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Digital twin for natural gas infrastructure operation and management via streaming dynamic mode decomposition with control

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  • Koo, Bonchan
  • Chang, Seungjoon
  • Kwon, Hweeung

Abstract

The gradual energy consumption increments and demand patterns diversification require an advanced operational protocol for natural gas infrastructure. However, conventional numerical methods suffer from low reliability and practicality due to the lack of usage of data that possess coherent physics in the system. The requirements to address this issue are system identification of data sequence and continuous modification to capture the changes of various environmental variables. Thus, we present the first data-driven digital twin model based on dynamic mode decomposition (DMD), which is well-suited for processing nonlinear, high-dimensional sequential data. It predicts pipeline flows and operating costs from measured data. Additionally, it adapts to changes in physical characteristics in pipelines. The proposed model was tested on real pipelines with a length of 5,000 km and demonstrated high accuracy, with a maximum error less than 0.2 MPa, and efficient computation, with a cost less than 2 s for 240 h. The proposed method constructs a reliable digital twin because it has the potential to be aware of the latent behavior of coherent physics in pipelines. The resulting digital twin forecast future status accurately and, based on this, may help establish advanced operating strategies in terms of safety and cost efficiency.

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  • 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).
    • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223007119
    DOI: 10.1016/j.energy.2023.127317
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    References listed on IDEAS

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    1. Koo, Bonchan & Ha, Youngcheol & Kwon, Hweeung, 2023. "Preliminary evaluation of hydrogen blending into high-pressure natural gas pipelines through hydraulic analysis," Energy, Elsevier, vol. 268(C).
    2. Mohan, Neethu & Soman, K.P. & Sachin Kumar, S., 2018. "A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model," Applied Energy, Elsevier, vol. 232(C), pages 229-244.
    3. Li, Zhuoran & Zhang, Caigong & Li, Changjun & Jia, Wenlong, 2022. "Thermodynamic study on the natural gas condensation in the throttle valve for the efficiency of the natural gas transport system," Applied Energy, Elsevier, vol. 322(C).
    4. Hong, Bingyuan & Li, Xiaoping & Li, Yu & Chen, Shilin & Tan, Yao & Fan, Di & Song, Shangfei & Zhu, Baikang & Gong, Jing, 2022. "An improved hydraulic model of gathering pipeline network integrating pressure-exchange ejector," Energy, Elsevier, vol. 260(C).
    5. Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
    6. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    7. 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).
    8. Kemfert, Claudia & Präger, Fabian & Braunger, Isabell & Hoffart, Franziska M. & Brauers, Hanna, 2022. "The expansion of natural gas infrastructure puts energy transitions at risk," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 582-587.
    9. 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).
    10. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    11. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
    12. Wen, Kai & Qiao, Dan & Nie, Chaofei & Lu, Yangfan & Wen, Feng & Zhang, Jing & Miao, Qing & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Multi-period supply and demand balance of large-scale and complex natural gas pipeline network: Economy and environment," Energy, Elsevier, vol. 264(C).
    13. Byoung-Kuk Ju & Seung-Hoon Yoo & Chulwoo Baek, 2022. "Economies of Scale in City Gas Sector in Seoul, South Korea: Evidence from an Empirical Investigation," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    14. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
    15. 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).
    16. Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
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    1. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    2. Xiao, Xiao & Zhang, Xuan & Song, Meiqi & Liu, Xiaojing & Huang, Qingyu, 2024. "NPP accident prevention: Integrated neural network for coupled multivariate time series prediction based on PSO and its application under uncertainty analysis for NPP data," Energy, Elsevier, vol. 305(C).

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