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Title: Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures

Abstract

Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.

Authors:
 [1];  [2];  [2];  [1];  [3];  [4];  [2]
  1. Univ. de Castilla-La Mancha, Albacete (Spain)
  2. Univ. Politecnica de Cartagena, Cartagena (Spain)
  3. The Univ. of Texas at Dallas, Richardson, TX (United States)
  4. 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)
OSTI Identifier:
1419591
Alternate Identifier(s):
OSTI ID: 1238038
Report Number(s):
NREL/JA-5D00-65841
Journal ID: ISSN 1996-1073; ENERGA
Grant/Contract Number:  
AC36-08GO28308; AC36-08-GO28308
Resource Type:
Published Article
Journal Name:
Energies (Basel)
Additional Journal Information:
Journal Name: Energies (Basel); Journal Volume: 9; Journal Issue: 2; Related Information: Energies; Journal ID: ISSN 1996-1073
Publisher:
MDPI AG
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; wind power generation; Weibull distributions; Weibull mixtures; Akaike information criterion; AIC; Bayesian information criterion; BIC

Citation Formats

Gomez-Lazaro, Emilio, Bueso, Maria C., Kessler, Mathieu, Martin-Martinez, Sergio, Zhang, Jie, Hodge, Bri -Mathias, and Molina-Garcia, Angel. Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures. United States: N. p., 2016. Web. doi:10.3390/en9020091.
Gomez-Lazaro, Emilio, Bueso, Maria C., Kessler, Mathieu, Martin-Martinez, Sergio, Zhang, Jie, Hodge, Bri -Mathias, & Molina-Garcia, Angel. Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures. United States. doi:10.3390/en9020091.
Gomez-Lazaro, Emilio, Bueso, Maria C., Kessler, Mathieu, Martin-Martinez, Sergio, Zhang, Jie, Hodge, Bri -Mathias, and Molina-Garcia, Angel. Tue . "Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures". United States. doi:10.3390/en9020091.
@article{osti_1419591,
title = {Probability density function characterization for aggregated large-scale wind power based on Weibull mixtures},
author = {Gomez-Lazaro, Emilio and Bueso, Maria C. and Kessler, Mathieu and Martin-Martinez, Sergio and Zhang, Jie and Hodge, Bri -Mathias and Molina-Garcia, Angel},
abstractNote = {Here, the Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment.},
doi = {10.3390/en9020091},
journal = {Energies (Basel)},
number = 2,
volume = 9,
place = {United States},
year = {2016},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3390/en9020091

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