Multi-criteria optimization of photovoltaic-based hydrogen refueling stations under behavioral and financial uncertainty: A case study

The large-scale deployment of hydrogen refueling stations remains critically constrained by renewable intermittency, temporal demand variability, and macroeconomic volatility, which is particularly acute in emerging economies such as Türkiye. Existing studies largely rely on static or annual-average demand profiles and seldom capture the effects of high economic uncertainty, including inflation, discount rate fluctuations, and investment risk, on the techno-economic feasibility of hydrogen projects. Unlike conventional approaches, this work develops a behavior-driven, high-resolution microsimulation framework that generates realistic, temporally detailed hydrogen demand profiles synchronized with solar availability, seasonal transitions, and user refueling behavior. A five-dimensional scenario matrix, spanning electrolyzer power, hydrogen storage capacity, photovoltaic capacity and investment cost, and discount rate, enables multi-criteria optimization of the Levelized Cost of Hydrogen (LCOH), self-sufficiency, and carbon footprint under real-world economic uncertainty. Results reveal that naïve oversizing strategies drive curtailment above 44% and LCOH beyond 11.8$/kg, while demand-synchronized, economically robust configurations achieve 7.06–8.76$/kg LCOH, 82% self-sufficiency, and up to 75% CO2 reduction. By explicitly incorporating behavioral variability, temporal demand dynamics, and macroeconomic risk, the proposed framework offers a policy-relevant, investment-oriented decision-support tool for designing hydrogen refueling stations that are cost-optimal and financially resilient, effectively bridging the gap between techno-economic modeling and real-world station deployment planning.