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基于ADMM的分布式有序充电调度算法
引用本文:柴致富,何高奇,卢兴见.基于ADMM的分布式有序充电调度算法[J].重庆大学学报(自然科学版),2020,43(7):101-110.
作者姓名:柴致富  何高奇  卢兴见
作者单位:华东理工大学 信息科学与工程学院, 上海 200237;华东理工大学 信息科学与工程学院, 上海 200237;华东师范大学 计算机科学与技术学院, 上海 200062
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资助项目(19ZR1415800);上海市科委科普资助项目(19DZ2301100)。
摘    要:随着电动汽车数量不断增加,大量电动汽车的无序充电行为会导致电网过载和电池寿命损耗。虽然当前已有很多研究关注电动汽车的有序充电行为,但如何在大规模有序充电过程中实现最大化车主便捷性同时减少电池寿命损耗尚未被研究。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提升具有重要意义。笔者提出一个实时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同时降低电池损耗。为减少充电过程中信息直接交换造成隐私泄露,同时降低算法计算复杂度,基于交替方向多乘子(ADMM,alternating direction method of multipliers)的分布式算法被提出。大量实验表明所提算法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。

关 键 词:充电系统  ADMM  调度方法  分布式算法
收稿时间:2020/3/12 0:00:00

ADMM-based coordinated EV charging scheduling algorithm
CHAI Zhifu,HE Gaoqi,LU Xingjian.ADMM-based coordinated EV charging scheduling algorithm[J].Journal of Chongqing University(Natural Science Edition),2020,43(7):101-110.
Authors:CHAI Zhifu  HE Gaoqi  LU Xingjian
Institution:School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China;School of Computer Science and Technology, East China Normal University, Shanghai 200062, P. R. China
Abstract:With the increasing number of electric vehicles (EVs), the out-of-order random charging behaviors cause the smart grid overload and the battery depreciation. Despite the fact that a lot of research has focused on EV charging coordination, it remains unexplored how to coordinate the EV charging while maximizing the convenience of EV drivers and minimizing the battery depreciation, which is a vital to the improvement of 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 studied the problem and a real-time charging scheme was proposed to coordinate the electric vehicle (EV) charging and decrease the battery depreciation. To prevent private leak and decrease calculation complexity, an ADMM-based distributed method was proposed. Extensive evaluations show that our distributed optimization method brings significant cost savings over existing methods. Simulation results show that the proposed algorithm could reduce the price cost of EV drivers and battery lifetime depreciation by up to 18.3% and 33.0% respectively.
Keywords:charging systems  ADMM  scheduling methods  distributed algorithms
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