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.


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    Titel :

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


    Beteiligte:
    Berk Saner, Can (Autor:in) / Saha, Jaydeep (Autor:in) / Srinivasan, Dipti (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    7596258 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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