Resilience Assessment of Green Hydrogen Production Facilities under Escalation Scenarios

Green hydrogen production facilities are crucial for low-carbon energy systems but are increasingly exposed to technical, natural, and cyber-physical threats that can evolve into escalation scenarios. In this context, resilience, the ability of a system to anticipate, absorb, respond to, and recover from disruptions while maintaining functionality, is a key property. However, traditional risk assessment approaches mainly focus on failure prevention, neglecting post-failure recovery and adaptation. To address this gap, this study proposes a quantitative, time-dependent framework to assess the resilience of green hydrogen production facilities under escalation scenarios using dynamic Bayesian networks. The model captures the evolving interactions among technical, human, and organizational factors during multiple disruptions. A case study of an alkaline electrolyzer shows that resilience is strongly influenced by escalation mechanisms, as well as by redundancy, monitoring, and safety barriers. Overall, the framework provides a dynamic and integrated perspective to support the design, operation, and management of resilient green hydrogen infrastructures under complex conditions.

Keywords: Resilience assessment, dynamic Bayesian networks, Escalation scenarios, Electrolyzers, Green hydrogen, Energy transition.