Operation optimization of large-scale natural gas pipeline networks based on intelligent algorithm

被引：0
|
作者：
Wei, Xuemei [1 ]
Qiu, Rui [1 ]
Zhang, Bo [1 ]
Liu, Chunying [1 ]
Wang, Guotao [2 ,3 ]
Wang, Bohong [4 ]
Liang, Yongtu [1 ]
机构：
[1] Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No.18, Changping District, Beijing,102249, China
[2] Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
[3] International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University, Hong Kong SAR, China
[4] National & Local Joint Engineering Research Center of Harbour Oil & Gas Storage and Transportation Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, Zhejiang Ocean University, No.1, Haida South Road, Zhoushan,316022, Chin
来源：
Energy | 2024年 / 310卷
关键词：
With the expansion of natural gas pipelines into larger networks; there is a growing concern regarding energy consumption in the operations of pipeline systems. However; the intricate network structure and hydraulic properties pose challenges for operation optimization. In this paper; a Mixed Integer Nonlinear Programming model is proposed to optimize the steady-state operation of natural gas pipeline network; aiming to minimize the energy consumption of compressor units. The model incorporates constraints related to flow direction; flow balance; pressure drop in pipeline sections; and pressure increase at compressor stations. To solve the complex nonlinear model efficiently; a stochastic optimization algorithm that integrates particle swarm optimization and high-fidelity simulation is proposed. The case study is conducted on a large-scale natural gas pipeline network consisting of forty-three pipeline sections and four compressor stations. The optimal operation scheme is calculated; and the outlet pressures of each compressor are determined. The results demonstrate that the stochastic optimization algorithm proposed in this paper can reduce energy consumption by 21.23 % at most and 19.77 % on average during pipeline operation; which can provide guidance for the operation management of large-scale natural gas pipeline network. © 2024;
D O I：
10.1016/j.energy.2024.133258
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