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Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Author

Listed:
  • Bozhen Jiang

    (Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Qin Wang

    (Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Shengyu Wu

    (State Grid Energy Research Institute, Beijing 102209, China)

  • Yidi Wang

    (China Electric Power Research Institute, Beijing 100055, China)

  • Gang Lu

    (State Grid Energy Research Institute, Beijing 102209, China)

Abstract

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

Suggested Citation

  • Bozhen Jiang & Qin Wang & Shengyu Wu & Yidi Wang & Gang Lu, 2024. "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, MDPI, vol. 17(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1381-:d:1356188
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    References listed on IDEAS

    as
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