Robust Optimization Scheduling of an Electric Vehicle Charging Station Based on Interval-Valued Intuitionistic Fuzzy Information

The ambiguity and incompleteness of electric vehicle (EV) user behavior preferences, as well as the uncertainty of randomly arriving EV charging demands, pose significant challenges for the scheduling of an EV charging station (EVCS). To address these issues, this paper proposes a bi-level distributionally robust optimization (DRO) model based on interval-valued intuitionistic fuzzy (IVIF) information. The IVIF information excels at capturing the dynamic and ambiguous nature of user preferences. An interpretable quantitative evaluation method based on IVIF is proposed to provide reliable prior knowledge for decision-making. The novel reward functions account for both group and individual characteristics, achieving a balance in their timely responses. In order to solve the problem of mixed-integer second-order cone and semidefinite programming (MI-SOCSDP) with sparse uncertain data, an alternating optimization procedure (AOP) is developed. The experimental results effectively demonstrate the consistency between the charging/discharging decision-making processes and users' fuzzy preferences. Moreover, the proposed method minimizes the worst-case expected cost over the ambiguity sets of probability distributions, effectively verifying the robustness of the model.