Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Development of onshore wind turbine fleet counteracts climate change-induced reduction in global capacity factor

Nature Energy volume 7, pages 608–619 (2022)Cite this article

Subjects

Abstract

The capacity factor (cf) is a critical variable for quantifying wind turbine efficiency. Climate change-induced wind resource variations and technical wind turbine fleet development will alter future cfs. Here we define 12 techno-climatic change scenarios to assess regional and global onshore cfs in 2021–2060. Despite a decreasing global wind resource, we find an increase in future global cf caused by fleet development. The increase is significant under all evaluated techno-climatic scenarios. Under the likely emissions scenario Shared Socioeconomic Pathway 2–4.5, global cf increases from 0.251 in 2021 up to 0.310 in 2035 under ambitious fleet development. This cf enhancement is equivalent to a 361 TWh yield improvement under the globally installed capacity of 2020 (698 GW). To increase the contribution of the future wind turbine fleet to the Intergovernmental Panel on Climate Change climate protection goals, we recommend a rapid wind turbine fleet conversion.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial distribution of the 2020 onshore wind turbine fleet.
Fig. 2: Historical and projected shares of rated PCs in total installed capacity under four techno scenarios.
Fig. 3: Historical and projected development of the global cf under the evaluated techno-climatic change scenarios.
Fig. 4: Non-exceedance probabilities of annual means of the global cf.
Fig. 5: Projected changes of national cf in 2035.
Fig. 6: Projected changes of wind turbine site-related cf in 2035.
Fig. 7: Influence of installed capacity on site development, cf and wind energy generation in 2035.

Similar content being viewed by others

Data availability

The datasets analysed and generated during the current study are included in the paper and its Supplementary Information. An Excel spreadsheet containing the scenario internal uncertainties, validation results, the detrended cf annual means for each GCM and the PHC combinations and their power curves is available as Supplementary Data 1. CMIP6 simulations are available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=form. Source data are provided with this paper.

Code availability

A Matlab code regarding cf estimation is available. A Matlab code regarding bias correction is not publicly available due to the large data size of the required GloWiSMo input data but is available upon reasonable request from the authors.

References

  1. Glasgow Climate Pact (UNFCC, 2021); https://unfccc.int/sites/default/files/resource/cma2021_L16_adv.pdf

  2. Statistical Review of World Energy 2021 (BP, 2021); https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf

  3. Renewable Energy Statistics 2021 (International Renewable Energy Agency, 2021); https://irena.org/-/media/Files/IRENA/Agency/Publication/2021/Aug/IRENA_Renewable_Energy_Statistics_2021.pdf

  4. Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).

    Article  Google Scholar 

  5. Rinne, E., Holttinen, H., Kiviluoma, J. & Rissanen, S. Effects of turbine technology and land use on wind power resource potential. Nat. Energy 3, 494–500 (2018).

    Article  Google Scholar 

  6. Karnauskas, K. B., Lundquist, J. K. & Zhang, L. Southward shift of the global wind energy resource under high carbon dioxide emissions. Nat. Geosci. 11, 38–43 (2018).

    Article  Google Scholar 

  7. Jung, C. & Schindler, D. Changing wind speed distributions under future global climate. Energy Convers. Manag. 198, 111841 (2019).

    Article  Google Scholar 

  8. Carvalho, D., Rocha, A., Costoya, X., deCastro, M. & Gómez-Gesteira, M. Wind energy resource over Europe under CMIP6 future climate projections: what changes from CMIP5 to CMIP6. Renew. Sustain. Energy Rev. 151, 111594 (2021).

    Article  Google Scholar 

  9. Sherman, P., Chen, X. & McElroy, M. B. Wind-generated electricity in China: decreasing potential, inter-annual variability and association with changing climate. Sci. Rep. 7, 16294 (2017).

  10. Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R. & Sakaguchi, K. Climate change impacts on wind power generation. Nat. Rev. Earth Environ. 1, 627–643 (2020).

    Article  Google Scholar 

  11. Zhuo, C. et al. Changes in wind energy potential over China using a regional climate model ensemble. Renew. Sustain. Energy Rev. 159, 112219 (2022).

