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Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

Author

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  • Hou, Guolian
  • Ye, Lingling
  • Huang, Ting
  • Huang, Congzhi

Abstract

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

Suggested Citation

  • Hou, Guolian & Ye, Lingling & Huang, Ting & Huang, Congzhi, 2024. "Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224026537
    DOI: 10.1016/j.energy.2024.132879
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    References listed on IDEAS

    as
    1. Chen, Youliang & Huang, Xiaoguang & Li, Wei & Fan, Rong & Zi, Pingyang & Wang, Xin, 2023. "Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station," Energy, Elsevier, vol. 285(C).
    2. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    3. Wang, Runchen & Du, Xiaonan & Shi, Yuetao & Deng, Weipeng & Wang, Yuhao & Sun, Fengzhong, 2024. "A novel system for reducing power plant electricity consumption and enhancing deep peak-load capability," Energy, Elsevier, vol. 295(C).
    4. Hou, Guolian & Xiong, Jian & Zhou, Guiping & Gong, Linjuan & Huang, Congzhi & Wang, Shunjiang, 2021. "Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network," Energy, Elsevier, vol. 234(C).
    5. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
    6. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    7. Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
    8. Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
    9. Zhang, Guangming & Zhang, Chao & Wang, Wei & Cao, Huan & Chen, Zhenyu & Niu, Yuguang, 2023. "Offline reinforcement learning control for electricity and heat coordination in a supercritical CHP unit," Energy, Elsevier, vol. 266(C).
    10. Wu, Huijuan & Meng, Keqilao & Fan, Daoerji & Zhang, Zhanqiang & Liu, Qing, 2022. "Multistep short-term wind speed forecasting using transformer," Energy, Elsevier, vol. 261(PA).
    11. Fan, He & Su, Zhi-gang & Wang, Pei-hong & Lee, Kwang Y., 2021. "A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units," Energy, Elsevier, vol. 226(C).
    12. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).
    13. Toopshekan, Ashkan & Abedian, Ali & Azizi, Arian & Ahmadi, Esmaeil & Vaziri Rad, Mohammad Amin, 2023. "Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm," Energy, Elsevier, vol. 285(C).
    14. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
    15. Bai, Weizheng & Shi, Xilin & Yang, Chunhe & Zhu, Shijie & Wei, Xinxing & Li, Yinping & Liu, Xin, 2024. "Assessment of the potential of salt mines for renewable energy peaking in China," Energy, Elsevier, vol. 300(C).
    16. Han, Zhonghe & Xiang, Peng, 2020. "Modeling condensate throttling to improve the load change performance of cogeneration units," Energy, Elsevier, vol. 192(C).
    17. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    18. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    19. Zhou, Jiayin & Huang, Diangui & Zhou, Lingzhe, 2024. "Modeling and thermal economy analysis of the coupled system of compressed steam energy storage and Rankine cycle in thermal power plant," Energy, Elsevier, vol. 291(C).
    20. Huang, Congzhi & Sheng, Xinxin, 2020. "Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm," Energy, Elsevier, vol. 205(C).
    21. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    22. Hou, Guolian & Gong, Linjuan & Hu, Bo & Huang, Ting & Su, Huilin & Huang, Congzhi & Zhou, Guiping & Wang, Shunjiang, 2022. "Flexibility oriented adaptive modeling of combined heat and power plant under various heat-power coupling conditions," Energy, Elsevier, vol. 242(C).
    23. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2023. "Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy," Energy, Elsevier, vol. 263(PB).
    24. Wang, Xu & Bie, Zhaohong & Liu, Fan & Kou, Yu, 2021. "Co-optimization planning of integrated electricity and district heating systems based on improved quadratic convex relaxation," Applied Energy, Elsevier, vol. 285(C).
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