Lowering Post‐Construction Yield Assessment Uncertainty Through Better Wind Plant Power Curves

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations

Abstract

Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post-construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine-learning algorithms. We utilize the National Renewable Energy Laboratory's open-source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0% and 1.2% (corresponding to a −59% and −80% relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long-term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5% (−43% relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine-learning model at daily resolution can further reduce regression uncertainty, with up to a −10% relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

Original languageAmerican English
Pages (from-to)5-22
Number of pages18
JournalWind Energy
Volume25
Issue number1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Wind Energy published by John Wiley & Sons Ltd.

NREL Publication Number

  • NREL/JA-5000-78511

Keywords

  • AEP assessment
  • machine learning
  • OpenOA
  • operational analysis
  • uncertainty

Fingerprint

Dive into the research topics of 'Lowering Post‐Construction Yield Assessment Uncertainty Through Better Wind Plant Power Curves'. Together they form a unique fingerprint.

Cite this