Optimal long-term planning of a green hydrogen production system under alternative technological options

Green hydrogen (H 2 ) is a key pillar of sustainable energy strategies but remains more expensive than fossil alternatives. This study develops a mixed integer linear programming model to optimize long-term green H 2 production, maximizing economic returns under varying technologies, water sources, and demand scenarios. The system integrates electrolyzers, photovoltaic (PV) panels, PV-thermal (PV-T) collectors, and two water sources: treated sewage effluent (TSE) and industrial produced (IP) water. Revenues from oxygen by-products and surplus electricity are included. A key novelty is comparing PV-T collectors with PV panels. Using Qatar as the reference context for 2025–2050, results show that 99% of H 2 is produced using PV-T collectors due to higher efficiency, while PV panels mainly supply electricity to the grid. TSE is the preferred water source, and oxygen sales are the largest revenue stream. The Levelized Cost of Hydrogen (LCOH) falls by 2.8% under medium demand but rises by 18.3% under high demand. A carbon tax shifts water use from TSE to IP, with little change in system configuration. Sensitivity analysis identifies oxygen prices and PV-T fixed costs as key LCOH drivers. Scenario analysis incorporates demand growth, carbon taxation, and technology improvements. Three combined scenarios are evaluated with assigned probabilities. The medium-demand scenario yields the lowest expected LCOH, offering a resilient pathway that balances scalability and cost efficiency for large-scale green H 2 deployment in Qatar. • MILP model optimizes green H2 production under varied scenarios. • PV-T collectors dominate due to higher efficiency and output. • TSE water and oxygen sales drive economic performance. • Medium demand offers lowest LCOH and the most resilient pathway.