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AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Author

Listed:
  • Younes Zahraoui

    (Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway)

  • Tarmo Korõtko

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn 19086, Estonia)

  • Argo Rosin

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn 19086, Estonia)

  • Saad Mekhilef

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Mehdi Seyedmahmoudian

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Alex Stojcevski

    (School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Ibrahim Alhamrouni

    (British Malaysian Institute, Universiti Kuala Lumpur, Kuala Lumpur 50250, Malaysia)

Abstract

This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control.

Suggested Citation

  • Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski & Ibrahim Alhamrouni, 2024. "AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review," Sustainability, MDPI, vol. 16(12), pages 1-35, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4959-:d:1412098
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    Cited by:

    1. Sujan Shrestha & Dewasis Dahal & Nishan Bhattarai & Sunil Regmi & Roshan Sewa & Ajay Kalra, 2025. "Machine Learning-Based Flood Risk Assessment in Urban Watershed: Mapping Flood Susceptibility in Charlotte, North Carolina," Geographies, MDPI, vol. 5(3), pages 1-17, August.
    2. Hirohito Yamada, 2025. "Autonomous Decentralized Cooperative Control DC Microgrid Deployed in Residential Areas," Energies, MDPI, vol. 18(18), pages 1-22, September.
    3. Josue N. Otshwe & Bin Li & Songsong Chen & Feixiang Gong & Bing Qi & Ngouokoua J. Chabrol, 2025. "Adaptive Control and Market Integration: Optimizing Distributed Power Resources for a Sustainable Grid," Energies, MDPI, vol. 18(7), pages 1-14, March.
    4. Kayode Ebenezer Ojo & Akshay Kumar Saha & Viranjay Mohan Srivastava, 2025. "Review of Advances in Renewable Energy-Based Microgrid Systems: Control Strategies, Emerging Trends, and Future Possibilities," Energies, MDPI, vol. 18(14), pages 1-26, July.
    5. Vadim Bol’shev & Dmitry Budnikov & Andrei Dzeikalo & Roman Korolev, 2025. "Power Outage Prediction on Overhead Power Lines on the Basis of Their Technical Parameters: Machine Learning Approach," Energies, MDPI, vol. 18(18), pages 1-20, September.
    6. Temitope Adefarati & Gulshan Sharma & Pitshou N. Bokoro & Rajesh Kumar, 2025. "Advancing Renewable-Dominant Power Systems Through Internet of Things and Artificial Intelligence: A Comprehensive Review," Energies, MDPI, vol. 18(19), pages 1-54, October.

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