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The growing integration of electric vehicles (EVs) presents a significant challenge to the economic operation and physical integrity of smart grids, primarily due to uncontrolled charging loads that can create new demand peaks that violate grid operational limits. This paper proposes a novel two-stage optimization framework to address this challenge by co-optimizing microgrid assets with a flexible, hierarchically-managed EV charging load. The first stage employs a fuzzy inference system, consistent with state-of-the-art demand modeling techniques, to forecast the composition of the EV fleet based on uncertain user behaviors. The second stage formulates a Mixed-Integer Linear Program (MILP) that disaggregates this forecast and allocates charging power across multiple State of Charge (SoC) categories, enforcing a strict fairness protocol that prioritizes the most urgent charging needs. The framework’s performance was rigorously evaluated against an uncontrolled baseline and a standard deterministic MILP benchmark. Simulation results demonstrate that the proposed model achieves a 7.1% reduction in total operational costs compared to the standard MILP, all while successfully adhering to the physical grid import limit that the uncontrolled baseline violates. Furthermore, a final analysis reveals that the implementation of the fairness-based hierarchy incurs no additional economic penalty, demonstrating that in a well co-optimized system, user equity and economic efficiency are not mutually exclusive goals. This model offers a scalable solution for future smart grid applications where EV integration is critical in sustainable energy systems.
Ikram et al. (Mon,) studied this question.