Research Papers

Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation

[+] Author and Article Information
Matthew A. Lackner1

Renewable Energy Research Laboratory, University of Massachusetts, Amherst, MA, 01003

Anthony L. Rogers, James F. Manwell

Renewable Energy Research Laboratory, University of Massachusetts, Amherst, MA, 01003


Corresponding author.

J. Sol. Energy Eng 130(3), 031006 (Jul 01, 2008) (10 pages) doi:10.1115/1.2931499 History: Received January 17, 2007; Revised October 04, 2007; Published July 01, 2008

This paper presents a mathematical framework to properly account for uncertainty in wind resource assessment and wind energy production estimation. A meteorological tower based wind measurement campaign is considered exclusively, in which measure-correlate-predict is used to estimate the long-term wind resource. The evaluation of a wind resource and the subsequent estimation of the annual energy production (AEP) is a highly uncertain process. Uncertainty arises at all points in the process, from measuring the wind speed to the uncertainty in a power curve. A proper assessment of uncertainty is critical for judging the feasibility and risk of a potential wind energy development. The approach in this paper provides a framework for an accurate and objective accounting of uncertainty and, therefore, better decision making when assessing a potential wind energy site. It does not investigate the values of individual uncertainty sources. Three major aspects of site assessment uncertainty are presented here. First, a method is presented for combining uncertainty that arises in assessing the wind resource. Second, methods for handling uncertainty sources in wind turbine power output and energy losses are presented. Third, a new method for estimating the overall AEP uncertainty when using a Weibull distribution is presented. While it is commonly assumed that the uncertainty in the wind resource should be scaled by a factor between 2 and 3 to yield the uncertainty in the AEP, this work demonstrates that this assumption is an oversimplification and also presents a closed form solution for the sensitivity factors of the Weibull parameters.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Empirical availability data and Weibull fit

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Figure 2

Distribution of lifetime availability values

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Figure 3

Sample power curve

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Figure 4

Dependence of CF on c and k

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Figure 5

Dependence of SFCF,c on c and k

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Figure 6

Dependence of SFCF,k on c and k



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