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Wind farm layout optimization for wake effect uniformity

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  • Yang, Kyoungboo
  • Kwak, Gyeongil
  • Cho, Kyungho
  • Huh, Jongchul

Abstract

The basic objective of wind farm layout optimization is to maximize the energy produced by wind farms. However, when wind turbines are arranged in a limited space like an onshore wind farm, specific wind turbines may have greater wake exposure than other wind turbines. This phenomenon can be conspicuous in a mixed layout that consists of turbines with different capacities and hub heights. In this study, we developed and tested a new objective function to increase wind farm energy output while making the wake loss of each wind turbine uniform. The purpose of this function is to adjust the wake effects of all of the wind turbines on a wind farm to similar levels, thereby promoting the operational stability of all of the wind turbines. Layout optimization was performed using a simulated annealing algorithm, which is a heuristic method, with actual wind conditions for an existing wind farm in operation. Then, the results obtained using the proposed method were compared with those yielded by layout optimization for energy maximization. The layout generated using the proposed objective function had lower energy output than that obtained by energy maximization. However, this difference was small and the proposed method prevented wake effect concentration on specific turbines by making the wake effect levels uniform.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:983-995
    DOI: 10.1016/j.energy.2019.07.019
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    1. 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.
    2. 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.
    3. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2018. "Optimal design of neighbouring offshore wind farms: A co-evolutionary approach," Applied Energy, Elsevier, vol. 209(C), pages 140-152.
    4. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    5. 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.
    6. 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.
    7. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    8. 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.
    9. 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.
    10. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    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. 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.
    13. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    14. 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.
    15. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    16. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2017. "Wind farm layout optimization using a Gaussian-based wake model," Renewable Energy, Elsevier, vol. 107(C), pages 531-541.
    17. 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.
    18. Song, Zhe & Zhang, Zijun & Chen, Xingying, 2016. "The decision model of 3-dimensional wind farm layout design," Renewable Energy, Elsevier, vol. 85(C), pages 248-258.
    19. 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.
    20. Chen, K. & Song, M.X. & Zhang, X. & Wang, S.F., 2016. "Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm," Renewable Energy, Elsevier, vol. 96(PA), pages 676-686.
    21. Kuo, Jim Y.J. & Romero, David A. & Amon, Cristina H., 2015. "A mechanistic semi-empirical wake interaction model for wind farm layout optimization," Energy, Elsevier, vol. 93(P2), pages 2157-2165.
    22. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    23. Song, Mengxuan & Wen, Yi & Duan, Bin & Wang, Jun & Gong, Qi, 2017. "Micro-siting optimization of a wind farm built in multiple phases," Energy, Elsevier, vol. 137(C), pages 95-103.
    24. 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.
    25. 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.
    26. Mayo, Michael & Daoud, Maisa, 2016. "Informed mutation of wind farm layouts to maximise energy harvest," Renewable Energy, Elsevier, vol. 89(C), pages 437-448.
    27. Tingey, Eric B. & Ning, Andrew, 2017. "Trading off sound pressure level and average power production for wind farm layout optimization," Renewable Energy, Elsevier, vol. 114(PB), pages 547-555.
    28. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    29. 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.
    30. 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.
    31. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization," Renewable Energy, Elsevier, vol. 107(C), pages 508-521.
    32. DuPont, Bryony & Cagan, Jonathan & Moriarty, Patrick, 2016. "An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm," Energy, Elsevier, vol. 106(C), pages 802-814.
    33. MirHassani, S.A. & Yarahmadi, A., 2017. "Wind farm layout optimization under uncertainty," Renewable Energy, Elsevier, vol. 107(C), pages 288-297.
    34. 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.
    35. Wagner, Markus & Day, Jareth & Neumann, Frank, 2013. "A fast and effective local search algorithm for optimizing the placement of wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 64-70.
    36. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
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