A Spatio-techno-economic Analysis for Wind-powered Hydrogen Production in Tunisia

This study investigated the potential of large-scale wind-powered green hydrogen production in Tunisia through a combined spatio-techno-economic analysis. Using a geographic information system-based Multi-Criteria Decision-Making approach, optimal locations for wind-hydrogen systems were identified based on criteria such as hydrogen potential, slope, land use, and proximity to essential infrastructure (water resources, grid network, transportation, and urban areas). The Best worst method (BMW) technique was employed to assign weights to the identified criteria. Subsequently, a techno-economic assessment was conducted at six prospective onshore wind project sites to evaluate the economic feasibility of hydrogen production. Therefore, the main contribution of this study lies in the synergistic combination of a wind-specific focus, application of an efficient and consistent BWM methodology within a GIS framework, and detailed site-specific techno-economic validation of the spatially identified optimal locations. The results of the spatial analysis indicated that 15.91 % (21,185 km²) of Tunisia’s land was suitable for wind-based hydrogen production, with 1110 km² exhibiting exceptional suitability, primarily in the central-western, southwestern, southeastern, and coastal regions. Among the five evaluated wind turbine models, the E115-3000 proved to be the most efficient. Site S3 (Sidi Abdelrahman) demonstrated the highest annual energy output (117.7 GWh) and hydrogen production potential (1267–1482 t), while S5 (Souk El Ahed) yielded the lowest energy output (50.121 GWh). Economically, S3 emerged as the most advantageous site, with the lowest Levelized Cost of Electricity (0.0446 $/kWh) and Levelized Cost of Hydrogen (3.581 $/kg), followed by S4. S5 had the highest LCOE (0.0643 $/kWh) and LCOH (5.169 $/kg). These findings highlight Tunisia’s promising potential for cost-competitive green hydrogen production, particularly in identified optimal locations, thus contributing to renewable energy targets and sustainable development.