Risk Analysis of Green Hydrogen Storage Systems Using Fault Tree and Bayesian Network

Hydrogen is increasingly recognised as a key element in the global transition towards sustainable and clean energy, offering a promising alternative to fossil fuels. Its production from renewable sources via electrolysis aligns with the goals of reducing carbon emissions and achieving energy security. However, despite its advantages, hydrogen poses significant challenges, particularly in terms of safe and efficient storage. Due to its low molecular weight, high diffusivity, and extreme flammability, hydrogen storage systems require rigorous risk assessment to prevent accidents and ensure operational reliability. This study focuses on evaluating the unavailability and safety of green hydrogen storage systems using two well-established risk analysis methods: Fault Tree Analysis (FTA) and Bayesian Networks (BNs). These complementary approaches enable the identification of critical failure modes and the quantification of system reliability under various scenarios. The comparative analysis reveals that both methods yield nearly identical results, underscoring the robustness and validity of the risk assessment. Based on these findings, the study proposes targeted safety measures aimed at mitigating identified risks and enhancing the secure deployment of hydrogen storage infrastructure. Ultimately, this work contributes to the advancement of safer hydrogen technologies and supports the broader energy transition by addressing key safety concerns associated with green hydrogen storage.