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

Abstract

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 T & uuml;rkiye. 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 na & iuml;ve 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.