Dynamic Pricing, Scheduling, and Energy Management for Profit Maximization in PHEV Charging Stations

• Published in: IEEE transactions on vehicular technology Vol. 66; no. 2; pp. 1011 - 1026
• Main Authors: Kim, Yeongjin, Kwak, Jeongho, Chong, Song
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Charging service provider (CSP); Charging stations; dropping; Energy management; Energy storage; Hybrid electric vehicles; plug-in hybrid electric vehicle (PHEV); Pricing; profit maximization; Renewable energy; Renewable energy sources; scheduling; Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2016.2567066

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the "Lyapunov drift-plus-penalty" technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm.