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Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

Juliane Weber1,2, Christopher Zachow2, Dirk Witthaut1,2

  • 1Institute of Energy and Climate Research-Systems Analysis and Technology Evaluation, Forschungszentrum Jülich, 52425 Jülich, Germany.

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Summary
This summary is machine-generated.

This study models wind power generation using additive binary Markov chains to capture temporal fluctuations. The method accurately reconstructs wind generation patterns, backup needs, and storage requirements in renewable energy systems.

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Area of Science:

  • Renewable Energy Systems
  • Stochastic Modeling
  • Power System Integration

Background:

  • Wind power generation exhibits significant temporal variability, posing challenges for grid integration.
  • Existing simulation methods often fail to accurately represent crucial temporal fluctuations in wind power.

Purpose of the Study:

  • To develop a novel method for modeling wind power generation time series, focusing on temporal fluctuations.
  • To assess the impact of wind power variability on backup and storage needs in power systems.

Main Methods:

  • Application of additive binary Markov chains to model wind generation as two states: high and low output periods.
  • Utilizing the empirical autocorrelation function as the sole input parameter for the model.
  • Extension of the two-state model to stochastically reproduce actual generation levels per period.

Main Results:

  • The additive binary Markov chain model successfully captures temporal correlations in wind power generation.
  • The model allows for the reconstruction of backup needs as a function of storage capacity.
  • The resting time distribution of high and low wind events is accurately reproduced for varying wind generation shares.

Conclusions:

  • Additive binary Markov chains provide a robust method for simulating wind power temporal variability.
  • The developed model aids in understanding and quantifying backup and storage requirements for high renewable energy penetration.
  • This approach enhances the reliability and integration of wind power into electrical grids.