TY - GEN
T1 - ADMM-Based decentralized electric vehicle charging with trip duration limits
AU - He, Gaoqi
AU - Chai, Zhifu
AU - Lu, Xingjian
AU - Kong, Fanxin
AU - Sheng, Bing
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - ADMM
KW - Decentralized
KW - Electtic Vehicle Charging
KW - Trip Duration Limits
UR - http://www.scopus.com/inward/record.url?scp=85083230986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083230986&partnerID=8YFLogxK
U2 - 10.1109/RTSS46320.2019.00020
DO - 10.1109/RTSS46320.2019.00020
M3 - Conference contribution
AN - SCOPUS:85083230986
T3 - Proceedings - Real-Time Systems Symposium
SP - 107
EP - 119
BT - Proceedings - 2019 IEEE 40th Real-Time Systems Symposium, RTSS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th IEEE Real-Time Systems Symposium, RTSS 2019
Y2 - 3 December 2019 through 6 December 2019
ER -