skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Uncertainty quantification in the analyses of operational wind power plant performance

Abstract

In the present work, we examine the variation introduced in the evaluation of an operating plant's wind power production as a result of the choices analysts make in the processing of the operational data. For this study, an idealized power production for individual turbines over an operational period was predicted by fitting power curves to the turbine production data collected during expected operation (that is, without curtailment or availability losses). A set of 240 possible methods were developed for (a) defining what data represented expected operation and (b) modeling the power curve. The spread in the idealized power production as predicted by the different methods was on average almost 3% for the 100 turbines considered. Such significant variation places a lower bound on the precision with which analysts may employ such data as benchmarks for calibration of their energy estimation processes and limits the potential for identification of refinements to the energy estimation models for improved accuracy.

Authors:
 [1];  [1];  [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; NREL Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1462320
Report Number(s):
NREL/JA-5000-71397
Journal ID: ISSN 1742-6588; TRN: US1902138
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
Journal Volume: 1037; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 42 ENGINEERING

Citation Formats

Craig, Anna, Optis, Mike, Fields, Michael Jason, and Moriarty, Patrick. Uncertainty quantification in the analyses of operational wind power plant performance. United States: N. p., 2018. Web. doi:10.1088/1742-6596/1037/5/052021.
Craig, Anna, Optis, Mike, Fields, Michael Jason, & Moriarty, Patrick. Uncertainty quantification in the analyses of operational wind power plant performance. United States. doi:10.1088/1742-6596/1037/5/052021.
Craig, Anna, Optis, Mike, Fields, Michael Jason, and Moriarty, Patrick. Fri . "Uncertainty quantification in the analyses of operational wind power plant performance". United States. doi:10.1088/1742-6596/1037/5/052021. https://www.osti.gov/servlets/purl/1462320.
@article{osti_1462320,
title = {Uncertainty quantification in the analyses of operational wind power plant performance},
author = {Craig, Anna and Optis, Mike and Fields, Michael Jason and Moriarty, Patrick},
abstractNote = {In the present work, we examine the variation introduced in the evaluation of an operating plant's wind power production as a result of the choices analysts make in the processing of the operational data. For this study, an idealized power production for individual turbines over an operational period was predicted by fitting power curves to the turbine production data collected during expected operation (that is, without curtailment or availability losses). A set of 240 possible methods were developed for (a) defining what data represented expected operation and (b) modeling the power curve. The spread in the idealized power production as predicted by the different methods was on average almost 3% for the 100 turbines considered. Such significant variation places a lower bound on the precision with which analysts may employ such data as benchmarks for calibration of their energy estimation processes and limits the potential for identification of refinements to the energy estimation models for improved accuracy.},
doi = {10.1088/1742-6596/1037/5/052021},
journal = {Journal of Physics. Conference Series},
number = ,
volume = 1037,
place = {United States},
year = {2018},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Advanced Algorithms for Wind Turbine Power Curve Modeling
journal, July 2013

  • Lydia, M.; Selvakumar, A. I.; Kumar, S. S.
  • IEEE Transactions on Sustainable Energy, Vol. 4, Issue 3
  • DOI: 10.1109/TSTE.2013.2247641

A comprehensive review on wind turbine power curve modeling techniques
journal, February 2014

  • Lydia, M.; Kumar, S. Suresh; Selvakumar, A. Immanuel
  • Renewable and Sustainable Energy Reviews, Vol. 30
  • DOI: 10.1016/j.rser.2013.10.030

    Works referencing / citing this record:

    Monetary‐based availability: A novel approach to assess the performance of wind turbines
    journal, November 2019

    • Lutz, Marc‐Alexander; Görg, Philip; Faulstich, Stefan
    • Wind Energy, Vol. 23, Issue 1
    • DOI: 10.1002/we.2411

    Powering the 21st century by wind energy—Options, facts, figures
    journal, September 2019

    • Rohrig, K.; Berkhout, V.; Callies, D.
    • Applied Physics Reviews, Vol. 6, Issue 3
    • DOI: 10.1063/1.5089877

    Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
    journal, January 2020

    • Pfaffel, Sebastian; Faulstich, Stefan; Rohrig, Kurt
    • Applied Sciences, Vol. 10, Issue 3
    • DOI: 10.3390/app10030898