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A Comparison of Wind Flow Models for Wind Resource Assessment in Wind Energy Applications

Author

Listed:
  • Nicolas Gasset

    (École de Technologie Supérieure, 1100 Notre-Dame Ouest, Montréal, QC H3C 1K3, Canada)

  • Mathieu Landry

    (K.C. Irving Chair in Sustainable Development, Université de Moncton, Moncton, NB E1A 3E9, Canada)

  • Yves Gagnon

    (K.C. Irving Chair in Sustainable Development, Université de Moncton, Moncton, NB E1A 3E9, Canada)

Abstract

The objective of this work was to assess the accuracy of various coupled mesoscale-microscale wind flow modeling methodologies for wind energy applications. This is achieved by examining and comparing mean wind speeds from several wind flow modeling methodologies with observational measurements from several 50 m met towers distributed across the study area. At the mesoscale level, with a 5 km resolution, two scenarios are examined based on the Mesoscale Compressible Community Model (MC2) model: the Canadian Wind Energy Atlas (CWEA) scenario, which is based on standard input data, and the CWEA High Definition (CWEAHD) scenario where high resolution land cover input data is used. A downscaling of the obtained mesoscale wind climate to the microscale level is then performed, where two linear microscale models, i.e. , MsMicro and the Wind Atlas Analysis and Application Program (WAsP), are evaluated following three downscaling scenarios: CWEA-WAsP, CWEA-MsMicro and CWEAHD-MsMicro. Results show that, for the territory studied, with a modeling approach based on the MC2 and MsMicro models, also known as Wind Energy Simulation Toolkit (WEST), the use of high resolution land cover and topography data at the mesoscale level helps reduce modeling errors for both the mesoscale and microscale models, albeit only marginally. At the microscale level, results show that the MC2-WAsP modeling approach gave substantially better results than both MC2 and MsMicro modeling approaches due to tweaked meso-micro coupling.

Suggested Citation

  • Nicolas Gasset & Mathieu Landry & Yves Gagnon, 2012. "A Comparison of Wind Flow Models for Wind Resource Assessment in Wind Energy Applications," Energies, MDPI, vol. 5(11), pages 1-35, October.
  • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4288-4322:d:21054
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    Citations

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    Cited by:

    1. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    2. Ewa Chomać-Pierzecka & Anna Sobczak & Dariusz Soboń, 2022. "Wind Energy Market in Poland in the Background of the Baltic Sea Bordering Countries in the Era of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, March.
    3. Takanori Uchida & Susumu Takakuwa, 2019. "A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain," Energies, MDPI, vol. 12(10), pages 1-19, May.
    4. Fang, Hsin-Fa, 2014. "Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago," Renewable Energy, Elsevier, vol. 67(C), pages 237-241.
    5. Mizuki Konagaya & Teruo Ohsawa & Toshinari Mito & Takeshi Misaki & Taro Maruo & Yasuyuki Baba, 2022. "Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models," Resources, MDPI, vol. 11(11), pages 1-18, October.
    6. Waewsak, Jompob & Landry, Mathieu & Gagnon, Yves, 2015. "Offshore wind power potential of the Gulf of Thailand," Renewable Energy, Elsevier, vol. 81(C), pages 609-626.
    7. Optis, Mike & Perr-Sauer, Jordan, 2019. "The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 27-41.
    8. Waewsak, Jompob & Landry, Mathieu & Gagnon, Yves, 2013. "High resolution wind atlas for Nakhon Si Thammarat and Songkhla provinces, Thailand," Renewable Energy, Elsevier, vol. 53(C), pages 101-110.
    9. Javier Sanz Rodrigo & Roberto Aurelio Chávez Arroyo & Patrick Moriarty & Matthew Churchfield & Branko Kosović & Pierre‐Elouan Réthoré & Kurt Schaldemose Hansen & Andrea Hahmann & Jeffrey D. Mirocha & , 2017. "Mesoscale to microscale wind farm flow modeling and evaluation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(2), March.
    10. Ewa Chomać-Pierzecka, 2024. "Investment in Offshore Wind Energy in Poland and Its Impact on Public Opinion," Energies, MDPI, vol. 17(16), pages 1-19, August.
    11. Jompob Waewsak & Chana Chancham & Somphol Chiwamongkhonkarn & Yves Gagnon, 2019. "Wind Resource Assessment of the Southernmost Region of Thailand Using Atmospheric and Computational Fluid Dynamics Wind Flow Modeling," Energies, MDPI, vol. 12(10), pages 1-18, May.
    12. 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).
    13. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    14. Lattawan Niyomtham & Charoenporn Lertsathittanakorn & Jompob Waewsak & Yves Gagnon, 2022. "Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand," Energies, MDPI, vol. 15(9), pages 1-19, April.
    15. Yang, Mao & Huang, Yutong & Guo, Yunfeng & Zhang, Wei & Wang, Bo, 2024. "Ultra-short-term wind farm cluster power prediction based on FC-GCN and trend-aware switching mechanism," Energy, Elsevier, vol. 290(C).
    16. Ewa Chomać-Pierzecka & Hubert Gąsiński & Joanna Rogozińska-Mitrut & Dariusz Soboń & Sebastian Zupok, 2023. "Review of Selected Aspects of Wind Energy Market Development in Poland and Lithuania in the Face of Current Challenges," Energies, MDPI, vol. 16(1), pages 1-17, January.
    17. Kotroni, V. & Lagouvardos, K. & Lykoudis, S., 2014. "High-resolution model-based wind atlas for Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 479-489.
    18. Hanslian, David & Hošek, Jiří, 2015. "Combining the VAS 3D interpolation method and Wind Atlas methodology to produce a high-resolution wind resource map for the Czech Republic," Renewable Energy, Elsevier, vol. 77(C), pages 291-299.
    19. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.

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