What can reanalysis data tell us about wind power?

Stephen Rose, Jay Apt

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


Reanalysis data sets have become a popular data source for large-scale wind power analyses because they cover large areas and long time spans, but those data are uncertain representations of "true" wind speeds. In this work we develop a model that systematically quantifies the uncertainties across many sites and corrects for biases of the reanalysis data. We apply this model to 32 years of reanalysis data for 1002 plausible wind-plant sites in the U.S. Great Plains to estimate variability of wind energy generation and the smoothing effect of aggregating distant wind plants. We find the coefficient of variation (COV) of annual energy generation of individual wind plants in the Great Plains is 5-12%, but the COV of all those plants aggregated together is 3.0%. The year-to-year variability (of interest to system planners) shows a maximum step change of ~10%, and the wind power varies by ±7.5% over a 32-year period. Similarly, the average variability of quarterly cash flow to equity investors in a single wind plant is 29%, but that can be reduced to 18-21% by creating small portfolios of two wind plants selected from regions with low correlations of wind speed.

Original languageEnglish (US)
Pages (from-to)963-969
Number of pages7
JournalRenewable Energy
StatePublished - Nov 1 2015

Bibliographical note

Funding Information:
This work was funded by the R. K. Mellon Foundation through a grant to The RenewElec Project at Carnegie Mellon University. The authors would like to thank Bob Dattore, John Zack, George Young, Paul Knight, Fallaw Sowell, and Christopher Wikle for their insights and assistance.

Publisher Copyright:
© 2015 Elsevier Ltd.


  • Reanalysis
  • Wind integration
  • Wind power finance
  • Wind power variability


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