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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
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Wind Power Persistence Characterized by Superstatistics.

Juliane Weber1,2, Mark Reyers3, Christian Beck4

  • 1Forschungszentrum Jülich, Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), 52428, Jülich, Germany.

Scientific Reports
|December 29, 2019
PubMed
Summary
This summary is machine-generated.

Understanding wind persistence is crucial for renewable energy. This study reveals that wind power variability requires advanced statistical models, like q-exponential distributions, for reliable energy storage solutions.

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Area of Science:

  • Renewable Energy Systems
  • Atmospheric Science
  • Statistical Physics

Background:

  • Climate change mitigation necessitates a shift to renewable electricity sources, with wind power being a key technology.
  • Effective integration of wind power requires understanding and quantifying wind persistence to determine necessary energy storage capacity.
  • Existing research extensively covers wind velocity statistics but lacks a deep understanding of wind persistence, particularly for high and low wind events.

Purpose of the Study:

  • To investigate the statistical properties of high- and low-wind persistence.
  • To identify appropriate statistical distributions that accurately model wind persistence.
  • To explore the underlying atmospheric dynamics contributing to persistent wind conditions.

Main Methods:

  • Analysis of wind velocity data to characterize persistence durations.
  • Application of statistical modeling, comparing exponential and q-exponential distributions.
  • Investigation of extreme value statistics, contrasting Gumbel and Fréchet distributions.
  • Synoptic analysis to correlate persistence with atmospheric circulation patterns and weather types.

Main Results:

  • Wind persistence exhibits heavy tails, indicating a greater likelihood of prolonged high or low wind periods than predicted by standard exponential distributions.
  • Q-exponential distributions provide a more accurate fit for wind persistence statistics.
  • Persistent wind conditions arise from a complex superposition of various weather types and circulation patterns, not solely from stationary or recurring phenomena.
  • Extreme value statistics for wind persistence follow a Fréchet distribution, differing from the typically assumed Gumbel distribution.

Conclusions:

  • Accurate statistical modeling of wind persistence, using q-exponential and Fréchet distributions, is essential for optimizing energy storage solutions.
  • Understanding the synoptic origins of wind persistence aids in predicting and managing variability in wind power generation.
  • Improved statistical and synoptic understanding of wind persistence supports the development of a reliable and economically viable energy system with high wind power penetration.