    Article  Google Scholar 

  12. Bloomfield, H. C. et al. Quantifying the sensitivity of European power systems to energy scenarios and climate change projections. Renew. Energy 164, 1062–1075 (2021).

    Article  Google Scholar 

  13. Hausfather, Z. & Peters, G. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).

    Article  Google Scholar 

  14. Jung, C. & Schindler, D. Distance to power grids and consideration criteria reduce global wind energy potential the most. J. Clean. Prod. 317, 128472 (2021).

    Article  Google Scholar 

  15. Eurek, K. et al. An improved global wind resource estimate for integrated assessment models. Energy Econ. 64, 552–567 (2017).

    Article  Google Scholar 

  16. Pryor, S. C. & Barthelmie, R. J. A global assessment of extreme wind speeds for wind energy applications. Nat. Energy 6, 268–276 (2021).

    Article  Google Scholar 

  17. Kitzing, L., Jensen, M. K., Telsnig, T. & Lantz, E. Multifaceted drivers for onshore wind energy repowering and their implications for energy transition. Nat. Energy 5, 1012–1021 (2020).

    Article  Google Scholar 

  18. Reyers, M., Moemken, J. & Pinto, J. G. Future changes of wind energy potentials over Europe in a large CMIP5 multi‐model ensemble. Int. J. Climatol. 36, 783–796 (2016).

    Article  Google Scholar 

  19. Meehl, G. A., Boer, G. J., Covey, C., Latif, M. & Stouffer, R. J. The Coupled Model Intercomparison Project (CMIP). Bull. Am. Meteor. 81, 313–318 (2000).

    Article  Google Scholar 

  20. Jung, C. & Schindler, D. Integration of small-scale surface properties in a new high resolution global wind speed model. Energy Convers. Manag. 210, 112733 (2020).

    Article  Google Scholar 

  21. Tapetado, P., Victoria, M., Greiner, M. & Usaola, J. Exploring backup requirements to complement wind, solar and hydro generation in a highly renewable Spanish power system. Energy Strategy Rev. 38, 100729 (2021).

    Article  Google Scholar 

  22. Jung, C. & Schindler, D. On the inter-annual variability of wind energy generation—a case study from Germany. Appl. Energy 230, 845–854 (2018).

    Article  Google Scholar 

  23. Vautard, R., Cattiaux, J., Yiou, P., Thépaut, J. N. & Ciais, P. Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat. Geosci. 3, 756–761 (2010).

    Article  Google Scholar 

  24. Jung, C., Schindler, D. & Grau, L. Achieving Germany’s wind energy expansion target with an improved wind turbine siting approach. Energy Convers. Manag. 173, 383–398 (2018).

    Article  Google Scholar 

  25. Diyoke, C. A new approximate capacity factor method for matching wind turbines to a site: case study of Humber region, UK. Int. J. Energy Environ. 10, 451–462 (2019).

    Article  Google Scholar 

  26. Jung, C. & Schindler, D. A global wind farm potential index to increase energy yields and accessibility. Energy 231, 120923 (2021).

    Article  Google Scholar 

  27. Future of Wind (International Renewable Energy Agency, 2019).

  28. Firestone, J. & Kirk, H. A strong relative preference for wind turbines in the United States among those who live near them. Nat. Energy 4, 311–320 (2019).

    Article  Google Scholar 

  29. Jacobson, M. Z. & Archer, C. L. Saturation wind power potential and its implications for wind energy. Proc. Natl Acad. Sci. USA 109, 15679–15684 (2012).

    Article  Google Scholar 

  30. Li, Y., Huang, X., Tee, K. F., Li, Q. & Wu, X. P. Comparative study of onshore and offshore wind characteristics and wind energy potentials: a case study for southeast coastal region of China. Sustain. Energy Technol. Assess. 39, 100711 (2020).

    Google Scholar 

  31. Costoya, X., DeCastro, M., Carvalho, D. & Gómez-Gesteira, M. On the suitability of offshore wind energy resource in the United States of America for the 21st century. Appl. Energy 262, 114537 (2020).

