Multi-level optimization of battery energy storage systems for electric vehicle charging applications under uncertainty

The rapid growth of electric vehicle charging demand introduces significant operational challenges for distribution networks. Battery energy storage systems (BESS) have emerged as a key solution to enhance grid flexibility and support reliable charging services. This paper proposes a multi-level optimization framework for the optimal sizing, placement, and operation of stationary battery energy storage systems integrated with electric vehicle charging infrastructure under uncertainty in mobility patterns and renewable generation. A Pareto-based optimization approach is adopted to capture trade-offs between system cost, network performance, and reliability. Simulation results on the IEEE 33-bus and IEEE 69-bus distribution networks demonstrate that, relative to the electric-vehicle-only configuration, the integrated electric-vehicle–photovoltaic–battery configuration increases electric-vehicle hosting capacity by 42.5%, reduces feeder peak load by 23.5%, improves the minimum bus voltage from 0.951 p.u. to 0.969 p.u., and reduces the charging-shortfall probability by more than 60% compared with deterministic planning. These results highlight the critical role of stationary battery energy storage systems in enabling reliable, efficient, and scalable electric vehicle charging under uncertainty.