Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model

Cristina L. Archer aCenter for Research in Wind, University of Delaware, Newark, Delaware

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Sicheng Wu aCenter for Research in Wind, University of Delaware, Newark, Delaware

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Yulong Ma aCenter for Research in Wind, University of Delaware, Newark, Delaware

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Pedro A. Jiménez bNational Center for Atmospheric Research, Boulder, Colorado

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Abstract

As wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called CTKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected CTKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cristina L. Archer, carcher@udel.edu

Abstract

As wind farms grow in number and size worldwide, it is important that their potential impacts on the environment are studied and understood. The Fitch parameterization implemented in the Weather Research and Forecasting (WRF) Model since version 3.3 is a widely used tool today to study such impacts. We identified two important issues related to the way the added turbulent kinetic energy (TKE) generated by a wind farm is treated in the WRF Model with the Fitch parameterization. The first issue is a simple “bug” in the WRF code, and the second issue is the excessive value of a coefficient, called CTKE, that relates TKE to the turbine electromechanical losses. These two issues directly affect the way that a wind farm wake evolves, and they impact properties like near-surface temperature and wind speed at the wind farm as well as behind it in the wake. We provide a bug fix and a revised value of CTKE that is one-quarter of the original value. This 0.25 correction factor is empirical; future studies should examine its dependence on parameters such as atmospheric stability, grid resolution, and wind farm layout. We present the results obtained with the Fitch parameterization in the WRF Model for a single turbine with and without the bug fix and the corrected CTKE and compare them with high-fidelity large-eddy simulations. These two issues have not been discovered before because they interact with one another in such a way that their combined effect is a somewhat realistic vertical TKE profile at the wind farm and a realistic wind speed deficit in the wake. All WRF simulations that used the Fitch wind farm parameterization are affected, and their conclusions may need to be revisited.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Cristina L. Archer, carcher@udel.edu
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  • Abkar, M., and F. Porté-Agel, 2015: A new wind-farm parameterization for large-scale atmospheric models. J. Renewable Sustainable Energy, 7, 013121, https://doi.org/10.1063/1.4907600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adams, A. S., and D. W. Keith, 2013: Are global wind power resource estimates overstated? Environ. Res. Lett., 8, 015021, https://doi.org/10.1088/1748-9326/8/1/015021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Archer, C. L., and A. Vasel-Be-Hagh, 2019: Wake steering via yaw control in multi-turbine wind farms: Recommendations based on large-eddy simulation. Sustainable Energy Technol. Assess., 33, 3443, https://doi.org/10.1016/j.seta.2019.03.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Archer, C. L., S. Mirzaeisefat, and S. Lee, 2013: Quantifying the sensitivity of wind farm performance to array layout options using large-eddy simulation. Geophys. Res. Lett., 40, 49634970, https://doi.org/10.1002/grl.50911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Archer, C. L., S. Wu, A. Vasel-Be-Hagh, J. F. Brodie, R. Delgado, A. St. Pé, S. Oncley, and S. Semmer, 2019: The VERTEX field campaign: Observations of near-ground effects of wind turbine wakes. J. Turbul., 20, 6492, https://doi.org/10.1080/14685248.2019.1572161.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baidya Roy, S., S. Pacala, and R. Walko, 2004: Can large wind farms affect local meteorology? J. Geophys. Res., 109, D19101, https://doi.org/10.1029/2004JD004763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrie, D. B., and D. B. Kirk-Davidoff, 2010: Weather response to a large wind turbine array. Atmos. Chem. Phys., 10, 769775, https://doi.org/10.5194/acp-10-769-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhaganagar, K., and M. Debnath, 2015: The effects of mean atmospheric forcings of the stable atmospheric boundary layer on wind turbine wake. J. Renewable Sustainable Energy, 7, 013124, https://doi.org/10.1063/1.4907687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blahak, U., B. Goretzki, and J. Meis, 2010: A simple parameterization of drag forces induced by large wind farms for numerical weather prediction models. Proc. European Wind Energy Conf. and Exhibition, PO ID 445, Warsaw, Poland, EWEC, 186189.

    • Search Google Scholar
    • Export Citation
  • Chaudhari, A., O. Agafonova, A. Hellsten, and J. Sorvari, 2017: Numerical study of the impact of atmospheric stratification on a wind-turbine performance. J. Phys.: Conf. Ser., 854, 012007, https://doi.org/10.1088/1742-6596/854/1/012007.

    • Search Google Scholar
    • Export Citation
  • Churchfield, M. J., S. Lee, J. Michalakes, and P. J. Moriarty, 2012a: A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics. J. Turbul., 13, N14, https://doi.org/10.1080/14685248.2012.668191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Churchfield, M. J., S. Lee, P. J. Moriarty, L. Martinez, S. Leonardi, G. Vijayakumar, and J. Brasseur, 2012b:A large-eddy simulations of wind-plant aerodynamics. 50th AIAA Aerospace Sciences Meeting, Nashville, TN, AIAA, https://www.nrel.gov/docs/fy12osti/53554.pdf .

