IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v264y2023ics0360544222029796.html
   My bibliography  Save this article

Rapid transient operation control method of natural gas pipeline networks based on user demand prediction

Author

Listed:
  • Wen, Kai
  • Jiao, Jianfeng
  • Zhao, Kang
  • Yin, Xiong
  • Liu, Yuan
  • Gong, Jing
  • Li, Cuicui
  • Hong, Bingyuan

Abstract

The natural gas pipeline networks play a vital role in Integrated Energy System (IES). Simultaneously, with the increase in the number and types of users, the operational model needs to be transformed from plan-oriented to demand-oriented. Hence, higher requirements are put forward for the flexibility and rapidity of the control methods. In this paper, a novel rapid operation control method for natural gas pipeline networks is proposed based on multi-user demand prediction and pipeline flow state inversion. First, the Nonlinear autoregressive (NAR) neural network is used to analyze the historical consumption of various users and predict each user's demand in the short term. Second, the rapid transient inversion method is derived from the partial differential control equation to realize the inverse of the pipeline flow state. Third, combined with the equipment model, the control method is formed to realize rapid control of the pipeline networks. The control method is applied to classical triangular networks and existing pipeline networks to demonstrate accuracy and effectiveness. The time-consuming ratio of the commercial software and proposed model for the triangular pipeline networks is 1:0.22. For the existing pipeline networks, six users' average demand prediction errors are 5.58%, and the control errors of pressure at three markets are 1.00%, 1.22%, and 0.81%. The results show that the proposed method can respond to users' demands rapidly and provide decision support for the safe and efficient operation of the natural gas pipeline networks.

Suggested Citation

  • Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222029796
    DOI: 10.1016/j.energy.2022.126093
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222029796
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126093?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    2. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    3. Wang, Qiang & Song, Xiaoxing & Li, Rongrong, 2018. "A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production," Energy, Elsevier, vol. 165(PB), pages 1320-1331.
    4. Kai Wen & Zijie Xia & Weichao Yu & Jing Gong, 2018. "A New Lumped Parameter Model for Natural Gas Pipelines in State Space," Energies, MDPI, vol. 11(8), pages 1-17, July.
    5. Liu, Qilu & Cheng, Kaiming & Zhuang, Yanjie, 2022. "Estimation of city energy consumption in China based on downscaling energy balance tables," Energy, Elsevier, vol. 256(C).
    6. Vidović, Danko & Sutlović, Elis & Majstrović, Matislav, 2019. "Steady state analysis and modeling of the gas compressor station using the electrical analogy," Energy, Elsevier, vol. 166(C), pages 307-317.
    7. Safari, Ali & Davallou, Maryam, 2018. "Oil price forecasting using a hybrid model," Energy, Elsevier, vol. 148(C), pages 49-58.
    8. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    9. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    10. Fan, Di & Gong, Jing & Zhang, Shengnan & Shi, Guoyun & Kang, Qi & Xiao, Yaqi & Wu, Changchun, 2021. "A transient composition tracking method for natural gas pipe networks," Energy, Elsevier, vol. 215(PA).
    11. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    12. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    13. Ríos-Mercado, Roger Z. & Borraz-Sánchez, Conrado, 2015. "Optimization problems in natural gas transportation systems: A state-of-the-art review," Applied Energy, Elsevier, vol. 147(C), pages 536-555.
    14. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    15. Jiang, Yunpeng & Ren, Zhouyang & Yang, Xin & Li, Qiuyan & Xu, Yan, 2022. "A steady-state energy flow analysis method for integrated natural gas and power systems based on topology decoupling," Applied Energy, Elsevier, vol. 306(PA).
    16. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    17. Svoboda, Radek & Kotik, Vojtech & Platos, Jan, 2021. "Short-term natural gas consumption forecasting from long-term data collection," Energy, Elsevier, vol. 218(C).
    18. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    19. Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
    20. Wen, Hao & Sang, Song & Qiu, Chenhui & Du, Xiangrui & Zhu, Xiao & Shi, Qian, 2019. "A new optimization method of wind turbine airfoil performance based on Bessel equation and GABP artificial neural network," Energy, Elsevier, vol. 187(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Koo, Bonchan & Chang, Seungjoon & Kwon, Hweeung, 2023. "Digital twin for natural gas infrastructure operation and management via streaming dynamic mode decomposition with control," Energy, Elsevier, vol. 274(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
    2. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    3. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    4. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    5. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    6. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    7. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    8. Pan, Shaowei & Yang, Bo & Wang, Shukai & Guo, Zhi & Wang, Lin & Liu, Jinhua & Wu, Siyu, 2023. "Oil well production prediction based on CNN-LSTM model with self-attention mechanism," Energy, Elsevier, vol. 284(C).
    9. Xu, Guangyue & Chen, Yaqiang & Yang, Mengge & Li, Shuang & Marma, Kyaw Jaw Sine, 2023. "An outlook analysis on China's natural gas consumption forecast by 2035: Applying a seasonal forecasting method," Energy, Elsevier, vol. 284(C).
    10. Wang, Yong & Yang, Zhongsen & Wang, Li & Ma, Xin & Wu, Wenqing & Ye, Lingling & Zhou, Ying & Luo, Yongxian, 2022. "Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model," Energy, Elsevier, vol. 259(C).
    11. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    12. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    13. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
    14. Deng, Zhihua & Chan, Siew Hwa & Chen, Qihong & Liu, Hao & Zhang, Liyan & Zhou, Keliang & Tong, Sirui & Fu, Zhichao, 2023. "Efficient degradation prediction of PEMFCs using ELM-AE based on fuzzy extension broad learning system," Applied Energy, Elsevier, vol. 331(C).
    15. Kai Wen & Hailong Xu & Wei Qi & Haichuan Li & Yichen Li & Bingyuan Hong, 2023. "Heat Transfer Model of Natural Gas Pipeline Based on Data Feature Extraction and First Principle Models," Energies, MDPI, vol. 16(3), pages 1-21, January.
    16. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    17. Mihai Daniel Roman & Diana Mihaela Stanculescu, 2021. "An Analysis of Countries’ Bargaining Power Derived from the Natural Gas Transportation System Using a Cooperative Game Theory Model," Energies, MDPI, vol. 14(12), pages 1-13, June.
    18. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    19. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    20. Wu, Kangcheng & Du, Qing & Zu, Bingfeng & Wang, Yupeng & Cai, Jun & Gu, Xin & Xuan, Jin & Jiao, Kui, 2021. "Enabling real-time optimization of dynamic processes of proton exchange membrane fuel cell: Data-driven approach with semi-recurrent sliding window method," Applied Energy, Elsevier, vol. 303(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:264:y:2023:i:c:s0360544222029796. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.