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Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization

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  • Wang, Longyan
  • Cholette, Michael E.
  • Zhou, Yunkai
  • Yuan, Jianping
  • Tan, Andy C.C.
  • Gu, Yuantong

Abstract

In this paper, a real wind farm layout optimization is carried out by applying the novel optimized wind farm control strategy (coordinating wind turbine operations by setting different axial induction values) and multiple wind turbine hub height selections. The unrestricted coordinate method (using Cartesian coordinates to determine wind turbine positions) is applied to study the effectiveness of different wind farm control strategies and wind turbine hub heights on the wind farm optimization. Means of handling the irregular boundary constraints and calculating the wind speed over non-flat terrain at different heights are proposed for the real wind farm. The results show that the optimized control strategy can always yield better results than the self-optimum control strategy, achieving a smaller cost of energy production and a larger wind farm efficiency. Quantitatively, the optimized control strategy yields approximately 9 kW more power per turbine than the self-optimum control strategy and reduces the cost of energy by 0.08 million dollars per megawatt with 39 wind turbines installed. The application of different hub height turbines facilitates to place wind turbines more flexibly achieving better optimization results than that with constant hub height turbines.

Suggested Citation

  • Wang, Longyan & Cholette, Michael E. & Zhou, Yunkai & Yuan, Jianping & Tan, Andy C.C. & Gu, Yuantong, 2018. "Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization," Renewable Energy, Elsevier, vol. 126(C), pages 819-829.
    • Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:819-829
    DOI: 10.1016/j.renene.2018.04.004
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    Cited by:

    1. Wang, Longyan & Zuo, Ming J. & Xu, Jian & Zhou, Yunkai & Tan, Andy C., 2019. "Optimizing wind farm layout by addressing energy-variance trade-off: A single-objective optimization approach," Energy, Elsevier, vol. 189(C).
    2. Lin, Jian Wei & Zhu, Wei Jun & Shen, Wen Zhong, 2022. "New engineering wake model for wind farm applications," Renewable Energy, Elsevier, vol. 198(C), pages 1354-1363.
    3. Wang, Longyan & Luo, Wei & Xu, Jian & Xie, Junhang & Luo, Zhaohui & Tan, Andy C.C., 2022. "Comparative study of decentralized instantaneous and wind-interval-based controls for in-line two scale wind turbines," Renewable Energy, Elsevier, vol. 189(C), pages 1218-1233.
    4. Muhammad Bin Ali & Zeshan Ahmad & Saad Alshahrani & Muhammad Rizwan Younis & Irsa Talib & Muhammad Imran, 2022. "A Case Study: Layout Optimization of Three Gorges Wind Farm Pakistan, Using Genetic Algorithm," Sustainability, MDPI, vol. 14(24), pages 1-18, December.

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