    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad, 2012: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Wea. Rev., 140, 30173038, https://doi.org/10.1175/MWR-D-11-00352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitch, A. C., J. B. Olson, and J. K. Lundquist, 2013: Parameterization of wind farms in climate models. J. Climate, 26, 64396458, https://doi.org/10.1175/JCLI-D-12-00376.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fleming, P. A., and Coauthors, 2014: Evaluating techniques for redirecting turbine wakes using SOWFA. Renewable Energy, 70, 211218, https://doi.org/10.1016/j.renene.2014.02.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghaisas, N. S., and C. L. Archer, 2016: Geometry-based models for studying the effects of wind farm layout. J. Atmos. Oceanic Technol., 33, 481501, https://doi.org/10.1175/JTECH-D-14-00199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghaisas, N. S., C. L. Archer, S. Xie, S. Wu, and E. Maguire, 2017: Evaluation of layout and atmospheric stability effects in wind farms using large-eddy simulation. Wind Energy, 20, 12271240, https://doi.org/10.1002/we.2091.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, Y., M. Stoellinger, and J. Naughton, 2016: Large eddy simulation for atmospheric boundary layer flow over flat and complex terrains. J. Phys.: Conf. Ser., 753, 032044, https://doi.org/10.1088/1742-6596/753/3/032044.

    • Search Google Scholar
    • Export Citation
  • Jacobson, M. Z., and C. L. Archer, 2012: Saturation wind power potential and its implications for wind energy. Proc. Natl. Acad. Sci. USA, 109, 15 67915 684, https://doi.org/10.1073/pnas.1208993109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., J. Dudhia, J. F. González-Rouco, J. Navarro, J. P. Montávez, and E. García-Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev., 140, 898918, https://doi.org/10.1175/MWR-D-11-00056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keith, D. W., J. F. DeCarolis, D. C. Denkenberger, D. H. Lenschow, S. L. Malyshev, S. Pacala, and P. J. Rasch, 2004: The influence of large-scale wind power on global climate. Proc. Natl. Acad. Sci. USA, 101, 16 11516 120, https://doi.org/10.1073/pnas.0406930101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirk-Davidoff, D. B., and D. W. Keith, 2008: On the climate impact of surface roughness anomalies. J. Atmos. Sci., 65, 22152234, https://doi.org/10.1175/2007JAS2509.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martínez-Tossas, L. A., M. J. Churchfield, and C. Meneveau, 2015: Large eddy simulation of wind turbine wakes: Detailed comparisons of two codes focusing on effects of numerics and subgrid modeling. J. Phys.: Conf. Ser., 625, 012024, https://doi.org/10.1088/1742-6596/625/1/012024.

    • Search Google Scholar
    • Export Citation
  • Marvel, K., B. Kravitz, and K. Caldeira, 2013: Geophysical limits to global wind power. Nat. Climate Change, 3, 118121, https://doi.org/10.1038/nclimate1683.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, L. M., F. Gans, and A. Kleidon, 2011: Estimating maximum global land surface wind power extractability and associated climatic consequences. Earth Syst. Dyn., 2, 112, https://doi.org/10.5194/esd-2-1-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, Y., and C. L. Archer, 2018: A hybrid wind-farm parametrization for mesoscale and climate models. Bound.-Layer Meteor., 168, 469495, https://doi.org/10.1007/s10546-018-0351-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajewski, D. A., and Coauthors, 2013: Crop Wind Energy Experiment (CWEX): Observations of surface-layer, boundary layer, and mesoscale interactions with a wind farm. Bull. Amer. Meteor. Soc., 94, 655672, https://doi.org/10.1175/BAMS-D-11-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volker, P., J. Badger, A. N. Hahmann, and S. Ott, 2015: The explicit wake parametrisation v1.0: A wind farm parametrisation in the mesoscale model WRF. Geosci. Model Dev., 8, 37153731, https://doi.org/10.5194/gmd-8-3715-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vollmer, L., G. Steinfeld, D. Heinemann, and M. Kühn, 2016: Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: An LES study. Wind Energy Sci., 1, 129141, https://doi.org/10.5194/wes-1-129-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., and R. G. Prinn, 2010: Potential climatic impacts and reliability of very large-scale wind farms. Atmos. Chem. Phys., 10, 20532061, https://doi.org/10.5194/acp-10-2053-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S., and C. Archer, 2015: Self-similarity and turbulence characteristics of wind turbine wakes via large-eddy simulation. Wind Energy, 18, 18151838, https://doi.org/10.1002/we.1792.

    • Crossref
    • Search Google Scholar
    • Export Citation
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