A methodology of natural gas pipeline network system supply resilience optimization: Based on demand-side and data science-driven approach

Zhiwei Zhao, Zhaoming Yang, Huai Su, Michael H. Faber and Jinjun Zhang

Reliability Engineering and System Safety, 2025, vol. 261, issue C

Abstract: This paper proposes a method for optimizing the supply resilience of natural gas pipeline networks, driven by demand-side dynamics and data science. The method is divided into two main components: user demand characteristic modeling and system supply resilience optimization modeling. In the user demand characteristic modeling phase, preprocessed user demand data is used, combining the Tabular Variational Autoencoder (TVAE) with probability density distribution curve fitting to provide an in-depth characterization of user demand patterns. For the system supply resilience optimization modeling, constraints are established based on the functional characteristics of the system's components, and specific objective functions are designed for different operational scenarios. Additionally, the Latin Hypercube Sampling (LHS) method is employed to capture fluctuations in user demand. Finally, this paper introduces a set of evaluation indicators for gas supply resilience and validates the proposed methodology through five scenario-based case studies. The results confirm the effectiveness and feasibility of this approach in improving the resilience of natural gas pipeline systems.

Keywords: Data augmentation; Supply resilience; Optimization; Natural gas pipeline system (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025002728

DOI: 10.1016/j.ress.2025.111071

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