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Modeling forecast errors for microgrid operation using Gaussian process regression.

Yeuntae Yoo1, Seungmin Jung2

  • 1Department of Electrical Engineering, Myongji University, Yongin, 17058, Republic of Korea.

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Summary
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This study introduces a novel method for modeling net-load forecast errors in microgrids. It uses Gaussian process regression to better predict uncertainties from renewable energy sources.

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Area of Science:

  • Electrical Engineering
  • Power Systems
  • Renewable Energy Integration

Background:

  • Microgrids are crucial for integrating renewable energy sources like solar and wind.
  • Renewable energy's inherent variability requires effective uncertainty management strategies.
  • Accurate assessment of uncertainty factors is vital for cost-effective microgrid operation.

Purpose of the Study:

  • To develop a method for modeling the probability distribution of net-load forecast errors in microgrids.
  • To account for the temporal inter-dependencies among various uncertainty factors.
  • To improve the accuracy of net-load error distribution estimation.

Main Methods:

  • Utilized Gaussian process regression for a data-driven approach.
  • Transformed diverse uncertainty factors into normal distributions while preserving marginal characteristics.
  • Modeled conditional probability distributions among uncertainty factors.

Main Results:

  • The proposed method effectively models net-load forecast error distribution.
  • Conditional probability density functions were trained and validated.
  • The approach enhances the suitability of density functions for net-load error approximation.

Conclusions:

  • The developed methodology provides a superior approach to approximating net load error distribution in microgrids.
  • This enhances the reliability and efficiency of microgrid operations with high renewable penetration.
  • The technique offers a robust framework for managing complex uncertainties in power systems.