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Computationally efficient tail distribution-aware large-scale power system overloading risk assessment.

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  • 1Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA.

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This study introduces an efficient method to assess power system overloading risks from renewable energy sources. The approach significantly speeds up risk analysis for large power grids, ensuring accuracy.

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Area of Science:

  • Power Systems Engineering
  • Computational Electromagnetics
  • Risk Analysis

Background:

  • Renewable energy sources (solar, wind) introduce intermittency and uncertainty.
  • This variability poses significant overloading risks to power systems, potentially causing cascading failures.
  • Quantifying these risks in large-scale power systems is computationally challenging.

Purpose of the Study:

  • To develop a computationally efficient and accurate method for quantifying overloading risks in large-scale power systems.
  • To address the challenges posed by intermittent renewable generation and N-k contingencies.
  • To improve the fidelity of risk assessment near the overloading threshold.

Main Methods:

  • A deep-kernel sparse vector-valued Gaussian process (GP) was developed as a surrogate model.
  • The GP model incorporates generation dispatch, contingencies, and uncertain inputs (photovoltaic power, load demand).
  • An adaptive resampling mechanism using a power flow solver was introduced to correct surrogate model biases.

Main Results:

  • The proposed method accelerates risk assessment by 22 times compared to Monte Carlo sampling on a 21k+ bus system.
  • High accuracy was maintained in overloading risk quantification.
  • The approach demonstrated robustness across various distribution types and correlation scenarios.

Conclusions:

  • The developed surrogate modeling approach provides a computationally efficient and accurate solution for power system risk assessment.
  • The adaptive resampling mechanism enhances the fidelity of predictions near the overloading threshold.
  • The method is effective for large-scale power systems with significant renewable integration.