基于ADMM的分布式有序充电调度算法
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作者单位:

1.华东理工大学 信息科学与工程学院;2.华东师范大学 计算机科学与技术学院 上海

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TP

基金项目:

国家自然科学青年基金资助项目(61602175),上海市自然科学基金(19ZR1415800),上海市科委科普项目(19DZ2301100))


ADMM-based Coordinated EV Charging Scheduling Algorithm
Author:
Affiliation:

1.School of Information Science and Engineering,East China University of Science and Technology,Shanghai;2.School of Computer Science and Technology,East China Normal University,Shanghai

Fund Project:

Supported by the NSF of China (61602175), NSF of Shanghai (19ZR1415800), and Science popularization project of Shanghai Science and Technology Commission (19DZ2301100).

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    摘要:

    随着电动汽车数量的不断增加,大量电动汽车的无序充电行为会导致电网的过载和电池寿命损耗。虽然当前已经有很多研究关注电动汽车的有序充电行为,但是如何在大规模的有序充电过程中实现最大化车主便捷性的同时减少电池寿命损耗尚未被研究。充电的便捷性和电池的损坏是车主非常关心的两个方面,研究关注充电便捷性和减少电池损坏的充电服务调度优化对充电站充电服务质量和用户满意度的提升具有重要意义。本文系统地研究了这个问题,并提出了一个实时的充电服务调度策略来协调大量电动汽车的充电行为,以实现最大化车主便捷性的同时降低电池损耗。为了减少充电过程中信息直接交换造成的隐私泄露,同时降低算法的计算复杂度,一个基于交替方向多乘子Alternating Direction Method of Multipliers(ADMM)的分布式算法被提出。大量实验表明了所提算法相比已有算法有显著提升,能减少33.0%的电池寿命损耗和18.3%的电费支出。

    Abstract:

    With the increasing number of Electric Vehicles (EVs), the out-of-order random charging behaviors cause the smart grid overloaded and the battery depreciation. Though a great deal of works focusing on EV charging coordination are proposed, it still remains unexplored to coordinate the EV charging by maximizing the convenience of EV drivers and minimizing the battery depreciation. This is a vital problem for increasing the service quality of the charging station and the users' satisfaction, since the convenience and lifetime of battery are specially concerned by EV drivers. In this paper, we systematically study the problem and a real-time charging scheme is proposed to coordinate the electric vehicle (EV) charging and decrease the battery depreciation to the battery. To prevent private leak and decrease calculation complexity, a ADMM-based distributed method is proposed in our paper. Extensive evaluations show that our distributed optimization method brings significant cost savings over existing methods. Simulation results show that our proposed algorithm could reduce the price cost of EV drivers and battery lifetime depreciation by up to 18.3% and 33.0%.

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  • 收稿日期:2020-03-12
  • 最后修改日期:2020-04-13
  • 录用日期:2020-04-14
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