Most suitable locations for large scale PV-driven hydrogen production valleys implementation in Portugal using AHP-GIS combination approach

Leveraging solar resources for hydrogen production represents a key pathway to decarbonization and the advancement of a sustainable energy system. Against this backdrop, the objective of this study is to identify the most suitable locations for the development of large-scale solar-driven hydrogen valleys in Portugal. To achieve this, high-resolution input data were employed in combination with the Analytical Hierarchy Process and Geographic Information Systems to create a land suitability atlas for the country. Furthermore, a techno-economic analysis was conducted to assess the feasibility of implementing such projects in the selected areas, considering hydrogen production potential and costs associated with the electrolyser and the different photovoltaic plants configurations. Results show that the “highly suitable” locations, representing nearly 2% of the total land area -approximately 1,665.67 km 2 -, offer significant potential for hydrogen production. Moreover, for the four selected hotspots, the techno-economic results were very promising, especially at Hotspot 2, near Sines, where the levelized cost of hydrogen is of 2.95 €/kg with a high annual “green” hydrogen production of 2,351 tons for 50 MW DC photovoltaic capacity, which means 47 kg for each kW of capacity. This result demonstrates the ability of Portugal to be positioned in the “green” hydrogen market, especially with the existing concurrence from Morocco and Spain and also it supports the energy programs in Portugal for a “green” and sustainable development of the country. • GIS–AHP framework developed for national-scale PV–H 2 suitability analysis • Highly suitable PV–H 2 zones identified, representing 1.89% of mainland Portugal • Techno-economic analysis of fixed, one- and two-axis PV systems at four sites • One-axis PV systems yield the minimum LCOH 2 (2.95–3.20 €/kg) • Robustness examined through spatial sensitivity analyses.