Journal Article

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

Fields, M. Jason, Bodini, Nicola, Optis, Mike, Perr-Sauer, Jordan, Simley, Eric

2021

Overview

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.

• Publisher: Wiley
• Subject: AEP assessment / EE - Wind and Water Power Program - Wind (EE-4W) / machine learning / OpenOA / operational analysis / uncertainty