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In recent years, smart grid technologies have attracted significant attention. This is driven by the rising cost and depletion of fossil fuels, accelerating the transition towards cleaner energy. This shift is exemplified by the growing popularity of electric vehicles (EVs) for their lower carbon emissions and operational benefits. However, rapid EV adoption and uncoordinated charging pose challenges for power grids. Managing peak demand and maintaining grid stability are becoming increasingly difficult. To address this, this study proposes a multi-factor, priority-based framework for scheduling EV charging at centralized stations. The framework integrates a day-ahead scheduling algorithm with a valley-filling strategy to shift charging to off-peak hours, supported by a dynamic time-of-use (TOU) tariff. Each EV is assigned a priority factor, giving higher-priority vehicles precedence during overloads. A genetic algorithm (GA) is used to optimally assign EVs to vehicle-to-grid (V2G) operations. If V2G support is insufficient, lower-priority vehicles are shifted to adjacent slots or deferred to the next cycle, ensuring fairness. Simulation results with 50 vehicles for charging and 10 for discharging validate the proposed framework. The average-to-peak demand ratio improves from 70.68 % before EV deployment to 97.4 % afterward using the valley-filling technique, enhancing load profile and resource utilization. In addition, the dynamic TOU tariff reduces total charging costs by 5 % compared to existing static tariffs. The scope of this study focused on a fixed EV fleet; relaxing this is a potential avenue for future research. Overall, the framework offers a scalable and adaptive solution for sustainable EV charging in smart grids.
Hamim et al. (Thu,) studied this question.