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Large-scale wind farm control using distributed economic model predictive scheme

Author

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  • Kong, Xiaobing
  • Ma, Lele
  • Wang, Ce
  • Guo, Shifan
  • Abdelbaky, Mohamed Abdelkarim
  • Liu, Xiangjie
  • Lee, Kwang Y.

Abstract

The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is difficult to work. Regarding the large-scale, geographically dispersed wind farm, an efficient distributed economic model predictive control strategy is proposed, which integrates the power tracking and economic optimization of the wind farm into one optimal control framework. By adopting the global economic cost function, the Nash optimal solutions under distributed framework approach the Pareto optimum. Thus, the reference power from the transmission system operator is accurately tracked, while the global dynamic economic optimality is guaranteed. The simulation results under step wind speed and practical wind speed variations verify the efficiency and reliability of the proposed control strategy.

Suggested Citation

  • Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:581-591
    DOI: 10.1016/j.renene.2021.09.048
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    References listed on IDEAS

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    1. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    2. Perveen, Rehana & Kishor, Nand & Mohanty, Soumya R., 2014. "Off-shore wind farm development: Present status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 780-792.
    3. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
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    Cited by:

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    3. Kong, Xiaobing & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2023. "Stable feedback linearization-based economic MPC scheme for thermal power plant," Energy, Elsevier, vol. 268(C).
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    6. Arabgolarcheh, Alireza & Rouhollahi, Amirhossein & Benini, Ernesto, 2023. "Analysis of middle-to-far wake behind floating offshore wind turbines in the presence of multiple platform motions," Renewable Energy, Elsevier, vol. 208(C), pages 546-560.
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    9. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    10. Zhu, Zheng & Liu, Xiangjie & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y. & Xu, Yuping, 2024. "PV/Hydrogen DC microgrid control using distributed economic model predictive control," Renewable Energy, Elsevier, vol. 222(C).
    11. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
    12. Sun, Kang & Xu, Zifei & Li, Shujun & Jin, Jiangtao & Wang, Peilin & Yue, Minnan & Li, Chun, 2023. "Dynamic response analysis of floating wind turbine platform in local fatigue of mooring," Renewable Energy, Elsevier, vol. 204(C), pages 733-749.

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