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Mesoscale to microscale wind farm flow modeling and evaluation

Author

Listed:
  • Javier Sanz Rodrigo
  • Roberto Aurelio Chávez Arroyo
  • Patrick Moriarty
  • Matthew Churchfield
  • Branko Kosović
  • Pierre‐Elouan Réthoré
  • Kurt Schaldemose Hansen
  • Andrea Hahmann
  • Jeffrey D. Mirocha
  • Daran Rife

Abstract

The increasing size of wind turbines, with rotors already spanning more than 150 m diameter and hub heights above 100 m, requires proper modeling of the atmospheric boundary layer (ABL) from the surface to the free atmosphere. Furthermore, large wind farm arrays create their own boundary layer structure with unique physics. This poses significant challenges to traditional wind engineering models that rely on surface‐layer theories and engineering wind farm models to simulate the flow in and around wind farms. However, adopting an ABL approach offers the opportunity to better integrate wind farm design tools and meteorological models. The challenge is how to build the bridge between atmospheric and wind engineering model communities and how to establish a comprehensive evaluation process that identifies relevant physical phenomena for wind energy applications with modeling and experimental requirements. A framework for model verification, validation, and uncertainty quantification is established to guide this process by a systematic evaluation of the modeling system at increasing levels of complexity. In terms of atmospheric physics, ‘building the bridge’ means developing models for the so‐called ‘terra incognita,’ a term used to designate the turbulent scales that transition from mesoscale to microscale. This range of scales within atmospheric research deals with the transition from parameterized to resolved turbulence and the improvement of surface boundary‐layer parameterizations. The coupling of meteorological and wind engineering flow models and the definition of a formal model evaluation methodology, is a strong area of research for the next generation of wind conditions assessment and wind farm and wind turbine design tools. Some fundamental challenges are identified in order to guide future research in this area. WIREs Energy Environ 2017, 6:e214. doi: 10.1002/wene.214 This article is categorized under: Wind Power > Climate and Environment Energy and Climate > Climate and Environment Energy Policy and Planning > Climate and Environment

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  • 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.
    • Handle: RePEc:bla:wireae:v:6:y:2017:i:2:n:e214
    DOI: 10.1002/wene.214
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    References listed on IDEAS

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    1. Radünz, William Corrêa & de Almeida, Everton & Gutiérrez, Alejandro & Acevedo, Otávio Costa & Sakagami, Yoshiaki & Petry, Adriane Prisco & Passos, Júlio César, 2022. "Nocturnal jets over wind farms in complex terrain," Applied Energy, Elsevier, vol. 314(C).
    2. Garrido-Perez, Jose M. & Ordóñez, Carlos & Barriopedro, David & García-Herrera, Ricardo & Paredes, Daniel, 2020. "Impact of weather regimes on wind power variability in western Europe," Applied Energy, Elsevier, vol. 264(C).
    3. Abedi, Hamidreza, 2023. "Assessment of flow characteristics over complex terrain covered by the heterogeneous forest at slightly varying mean flow directions," Renewable Energy, Elsevier, vol. 202(C), pages 537-553.
    4. Abedi, Hamidreza & Sarkar, Saptarshi & Johansson, Håkan, 2021. "Numerical modelling of neutral atmospheric boundary layer flow through heterogeneous forest canopies in complex terrain (a case study of a Swedish wind farm)," Renewable Energy, Elsevier, vol. 180(C), pages 806-828.
    5. Michael F. Howland & John O. Dabiri, 2019. "Wind Farm Modeling with Interpretable Physics-Informed Machine Learning," Energies, MDPI, vol. 12(14), pages 1-21, July.
    6. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    7. Renko Buhr & Hassan Kassem & Gerald Steinfeld & Michael Alletto & Björn Witha & Martin Dörenkämper, 2021. "A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification," Energies, MDPI, vol. 14(4), pages 1-22, February.
    8. Durán, Pablo & Meiβner, Cathérine & Casso, Pau, 2020. "A new meso-microscale coupled modelling framework for wind resource assessment: A validation study," Renewable Energy, Elsevier, vol. 160(C), pages 538-554.
    9. Radünz, William Corrêa & Sakagami, Yoshiaki & Haas, Reinaldo & Petry, Adriane Prisco & Passos, Júlio César & Miqueletti, Mayara & Dias, Eduardo, 2021. "Influence of atmospheric stability on wind farm performance in complex terrain," Applied Energy, Elsevier, vol. 282(PA).
    10. Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Fernando Rueda-Martinez & Gerardo Alcalá & Alberto-Jesus Perea-Moreno, 2019. "Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico," Energies, MDPI, vol. 12(12), pages 1-22, June.
    11. Dimitris Drikakis & Talib Dbouk, 2022. "The Role of Computational Science in Wind and Solar Energy: A Critical Review," Energies, MDPI, vol. 15(24), pages 1-20, December.
    12. Asmae El Bahlouli & Alexander Rautenberg & Martin Schön & Kjell zum Berge & Jens Bange & Hermann Knaus, 2019. "Comparison of CFD Simulation to UAS Measurements for Wind Flows in Complex Terrain: Application to the WINSENT Test Site," Energies, MDPI, vol. 12(10), pages 1-21, May.
    13. 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.
    14. Rafael V. Rodrigues & Corinne Lengsfeld, 2019. "Development of a Computational System to Improve Wind Farm Layout, Part II: Wind Turbine Wakes Interaction," Energies, MDPI, vol. 12(7), pages 1-27, April.

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