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Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction

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  • Song, Mengxuan
  • Chen, Kai
  • Zhang, Xing
  • Wang, Jun

Abstract

In the optimization of wind turbine micro-siting of wind farms, the major target is to maximize the total energy yield. But considering from the aspect of the power grid, the sensitivity of wind power generation to varying incoming wind direction is also an essential factor. However, most existing optimization approaches on wind turbine micro-siting are focused on increasing the total power yield only. In this paper, by employing computational fluid dynamics and the virtual particle model for the simulation of turbine wake flow, a sensitivity index is proposed to quantitatively evaluate the variation of power generation under varying wind direction. Typical turbine layouts obtained by existing power optimization approaches are evaluated for stability. Results indicate that regularly arranged turbine layouts are not suitable for stable power production. Based on solutions from the power optimization, a second-stage optimization using Particle Swarm Optimization algorithm is presented. The proposed optimization method adjusts the positions of the turbines locally, aiming at increasing the stability of wind farm power generation without damaging its advantage of high power yield. Case studies on flat terrain and complex terrain both demonstrate the effectiveness of the present local adjustment optimization method.

Suggested Citation

  • Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:57-65
    DOI: 10.1016/j.renene.2015.06.033
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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.
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    5. Song, M.X. & Chen, K. & Zhang, X. & Wang, J., 2015. "The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain," Energy, Elsevier, vol. 80(C), pages 567-574.
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    7. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2014. "Optimization of wind farm micro-siting for complex terrain using greedy algorithm," Energy, Elsevier, vol. 67(C), pages 454-459.
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    Cited by:

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    3. Silvio Rodrigues & Carlos Restrepo & George Katsouris & Rodrigo Teixeira Pinto & Maryam Soleimanzadeh & Peter Bosman & Pavol Bauer, 2016. "A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures," Energies, MDPI, vol. 9(3), pages 1-42, March.
    4. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2018. "Assessing the energy benefit of using a wind turbine micro-siting model," Renewable Energy, Elsevier, vol. 118(C), pages 591-601.
    5. Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.
    6. Mohammad Arashi & Priyanka Nagar & Andriette Bekker, 2020. "Joint Probabilistic Modeling of Wind Speed and Wind Direction for Wind Energy Analysis: A Case Study in Humansdorp and Noupoort," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    7. Han, Qinkai & Chu, Fulei, 2021. "Directional wind energy assessment of China based on nonparametric copula models," Renewable Energy, Elsevier, vol. 164(C), pages 1334-1349.
    8. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.
    9. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
    10. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
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