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A two-stage optimization model for floating offshore wind farm layout and control

Author

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  • Tao, Siyu
  • Yang, Jisheng
  • Zheng, Gang
  • He, Ruiyang
  • Feijóo-Lorenzo, Andrés E.

Abstract

The effective planning of wind turbine (WT) positions in a wind farm (WF) plays a crucial role in capturing wind energy and increasing power generation. Control optimization of WT attitudes can also help reduce wake effects and increase power generation, thereby maximizing the annual electricity production (AEP) of a WF. In this paper, a two-stage optimization model is proposed for planning the stationary layout of floating offshore wind farms (FOWFs), as well as the positions and attitudes of the floating offshore wind turbines (FOWTs) via optimal control. In the first stage, a competitive particle swarm optimizer (CPSO) is employed to generate multiple FOWF layouts. In the second stage, the genetic algorithm (GA) is utilized to optimize the yaw angles and axial induction factors of the FOWTs for operation control. Unlike the existing approaches that address WT layout design and operation control sequentially and separately, the proposed method integrates these aspects to simplify the FOWTs operation and to improve their performances. Additionally, taking the movable characteristics of the FOWTs into consideration, the proposed model incorporates the positional movement of the FOWTs under varying wind conditions, making the power calculations more realistic. Case studies demonstrate that compared with the traditional layout-only optimization, the proposed two-stage optimization model can help increase the AEP of the FOWFs by 4.72 %.

Suggested Citation

  • Tao, Siyu & Yang, Jisheng & Zheng, Gang & He, Ruiyang & Feijóo-Lorenzo, Andrés E., 2025. "A two-stage optimization model for floating offshore wind farm layout and control," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125012765
    DOI: 10.1016/j.renene.2025.123614
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    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. 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).
    3. Long, Huan & Li, Peikun & Gu, Wei, 2020. "A data-driven evolutionary algorithm for wind farm layout optimization," Energy, Elsevier, vol. 208(C).
    4. Peng, Wangxuan & Li, Baoliang & Ge, Mingwei & Li, Xintao & Ding, Wei & Li, Bo, 2025. "Layout optimization for offshore wind farms considering both fatigue damage and power generation," Renewable Energy, Elsevier, vol. 246(C).
    5. Thomas, B. & Costoya, X. & deCastro, M. & Iglesias, G. & Gómez-Gesteira, M., 2025. "Levelized cost of energy for various floating offshore wind farm designs in the areas covered by the Spanish maritime spatial planning," Applied Energy, Elsevier, vol. 381(C).
    6. 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).
    7. 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.
    8. Ferri, Giulio & Marino, Enzo, 2023. "Site-specific optimizations of a 10 MW floating offshore wind turbine for the Mediterranean Sea," Renewable Energy, Elsevier, vol. 202(C), pages 921-941.
    9. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    10. Song, Dongran & Shen, Xutao & Gao, Yang & Wang, Lei & Du, Xin & Xu, Zhiliang & Zhang, Zhihong & Huang, Chaoneng & Yang, Jian & Dong, Mi & Joo, Young Hoo, 2023. "Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm," Applied Energy, Elsevier, vol. 351(C).
    11. Nouri, Reza & Vasel-Be-Hagh, Ahmad & Archer, Cristina L., 2020. "The Coriolis force and the direction of rotation of the blades significantly affect the wake of wind turbines," Applied Energy, Elsevier, vol. 277(C).
    12. Devoy McAuliffe, Fiona & Judge, Frances M. & Murphy, Jimmy, 2024. "Modelling the installation of next generation floating offshore wind farms," Applied Energy, Elsevier, vol. 374(C).
    13. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    14. Hanifa Teimourian & Mahmoud Abubakar & Melih Yildiz & Amir Teimourian, 2022. "A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution," Energies, MDPI, vol. 15(15), pages 1-15, August.
    15. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    16. Qian, Guo-Wei & Ishihara, Takeshi, 2021. "Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity," Energy, Elsevier, vol. 220(C).
    17. 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.
    18. Danovaro, Roberto & Bianchelli, Silvia & Brambilla, Paola & Brussa, Gaia & Corinaldesi, Cinzia & Del Borghi, Adriana & Dell’Anno, Antonio & Fraschetti, Simonetta & Greco, Silvestro & Grosso, Mario & N, 2024. "Making eco-sustainable floating offshore wind farms: Siting, mitigations, and compensations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
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    1. Tao, Siyu & Yang, Jisheng & Zheng, Gang & Feijóo-Lorenzo, Andrés E., 2026. "Three-dimensional spatial planning of collection systems in floating offshore wind farms: A two-stage optimization method," Renewable Energy, Elsevier, vol. 257(C).

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