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Wind Farm Yaw Optimization via Random Search Algorithm

Author

Listed:
  • Jim Kuo

    (Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA)

  • Kevin Pan

    (Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA)

  • Ni Li

    (Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
    School of Aeronautics, Northwestern Polytechnical University, Xian 710072, China)

  • He Shen

    (Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA)

Abstract

One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

Suggested Citation

  • Jim Kuo & Kevin Pan & Ni Li & He Shen, 2020. "Wind Farm Yaw Optimization via Random Search Algorithm," Energies, MDPI, vol. 13(4), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:865-:d:321358
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    References listed on IDEAS

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    Cited by:

    1. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    2. Huang, Zishuo & Wu, Wenchuan, 2024. "An efficient solution for large offshore wind farm power optimization with the Porté-Agel wake model: Optimality and efficiency," Energy, Elsevier, vol. 306(C).
    3. Antonio Crespo, 2022. "Wakes of Wind Turbines in Yaw for Wind Farm Power Optimization," Energies, MDPI, vol. 15(18), pages 1-3, September.
    4. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).

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