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Optimization of wind farm layout with complex land divisions

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  • Wang, Longyan
  • Tan, Andy C.C.
  • Cholette, Michael E.
  • Gu, Yuantong

Abstract

The study of wind farm layout optimization considering the decisions of land owners has rarely been reported in literature. In this paper, the common situation of complex land divisions (e.g. unequally-spaced plots) is addressed for the first time. A new constraint handling and fitness evaluation technique is developed to address the more complex wind farm boundaries and integrated into two common wind farm optimization approaches: the grid based method and the unrestricted coordinate method. Enable by the new technique, a numerical optimization study is conducted with the goal of evaluating the impact of the participation of land owners on the economic performance of the wind farm. In particular, two scenarios are considered: 1) the varying land plot scenario, where the land plot availability is included in the decision variables of the optimization, and 2) the sequential land plot scenario, where the land plot availability is fixed prior to optimization. The study reveals that the unrestricted coordinate method under the sequential land plot scenario yields the best optimization results, with the smallest cost of energy and the largest wind farm efficiency.

Suggested Citation

  • Wang, Longyan & Tan, Andy C.C. & Cholette, Michael E. & Gu, Yuantong, 2017. "Optimization of wind farm layout with complex land divisions," Renewable Energy, Elsevier, vol. 105(C), pages 30-40.
    • Handle: RePEc:eee:renene:v:105:y:2017:i:c:p:30-40
    DOI: 10.1016/j.renene.2016.12.025
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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).
    2. Emin Sertaç Ari & Cevriye Gencer, 2020. "Proposal of a novel mixed integer linear programming model for site selection of a wind power plant based on power maximization with use of mixed type wind turbines," Energy & Environment, , vol. 31(5), pages 825-841, August.
    3. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
    4. 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.
    5. Wu, Yan & Zhang, Shuai & Wang, Ruiqi & Wang, Yufei & Feng, Xiao, 2020. "A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner," Renewable Energy, Elsevier, vol. 146(C), pages 687-698.
    6. Christos A. Christodoulou & Vasiliki Vita & George-Calin Seritan & Lambros Ekonomou, 2020. "A Harmony Search Method for the Estimation of the Optimum Number of Wind Turbines in a Wind Farm," Energies, MDPI, vol. 13(11), pages 1-8, June.
    7. Reddy, Sohail R., 2021. "An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization," Renewable Energy, Elsevier, vol. 165(P1), pages 162-173.
    8. Wang, Longyan & Zuo, Ming J. & Xu, Jian & Zhou, Yunkai & Tan, Andy C., 2019. "Optimizing wind farm layout by addressing energy-variance trade-off: A single-objective optimization approach," Energy, Elsevier, vol. 189(C).
    9. Shaaban, S. & Albatal, A. & Mohamed, M.H., 2018. "Optimization of H-Rotor Darrieus turbines' mutual interaction in staggered arrangements," Renewable Energy, Elsevier, vol. 125(C), pages 87-99.
    10. Miao, Shuwei & Yang, Hejun & Gu, Yingzhong, 2018. "A wind vector simulation model and its application to adequacy assessment," Energy, Elsevier, vol. 148(C), pages 324-340.
    11. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.

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