Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures

Emilio Gómez-Lázaro, María C. Bueso, Mathieu Kessler, Sergio Martín-Martínez, Jie Zhang, Bri-Mathias Hodge and Angel Molina-García
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Emilio Gómez-Lázaro: Renewable Energy Research Institute and DIEEAC/EDII-AB, Universidad de Castilla-La Mancha, Albacete 02071, Spain
María C. Bueso: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena 30202, Spain
Mathieu Kessler: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena 30202, Spain
Sergio Martín-Martínez: Renewable Energy Research Institute and DIEEAC/EDII-AB, Universidad de Castilla-La Mancha, Albacete 02071, Spain
Jie Zhang: Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA
Bri-Mathias Hodge: National Renewable Energy Laboratory, Golden, CO 80401, USA
Angel Molina-García: Department of Electrical Engineering, Universidad Politécnica de Cartagena, Cartagena 30202, Spain

Energies, 2016, vol. 9, issue 2, 1-15

Abstract: 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.

Keywords: wind power generation; Weibull distributions; Weibull mixtures; Akaike information criterion (AIC); Bayesian information criterion (BIC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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