IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v309y2024ics0360544224028810.html
   My bibliography  Save this article

Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm

Author

Listed:
  • Zhang, Xiaofeng
  • Wang, Qiang
  • Ye, Shitong
  • Luo, Kun
  • Fan, Jianren

Abstract

Wind farm layout optimization (WFLO) has become a significant approach to enhancing the efficiency of wind energy utilization. However, load also represents a critical factor that must be considered during optimization. To enhance power generation while controlling loads within limitations, an innovative load-constrained layout optimization method that employs a surrogate model based on artificial neural networks and genetic algorithms was proposed. This paper verified the accuracy of the surrogate model and then conducted layout optimizations on a single and a full wind condition case to assess the proposed method. The results indicated that the mean absolute percentage errors of load channels can meet the precision requirements for WFLO. A comparison of layout optimization results between the method proposed in this paper and the traditional method showed that in the single-wind-condition case, the method proposed in this paper reduced the maximum load by 5.64 % compared to the traditional method, with nearly identical power output; in the full-wind-condition case, the reduction of maximum load was 1.70 %, while only sacrificing the annual power generation by 0.10 %. This study provides a load-constrained WFLO method, promising to effectively ensure the lifespan of wind turbines while increasing power generation and offering significant engineering value.

Suggested Citation

  • Zhang, Xiaofeng & Wang, Qiang & Ye, Shitong & Luo, Kun & Fan, Jianren, 2024. "Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028810
    DOI: 10.1016/j.energy.2024.133106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224028810
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133106?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. Kaldellis, John K. & Triantafyllou, Panagiotis & Stinis, Panagiotis, 2021. "Critical evaluation of Wind Turbines’ analytical wake models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. 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.
    3. 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).
    4. Al-Yahyai, Sultan & Charabi, Yassine & Gastli, Adel, 2010. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3192-3198, December.
    5. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm," Energy, Elsevier, vol. 73(C), pages 430-442.
    6. de N Santos, Francisco & D’Antuono, Pietro & Robbelein, Koen & Noppe, Nymfa & Weijtjens, Wout & Devriendt, Christof, 2023. "Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks," Renewable Energy, Elsevier, vol. 205(C), pages 461-474.
    7. Changshui, Zhang & Guangdong, Hou & Jun, Wang, 2011. "A fast algorithm based on the submodular property for optimization of wind turbine positioning," Renewable Energy, Elsevier, vol. 36(11), pages 2951-2958.
    8. 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.
    9. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Yan, Gary Kai & Gu, Michelle Dao & Moran, Joaquin & Morgenroth, Michael & Amon, Cristina H., 2016. "The impact of land use constraints in multi-objective energy-noise wind farm layout optimization," Renewable Energy, Elsevier, vol. 85(C), pages 359-370.
    10. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    11. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    12. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    13. Georgios Gasparis & Wai Hou Lio & Fanzhong Meng, 2020. "Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm," Energies, MDPI, vol. 13(23), pages 1-15, December.
    14. 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.
    15. Abdelsalam, Ali M. & El-Shorbagy, M.A., 2018. "Optimization of wind turbines siting in a wind farm using genetic algorithm based local search," Renewable Energy, Elsevier, vol. 123(C), pages 748-755.
    16. Wang, Qiang & Luo, Kun & Wu, Chunlei & Zhu, Zhaofan & Fan, Jianren, 2022. "Mesoscale simulations of a real onshore wind power base in complex terrain: Wind farm wake behavior and power production," Energy, Elsevier, vol. 241(C).
    17. Wang, Longyan & Cholette, Michael E. & Tan, Andy C.C. & Gu, Yuantong, 2017. "A computationally-efficient layout optimization method for real wind farms considering altitude variations," Energy, Elsevier, vol. 132(C), pages 147-159.
    18. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    19. Davide Astolfi & Fabrizio De Caro & Alfredo Vaccaro, 2023. "Characterizing the Wake Effects on Wind Power Generator Operation by Data-Driven Techniques," Energies, MDPI, vol. 16(15), pages 1-19, August.
    20. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Guo, Kunpeng & Zhou, Tong & Liu, Min & Zhang, Jian & Yuan, Ziting, 2022. "A novel approach for wind farm micro-siting in complex terrain based on an improved genetic algorithm," Energy, Elsevier, vol. 251(C).
    21. 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.
    22. Hu, Weicheng & Yang, Qingshan & Yuan, Ziting & Yang, Fucheng, 2024. "Wind farm layout optimization in complex terrain based on CFD and IGA-PSO," Energy, Elsevier, vol. 288(C).
    23. 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).
    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. Amar Maafa & Hacene Mellah & Karim Benaouicha & Badreddine Babes & Abdelghani Yahiou & Hamza Sahraoui, 2024. "Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator," Sustainability, MDPI, vol. 16(21), pages 1-21, October.

    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. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    3. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    4. 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).
    5. 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.
    6. Wang, Longyan & Cholette, Michael E. & Zhou, Yunkai & Yuan, Jianping & Tan, Andy C.C. & Gu, Yuantong, 2018. "Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization," Renewable Energy, Elsevier, vol. 126(C), pages 819-829.
    7. 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.
    8. 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).
    9. 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).
    10. Nicolas Kirchner-Bossi & Fernando Porté-Agel, 2018. "Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model," Energies, MDPI, vol. 11(12), pages 1-26, November.
    11. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2018. "Continuous adjoint formulation for wind farm layout optimization: A 2D implementation," Applied Energy, Elsevier, vol. 228(C), pages 2333-2345.
    17. Ling, Ziyan & Zhao, Zhenzhou & Liu, Yige & Liu, Huiwen & Ali, Kashif & Liu, Yan & Wen, Yifan & Wang, Dingding & Li, Shijun & Su, Chunhao, 2024. "Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model," Renewable Energy, Elsevier, vol. 227(C).
    18. Javier Serrano González & Manuel Burgos Payán & Jesús Manuel Riquelme Santos & Ángel Gaspar González Rodríguez, 2021. "Optimal Micro-Siting of Weathervaning Floating Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-19, February.
    19. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    20. 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.

    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:309:y:2024:i:c:s0360544224028810. 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.