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Optimal control of a wind farm in time-varying wind using deep reinforcement learning

Author

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  • Kim, Taewan
  • Kim, Changwook
  • Song, Jeonghwan
  • You, Donghyun

Abstract

A deep-reinforcement-learning (DRL) based control method to take the advantage of complex wake interactions in a wind farm is developed. Although the wind over a wind farm is changing, steady wind has been assumed in the most conventional methods for wind farm control. Under unsteady wind, the generated power of a wind farm becomes stochastic due to intermittent and fluctuating wind. To tackle the difficulty, a DRL-based method with which the pitch and yaw angles of wind turbines in a wind farm are strategically controlled is developed. Time-histories of the past wind and the predicted future wind are both utilized to identify the relation between the generated power and control. The present neural network is trained and validated using an experimental wind farm. A multi-fan wind tunnel is developed to generate unsteady wind for experiments with miniature wind farms, where the improvement in the generated power by the present DRL-based control method is demonstrated.

Suggested Citation

  • Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:energy:v:303:y:2024:i:c:s0360544224017237
    DOI: 10.1016/j.energy.2024.131950
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    References listed on IDEAS

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    1. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Doekemeijer, Bart M. & van der Hoek, Daan & van Wingerden, Jan-Willem, 2020. "Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions," Renewable Energy, Elsevier, vol. 156(C), pages 719-730.
    3. Padullaparthi, Venkata Ramakrishna & Nagarathinam, Srinarayana & Vasan, Arunchandar & Menon, Vishnu & Sudarsanam, Depak, 2022. "FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning," Renewable Energy, Elsevier, vol. 181(C), pages 445-456.
    4. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    5. Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.
    6. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    7. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    8. Liu, Zhenkun & Jiang, Ping & Zhang, Lifang & Niu, Xinsong, 2020. "A combined forecasting model for time series: Application to short-term wind speed forecasting," Applied Energy, Elsevier, vol. 259(C).
    9. Wang, Tengyuan & Cai, Chang & Wang, Xinbao & Wang, Zekun & Chen, Yewen & Song, Juanjuan & Xu, Jianzhong & Zhang, Yuning & Li, Qingan, 2023. "A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow," Energy, Elsevier, vol. 271(C).
    10. Dou, Bingzheng & Qu, Timing & Lei, Liping & Zeng, Pan, 2020. "Optimization of wind turbine yaw angles in a wind farm using a three-dimensional yawed wake model," Energy, Elsevier, vol. 209(C).
    11. Chen, Kaixuan & Lin, Jin & Qiu, Yiwei & Liu, Feng & Song, Yonghua, 2022. "Joint optimization of wind farm layout considering optimal control," Renewable Energy, Elsevier, vol. 182(C), pages 787-796.
    12. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    13. He, Ruiyang & Yang, Hongxing & Sun, Shilin & Lu, Lin & Sun, Haiying & Gao, Xiaoxia, 2022. "A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control," Applied Energy, Elsevier, vol. 326(C).
    14. Jeon, Sanghyeon & Kim, Bumsuk & Huh, Jongchul, 2015. "Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine," Energy, Elsevier, vol. 93(P2), pages 1769-1777.
    15. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    16. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    17. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    18. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Experimental investigation of the performance and wake effect of a small-scale wind turbine in a wind tunnel," Energy, Elsevier, vol. 166(C), pages 819-833.
    19. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    20. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    21. Zong, Haohua & Porté-Agel, Fernando, 2021. "Experimental investigation and analytical modelling of active yaw control for wind farm power optimization," Renewable Energy, Elsevier, vol. 170(C), pages 1228-1244.
    22. Wang, Zhenyu & Ozbay, Ahmet & Tian, Wei & Hu, Hui, 2018. "An experimental study on the aerodynamic performances and wake characteristics of an innovative dual-rotor wind turbine," Energy, Elsevier, vol. 147(C), pages 94-109.
    23. Majid Bastankhah & Fernando Porté-Agel, 2017. "A New Miniature Wind Turbine for Wind Tunnel Experiments. Part I: Design and Performance," Energies, MDPI, vol. 10(7), pages 1-19, July.
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    3. Mahmud, Sakib & Sayed, Aya Nabil & Himeur, Yassine & Nhlabatsi, Armstrong & Bensaali, Faycal, 2026. "A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).

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