电动汽车充电站分散式电力调度策略

IF 1.9 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
He Yin, Amro Alsabbagh, Chengbin Ma
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引用次数: 2

摘要

本文讨论了电动汽车充电站的分散电力调度问题。通过一个实时的Stackelberg博弈来解决电力调度问题。在这个博弈中,领导者是电动汽车充电站(EVCS),追随者是电动汽车。EVCS的偏好被设计为自给自足、为电动汽车提供充电服务和维持电池储能系统(BESS)的能量水平,通过不同的效用函数来描述。此外,追随者的偏好是最大化他们的电动汽车充电功率。该学习算法利用共识网络以迭代去中心化的方式达到通用Stackelberg均衡作为电动汽车间的电力调度。仿真中的静态和动态案例研究验证了该策略的成功实施、对不确定性的灵活性和电动汽车数量的可重构性。与以电动汽车平均充电时间、BESS充放电次数和向电网的能量交换为标准的集中式基准策略相比,该策略也具有优异的性能。最后,建立了一个缩小规模的实验实现,以验证基于博弈论的策略的功能和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A decentralized power dispatch strategy in an electric vehicle charging station

A decentralized power dispatch strategy in an electric vehicle charging station

In this study, a decentralized power dispatch in a charging station serving electric vehicles (EVs) is discussed. The power dispatch problem is solved through a Stackelberg game in real time. In this game, the leader is the EV charging station (EVCS) while the followers are EVs. The preferences of the EVCS are designed as being self-sufficient, providing charging services to the EVs, and maintaining the energy level of the battery energy storage system (BESS), which are described through different utility functions. In addition, the preferences of followers are to maximize their EV charging powers. The learning algorithm utilizes the consensus network to reach the generalized Stackelberg equilibrium as the power dispatch among EVs in an iterative decentralized manner. Both the static and dynamic case studies in the simulation verify the successful implementation of the proposed strategy, the flexibility to uncertainties and the re-configurability to the number of EVs. It also has an excellent performance compared with the centralized benchmark strategy with criteria, that is, the average EV charging time, the number of charge and discharge rate of the BESS and energy exchange to the grid. Finally, a down-scaled experiment implementation is set up to validate the functionality and the effectiveness of the game theory-based strategy.

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来源期刊
CiteScore
5.80
自引率
4.30%
发文量
18
审稿时长
29 weeks
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