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An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm

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  • DuPont, Bryony
  • Cagan, Jonathan
  • Moriarty, Patrick

Abstract

This paper presents a system of modeling advances that can be applied in the computational optimization of wind plants. These modeling advances include accurate cost and power modeling, partial wake interaction, and the effects of varying atmospheric stability. To validate the use of this advanced modeling system, it is employed within an Extended Pattern Search (EPS)-Multi-Agent System (MAS) optimization approach for multiple wind scenarios. The wind farm layout optimization problem involves optimizing the position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, which increases the effective wind speed and resultant power at each turbine. The EPS-MAS optimization algorithm employs a profit objective, and an overarching search determines individual turbine positions, with a concurrent EPS-MAS determining the optimal hub height and rotor diameter for each turbine. Two wind cases are considered: (1) constant, unidirectional wind, and (2) three discrete wind speeds and varying wind directions, each of which have a probability of occurrence. Results show the advantages of applying the series of advanced models compared to previous application of an EPS with less advanced models to wind farm layout optimization, and imply best practices for computational optimization of wind farms with improved accuracy.

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  • 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.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:802-814
    DOI: 10.1016/j.energy.2015.12.033
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    References listed on IDEAS

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    Cited by:

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    11. Dhunny, A.Z. & Doorga, J.R.S. & Allam, Z. & Lollchund, M.R. & Boojhawon, R., 2019. "Identification of optimal wind, solar and hybrid wind-solar farming sites using fuzzy logic modelling," Energy, Elsevier, vol. 188(C).
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    14. 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.
    15. Chen, Jian & Zhang, Yu & Xu, Zhongyun & Li, Chun, 2023. "Flow characteristics analysis and power comparison for two novel types of vertically staggered wind farms," Energy, Elsevier, vol. 263(PE).
    16. 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.
    17. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    18. 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).
    19. Muhammad Bin Ali & Zeshan Ahmad & Saad Alshahrani & Muhammad Rizwan Younis & Irsa Talib & Muhammad Imran, 2022. "A Case Study: Layout Optimization of Three Gorges Wind Farm Pakistan, Using Genetic Algorithm," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    20. Masoudi, Seiied Mohsen & Baneshi, Mehdi, 2022. "Layout optimization of a wind farm considering grids of various resolutions, wake effect, and realistic wind speed and wind direction data: A techno-economic assessment," Energy, Elsevier, vol. 244(PB).

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