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Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation

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  • Ju, Xinglong
  • Liu, Feng

Abstract

Wind energy which is known for its cleanliness and cost-effectiveness has been one of the main alternatives for fossil fuels. An integral part is to maximize the wind energy output by optimizing the layout of wind turbines. In this paper, we first discuss the drawbacks of Conventional Genetic Algorithm (CGA) by investigating into the implications of crossover and mutation steps of CGA for the wind farm layout problem, which explains why CGA has a higher possibility of convergence to a suboptimal solution. To address the limitations of CGA, we propose novel algorithms by incorporating the self-adaptivity capability of individuals, which is an essential step observed in the natural world, called Adaptive Genetic Algorithm (AGA) and Self-Informed Genetic Algorithm (SIGA). To be specific, the individual’s chromosomes in a population will conduct a self-examination on the efficiency of all the wind turbines, and thus gaining self-awareness on which part of the solution is currently the bottleneck for further improvement. In order to relocate the worst turbine, we first propose to relocate the worst turbine randomly with AGA, and then an improved version called SIGA is developed with information guided relocation to find a good location using a surrogate model from Multivariate Adaptive Regression Splines (MARS) regression based on Monte Carlo Simulation. Extensive numerical results under multiple wind distributions and different wind farm sizes illustrate the improved efficiency of SIGA and AGA over CGA. In the end, an open-source Python package is made available on github (https://github.com/JuXinglong/WFLOP_Python).

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:429-445
    DOI: 10.1016/j.apenergy.2019.04.084
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Sun, Haiying & Yang, Hongxing, 2018. "Study on an innovative three-dimensional wind turbine wake model," Applied Energy, Elsevier, vol. 226(C), pages 483-493.
    7. 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.
    8. Yang, Jin & Chen, Bin, 2013. "Integrated evaluation of embodied energy, greenhouse gas emission and economic performance of a typical wind farm in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 559-568.
    9. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    10. 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.
    11. 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.
    12. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.
    13. Vasel-Be-Hagh, Ahmadreza & Archer, Cristina L., 2017. "Wind farm hub height optimization," Applied Energy, Elsevier, vol. 195(C), pages 905-921.
    14. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    15. Salcedo-Sanz, S. & Gallo-Marazuela, D. & Pastor-Sánchez, A. & Carro-Calvo, L. & Portilla-Figueras, A. & Prieto, L., 2014. "Offshore wind farm design with the Coral Reefs Optimization algorithm," Renewable Energy, Elsevier, vol. 63(C), pages 109-115.
    16. Yang, Xiaolei & Pakula, Maggie & Sotiropoulos, Fotis, 2018. "Large-eddy simulation of a utility-scale wind farm in complex terrain," Applied Energy, Elsevier, vol. 229(C), pages 767-777.
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