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AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Author

Listed:
  • Jiaojiao Dong

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA)

  • Mirka Mandich

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA)

  • Yinfeng Zhao

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA)

  • Yang Liu

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA)

  • Shutang You

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA)

  • Yilu Liu

    (Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
    Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA)

  • Hongming Zhang

    (Stronghold Resource Partners, Dallas, TX 75219, USA)

Abstract

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

Suggested Citation

  • Jiaojiao Dong & Mirka Mandich & Yinfeng Zhao & Yang Liu & Shutang You & Yilu Liu & Hongming Zhang, 2023. "AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System," Energies, MDPI, vol. 16(3), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1401-:d:1052401
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    References listed on IDEAS

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    1. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2022. "Artificial Intelligence Techniques for Power System Transient Stability Assessment," Energies, MDPI, vol. 15(2), pages 1-21, January.
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