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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), Wind Energy Technologies Office (EE-4WE); 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}
}

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