New method for the transient simulation of natural gas pipeline networks based on the fracture-dimension-reduction algorithm

被引:1
|
作者
Guo, Qiao [1 ]
Xie, Wenhao [2 ]
Nie, Zihao [3 ]
Lu, Pengfei [4 ]
Xi, Xi [5 ]
Wang, Shouxi [1 ]
机构
[1] Xian Shiyou Univ, Coll Petr Engn, Xian 710065, Shaanxi, Peoples R China
[2] Xian Shiyou Univ, Coll Sci, Xian 710065, Shaanxi, Peoples R China
[3] PipeChina North Pipeline Co Ltd, Langfang 065000, Hebei, Peoples R China
[4] China Changqing Engn Design Co Ltd, Xian 710018, Shaanxi, Peoples R China
[5] PetroChina Southwest Oil & Gasfield Co, Chengdu Nat Gas Chem Plant Gen, Chengdu 610051, Sichuan, Peoples R China
关键词
Natural gas pipeline network; Station model; Inter-station pipeline network model; Transient simulation; Calculation efficiency; Nonlinear equations; Fracture-dimension-reduction algorithm; Equation set dimension; EQUATION;
D O I
10.1016/j.ngib.2023.09.004
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The transient simulation technology of natural gas pipeline networks plays an increasingly prominent role in the scheduling management of natural gas pipeline network system. The increasingly large and complex natural gas pipeline network requires more strictly on the calculation efficiency of transient simulation. To this end, this paper proposes a new method for the transient simulation of natural gas pipeline networks based on fracture-dimension-reduction algorithm. Firstly, a pipeline network model is abstracted into a station model, inter-station pipeline network model and connection node model. Secondly, the pressure at the connection node connecting the station and the inter-station pipeline network is used as the basic variable to solve the general solution of the flow rate at the connection node, reconstruct the simulation model of the inter-station pipeline network, and reduce the equation set dimension of the inter-station pipeline network model. Thirdly, the transient simulation model of the natural gas pipeline network system is constructed based on the reconstructed simulation model of the inter-station pipeline network. Finally, the calculation accuracy and efficiency of the proposed algorithm are compared and analyzed for the two working conditions of slow change of compressor speed and rapid shutdown of the compressor. And the following research results are obtained. First, the fracture-dimension-reduction algorithm has a high calculation accuracy, and the relative error of compressor outlet pressure and user pressure is less than 0.1%. Second, the calculation efficiency of the new fracture-dimension-reduction algorithm is high, and compared with the nonlinear equations solving method, the speed-up ratios under the two conditions are high up to 17.3 and 12.2 respectively. Third, the speed-up ratio of the fracture-dimension-reduction algorithm is linearly related to the equation set dimension of the transient simulation model of the pipeline network system. The larger the equation set dimension, the higher the speed-up ratio, which means the more complex the pipeline network model, the more remarkable the calculation speed-up effect. In conclusion, this new method improves the calculation speed while keeping the calculation accuracy, which is of great theoretical value and reference significance for improving the calculation efficiency of the transient simulation of complex natural gas pipeline network systems. (c) 2023 Sichuan Petroleum Administration. Publishing services by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:490 / 501
页数:12
相关论文
共 26 条
  • [11] A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning
    Fan, Lin
    Su, Huai
    Wang, Wei
    Zio, Enrico
    Zhang, Li
    Yang, Zhaoming
    Peng, Shiliang
    Yu, Weichao
    Zuo, Lili
    Zhang, Jinjun
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 225
  • [12] A deep reinforcement learning-based method for predictive management of demand response in natural gas pipeline networks
    Fan, Lin
    Su, Huai
    Zio, Enrico
    Chi, Lixun
    Zhang, Li
    Zhou, Jing
    Liu, Zhe
    Zhan, Jinjun
    JOURNAL OF CLEANER PRODUCTION, 2022, 335
  • [13] Topology Analysis of Natural Gas Pipeline Networks Based on Complex Network Theory
    Ye, Heng
    Li, Zhiping
    Li, Guangyue
    Liu, Yiran
    ENERGIES, 2022, 15 (11)
  • [14] Study on optimal operation of natural gas pipeline network based on improved genetic algorithm
    Zhang, Zhenwu
    Liu, Xiantao
    ADVANCES IN MECHANICAL ENGINEERING, 2017, 9 (08) : 1 - 8
  • [15] Study on Topology-Based Identification of Sources of Vulnerability for Natural Gas Pipeline Networks
    Wang, Peng
    Yu, Bo
    Sun, Dongliang
    Ao, Shangmin
    Zhai, Huaxing
    COMPUTATIONAL SCIENCE - ICCS 2018, PT III, 2018, 10862 : 163 - 173
  • [16] Recommendations on the improvement of the proposed government new rules for the operation of natural gas pipeline networks and management of natural gas supply emergency in China
    Bai J.
    Zhang X.
    Zhang L.
    Natural Gas Industry, 2021, 41 (07) : 172 - 178
  • [17] A New Strategy for the Simulation of Gas Pipeline Network Based on System Topology Identification
    Du, Zengzhi
    Li, Chunxi
    Sun, Wei
    Wang, Jianhong
    12TH INTERNATIONAL SYMPOSIUM ON PROCESS SYSTEMS ENGINEERING (PSE) AND 25TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING (ESCAPE), PT A, 2015, 37 : 395 - 400
  • [18] A Robustness Evaluation Method of Natural Gas Pipeline Network Based on Topological Structure Analysis
    Li, Xueyi
    Su, Huai
    Zhang, Jinjun
    Yang, Nan
    FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [19] Resilience-based supply assurance of natural gas pipeline networks and its research prospects
    Zhang J.
    Su H.
    Gao P.
    Shiyou Xuebao/Acta Petrolei Sinica, 2020, 41 (12): : 1665 - 1678
  • [20] A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods
    Yin, Xiong
    Wen, Kai
    Huang, Weihe
    Luo, Yinwei
    Ding, Yi
    Gong, Jing
    Gao, Jianfeng
    Hong, Bingyuan
    APPLIED ENERGY, 2023, 333