IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v182y2022icp787-796.html
   My bibliography  Save this article

Joint optimization of wind farm layout considering optimal control

Author

Listed:
  • Chen, Kaixuan
  • Lin, Jin
  • Qiu, Yiwei
  • Liu, Feng
  • Song, Yonghua

Abstract

The wake effect is one of the leading causes of energy losses in offshore wind farms (WFs). Both turbine placement and cooperative control can influence the wake interactions inside the WF and thus the overall WF power production. Traditionally, greedy control strategy is assumed in the layout design phase. To exploit the potential synergy between the WF layout and control so that a system-level optimal layout can be obtained with the greatest energy yields, the layout optimization should be performed with cooperative control considerations. For this purpose, a novel two-stage WF layout optimization model is developed in this paper. Cooperative WF control of both turbine yaw and axis-induction are considered in the WF layout design. However, the integration of WF control makes the layout optimization much more complicated and results in a large-scale nonconvex problem, hindering the application of current layout optimization methods. To increase the computational efficiency, we leverage the hierarchy and decomposability of the joint optimization problem and design a decomposition-based hybrid method (DBHM). Instead of manipulating all the variables simultaneously, the joint optimization problem is decomposed into several subproblems and their coordination. Case studies are carried out on different WFs. It is shown that WF layouts with higher energy yields can be obtained by the proposed joint optimization compared to traditional separate layout optimization. Moreover, the computational advantages of the proposed DBHM on the considered joint layout optimization problem are also demonstrated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:182:y:2022:i:c:p:787-796
    DOI: 10.1016/j.renene.2021.10.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121014890
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.10.032?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Pookpunt, Sittichoke & Ongsakul, Weerakorn, 2013. "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, Elsevier, vol. 55(C), pages 266-276.
    2. Wan, Chunqiu & Wang, Jun & Yang, Geng & Gu, Huajie & Zhang, Xing, 2012. "Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy," Renewable Energy, Elsevier, vol. 48(C), pages 276-286.
    3. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    4. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    5. Park, Jinkyoo & Law, Kincho H., 2015. "Layout optimization for maximizing wind farm power production using sequential convex programming," Applied Energy, Elsevier, vol. 151(C), pages 320-334.
    6. van der Hoek, Daan & Kanev, Stoyan & Allin, Julian & Bieniek, David & Mittelmeier, Niko, 2019. "Effects of axial induction control on wind farm energy production - A field test," Renewable Energy, Elsevier, vol. 140(C), pages 994-1003.
    7. Wilson, Dennis & Rodrigues, Silvio & Segura, Carlos & Loshchilov, Ilya & Hutter, Frank & Buenfil, Guillermo López & Kheiri, Ahmed & Keedwell, Ed & Ocampo-Pineda, Mario & Özcan, Ender & Peña, Sergio Iv, 2018. "Evolutionary computation for wind farm layout optimization," Renewable Energy, Elsevier, vol. 126(C), pages 681-691.
    8. Turner, S.D.O. & Romero, D.A. & Zhang, P.Y. & Amon, C.H. & Chan, T.C.Y., 2014. "A new mathematical programming approach to optimize wind farm layouts," Renewable Energy, Elsevier, vol. 63(C), pages 674-680.
    9. Wang, Longyan & Tan, Andy & Gu, Yuantong, 2016. "A novel control strategy approach to optimally design a wind farm layout," Renewable Energy, Elsevier, vol. 95(C), pages 10-21.
    10. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    11. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    12. Cao, Jiu Fa & Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær & Sun, Zhen Ye, 2020. "Optimizing wind energy conversion efficiency with respect to noise: A study on multi-criteria wind farm layout design," Renewable Energy, Elsevier, vol. 159(C), pages 468-485.
    13. Pérez, Beatriz & Mínguez, Roberto & Guanche, Raúl, 2013. "Offshore wind farm layout optimization using mathematical programming techniques," Renewable Energy, Elsevier, vol. 53(C), pages 389-399.
    14. 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).
    15. 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.
    16. Eroğlu, Yunus & Seçkiner, Serap Ulusam, 2012. "Design of wind farm layout using ant colony algorithm," Renewable Energy, Elsevier, vol. 44(C), pages 53-62.
    17. Feng, Ju & Shen, Wen Zhong, 2015. "Solving the wind farm layout optimization problem using random search algorithm," Renewable Energy, Elsevier, vol. 78(C), pages 182-192.
    18. Ju, Xinglong & Liu, Feng, 2019. "Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation," Applied Energy, Elsevier, vol. 248(C), pages 429-445.
    19. 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.
    20. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2018. "Constrained multi-objective wind farm layout optimization: Novel constraint handling approach based on constraint programming," Renewable Energy, Elsevier, vol. 126(C), pages 341-353.
    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.
    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. 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. 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).

    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. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(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. 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.
    4. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    5. Hou, Peng & Hu, Weihao & Chen, Cong & Soltani, Mohsen & Chen, Zhe, 2016. "Optimization of offshore wind farm layout in restricted zones," Energy, Elsevier, vol. 113(C), pages 487-496.
    6. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2021. "Wind Farm Area Shape Optimization Using Newly Developed Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 14(14), pages 1-25, July.
    7. 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.
    8. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
    9. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    10. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2018. "Assessing the energy benefit of using a wind turbine micro-siting model," Renewable Energy, Elsevier, vol. 118(C), pages 591-601.
    11. Ulku, I. & Alabas-Uslu, C., 2019. "A new mathematical programming approach to wind farm layout problem under multiple wake effects," Renewable Energy, Elsevier, vol. 136(C), pages 1190-1201.
    12. Wu, Chutian & Yang, Xiaolei & Zhu, Yaxin, 2021. "On the design of potential turbine positions for physics-informed optimization of wind farm layout," Renewable Energy, Elsevier, vol. 164(C), pages 1108-1120.
    13. 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).
    14. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2020. "Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods," Applied Energy, Elsevier, vol. 261(C).
    15. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2019. "Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines," Energy, Elsevier, vol. 168(C), pages 637-650.
    16. Nagpal, Shriya V. & Liu, M. Vivienne & Anderson, C. Lindsay, 2021. "A comparison of deterministic refinement techniques for wind farm layout optimization," Renewable Energy, Elsevier, vol. 168(C), pages 581-592.
    17. Muhammad Nabeel Hussain & Nadeem Shaukat & Ammar Ahmad & Muhammad Abid & Abrar Hashmi & Zohreh Rajabi & Muhammad Atiq Ur Rehman Tariq, 2022. "Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines," Sustainability, MDPI, vol. 14(14), pages 1-24, July.
    18. Brogna, Roberto & Feng, Ju & Sørensen, Jens Nørkær & Shen, Wen Zhong & Porté-Agel, Fernando, 2020. "A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain," Applied Energy, Elsevier, vol. 259(C).
    19. Lo Brutto, Ottavio A. & Thiébot, Jérôme & Guillou, Sylvain S. & Gualous, Hamid, 2016. "A semi-analytic method to optimize tidal farm layouts – Application to the Alderney Race (Raz Blanchard), France," Applied Energy, Elsevier, vol. 183(C), pages 1168-1180.
    20. Wędzik, Andrzej & Siewierski, Tomasz & Szypowski, Michał, 2016. "A new method for simultaneous optimizing of wind farm’s network layout and cable cross-sections by MILP optimization," Applied Energy, Elsevier, vol. 182(C), pages 525-538.

    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:renene:v:182:y:2022:i:c:p:787-796. 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/renewable-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.