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Operational optimization of large-scale thermal constrained natural gas pipeline networks: A novel iterative decomposition approach

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  • Wang, Guotao
  • Zhao, Wei
  • Qiu, Rui
  • Liao, Qi
  • Lin, Zhenjia
  • Wang, Chang
  • Zhang, Haoran

Abstract

Operation optimization is an effective but quite challenging way to reduce the energy consumption of large-scale pipeline networks. The thermal-hydraulic performance of natural gas, combined with the detailed constraints of compressors, creates many nonlinear constraints that make optimization models difficult to solve. Previous studies aimed at optimizing large-scale natural gas pipeline networks have often simplified multiple compressors into a single compressor station and ignored the operating envelopes of centrifugal compressors. This oversimplification can lead to solutions that deviate from reality. To address this issue, this paper presents an optimization model that is more practical to real-world scenarios. The model incorporates thermal-hydraulic constraints, reverse processes, and detailed constraints of compressors, including the operating envelopes of centrifugal compressors. To solve the Mixed Integer Nonlinear Programming (MINLP) model, an iterative four-stage decomposition algorithm is proposed. The proposed model and algorithm are verified by three large-scale natural gas pipeline networks, demonstrating advantages in solving time and scalability. Specifically, the solving time only requires 9.9 s for a pipeline network containing 406 nodes, even when considering the operating envelopes of centrifugal compressors. These characteristics of the model and algorithm enable them to be portable for similar problems and future research.

Suggested Citation

  • Wang, Guotao & Zhao, Wei & Qiu, Rui & Liao, Qi & Lin, Zhenjia & Wang, Chang & Zhang, Haoran, 2023. "Operational optimization of large-scale thermal constrained natural gas pipeline networks: A novel iterative decomposition approach," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223022508
    DOI: 10.1016/j.energy.2023.128856
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    References listed on IDEAS

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