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Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit

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  • Hou, Guolian
  • Gong, Linjuan
  • Hu, Bo
  • Su, Huilin
  • Huang, Ting
  • Huang, Congzhi
  • Fan, Wei
  • Zhao, Yuanzhu

Abstract

As main measure to stabilize fluctuation of power grid under renewable energy utilization, the flexible operation of thermal power unit reveals remarkable significant. To serve for peak shaving and fast load varying, a novel T-S fuzzy modeling approach based on fast adaptive moth-flame optimization (FAMFO) algorithm is proposed for dynamics investigation of supercritical unit. This modeling scheme is deemed as hierarchical process with data partition stage and parameter determination stage. Firstly, an automatic clustering strategy relying on FAMFO is fulfilled for more reasonable data partition result, which devotes to accuracy modeling without subjective interference. In this stage, the FAMFO is attained with assistance of four improvements and its merits in balancing search exploration and exploitation guarantee the width of covered operation range of supercritical unit for peak shaving. Secondly, inherent advantages of least square technics in parameter identification witness the successful implementation of a well-performed exponentially-weighted least squares algorithm in later stage of fuzzy modeling. In effectiveness verification of the proposed modeling approach, the superiorities of FAMFO in promoting search precision and speed are demonstrated via benchmark function test and Friedman test. Then, the flexible operation modeling performance is proved via extensive simulation experiments using on-site data of supercritical unit.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221020910
    DOI: 10.1016/j.energy.2021.121843
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    as
    1. Lin, Xing-Min & Kireeva, Natalia & Timoshin, A.V. & Naderipour, Amirreza & Abdul-Malek, Zulkurnain & Kamyab, Hesam, 2021. "A multi-criteria framework for designing of stand-alone and grid-connected photovoltaic, wind, battery clean energy system considering reliability and economic assessment," Energy, Elsevier, vol. 224(C).
    2. Chen, Siyuan & Liu, Pei & Li, Zheng, 2020. "Low carbon transition pathway of power sector with high penetration of renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    3. Zhang, Kezhen & Zhao, Yongliang & Liu, Ming & Gao, Lin & Fu, Yue & Yan, Junjie, 2021. "Flexibility enhancement versus thermal efficiency of coal-fired power units during the condensate throttling processes," Energy, Elsevier, vol. 218(C).
    4. 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).
    5. Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2021. "Prediction of lignocellulosic biomass structural components from ultimate/proximate analysis," Energy, Elsevier, vol. 222(C).
    6. Fathy, Ahmed & Rezk, Hegazy & Mohamed Ramadan, Haitham Saad, 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process," Energy, Elsevier, vol. 207(C).
    7. Wang, Yanhong & Cao, Lihua & Li, Xingcan & Wang, Jiaxing & Hu, Pengfei & Li, Bo & Li, Yong, 2020. "A novel thermodynamic method and insight of heat transfer characteristics on economizer for supercritical thermal power plant," Energy, Elsevier, vol. 191(C).
    8. 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).
    9. Hundi, Prabhas & Shahsavari, Rouzbeh, 2020. "Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants," Applied Energy, Elsevier, vol. 265(C).
    10. Hou, Guolian & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2020. "Fuzzy modeling and fast model predictive control of gas turbine system," Energy, Elsevier, vol. 200(C).
    11. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    12. Wang, Zhu & Liu, Ming & Yan, Junjie, 2021. "Flexibility and efficiency co-enhancement of thermal power plant by control strategy improvement considering time varying and detailed boiler heat storage characteristics," Energy, Elsevier, vol. 232(C).
    13. Wu, Xiao & Wang, Meihong & Lee, Kwang Y., 2020. "Flexible operation of supercritical coal-fired power plant integrated with solvent-based CO2 capture through collaborative predictive control," Energy, Elsevier, vol. 206(C).
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    3. Zima, Wiesław & Grądziel, Sławomir & Cebula, Artur & Rerak, Monika & Kozak-Jagieła, Ewa & Pilarczyk, Marcin, 2023. "Mathematical model of a power boiler operation under rapid thermal load changes," Energy, Elsevier, vol. 263(PC).
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    5. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    6. 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).
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