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Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven

Author

Listed:
  • Zhou, Jun
  • Qin, Can
  • Fu, Tiantian
  • Liu, Shitao
  • Liang, Guangchuan
  • Li, Cuicui
  • Hong, Bingyuan

Abstract

In recent years, the scale of gas transmission networks has been continuously expanding, and the operational conditions are becoming increasingly complex. This poses higher requirements for the centralized control of the pipeline network operation. Relying solely on the experience of scheduling personnel may not comprehensively address the operational issues within the network. There is an urgent need for efficient automatic response methods (ARM) to assist in formulating operation schemes and ensuring the safe and stable operation of the pipeline network. Therefore, this paper proposes an automatic response framework of large complex natural gas pipeline operation optimization based on data-mechanism coupling to provide operation schemes that ensure safety, stability, and economic benefits. First, two ARM is proposed, namely the Data-Based ARM using operation scheme database, and the Opti-Model ARM based on optimization modeling. Subsequently, the rapid response features of Data-Based ARM and the optimal response characteristics of Opti-Model ARM are combined to establish the Integrated ARM. Finally, these three ARMs are compared and analyzed through a regional natural gas pipeline network in China. The result indicates that the Data-Based ARM can quickly produce a variety of matched solutions but cannot ensure economic optimality. Response solutions obtained through Opti-Model ARM reduce the compressor energy costs by 4.6–10.8 % compared to on-site operational schemes, but they take longer in response time. In contrast, the economic attributes of solutions derived from the Integrated ARM are on par with Opti-Model ARM, but with a 58.7 % improvement in response speed. The proposed Integrated ARM can swiftly and accurately offer economically viable operation schemes tailored to the varying needs of pipeline operators, which can help to address the energy demand challenges of the future, fostering sustainable development.

Suggested Citation

  • Zhou, Jun & Qin, Can & Fu, Tiantian & Liu, Shitao & Liang, Guangchuan & Li, Cuicui & Hong, Bingyuan, 2024. "Automatic response framework for large complex natural gas pipeline operation optimization based on data-mechanism hybrid-driven," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023843
    DOI: 10.1016/j.energy.2024.132610
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