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Supply-demand balance of natural gas pipeline network integrating hydraulic and thermal characteristics, energy conservation and carbon reduction

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  • Hong, Bingyuan
  • Qiao, Dan
  • Li, Yichen
  • Sun, Xiaoqing
  • Yang, Baolong
  • Li, Li
  • Gong, Jing
  • Wen, Kai

Abstract

Under the background of “One national network”, the balance between the natural gas supply side and the demand side is prominent. This paper proposes a supply-demand balance method of natural gas pipeline network coupled with the hydraulic and thermal characteristics, and decides the transportation scheme through flow rate allocation. This paper designs a two-stage relaxation optimization algorithm, coupled the thermal process into the hydraulic calculation to improve the hydraulic calculation accuracy, making the natural gas transportation scheme more applicable to engineering. The result shows the feasibility of transportation decision-making and the accuracy of hydro thermal calculation of the proposed model through a real case. The relative error of thermal calculation and TGNET is below 2%, and the hydraulic calculation error of coupled thermal can also be within 10%. After optimization, the carbon emissions decrease from 418,100 tons to 260,000 tons, reducing by about 37.81%. This study also analyzes the impact of hydraulic calculation on carbon emissions calculation, and further illustrates the necessity of coupled thermal characteristics in transportation scheme. This study can provide decision support for the transportation operation and help the natural gas pipeline network to meet the dual carbon requirements and achieve sustainable development.

Suggested Citation

  • Hong, Bingyuan & Qiao, Dan & Li, Yichen & Sun, Xiaoqing & Yang, Baolong & Li, Li & Gong, Jing & Wen, Kai, 2023. "Supply-demand balance of natural gas pipeline network integrating hydraulic and thermal characteristics, energy conservation and carbon reduction," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018212
    DOI: 10.1016/j.energy.2023.128427
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    References listed on IDEAS

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    Cited by:

    1. Lin, Zijie & Xie, Linbo & Zhang, Siyuan, 2024. "A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction," Energy, Elsevier, vol. 298(C).

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