Stochastic Modeling of Finite-Capacity Electric Vehicle Charging Infrastructures with Flexible Service Provisioning

Abstract In the recent past, the management of electric vehicle (EV) charging infrastructure received increased attention due to its significant role in enabling the transition to sustainable transportation. The current study investigates a flexible queueing mechanism for EV charging stations under two types of service disruptions. The model incorporates negative and positive arrivals, partial server breakdowns, and a threshold-based repair mechanism. The proposed modeling reveals that negative arrivals, indicating cancelled or withdrawn charge requests, interrupt ongoing services. Such disruption adversely impacts the overall operational efficiency of the charging infrastructure. For modeling purposes, the hypothesis of the quasi-birth-and-death (QBD) process in unification with the matrix geometric solution approach is utilized. The framework establishes the stationary states of the system, determines the steady-state probability vectors, and estimates numerous essential performance metrics, including average waiting time at the charging system, system utilization, reliability measures, etc. Numerical studies reveal that when the negative arrival rate increases, the average waiting time and system utilization decrease progressively, while the negative arrival rate also has a decreasing congestion effect on the system. Further investigations indicate that repair rates have a higher impact on system throughput than individual breakdown rates, emphasizing the necessity of prompt repair provisioning. Further, graphical representations demonstrate how variations in critical parameters influence system performance. These insights help improve the operational efficiency of EV charging systems in practice. The proposed research addresses how adverse demand fluctuations could impact initiatives to improve the adaptability and flexibility of EV charging infrastructures, thereby facilitating the wider adoption of EV transportation.