IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223026944.html

Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization

Author

Listed:
  • Yu, Xiaobing
  • Lu, Yangchen

Abstract

Wind farm layout optimization is a challenging issue which demands to discover some trade-off solutions considering various criteria, such as the power generated and the cost of the farm. Due to the complexity of the problem, we developed a reinforcement learning-based multi-objective differential evolution (RLMODE) algorithm to address the issue. In the developed algorithm, RL technique is applied to coordinate the parameter of DE algorithm, which can balance the local and global search. A tournament-based mutation operator is used to accelerate the convergence of the RLMODE algorithm. We tested the performance of the proposed RLMODE in two wind scenarios. The spread and spacing indicators of the algorithm are the best; the power generated by the solution from the RLMODE algorithm is the most when compared with some representative optimization algorithms and existing methods.

Suggested Citation

  • Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026944
    DOI: 10.1016/j.energy.2023.129300
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223026944
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129300?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
    2. 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.
    3. Li, Wenwen & Özcan, Ender & John, Robert, 2017. "Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation," Renewable Energy, Elsevier, vol. 105(C), pages 473-482.
    4. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    5. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    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. Rodrigues, S. & Bauer, P. & Bosman, Peter A.N., 2016. "Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 587-609.
    8. Reddy, Sohail R., 2021. "An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization," Renewable Energy, Elsevier, vol. 165(P1), pages 162-173.
    9. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    10. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    11. Cazzaro, Davide & Koza, David Franz & Pisinger, David, 2023. "Combined layout and cable optimization of offshore wind farms," European Journal of Operational Research, Elsevier, vol. 311(1), pages 301-315.
    12. Reddy, Sohail R., 2020. "Wind Farm Layout Optimization (WindFLO) : An advanced framework for fast wind farm analysis and optimization," Applied Energy, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Mingwei & Zhang, Mingming & Qin, Caiyan & Sun, Haiying & Deng, Xiaowei, 2026. "A data-driven double-Gaussian wake model reflecting the wake evolution process," Renewable Energy, Elsevier, vol. 257(C).
    2. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    3. Zhang, Zihang & Li, Jiayi & Lei, Zhenyu & Zhu, Qianyu & Cheng, Jiujun & Gao, Shangce, 2024. "Reinforcement learning-based particle swarm optimization for wind farm layout problems," Energy, Elsevier, vol. 313(C).
    4. Tu, Yu & Dong, Zhikun & Chen, Yaoran & Zhang, Kai & Zhou, Dai & Yang, Hongxing, 2026. "Global optimization of wake steering for large-scale wind farms using generalized serial refinement method," Applied Energy, Elsevier, vol. 406(C).
    5. Kim, Taewan & Song, Jeonghwan & You, Donghyun, 2024. "Optimization of a wind farm layout to mitigate the wind power intermittency," Applied Energy, Elsevier, vol. 367(C).
    6. Houssem R. E. H. Bouchekara & Makbul A. M. Ramli & Mohammad S. Javaid, 2024. "Wind Farm Layout Optimization/Expansion of Real Wind Turbines with a Parallel Collaborative Multi-Objective Optimization Algorithm," Energies, MDPI, vol. 17(22), pages 1-32, November.
    7. Dong, Guodan & Qin, Jianhua & Wu, Chutian & Xu, Chang & Yang, Xiaolei, 2026. "Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization," Renewable Energy, Elsevier, vol. 259(C).
    8. Cui, Can & Xue, Jiahui & Liu, Lanjun, 2025. "Optimal control of HVAC systems through active disturbance rejection control-assisted reinforcement learning," Energy, Elsevier, vol. 323(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Taewan & Song, Jeonghwan & You, Donghyun, 2024. "Optimization of a wind farm layout to mitigate the wind power intermittency," Applied Energy, Elsevier, vol. 367(C).
    2. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).
    3. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    4. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    5. 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.
    6. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    7. 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).
    8. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    9. Guido Pantuza, 2025. "Exact Hybrid Approach to the Wind Farm Layout Optimisation Problem," SN Operations Research Forum, Springer, vol. 6(3), pages 1-18, September.
    10. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    11. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    12. 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.
    13. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    14. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    15. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    16. Eikrem, Kjersti Solberg & Lorentzen, Rolf Johan & Faria, Ricardo & Stordal, Andreas Størksen & Godard, Alexandre, 2023. "Offshore wind farm layout optimization using ensemble methods," Renewable Energy, Elsevier, vol. 216(C).
    17. Taufal Hidayat & Makbul A. M. Ramli & Mohammed M. Alqahtani, 2024. "Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics," Sustainability, MDPI, vol. 16(7), pages 1-28, March.
    18. Lu, Boan & Shen, Xinwei & Du, Yunfei & Huang, Zehai & Tan, Renshen, 2025. "Offshore wind farm micro-siting based on two-phase hybrid optimization," Applied Energy, Elsevier, vol. 381(C).
    19. Song, Mengxuan & Chen, Kai & Wang, Jun, 2020. "A two-level approach for three-dimensional micro-siting optimization of large-scale wind farms," Energy, Elsevier, vol. 190(C).
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223026944. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.