    Article  Google Scholar 

  32. Hoen, B. D. et al. United States Wind Turbine Database (US Geological Survey, American Clean Power Association & Lawrence Berkeley National Laboratory, 2020); https://www.sciencebase.gov/catalog/item/57bdfd8fe4b03fd6b7df5ff9

  33. Grau, L., Jung, C. & Schindler, D. Sounding out the repowering potential of wind energy—a scenario-based assessment from Germany. J. Clean. Prod. 293, 126094 (2021).

    Article  Google Scholar 

  34. Canadian Wind Turbine Database (Government of Canada, 2020); https://open.canada.ca/data/en/dataset/79fdad93-9025-49ad-ba16-c26d718cc070

  35. Global Wind Report, Annual Market Update 2017 (Global Wind Energy Council, 2018); https://gwec.net/wp-content/uploads/2020/11/GWEC_Global_Wind_2017_Report.pdf

  36. IEA Wind Technology Collaboration Programme (International Energy Agency Wind Technology Collaboration Programme, 2020); https://iea-wind.org/wp-content/uploads/2020/12/Annual-Report-2017.pdf

  37. Dunnett, S., Sorichetta, A., Taylor, G. & Eigenbrod, F. Harmonised global datasets of wind and solar farm locations and power. Sci. Data 7, 130 (2020).

  38. Marktstammdatenregister (Bundesnetzagentur/Federal Network Agency, 2022); https://www.marktstammdatenregister.de/MaStR/Einheit/Einheiten/OeffentlicheEinheitenuebersicht

  39. Jung, C. & Schindler, D. A review of recent studies on wind resource projections under climate change. Renew. Sustain. Energy Rev. 165, 112596 (2022).

    Article  Google Scholar 

  40. Costoya, X., Rocha, A. & Carvalho, D. Using bias-correction to improve future projections of offshore wind energy resource: a case study on the Iberian Peninsula. Appl. Energy 262, 114562 (2020).

    Article  Google Scholar 

  41. Jung, C. & Schindler, D. Introducing a new approach for wind energy potential assessment under climate change at the wind turbine scale. Energy Convers. Manag. 225, 113425 (2020).

    Article  Google Scholar 

  42. Gualtieri, G. A. Comprehensive review on wind resource extrapolation models applied in wind energy. Renew. Sust. Energy Rev. 102, 215–233 (2019).

    Article  Google Scholar 

  43. Stull R. B. Practical Meteorology: An Algebra-Based Survey of Atmospheric Science (Sundog Publishing, 2018).

  44. Wind Turbine Library (Reiner Lemoine Institut, 2019); https://openenergy-platform.org/dataedit/view/supply/wind_turbine_library

  45. The Economics of Wind Energy (European Wind Energy Association, 2009); https://www.ewea.org/fileadmin/files/library/publications/reports/Economics_of_Wind_Energy.pdf

  46. Wilcoxon, F. Individual comparisons by ranking methods. Biometrics 1, 80–83 (1945).

    Article  MathSciNet  Google Scholar 

  47. Dix, M. et al. ACCESS-CM2 Model Output Prepared for CMIP6 (Commonwealth Science and Industrial Research Organisation, 2019); https://doi.org/10.22033/ESGF/CMIP6.4239

  48. Ziehn, T. et al. ACCESS-ESM1.5 Model Output Prepared for CMIP6 (Commonwealth Science and Industrial Research Organisation, 2019); https://doi.org/10.22033/ESGF/CMIP6.2288

  49. Swart, N. C. et al. CanESM5 Model Output Prepared for CMIP6 (Canadian Centre for Climate Modelling and Analysis, 2019); https://doi.org/10.22033/ESGF/CMIP6.1317

  50. Danabasoglu, G. CESM2-WACCM Model Output Prepared for CMIP6 (National Center for Atmospheric Research, 2019); https://doi.org/10.22033/ESGF/CMIP6.10024

  51. Fogli, P. G., Iovino, D. & Lovato, T. CMCC-CM2-SR5 Model Output Prepared for CMIP6 (Euro-Mediterranean Center on Climate Change, 2020); https://doi.org/10.22033/ESGF/CMIP6.13162

