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Title: Wind Plant Performance Prediction (WP3) Project

Abstract

The methods for analysis of operational wind plant data are highly variable across the wind industry, leading to high uncertainties in the validation and bias-correction of preconstruction energy estimation methods. Lack of credibility in the preconstruction energy estimates leads to significant impacts on project financing and therefore the final levelized cost of energy for the plant. In this work, the variation in the evaluation of a wind plant's operational energy production as a result of variations in the processing methods applied to the operational data is examined. Preliminary results indicate that selection of the filters applied to the data and the filter parameters can have significant impacts in the final computed assessment metrics.

Authors:
 [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1419163
Report Number(s):
NREL/PR-5000-70716
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at WindTech2017, 24-26 October 2017, Boulder, Colorado
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; wind energy; wind plant; uncertainty quantification; operational analysis; performance

Citation Formats

Craig, Anna. Wind Plant Performance Prediction (WP3) Project. United States: N. p., 2018. Web.
Craig, Anna. Wind Plant Performance Prediction (WP3) Project. United States.
Craig, Anna. 2018. "Wind Plant Performance Prediction (WP3) Project". United States. doi:. https://www.osti.gov/servlets/purl/1419163.
@article{osti_1419163,
title = {Wind Plant Performance Prediction (WP3) Project},
author = {Craig, Anna},
abstractNote = {The methods for analysis of operational wind plant data are highly variable across the wind industry, leading to high uncertainties in the validation and bias-correction of preconstruction energy estimation methods. Lack of credibility in the preconstruction energy estimates leads to significant impacts on project financing and therefore the final levelized cost of energy for the plant. In this work, the variation in the evaluation of a wind plant's operational energy production as a result of variations in the processing methods applied to the operational data is examined. Preliminary results indicate that selection of the filters applied to the data and the filter parameters can have significant impacts in the final computed assessment metrics.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2018,
month = 1
}

Conference:
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