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Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development

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  • Yu, Jianxi
  • Petersen, Nils
  • Liu, Pei
  • Li, Zheng
  • Wirsum, Manfred

Abstract

Digital twin is a core technology for smart power plants aiming to increase the safety and efficiency of power generation in low-carbon transitions. High-precision modelling of in-service power plant thermal systems plays a key role to develop digital twins, but remains a challenge. There is a lack of high-precision modelling for in-service power plant thermal systems over the full working ranges. This work proposes a hybrid modelling framework combining physical mechanism and operation data to develop grey-box models of thermal systems. Key equipment characteristics are figured out through historical operation data. Moreover, system models consisting of mass and energy balances, process mechanism equations and characteristic equations are implemented. An in-service 660 MW ultra-supercritical double reheat power plant, one of the most advanced thermal power generation technologies, is selected as a case study. The grey-box model of high- and intermediate-pressure thermal system is established. An average simulation error of the model of 0.79% over the full working ranges is achieved. Furthermore, key system characteristics are quantified through the model. It demonstrates the high precision of the proposed modelling method over the full working ranges and provides necessary model support for the digital twin development of thermal power plants.

Suggested Citation

  • Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
  • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222019831
    DOI: 10.1016/j.energy.2022.125088
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    1. Yu, Wei & Patros, Panos & Young, Brent & Klinac, Elsa & Walmsley, Timothy Gordon, 2022. "Energy digital twin technology for industrial energy management: Classification, challenges and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(C).
    3. Li, Hongcheng & Yang, Dan & Cao, Huajun & Ge, Weiwei & Chen, Erheng & Wen, Xuanhao & Li, Chongbo, 2022. "Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system," Energy, Elsevier, vol. 239(PC).
    4. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "Data reconciliation and gross error detection for operational data in power plants," Energy, Elsevier, vol. 75(C), pages 14-23.
    5. Chen, Kang & Zhu, Xu & Anduv, Burkay & Jin, Xinqiao & Du, Zhimin, 2022. "Digital twins model and its updating method for heating, ventilation and air conditioning system using broad learning system algorithm," Energy, Elsevier, vol. 251(C).
    6. Guo, Sisi & Liu, Pei & Li, Zheng, 2016. "Data reconciliation for the overall thermal system of a steam turbine power plant," Applied Energy, Elsevier, vol. 165(C), pages 1037-1051.
    7. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    8. Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    9. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    10. Gu, Yujiong & Xu, Jing & Chen, Dongchao & Wang, Zhong & Li, Qianqian, 2016. "Overall review of peak shaving for coal-fired power units in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 723-731.
    11. Tariq, Rasikh & Torres-Aguilar, C.E. & Sheikh, Nadeem Ahmed & Ahmad, Tanveer & Xamán, J. & Bassam, A., 2022. "Data engineering for digital twining and optimization of naturally ventilated solar façade with phase changing material under global projection scenarios," Renewable Energy, Elsevier, vol. 187(C), pages 1184-1203.
    12. Spinti, Jennifer P. & Smith, Philip J. & Smith, Sean T., 2022. "Atikokan Digital Twin: Machine learning in a biomass energy system," Applied Energy, Elsevier, vol. 310(C).
    13. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    14. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    15. Zhao, Zhigang & Su, Sheng & Si, Ningning & Hu, Song & Wang, Yi & Xu, Jun & Jiang, Long & Chen, Gang & Xiang, Jun, 2017. "Exergy analysis of the turbine system in a 1000 MW double reheat ultra-supercritical power plant," Energy, Elsevier, vol. 119(C), pages 540-548.
    16. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    17. Wang, Chaoyang & Liu, Ming & Zhao, Yongliang & Qiao, Yongqiang & Chong, Daotong & Yan, Junjie, 2018. "Dynamic modeling and operation optimization for the cold end system of thermal power plants during transient processes," Energy, Elsevier, vol. 145(C), pages 734-746.
    18. Yu, Jianxi & Liu, Pei & Li, Zheng, 2021. "Data reconciliation of the thermal system of a double reheat power plant for thermal calculation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
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