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Title: Uncertainty Quantification in Wind Plant Energy Estimation

Abstract

Data characterizing a wind power plant's production is important for the validation, calibration, and refinement of the wake loss models used to simulate performance for pre-construction energy estimates. In this process, there is the potential for significant variation in the methods used by an analyst for, first, the preparation of the operational production data for comparison to model data and, second, the generation of analogous production data from simulations. In this work, the uncertainty introduced by these method variations is quantified by considering ensembles of analysis methods. Specifically, 1920 methods variations were developed for preparing the operational data as a predictable production benchmark and simulations taking as input sample sets of the observed wind resource ranged in size from covering 4% to 125% of a full year were run using 3 engineering wake loss models in FLORIS simulations. The benchmark and simulation input uncertainties place lower bounds on the precision of the calibration between the simulation model estimates and operational benchmark data. Even within subsets of method variations (representative of what might be seen within a single company), the uncertainty in the predictable expected production is estimated to be between 1% and 3% the median predictable expected production while themore » uncertainty in the simulated expected production remains at best on the order of 2%. To put this into context: the simulated expected production is very closely related to the annual energy production (AEP) metric commonly used to quantify a plant's predicted performance and a 3% error in the pre-construction AEP estimate can result in a $17M loss due to additional financing costs for a typical 100MW Texas plant.« less

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:
1547239
Report Number(s):
NREL/CP-5000-74482
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the AIAA SciTech 2019 Forum, 7-11 January 2019, San Diego, California
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; uncertainty quantification; wind plant; energy analysis; model calibration; variations

Citation Formats

Craig, Anna. Uncertainty Quantification in Wind Plant Energy Estimation. United States: N. p., 2019. Web. doi:10.2514/6.2019-0541.
Craig, Anna. Uncertainty Quantification in Wind Plant Energy Estimation. United States. https://doi.org/10.2514/6.2019-0541
Craig, Anna. 2019. "Uncertainty Quantification in Wind Plant Energy Estimation". United States. https://doi.org/10.2514/6.2019-0541.
@article{osti_1547239,
title = {Uncertainty Quantification in Wind Plant Energy Estimation},
author = {Craig, Anna},
abstractNote = {Data characterizing a wind power plant's production is important for the validation, calibration, and refinement of the wake loss models used to simulate performance for pre-construction energy estimates. In this process, there is the potential for significant variation in the methods used by an analyst for, first, the preparation of the operational production data for comparison to model data and, second, the generation of analogous production data from simulations. In this work, the uncertainty introduced by these method variations is quantified by considering ensembles of analysis methods. Specifically, 1920 methods variations were developed for preparing the operational data as a predictable production benchmark and simulations taking as input sample sets of the observed wind resource ranged in size from covering 4% to 125% of a full year were run using 3 engineering wake loss models in FLORIS simulations. The benchmark and simulation input uncertainties place lower bounds on the precision of the calibration between the simulation model estimates and operational benchmark data. Even within subsets of method variations (representative of what might be seen within a single company), the uncertainty in the predictable expected production is estimated to be between 1% and 3% the median predictable expected production while the uncertainty in the simulated expected production remains at best on the order of 2%. To put this into context: the simulated expected production is very closely related to the annual energy production (AEP) metric commonly used to quantify a plant's predicted performance and a 3% error in the pre-construction AEP estimate can result in a $17M loss due to additional financing costs for a typical 100MW Texas plant.},
doi = {10.2514/6.2019-0541},
url = {https://www.osti.gov/biblio/1547239}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 06 00:00:00 EST 2019},
month = {Sun Jan 06 00:00:00 EST 2019}
}

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Works referenced in this record:

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


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