Revenue Optimization Frameworks for Multi-Class PEV Charging Stations

The charging power of plug-in electric vehicles (PEVs) decreases significantly when the state of charge (SoC) gets closer to the fully charged state, which leads to a longer charging duration. Each time when the battery is charged at high rates, it incurs a significant degradation cost that shortens the battery life. Furthermore, the differences between demand preferences, battery types, and charging technologies make the operation of the charging stations a complex problem. Even though some of these issues have been addressed in the literature, the charging station modeling with battery models and different customer preferences have been neglected. To that end, this paper proposes two queueing-based optimization frameworks. In the first one, the goal is to maximize the system revenue for single class customers by limiting the requested SoC targets. The PEV cost function is composed of battery degradation cost, the waiting cost in the queue, and the admission fee. Under this framework, the charging station is modeled as a $M/G/S/K$ queue, and the system performance is assessed based on the numerical and simulation results. In the second framework, we describe an optimal revenue model for multi-class PEVs, building upon the approach utilized in the first framework. Two charging strategies are proposed: 1) a dedicated charger model and 2) a shared charger model for the multi-class PEVs. We evaluate and compare these strategies. Results show that the proposed frameworks improve both the station performance and quality of service provided to customers. The results show that the system revenue is more than doubled when compared with the baseline scenario which includes no limitations on the requested SoC.