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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Emilio Gómez-Lázaro & María C. Bueso & Mathieu Kessler & Sergio Martín-Martínez & Jie Zhang & Bri-Mathias Hodge & Angel Molina-García, 2016. "Probability Density Function Characterization for Aggregated Large-Scale Wind Power Based on Weibull Mixtures," Energies, MDPI, vol. 9(2), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:91-:d:63288
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    References listed on IDEAS

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    Cited by:

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    6. Krishnamoorthy R & Udhayakumar K & Kannadasan Raju & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2020. "An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization," Energies, MDPI, vol. 13(12), pages 1-41, June.
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