IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v121y2017icp561-569.html
   My bibliography  Save this article

Wind farm multi-objective wake redirection for optimizing power production and loads

Author

Listed:
  • van Dijk, Mike T.
  • van Wingerden, Jan-Willem
  • Ashuri, Turaj
  • Li, Yaoyu

Abstract

Clustering wind turbines as a wind farm to share the infrastructure is an effective strategy to reduce the cost of energy. However, this results in aerodynamic wake interaction among wind turbines. Yawing the upstream wind turbines can mitigate the losses in wind farm power output. Yaw-misalignment also affects the loads, as partial wake overlap can increase fatigue of downstream turbines. This paper studies multi-objective optimization of wind farm wake using yaw-misalignment to increase power production and reduce loads due to partial wake overlap. This is achieved using a computational framework consisting of an aerodynamic model for wind farm wake, a blade-element-momentum model to compute the power and the loads, and a gradient-based optimizer. The results show that yaw-misalignment is capable of increasing the power production of the wind farm, while reducing the loading due to partial wake overlap. A multi-objective optimization is able to further decrease the loads at the expense of a small amount of power production.

Suggested Citation

  • van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    • Handle: RePEc:eee:energy:v:121:y:2017:i:c:p:561-569
    DOI: 10.1016/j.energy.2017.01.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217300518
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.01.051?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. Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    2. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    3. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    4. Denny, Eleanor, 2009. "The economics of tidal energy," Energy Policy, Elsevier, vol. 37(5), pages 1914-1924, May.
    5. 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.
    6. Nikolić, Vlastimir & Shamshirband, Shahaboddin & Petković, Dalibor & Mohammadi, Kasra & Ćojbašić, Žarko & Altameem, Torki A. & Gani, Abdullah, 2015. "Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology," Energy, Elsevier, vol. 80(C), pages 361-372.
    7. Bolinger, Mark & Wiser, Ryan, 2009. "Wind power price trends in the United States: Struggling to remain competitive in the face of strong growth," Energy Policy, Elsevier, vol. 37(3), pages 1061-1071, March.
    8. Jeong, Min-Soo & Cha, Myung-Chan & Kim, Sang-Woo & Lee, In, 2015. "Numerical investigation of optimal yaw misalignment and collective pitch angle for load imbalance reduction of rigid and flexible HAWT blades under sheared inflow," Energy, Elsevier, vol. 84(C), pages 518-532.
    9. Na, Ji Sung & Koo, Eunmo & Muñoz-Esparza, Domingo & Jin, Emilia Kyung & Linn, Rodman & Lee, Joon Sang, 2016. "Turbulent kinetics of a large wind farm and their impact in the neutral boundary layer," Energy, Elsevier, vol. 95(C), pages 79-90.
    10. Sims, Ralph E. H. & Rogner, Hans-Holger & Gregory, Ken, 2003. "Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation," Energy Policy, Elsevier, vol. 31(13), pages 1315-1326, October.
    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. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
    2. Jennifer Marie Rinker & Esperanza Soto Sagredo & Leonardo Bergami, 2021. "The Importance of Wake Meandering on Wind Turbine Fatigue Loads in Wake," Energies, MDPI, vol. 14(21), pages 1-18, November.
    3. Xu, Zongyuan & Gao, Xiaoxia & Zhang, Huanqiang & Lv, Tao & Han, Zhonghe & Zhu, Xiaoxun & Wang, Yu, 2023. "Analysis of the anisotropy aerodynamic characteristics of downstream wind turbine considering the 3D wake expansion based on coupling method," Energy, Elsevier, vol. 263(PD).
    4. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    5. Stoyan Kanev & Edwin Bot & Jack Giles, 2020. "Wind Farm Loads under Wake Redirection Control," Energies, MDPI, vol. 13(16), pages 1-15, August.
    6. Kanev, Stoyan, 2020. "Dynamic wake steering and its impact on wind farm power production and yaw actuator duty," Renewable Energy, Elsevier, vol. 146(C), pages 9-15.
    7. 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).
    8. 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).
    9. 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.
    10. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
    11. 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).
    12. Yin, Xiuxing & Zhang, Wencan & Jiang, Zhansi & Pan, Li, 2020. "Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization," Renewable Energy, Elsevier, vol. 160(C), pages 974-986.
    13. Can Zhang & Jisheng Zhang & Athanasios Angeloudis & Yudi Zhou & Stephan C. Kramer & Matthew D. Piggott, 2023. "Physical Modelling of Tidal Stream Turbine Wake Structures under Yaw Conditions," Energies, MDPI, vol. 16(4), pages 1-21, February.
    14. Antonio Cioffi & Claudia Muscari & Paolo Schito & Alberto Zasso, 2020. "A Steady-State Wind Farm Wake Model Implemented in OpenFAST," Energies, MDPI, vol. 13(23), pages 1-16, November.
    15. Shibuya, Koichiro & Uchida, Takanori, 2023. "Wake asymmetry of yaw state wind turbines induced by interference with wind towers," Energy, Elsevier, vol. 280(C).
    16. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    17. 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).
    18. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    19. 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.
    20. Yingming Liu & Yingwei Wang & Xiaodong Wang & Jiangsheng Zhu & Wai Hou Lio, 2019. "Active Power Dispatch for Supporting Grid Frequency Regulation in Wind Farms Considering Fatigue Load," Energies, MDPI, vol. 12(8), pages 1-23, April.
    21. Francesco Castellani & Marco Buzzoni & Davide Astolfi & Gianluca D’Elia & Giorgio Dalpiaz & Ludovico Terzi, 2017. "Wind Turbine Loads Induced by Terrain and Wakes: An Experimental Study through Vibration Analysis and Computational Fluid Dynamics," Energies, MDPI, vol. 10(11), pages 1-19, November.
    22. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    23. 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).

