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Probabilistic Modeling and Statistical Characteristics of Aggregate Wind Power

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Book cover Large Scale Renewable Power Generation

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

The stochasticity of the electrical power output by wind turbines poses special challenges to power system operation and planning. Increasing penetration levels of wind and other weather-driven renewable resources exacerbate the uncertainty and variability that must be managed. This chapter focuses on the probabilistic modeling and statistical characteristics of aggregated wind power in large electrical systems. The mathematical framework for probabilistic models—accounting for geographic diversity and the smoothing effect—is developed, and the selection and application of parametric models is discussed. Statistical characteristics from several real systems with high levels of wind power penetration are provided and analyzed.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Seattle University, 901 12th Ave, Seattle, WA, 98122, USA

    H. Louie

  2. Department of Mathematics, Seattle University, 901 12th Ave, Seattle, WA, 98122, USA

    J. M. Sloughter

Authors
  1. H. Louie
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  2. J. M. Sloughter
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Corresponding author

Correspondence to H. Louie .

Editor information

Editors and Affiliations

  1. Griffith School of Engineering, Griffith University, Gold Coast, Queensland, Australia

    Jahangir Hossain

  2. Electrical & Electronics Engineering, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Apel Mahmud

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© 2014 Springer Science+Business Media Singapore

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Louie, H., Sloughter, J.M. (2014). Probabilistic Modeling and Statistical Characteristics of Aggregate Wind Power. In: Hossain, J., Mahmud, A. (eds) Large Scale Renewable Power Generation. Green Energy and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-4585-30-9_2

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  • DOI: https://doi.org/10.1007/978-981-4585-30-9_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-4585-29-3

  • Online ISBN: 978-981-4585-30-9

  • eBook Packages: EnergyEnergy (R0)

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