了快速增长。灵活的电动汽车能够增强电力系统处理可再生能源发电不确定性的能力。如何利用电动汽车灵活性参与电网运行已引起广泛关注。然而,由于单个电动汽车容量小、数量大,直接控制它们具有挑战性。因此,由聚合商代表电动汽车与电网交互,通过表征其聚合灵活性来实现。本文聚焦于聚合电动汽车功率灵活性表征问题。首先,构建离线模型以获得聚合电动汽车功率灵活性区域的下界和上界,确保该区域内的任何轨迹都是可行的。然后,考虑到实时电价和电动汽车到达/离开时间等参数无法预先获知,基于李雅普诺夫优化技术开发了一种在线算法。我们为所提出的在线算法提供了最大充电延迟的理论界限。此外,设计并集成实时反馈到所提出的在线算法中,以更好地释放电动汽车功率灵活性。进行了全面的性能比较,以展示所提出方法的优势。

英文摘要:

As an essential measure to combat global warming, electric vehicles (EVs) have witnessed rapid growth. Flexible EVs can enhance power systems' ability to handle renewable generation uncertainties. How EV flexibility can be utilized in power grid operation has captured great attention. However, the direct control of individual EVs is challenging due to their small capacity and large number. Hence, it is the aggregator that interacts with the grid on behalf of the EVs by characterizing their aggregate flexibility. In this paper, we focus on the aggregate EV power flexibility characterization problem. First, an offline model is built to obtain the lower and upper bounds of the aggregate EV power flexibility region. It ensures that any trajectory within the region is feasible. Then, considering that parameters such as real-time electricity prices and EV arrival/departure times are not known in advance, an online algorithm is developed based on Lyapunov optimization techniques. We provide a theoretical bound for the maximum charging delay under the proposed online algorithm. Furthermore, real-time feedback is designed and integrated into the proposed online algorithm to better unlock EV power flexibility. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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