A Bayesian Network Approach to Dynamic Risk Assessment of Hydrogen Refueling Stations

Hydrogen is a promising energy vector, especially for hard-to-abate sectors such as heavy-duty transport. However, establishing a robust and safe hydrogen infrastructure, including hydrogen refueling stations (HRS), is crucial to realize this potential. Risk assessment plays a pivotal role in identifying and mitigating potential hazards to ensure the safe and reliable operation of HRS.
Traditional risk assessments for new technologies like hydrogen often face challenges due to insufficient data and uncertainties. Bayesian Networks (BNs) offer a flexible framework to address these challenges by incorporating probabilistic reasoning and expert knowledge, enabling decision-making even with incomplete information.
In this study, BNs were applied to analyze an HRS, focusing on quantifying uncertainty using the concept of total probability bias. The methodology involved several key steps: First, an FMEA (Failure Modes and Effects Analysis) was employed for hazard identification, while a Bow-Tie (BT) diagram was used to model worst-case scenarios. Second, the BT was transformed into a BN to represent event connections and identify potential failure points visually. Third, Relevant reliability data for components and systems were integrated into the BN to provide estimates of failure probabilities. Finally, the BN was dynamically updated with new operational data, allowing for continuous refinement of risk assessments, improved risk mitigation strategies, and more informed decision-making processes.
Using Bayesian network modeling, this dynamic risk assessment method enables faster and more accurate risk evaluations, enhancing risk management and decision-making. The approach offers a flexible framework that incorporates uncertainty quantification, supporting the safe integration of hydrogen into the energy landscape.

Keywords: Hydrogen refueling stations, Risk assessment, Bayesian networks, Uncertainty.