Demand aware optimization of hybrid energy storage systems for renewable electric vehicle charging stations under variable electricity demand

This paper presents a demand-aware optimization framework for a renewable-powered electric vehicle charging station (EVCS) using a hybrid renewable energy system (HRES) comprising solar photovoltaic generation, wind power, battery energy storage systems (BESS), and hydrogen energy storage systems (HESS). The IbI Logics Optimization Algorithm (ILA) is employed to determine the optimal system configuration under varying charging demand conditions. To capture realistic charging behavior, three demand scenarios are developed considering variations in fleet size, charging intensity, and temporal clustering of EV arrivals. Although the peak demand remains nearly constant at 930 kW across all scenarios, annual electricity demand varies by 19.28% due to differences in demand distribution and vehicle penetration levels. Results reveal that temporal demand characteristics strongly influence storage allocation and system economics. Hydrogen storage capacity varies by 36.74% across scenarios, highlighting its role in long-duration energy balancing, whereas battery storage capacity changes by less than 9%. In contrast, battery discharge power varies by more than 40%, demonstrating its critical role in mitigating clustered charging peaks. Comparative evaluation over 30 independent runs against the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates the consistently superior economic performance of ILA, achieving average reductions in mean NPC of approximately 4.66% relative to GA and 4.10% relative to PSO across the three demand scenarios.