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• W2998263013 abstract "In order to minimize the peak load of electric vehicles (EVs) and enhance the resilience of fast EV charging stations, several sizing methods for deployment of the stationary energy storage system (ESS) have been proposed. However, methods for assessing the optimality of the obtained results and performance of the determined sizes under different conditions are missing. In order to address these issues, a two-step approach is proposed in this study, which comprises of optimality analysis and performance evaluation steps. In the case of optimality analysis, random sizes of battery and converter (scenarios) are generated using Monte Carlo simulations and their results are compared with the results of sizes obtained from sizing methods. In order to carry out this analysis, two performance analysis indices are proposed in this study, which are named the cost index and the power index. These indices respectively determine the performance of the determined sizes in terms of total network cost and performance ratio of power bought during peak intervals and investment cost of the ESS. During performance evaluation, the performance of the determined sizes (battery and converter) are analyzed for different seasons of the year and typical public holidays. Typical working days and holidays have been analyzed for each season of the year and suitability of the determined sizes is analyzed. Simulation results have proved that the proposed method is suitable for determining the optimality of results obtained by different sizing methods." @default.
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• W2998263013 date "2020-01-02" @default.
• W2998263013 modified "2023-10-13" @default.
• W2998263013 title "Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation" @default.
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• W2998263013 doi "https://doi.org/10.3390/en13010230" @default.
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