    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. Slattery, Michael C. & Johnson, Becky L. & Swofford, Jeffrey A. & Pasqualetti, Martin J., 2012. "The predominance of economic development in the support for large-scale wind farms in the U.S. Great Plains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3690-3701.
    2. Linnell, Peter, 2010. "Are Smaller Turbines the Way Forward for Wind Energy in Herefordshire?," MPRA Paper 58879, University Library of Munich, Germany.
    3. Linnell, Peter, 2010. "Are Smaller Turbines the Way Forward for Wind Energy in Herefordshire?," MPRA Paper 58227, University Library of Munich, Germany.
    4. Linnell, Peter, 2010. "Are Smaller Turbines the Way Forward for Wind Energy in Herefordshire?," MPRA Paper 58386, University Library of Munich, Germany.
    5. Dinica, Valentina, 2011. "Renewable electricity production costs--A framework to assist policy-makers' decisions on price support," Energy Policy, Elsevier, vol. 39(7), pages 4153-4167, July.
    6. Zhang, Jie & Jain, Rishabh & Hodge, Bri-Mathias, 2016. "A data-driven method to characterize turbulence-caused uncertainty in wind power generation," Energy, Elsevier, vol. 112(C), pages 1139-1152.
    7. Swofford, Jeffrey & Slattery, Michael, 2010. "Public attitudes of wind energy in Texas: Local communities in close proximity to wind farms and their effect on decision-making," Energy Policy, Elsevier, vol. 38(5), pages 2508-2519, May.
    8. Hernández-Escobedo, Q. & Manzano-Agugliaro, F. & Zapata-Sierra, A., 2010. "The wind power of Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2830-2840, December.
    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. Ehlers, Melf-Hinrich & Sutherland, Lee-Ann, 2016. "Patterns of attention to renewable energy in the British farming press from 1980 to 2013," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 959-973.
    11. Bolinger, Mark & Wiser, Ryan, 2012. "Understanding wind turbine price trends in the U.S. over the past decade," Energy Policy, Elsevier, vol. 42(C), pages 628-641.
    12. 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).
    13. Lai, N.Y.G. & Yap, E.H. & Lee, C.W., 2011. "Viability of CCS: A broad-based assessment for Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3608-3616.
    14. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    15. Valentine, Scott Victor, 2011. "Understanding the variability of wind power costs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3632-3639.
    16. Audoly, Richard & Vogt-Schilb, Adrien & Guivarch, Céline & Pfeiffer, Alexander, 2018. "Pathways toward zero-carbon electricity required for climate stabilization," Applied Energy, Elsevier, vol. 225(C), pages 884-901.
    17. Escalera Mendoza, Alejandra S. & Griffith, D. Todd & Jeong, Michael & Qin, Chris & Loth, Eric & Phadnis, Mandar & Pao, Lucy & Selig, Michael S., 2023. "Aero-structural rapid screening of new design concepts for offshore wind turbines," Renewable Energy, Elsevier, vol. 219(P2).
    18. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    19. Naeem, Muhammad Abubakr & Arfaoui, Nadia, 2023. "Exploring downside risk dependence across energy markets: Electricity, conventional energy, carbon, and clean energy during episodes of market crises," Energy Economics, Elsevier, vol. 127(PB).
    20. Shin, Dongheon & Ko, Kyungnam, 2022. "Experimental study on application of nacelle-mounted LiDAR for analyzing wind turbine wake effects by distance," Energy, Elsevier, vol. 243(C).

    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:121:y:2017:i:c:p:561-569. 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.