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Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD)

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  • Dhunny, A.Z.
  • Lollchund, M.R.
  • Rughooputh, S.D.D.V.

Abstract

Computational Fluid Dynamics (CFD) modeling is becoming an important tool in the wind industry to study wind flow patterns. Accurate CFD simulations of wind flow are essential for the selection of wind farm locations as well as the design of appropriate wind turbines. This article validates the average wind power estimated by the state of the art CFD tool WindSim using on-site measurements from nine meteorological stations scattered around a highly complex terrain at several heights. It is known that the numerical solver is very sensitive to the wide number of computational parameters that have to be taken into consideration by the user. This paper investigates those computational parameters in details including a grid dependency test, the order of the discretization schemes, the turbulence models (Standard k-ε, k-ε with Yap corrections, RNG k-ε and Modified k-ε) and the iterative convergence criteria. The best model is employed to investigate major hot spots identified where wind farming is feasible in Mauritius with due consideration to land use and topographical requirements. Wind maps are produced at four levels which are of typical hub heights of commercial wind turbines. These maps can be used to assist in the decision-making process when locating best placements for wind farming.

Suggested Citation

  • Dhunny, A.Z. & Lollchund, M.R. & Rughooputh, S.D.D.V., 2017. "Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD)," Renewable Energy, Elsevier, vol. 101(C), pages 1-9.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:1-9
    DOI: 10.1016/j.renene.2016.08.032
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    References listed on IDEAS

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    1. 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.
    2. A.Z. Dhunny & M.R. Lollchund & S.D.D.V. Rughooputh, 2016. "Numerical analysis of wind flow patterns over complex hilly terrains: comparison between two commonly used CFD software," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 39(3/4), pages 181-203.
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    Cited by:

    1. Dhunny, A.Z. & Allam, Z. & Lobine, D. & Lollchund, M.R., 2019. "Sustainable renewable energy planning and wind farming optimization from a biodiversity perspective," Energy, Elsevier, vol. 185(C), pages 1282-1297.
    2. Shea, Ryan P. & Ramgolam, Yatindra Kumar, 2019. "Applied levelized cost of electricity for energy technologies in a small island developing state: A case study in Mauritius," Renewable Energy, Elsevier, vol. 132(C), pages 1415-1424.
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    5. Shaohui Li & Xuejin Sun & Riwei Zhang & Chuanliang Zhang, 2019. "A Feasibility Study of Simulating the Micro-Scale Wind Field for Wind Energy Applications by NWP/CFD Model with Improved Coupling Method and Data Assimilation," Energies, MDPI, vol. 12(13), pages 1-19, July.
    6. Takanori Uchida & Kenichiro Sugitani, 2020. "Numerical and Experimental Study of Topographic Speed-Up Effects in Complex Terrain," Energies, MDPI, vol. 13(15), pages 1-38, July.
    7. Liu, Zhenqing & Diao, Zheng & Ishihara, Takeshi, 2019. "Study of the flow fields over simplified topographies with different roughness conditions using large eddy simulations," Renewable Energy, Elsevier, vol. 136(C), pages 968-992.
    8. Yang, Lin & Rojas, Jose I. & Montlaur, Adeline, 2020. "Advanced methodology for wind resource assessment near hydroelectric dams in complex mountainous areas," Energy, Elsevier, vol. 190(C).
    9. Yan, Bowen & Shen, Ruifang & Li, Ke & Wang, Zhenguo & Yang, Qingshan & Zhou, Xuhong & Zhang, Le, 2023. "Spatio-temporal correlation for simultaneous ultra-short-term wind speed prediction at multiple locations," Energy, Elsevier, vol. 284(C).
    10. Wen-Ko Hsu & Chung-Kee Yeh, 2021. "Offshore Wind Potential of West Central Taiwan: A Case Study," Energies, MDPI, vol. 14(12), pages 1-20, June.
    11. Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
    12. Sarah Jamal Mattar & Mohammad Reza Kavian Nezhad & Michael Versteege & Carlos F. Lange & Brian A. Fleck, 2021. "Validation Process for Rooftop Wind Regime CFD Model in Complex Urban Environment Using an Experimental Measurement Campaign," Energies, MDPI, vol. 14(9), pages 1-19, April.
    13. Huilai Ren & Xiaodong Zhang & Shun Kang & Sichao Liang, 2018. "Actuator Disc Approach of Wind Turbine Wake Simulation Considering Balance of Turbulence Kinetic Energy," Energies, MDPI, vol. 12(1), pages 1-19, December.
    14. Daniel Tabas & Jiannong Fang & Fernando Porté-Agel, 2019. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics," Energies, MDPI, vol. 12(7), pages 1-12, April.
    15. Francesco Castellani & Marco Buzzoni & Davide Astolfi & Gianluca D’Elia & Giorgio Dalpiaz & Ludovico Terzi, 2017. "Wind Turbine Loads Induced by Terrain and Wakes: An Experimental Study through Vibration Analysis and Computational Fluid Dynamics," Energies, MDPI, vol. 10(11), pages 1-19, November.
    16. Timmons, D. & Dhunny, A.Z. & Elahee, K. & Havumaki, B. & Howells, M. & Khoodaruth, A. & Lema-Driscoll, A.K. & Lollchund, M.R. & Ramgolam, Y.K. & Rughooputh, S.D.D.V. & Surroop, D., 2019. "Cost minimization for fully renewable electricity systems: A Mauritius case study," Energy Policy, Elsevier, vol. 133(C).
    17. Takanori Uchida, 2019. "Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain Using High-Resolution Elevation Data and Surface Roughness Data Constructed with a Drone," Energies, MDPI, vol. 12(19), pages 1-20, October.
    18. Yuan Song & Insu Paek, 2020. "Prediction and Validation of the Annual Energy Production of a Wind Turbine Using WindSim and a Dynamic Wind Turbine Model," Energies, MDPI, vol. 13(24), pages 1-15, December.
    19. Takanori Uchida & Yasushi Kawashima, 2019. "New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence," Energies, MDPI, vol. 12(13), pages 1-27, July.
    20. 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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