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A fast algorithm based on the submodular property for optimization of wind turbine positioning

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  • Changshui, Zhang
  • Guangdong, Hou
  • Jun, Wang

Abstract

In the design of a wind farm, the placement of turbines is an important factor that affects the efficiency and profit, but automatic placement of turbines is still a challenging problem.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:11:p:2951-2958
    DOI: 10.1016/j.renene.2011.03.045
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    6. 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.
    7. 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.
    8. Beşkirli, Mehmet & Koç, İsmail & Haklı, Hüseyin & Kodaz, Halife, 2018. "A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 301-308.
    9. 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).
    10. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.

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