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Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development

Nature Energy volume 4, pages 26–34 (2019)Cite this article

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A Publisher Correction to this article was published on 05 March 2019

This article has been updated

Abstract

Optimal wind farm locations require a strong and reliable wind resource and access to transmission lines. As onshore and offshore wind energy grows, preferred locations become saturated with numerous wind farms. An upwind wind farm generates ‘wake effects’ (decreases in downwind wind speeds) that undermine a downwind wind farm’s power generation and revenues. Here we use a diverse set of analysis tools from the atmospheric science, economic and legal communities to assess costs and consequences of these wake effects, focusing on a West Texas case study. We show that although wake effects vary with atmospheric conditions, they are discernible in monthly power production. In stably stratified atmospheric conditions, wakes can extend 50+ km downwind, resulting in economic losses of several million dollars over six years for our case study. However, our investigation of the legal literature shows no legal guidance for protecting existing wind farms from such significant impacts.

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Fig. 1: A map of the wind farms’ turbine positions plotted over an elevation map of the surrounding terrain.
Fig. 2: Comparing predicted capacity factors with the actual capacity factors at the downwind farm using econometric analysis.
Fig. 3: Time series of WRF-simulated power production for the Texas complex of wind farms for January 2013.
Fig. 4: WRF-calculated wind farm wakes.
Fig. 5: Wind turbine characteristics and wind farm wake variability.

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Data availability

The data that support all of the empirical findings in this study are based on publicly available data as referenced herein. National Weather Service Automated Surface Observing System data were accessed via http://mesonet.agron.iastate.edu/ASOS/. The WRF simulations employ the publicly available WRF code (http://www.wrf-model.org) with no custom code. The data that support the plots within this paper are available at https://github.com/julielundquist/NatureEnergyWindFarmWakes. These data, as well as the namelists for the WRF simulations, are also archived at the University of Colorado PetaLibrary (funded by the NSF under grant OCI-1126839) and can be obtained from the corresponding author upon request.

Change history

  • 05 March 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

The views expressed in the article do not necessarily represent the views of the US Department of Energy or the US government. The authors express great appreciation to the National Science Foundation’s (NSF’s) Coupled Natural and Human systems programme, which primarily funded this work under BCS-1413980. J.M.T. was partially supported by an NSF Graduate Research Fellowship under grant number 1144083. Simulations were conducted using the Extreme Science and Engineering Discovery Environment, which is supported by NSF grant number ACI1053575. K. King provided research assistance on the econometric model. We express appreciation to the West Texas Mesonet, Texas Tech University, for the use of the sodar data for the WRF model validation. K.K.D. was supported in her research efforts by D. Burkhardt, T. Witt, S. Lloyd, E. Montague, J. Calicchio, J. Dake, C. Wilden and B. Roche. She also benefitted from consultations with K. Diamond, R. Wetsel, T. Rule, E. Crivella, M. Safty, Y. Lifshitz and B. Diffen. The Alliance for Sustainable Energy, LLC (Alliance) is the manager and operator of the National Renewable Energy Laboratory. The National Renewable Energy Laboratory is a national laboratory of the US Department of Energy, Office of Energy Efficiency and Renewable Energy. This work was authored by the Alliance and supported by the US Department of Energy under contract no. DE-AC36-08GO28308. Funding was provided by the Wind Energy Technologies Office.

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, CO, USA

    J. K. Lundquist & J. M. Tomaszewski

  2. National Renewable Energy Laboratory, Golden, CO, USA

    J. K. Lundquist

  3. University of Denver Sturm College of Law, Denver, CO, USA

    K. K. DuVivier

  4. Department of Economics, University of Colorado Boulder, Boulder, CO, USA

    D. Kaffine

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Contributions

J.K.L., D.K. and K.K.D. conceived the research. D.K. designed and carried out the econometric study and wrote the economic sections. J.K.L. and J.M.T. designed the atmospheric simulations; J.M.T. carried out the atmospheric simulations; J.K.L. and J.M.T. wrote the atmospheric sections. K.K.D. designed the legal investigation and wrote the legal sections. All authors contributed significantly to writing the joint sections.

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Correspondence to J. K. Lundquist.

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Supplementary Notes 1–7, Supplementary Figures 1–13, Supplementary Tables 1–8, Supplementary References

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Lundquist, J.K., DuVivier, K.K., Kaffine, D. et al. Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development. Nat Energy 4, 26–34 (2019). https://doi.org/10.1038/s41560-018-0281-2

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