  52. Peano, D., Lovato, T. & Materia, S. CMCC-ESM2 Model Output Prepared for CMIP6 (Euro-Mediterranean Center on Climate Change, 2020); https://doi.org/10.22033/ESGF/CMIP6.13165

  53. EC-Earth3 Model Output Prepared for CMIP6 (EC-Earth Consortium, 2019); https://doi.org/10.22033/ESGF/CMIP6.181

  54. Li, L. FGOALS-g3 Model Output Prepared for CMIP6 (Institute of Atmospheric Physics of Chinese Academy of Science, 2019); https://doi.org/10.22033/ESGF/CMIP6.2056

  55. Prajesh, A. G. et al. IITM-ESM Model Output Prepared for CMIP6 (Indian Institute of Tropical Meteorology, 2019); https://doi.org/10.22033/ESGF/CMIP6.44

  56. Volodin, E. et al. INM-CM4-8 Model Output Prepared for CMIP6 (Institute of Numerical Mathematics, 2019); https://doi.org/10.22033/ESGF/CMIP6.12321

  57. Volodin, E. et al. INM-CM5-0 Model Output Prepared for CMIP6 (Institute of Numerical Mathematics, 2019); https://doi.org/10.22033/ESGF/CMIP6.12322

  58. Boucher, O. et al. IPSL-CM6A-LR Model Output Prepared for CMIP6 (Institut Pierre-Simon Laplace, 2018); https://doi.org/10.22033/ESGF/CMIP6.1534

  59. Shiogama, H., Abe, M. & Tatebe, H. MIROC MIROC6 Model Output Prepared for CMIP6 (Japanese Modeling Community, 2019); https://doi.org/10.22033/ESGF/CMIP6.898

  60. von Storch, J.-S. et al. MPIESM1.2-HR Model Output Prepared for CMIP6 (Max Planck Institute for Meteorology, 2017); https://doi.org/10.22033/ESGF/CMIP6.762

  61. Jungclaus, J. et al. MPIESM1.2-LR Model Output Prepared for CMIP6 (Max Planck Institute for Meteorology, 2019); https://doi.org/10.22033/ESGF/CMIP6.787

  62. Yukimoto, S. et al. MRI-ESM2.0 Model Output Prepared for CMIP6 (Meteorological Research Institute, 2019); https://doi.org/10.22033/ESGF/CMIP6.621

  63. Seland, Ø. et al. NorESM2-LM Model Output Prepared for CMIP6 (Norwegian Climate Center, 2019); https://doi.org/10.22033/ESGF/CMIP6.502

  64. Bentsen, M. et al. NorESM2-MM Model Output Prepared for CMIP6 (Norwegian Climate Center, 2019); https://doi.org/10.22033/ESGF/CMIP6.506

Download references

Author information

Authors and Affiliations

  1. Environmental Meteorology, Albert-Ludwigs-University of Freiburg, Freiburg, Germany

    Christopher Jung & Dirk Schindler

Authors
  1. Christopher Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dirk Schindler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.J.: conceptualization, data curation, formal analysis, methodology, project administration, supervision, validation, visualization, writing—original draft preparation. D.S.: resources, validation, supervision, visualization, writing—review and editing.

Corresponding author

Correspondence to Christopher Jung.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Energy thanks Hannah Bloomfield, Edgar Hertwich and Jianlei Mo for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Supplementary Table 1

Supplementary tables.

Source data

Source Data Fig. 1

GeoTiff for Fig. 1.

Source Data Fig. 2

Statistical source data for Fig. 2.

Source Data Fig. 3

Statistical source data for Fig. 3.

Source Data Fig. 4

Statistical source data for Fig. 4.

Source Data Fig. 5

Statistical source data for Fig. 5.

Source Data Fig. 6

Statistical source data for Fig. 6.

Source Data Fig. 7

Statistical source data for Fig. 7.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, C., Schindler, D. Development of onshore wind turbine fleet counteracts climate change-induced reduction in global capacity factor. Nat Energy 7, 608–619 (2022). https://doi.org/10.1038/s41560-022-01056-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41560-022-01056-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing