Techno-economic optimization of e-hydrogen production in Saudi Arabia

We present a comprehensive techno-economic analysis (TEA) that systematically optimizes e-hydrogen production costs in the Kingdom of Saudi Arabia, a region characterized by abundant solar irradiance and promising wind energy potential. We conducted a global systematic review of previous TEAs of e-hydrogen production. The results revealed a lack of user-friendly and intuitive analytical tools. To address this gap, we developed the e-Hydrogen Cost Optimizer, a Python-based optimization tool built on a Mixed-Integer Linear Programming framework that advances previous approaches. This optimizer improves upon prior art by explicitly accounting for hourly variability in renewable energy supply and hydrogen demand, and by integrating multiple system components within a single scalable model. The model was benchmarked against published case studies to ensure validity. Our optimization approach identifies the most cost-effective integration of photovoltaic (PV) solar and wind energy, electrolyzer capacity, and storage solutions, considering regional climate, system efficiencies, capital and operational expenditures, and hydrogen demand at daily and yearly resolutions. Case studies for 3 coastal Saudi cities (Duba, Yanbu, and Jubail) demonstrate location-specific trends: Duba and Yanbu rely heavily on solar PV due to high solar full load hours, while Jubail benefits from a diversified mix including wind energy. The optimized Levelized Cost of Hydrogen ranges from 2. 67 to 2. 89 /kg H₂ in Duba, 2. 78–2. 88 /kg H₂ in Yanbu, and 2. 95–3. 17 /kg H₂ in Jubail, reflecting competitive costs aligned with international benchmarks under favorable conditions. These findings demonstrate Saudi Arabia’s potential to achieve competitive renewable hydrogen costs and strengthen its role in emerging global hydrogen markets. • e-Hydrogen Cost Optimizer developed as a user-friendly tool for techno-economic optimization of e-hydrogen systems. • Optimization framework relies on Mixed Integer Linear Programming (MILP) • Levelized Cost of Hydrogen (LCOH) used as the main economic optimization metric. • Case studies: solar dominates in Duba/Yanbu; Jubail uses a solar–wind mix. • LCOH ranges 2. 67–3. 17 /kgH 2, aligning with global green hydrogen cost targets.