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Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050

Nature Energy volume 6, pages 555–565 (2021)Cite this article

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Abstract

Wind energy has experienced accelerated cost reduction over the past five years—far greater than predicted in a 2015 expert elicitation. Here we report results from a new survey on wind costs, compare those with previous results and discuss the accuracy of the earlier predictions. We show that experts in 2020 expect future onshore and offshore wind costs to decline 37–49% by 2050, resulting in costs 50% lower than predicted in 2015. This is due to cost reductions witnessed over the past five years and expected continued advancements. If realized, these costs might allow wind to play a larger role in energy supply than previously anticipated. Considering both surveys, we also conclude that there is considerable uncertainty about future costs. Our results illustrate the importance of considering cost uncertainty, highlight the value and limits of using experts to reveal those uncertainties, and yield possible lessons for energy modellers and expert elicitation.

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Fig. 1: Results from the 2015 expert elicitation compared with recent published estimates of realized LCOE.
Fig. 2: Expected LCOE changes in the median scenario in percentage terms relative to 2019 baseline values.
Fig. 3: Estimated change in LCOE over time across all three scenarios from the 2020 elicitation.
Fig. 4: Estimates of median-scenario LCOE in the 2020 and 2015 surveys.
Fig. 5: Estimates of median-scenario LCOE from the 2020 survey by region of the world.
Fig. 6: Expected turbine size in 2035 for onshore and offshore wind, compared with 2019 medians.
Fig. 7: Impact of five drivers for median-scenario LCOE reduction in 2035.
Fig. 8: Comparison of 2020 survey results with other contemporaneous LCOE forecasts.

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Data availability

Respondent-level data from the 2020 survey are provided in Supplementary Data 1, albeit with personal identifiers eliminated. Answers to the small number of questions that were open-ended, inviting narrative responses, are not included to help ensure the confidentiality of the respondents to the survey. Data underlying the figures in the Supplementary Information are provided as Supplementary Data 2. Source data are provided with this paper.

Code availability

Most data cleaning, analysis and figure creation was performed using R statistical software, including code from the tidyverse, scales and ggpmisc packages. The regressions were also implemented in R statistical software. All scripts are available upon request from the corresponding author.

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Acknowledgements

This study was conducted under the auspices of the IEA Wind Implementing Agreement for Cooperation in the Research, Development, and Deployment of Wind Energy Systems (IEA Wind). It is authored by staff at Lawrence Berkeley National Laboratory, funded by the US Department of Energy (DOE) under contract no. DE-AC02-05CH11231 (R.W., J.R., J.S.). It is also authored by staff at the NREL, operated by Alliance for Sustainable Energy, LLC, for the DOE under contract no. DE-AC36-08GO28308 (P.B., E.L.). Funding was provided by the DOE Office of Energy Efficiency and Renewable Energy, Wind Energy Technologies Office. The views expressed here do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes. We especially thank our IEA Wind collaborators: V. Berkhout, G. Bohan, J. Hethey, S. Kalash, L. Kitzing, Y. Kikuchi, S. Lüers, M. Noonan, A. M. Østenby, A. Dalla Riva, T. Stehly, M. Stenkvist and T. Telsnig. These collaborators were involved from the outset of this project: offering crucial feedback on overall objectives, survey design and specific questions; piloting draft versions of the survey; and suggesting experts to include in the sample. For additional assistance in identifying possible survey respondents and/or the curation of the leading expert group, we thank: I. Martí, K. Ohlenforst, H. Stiesdal, M. Hall, F. Zhao, D. Weir, P.-J. Rigole, F. Klein, S. Barth, C. Bottasso, G. Smart, P. Veers, K. Ralston, A. Gambhir, J. Hensley, W. Musial, G. Du, A. Smith, I. Komusanac, A. Lemke, J. Lee and A. Pek. For input on survey objectives and design, we also thank R. Tusing, M. Taylor, M. Bolinger, J. McCann, K. Ohlenforst, K. Dykes and A. Barr. The survey was implemented online via Qualtrics software, but required considerable customization. We greatly appreciate WALKER for assistance in survey implementation and execution, with special thanks to R. Fanning, J. Connolly and J. Wiggington. Of course, this work would not have been possible without the gracious contributions of the experts who chose to participate in the survey. We list those individuals and their affiliated organizations in Supplementary Table 5.

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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Ryan Wiser, Joseph Rand & Joachim Seel

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

    Philipp Beiter & Eric Lantz

  3. University of Massachusetts—Amherst, Amherst, MA, USA

    Erin Baker

  4. US Department of Energy, Washington DC, USA

    Patrick Gilman

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Contributions

All authors contributed to formulating the research, constructing the survey, and discussing, reviewing and revising the paper. R.W. led the overall effort and drafted most of the paper. R.W., P.B. and E.L. each contributed substantially to the creation of the survey sample. J.S. and J.R. led the implementation and execution of the online survey. J.R. led the analysis of the survey responses, with assistance from R.W. E.B. provided insight on expert elicitation design. P.G. provided overall guidance, including on survey goals and objectives.

Corresponding author

Correspondence to Ryan Wiser.

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Competing interests

The authors declare the following competing interest: P.G. is employed by the US Department of Energy, which provided research support funding for the work described in this article. P.G. contributed to formulating the research, constructing the survey, and discussing, reviewing and revising the paper. The authors contend that this competing interest had no bearing on the results or findings of the work, as presented in the current paper.

Additional information

Peer review information Nature Energy thanks Roger Cooke, John Paul Gosling and Malte Jansen for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Tables 1–5, Notes 1–4 and references.

Reporting Summary

Supplementary Data 1

Respondent-level data from the 2020 survey, with personal identifiers eliminated. Answers to the small number of questions that were open-ended, inviting narrative responses, are not included to help ensure the confidentiality of the respondents to the survey.

Supplementary Data 2

Data for the figures included in the Supplementary Information.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data.

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Wiser, R., Rand, J., Seel, J. et al. Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050. Nat Energy 6, 555–565 (2021). https://doi.org/10.1038/s41560-021-00810-z

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