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

Younes Zahraoui (), Tarmo Korõtko, Argo Rosin, Saad Mekhilef, Mehdi Seyedmahmoudian, Alex Stojcevski and Ibrahim Alhamrouni
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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

Sustainability, 2024, vol. 16, issue 12, 1-35

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.

Keywords: microgrid; energy-management system; Artificial Intelligence; deep reinforcement learning; machine learning; power system; forecasting; resilience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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