A Methodology to Evaluate Gas Supply Reliability of the Natural Gas Pipeline Network Considering the Linepack Effect

As a crucial bridge in the global transition toward sustainable and low-carbon energy systems, the resilient operation of natural gas pipeline networks (NGPNs) is essential for ensuring socio-economic stability and energy security. Gas supply reliability serves as a fundamental metric to quantify and monitor the sustainability of energy delivery infrastructure. However, the existing optimization-based model and maximum-flow algorithm often neglect the impact of linepack, defined here as the volume of gas stored within pipelines, particularly end-section linepack, on system state evolution. To address these limitations, a gas supply reliability assessment method, consisting of six interrelated components, is developed. Gas supply reliability indicators were defined from both the quantity and time perspectives at first. Second, system uncertainties were quantified by incorporating supply uncertainties, network units, and demand fluctuations. Third, the sequential Monte Carlo method was employed to simulate the state transitions by capturing the transition times, affected units, and post-transition states. Fourth, a calculation model for the gas supply capacity of the NGPN was established that incorporated the linepack effect and treated the forecasted gas consumption of downstream consumers as boundary conditions. Finally, a method was developed for gas supply reliability assessment, which incorporated the coefficient of variation to improve its engineering applicability for evaluating the NGPN dependability. By applying the proposed approach to a real NGPN in China, the system gas supply reliability increased by 0.379% and 0.051% in the time and quantity dimensions, respectively, compared with the method neglecting linepack. These results indicate that linepack provides an effective short-term supplementary gas source during unit failures or demand fluctuations and should be considered in practical NGPN reliability assessment.