Multiobjective MINLP–Based Optimal Design for Storage-Supported EV Charging Hub in Urban Areas

Integration of public electric vehicle (EV) charging stations into the existing power system presents a challenge in regard to stability, efficiency, and cost-effectiveness, particularly in urban areas with limited power grid capacity. In this paper we propose a comprehensive multiobjective optimization strategy for optimal sizing and configuration of storage-supported EV charging infrastructure using a mixed-integer nonlinear programming (MINLP) approach. The strategy aims to minimize the total system cost while maximizing energy storage contribution and improving quality of service, subject to technical constraints regarding power system capacity, charger utilization, and user waiting times. The proposed optimization framework is suitable for implementation of various optimization methods. Grid search and genetic algorithm are compared within MINLP to validate the robustness and solver independence of the optimization process. Additionally, the paper explores the impact of varying weighting factors in the objective function (OF), reflecting different strategic priorities during the planning phase of EV charging infrastructure development. Quantitative results for the considered case study indicate that the optimal configuration consists of five high-power chargers (150 kW each), 10 low-power chargers (22 kW each), and an energy storage system of 850 kWh. This solution achieves an internal rate of return (IRR) of 12.4%, a battery contribution factor (BCF) of 27.6%, and maintains user waiting times below 15 min even on peak demand days. These results confirm the capability of the proposed framework to balance economic viability, energy resilience, and quality of service within realistic urban grid constraints.