Risk-Informed Separation Distances For Hydrogen Refuelling Stations

The development of an infrastructure for the future hydrogen economy will require the simultaneous development 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 the technical basis for assessing the safety of hydrogen-based systems for use in the development/modification of relevant codes and standards. This work includes experimentation and modelling to understand the fluid mechanics and dispersion of hydrogen for different release scenarios, including investigations of hydrogen combustion and subsequent heat transfer from hydrogen flames. The resulting technical information is incorporated into engineering models that are used for assessment of different hydrogen release scenarios and for input into quantitative risk assessments (QRA) of hydrogen facilities. The QRAs are used to identify and quantify scenarios for the unintended release of hydrogen and to identify the significant risk contributors at different types of hydrogen facilities. The results of the QRAs are one input into a risk-informed codes and standards development 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: the minimum separation distances between a hydrogen refuelling station and other facilities and the public at large. An example application of the risk-informed approach has been performed to illustrate its utility 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 to key modelling assumptions and the component leakage rates used in the QRA models.