Autors: Adamova V., Popov, S. A., Baeva, S. K., Hinov, N. L. Title: Design Scenarios and Risk-Aware Performance Framework for Modular EV Fast Charging Stations Keywords: electric vehicle (EV) fast charging, grid constraints, modular charging stations, Pareto analysis, power quality, risk-aware performance, scenario-based simulationAbstract: The rapid growth of electric vehicles (EVs) requires the deployment of modular fast charging stations that balance charging performance, grid limitations, and investment costs. This study develops design scenarios for modular EV fast charging stations and introduces a risk-aware performance analysis framework under power and grid quality constraints. A simulation-based approach evaluates 286 station configurations with ten charging outlets (20–50 kW), grouped into 16 representative classes based on three key dimensions: total installed power, dominant charger type, and peak load risk. Performance metrics such as efficiency of charger utilization, load factor, and overload risk are used to construct Pareto frontiers and identify optimal trade-offs between capacity and operational safety. Results indicate that medium-power configurations (251–350 kW) achieve the best compromise between efficiency (>82%) and load factor (>50%) without exceeding safe operating limits, while high-power configurations enable maximum throughput at the expense of elevated overload risk. Sensitivity analysis confirms the robustness of the proposed grouping approach under variations in arrival rates, battery sizes, and grid constraints (400–600 kW). The findings provide practical insights into the design and risk management of modular charging stations, supporting urban planners and power engineers in developing efficient and reliable EV charging infrastructure. References - Fescioglu-Unver N. Yildiz Aktaş M. Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing Renew. Sustain. Energy Rev. 2023 188 113873 10.1016/j.rser.2023.113873
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