文献类型：会议论文

英文题名：ADMM-Based Decentralized Electric Vehicle Charging with Trip Duration Limits

作者：He, Gaoqi[1,2];Chai, Zhifu[2];Lu, Xingjian[1];Kong, Fanxin[3];Sheng, Bing[4]

机构：[1]East China Normal Univ, Sch Comp Sci & Technol, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China;[3]Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY USA;[4]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai, Peoples R China

会议论文集：40th IEEE Real-Time Systems Symposium (RTSS)

会议日期：DEC 03-06, 2019

会议地点：Hong Kong, HONG KONG

语种：英文

摘要：With the large-scale deployment of Electric Vehicles (EVs), the unbalanced distribution of charging needs and random charging behaviors cause charging stations (CSs) congestion. This degrades EV drivers' quality of experience by extending charging waiting time and increasing charging fee. Thus, EV owners are facing a critical issue on how to decrease the cost of charging, which consists of two parts: charging duration and charging fee. A great deal of existing work is confined to finding CSs to optimize the two parts individually. However, it still remains unexplored how to jointly minimize charging duration and charging fee under an overall time limit (i.e., deadline) of a scheduled trip. The problem is the focus of this paper. First, we formulate this problem as a 0-1 Integer Linear Programming problem and show its NP-Hardness. Then, we propose an efficient distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM). The algorithm decomposes the original problem into sub-problems that can be solved locally and in parallel between charging stations and the global coordinator. Finally, we carry out extensive simulations based on real-life transport network data, and the results show that the proposed approach brings significant cost savings over existing ones.