Resilience analysis for energy foresight scenarios: addressing the deep uncertainties of hydrogen deployment in industrial hubs

Overview & Synopsis

Hydrogen is emerging as a promising solution for reducing greenhouse gas emissions in key industrial sectors. However, its deployment faces significant uncertainties related to technological progress, economic viability, political decisions, and geopolitical dynamics. This study adopts a scenario-based approach to assess the resilience of a hydrogen ecosystem in an industrial hub. A two-step methodology is developed to explore how an optimised multi-energy system would respond to unanticipated disruptions such as fuel price shocks, policy changes, or slower-than-expected technological advancements. First, an integrated capacity expansion and operation model determines optimal system capacities assuming perfect foresight. Second, the resulting energy system is evaluated under an alternative context, where only operational decisions are optimised. Special attention is given to electricity price modelling, due to its crucial impact on electrolytic hydrogen production. Nine scenarios are investigated, reflecting major categories of deep uncertainty relevant to long-term energy planning, including policy directions, technological innovation, and resource availability. Results show that the most pessimistic scenarios in terms of hydrogen cost are also the most robust. Scenarios with more electrolysis capacities are generally more expensive but less sensitive to disruptions. In contrast, scenarios relying on early development of a European hydrogen transportation infrastructure are cheaper but less resilient. For policymakers, these findings highlight the need for coherent strategies integrating electricity and hydrogen systems to balance cost-efficiency, resilience, and environmental goals. Clear strategic direction, combined with support for local hydrogen industries, will be vital to securing Europe’s energy sovereignty and long-term sustainability in the face of evolving uncertainties.