A Social Welfare Theory-Inspired Lexicographic Optimal Charging Scheduling Framework for Modular EV Fast Charging Stations

Abstract

Fast charging technology is crucial for widespread electric vehicle (EV) adoption. To enhance efficiency and scalability, charging equipment manufacturers are shifting towards a modular architecture in fast charging stations (FCSs). This architecture features multiple converter modules and charging ports, allowing flexible power allocation through module-to-port assignment. However, it introduces challenges, particularly when ports operate with fewer modules, necessitating EV charging scheduling schemes to allocate limited FCS capacity while maintaining high quality-of-service (QoS). Traditional scheduling methods are ill-suited for modular FCS settings due to unique characteristics such as discrete module-to-port allocation, state-of-charge-dependent charge curves, and power ramp rate limits. This work proposes a social welfare-inspired EV scheduling framework for modular FCSs, using lexicographic optimization and receding horizon control. The framework includes a computationally efficient charge curve model based on sliding convex hulls and a mathematical model tailored for modular FCSs. The three-stage lexicographic model, derived from Rawlsian and Benthamite social welfare theories, accommodates customer preferences and EV characteristics for high QoS provision. A welfare score metric, adapted from social welfare theories, is also introduced for multi-faceted QoS assessment. Across ceteris paribus experiments, the proposed framework consistently outperforms three benchmark methods, with a margin of up to 34% in welfare scores over the second-best method. In a diverse set of randomized EV arrival scenarios, the framework enables a median welfare around 85%, outperforming the benchmarks by at least 7.8%, with statistical tests confirming its significance. Moreover, ramp rate violations are kept at a minimum, while the computational efficiency and scalability are verified.