Risk-Informed Separation Distances for Hydrogen Refueling Stations-Paper

The development of an infrastructure for the future hydrogen economy will require the simultaneousdevelopment of a set of codes and standards. As part of the U.S. Department of Energy Hydrogen,Fuel Cells & Infrastructure Technologies Program, Sandia National Laboratories is developing thetechnical basis for assessing the safety of hydrogen-based systems for use in thedevelopment/modification of relevant codes and standards. This work includes experimentation andmodeling to understand the fluid mechanics and dispersion of hydrogen for different releasescenarios, including investigations of hydrogen combustion and subsequent heat transfer fromhydrogen flames. The resulting technical information is incorporated into engineering models that areused for assessment of different hydrogen release scenarios and for input into quantitative riskassessments (QRA) of hydrogen facilities. The QRAs are used to identify and quantify scenarios forthe unintended release of hydrogen and to identify the significant risk contributors at different types ofhydrogen facilities. The results of the QRAs are one input into a risk-informed codes and standardsdevelopment process that can also include other considerations by the code and standard developers.This paper describes an application of QRA methods to help establish one key code requirement: theminimum separation distances between a hydrogen refueling station and other facilities and the publicat large. An example application of the risk-informed approach has been performed to illustrate itsutility and to identify key parameters that can influence the resulting selection of separation distances.Important parameters that were identified include the selected consequence measures and risk criteria,facility operating parameters (e.g., pressure and volume), and the availability of mitigation features(e.g., automatic leak detection and isolation). The results also indicate the sensitivity of the results tokey modeling assumptions and the component leakage rates used in the QRA models.