Electric Vehicle Charge Scheduling Mechanism to Maximize Cost Efficiency and User Convenience

• Published in: IEEE transactions on smart grid Vol. 10; no. 3; pp. 3020 - 3030
• Main Authors: Chung, Hwei-Ming, Li, Wen-Tai, Yuen, Chau, Wen, Chao-Kai, Crespi, Noel
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Automobile industry; Batteries; Charging; Charging stations; Complexity; Computation; Computer Science; Computer simulation; Distributed algorithms; Electric vehicles; Networking and Internet Architecture; online algorithm; Optimal scheduling; Optimization; Pareto optimality; Production scheduling; Scheduling; Scheduling algorithms; smart grid; Stakeholders; State of charge; Stations; Pareto optimality; Online algorithm; Scheduling; Smart grid; Electric Vehicles
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2018.2817067

This paper investigates the fee scheduling problem of electric vehicles (EVs) at the micro-grid scale. This problem contains a set of charging stations controlled by a central aggregator. One of the main stakeholders is the operator of the charging stations, who is motivated to minimize the cost incurred by the charging stations, while the other major stakeholders are vehicle owners who are mostly interested in user convenience, as they want their EVs to be fully charged as soon as possible. A bi-objective optimization problem is formulated to jointly optimize two factors that correspond to these stakeholders. An online centralized scheduling algorithm is proposed and proven to provide a Pareto-optimal solution. Moreover, a novel low-complexity distributed algorithm is proposed to reduce both the transmission data rate and the computation complexity in the system. The algorithms are evaluated through simulation, and results reveal that the charging time in the proposed method is 30% less than that of the compared methods proposed in the literature. The data transmitted by the distributed algorithm is 33.25% lower than that of a centralized one. While the performance difference between the centralized and distributed algorithms is only 2%, the computation time shows a significant reduction.