RAMS assessment methodology for road transport self-contained energy systems considering source-load dual uncertainty

Abstract

The reciprocal advancement of energy and transportation serves as a foundation for and accelerates the continuity of human civilization and technological development. The transportation sector represents a substantial proportion of energy consumption, thus, making the optimization of transportation assets and the greening of energy utilization are critical strategies for achieving dual carbon targets. This study addresses the deficiencies in performance assessment methodologies for self-sufficient energy systems in road transportation by proposing a Reliability, Availability, Maintainability, Safety (RAMS) assessment framework and corresponding indicator system that incorporates "source-load" uncertainties. Analysis of operational modes enables to determine the overall system performance. Initially, the study establishes the system architecture for self-sufficient energy systems in road transportation, complemented by a "source-storage-load" component model. Subsequently, 13 RAMS assessment indicators are developed incorporating uncertainties, and a RAMS assessment framework is presented. To mitigate the impact of uncertainties on system evaluations, the Latin Hypercube Sampling method and synchronous back-substitution scenario reduction technique are employed.

Finally, utilizing the Gela section of the Jing-Zang Expressway, China, as a case study, the validity of the proposed RAMS indicators and methods is assessed by comparing the operational results of three distinct modes, grouped by with and without hydrogen storage systems, and with and without gas turbines, across the four seasons. The findings indicate that, with increased system configurations with hydrogen storage systems and gas turbines, enhancements of 96.77 %, 88.57 %, 85.71 %, and 71.43 % in RAMS performance were obtained in spring, summer, autumn, and winter, respectively. Seasonal analysis demonstrates that the system performs optimally in summer and autumn, with minimal variation, followed by spring, and exhibits the lowest performance in winter.