Robust optimal design of hybrid renewable energy systems for constant green hydrogen supply

The optimal sizing of hybrid renewable energy systems for constant hydrogen supply – particularly suited for decarbonizing hard-to-abate industrial processes – requires managing temporal fluctuations of renewable energy sources through intermediate storage and is often tackled using mathematical optimization. However, efficiently finding a design that reliably meets constant hydrogen demand over long time horizons remains challenging. This work addresses the challenge using robust optimization techniques deriving a tractable robust counterpart for a constant supply system based on box uncertainty sets considering meteorological parameters as uncertainties. The investigated system comprises wind turbines, photovoltaic panels, an electrolyzer and electrical energy and hydrogen storage. To account for uneven geographical availability, three archetypal locations with distinct renewable resource profiles are investigated. As part of the comprehensive analysis, the impact of varying techno-economic assumptions and different operation scenarios, including grid connection and variable hydrogen supply, is examined. Results show baseline levelized cost of hydrogen values of 3.7–4.3 € kg −1 , rising to 7 € kg −1 under pessimistic techno-economic assumptions. Allowing variable demand trims costs by up to 1.4 € kg −1 , while grid interconnection yields up to 0.7 € kg −1 savings. Uncertainty consideration through robust optimization removes up to 23 % of the hourly shortfalls seen in nominal designs evaluated on historic profiles, but adds roughly 1 € kg −1 , an insurance premium for reliable supply. Sites with a consistently strong, complementary renewable mix (particularly wind-rich regions augmented by solar) turn out to be the most economical. This unified framework reveals the cost trade-offs among flexibility, grid interaction and robustness, equipping developers and policymakers with actionable insights for green hydrogen production. • Optimization framework for sizing hybrid renewable energy systems. • Location-specific optimal design for three archetypal locations. • Investigation of varying techno-economic assumptions and operation scenarios. • Robust designs for reduction of demand shortfall on historic weather profiles. • Cost trade-offs with respect to flexibility, grid connection and supply robustness.