AI-Driven Microgrids: A Review of Enabling Technologies and Future Prospects

Jiajian Lin, Yuting Sheng, Yutong Zhou and Jalal Tavalaei
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Jiajian Lin: Faculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Malaysia
Yuting Sheng: Department of Engineering Management, The Eleventh Metallurcial Construction Group CO., LTD, Liuzhou, China
Yutong Zhou: College of Mechanical Engineering, Taishan University, Taian, China
Jalal Tavalaei: Faculty of Engineering, Computing and Science, Swinburne University of Technology, Kuching, Malaysia

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 6, 630-658

Abstract: Microgrids represent a transformative paradigm in modern energy systems, enabling localized, efficient, and resilient energy management. With the growing urgency to decarbonize power systems and accommodate the increasing penetration of renewable energy sources, microgrids have emerged as a practical solution for integrating distributed energy resources (DERs), such as solar photovoltaics, wind turbines, and energy storage systems. Their ability to operate in grid-connected and islanded modes enhances energy reliability and autonomy, particularly in remote or disaster-prone areas. However, microgrids face significant operational challenges, including the intermittency of renewables, load uncertainty, and communication latency. To address these issues, artificial intelligence (AI) technologies have become increasingly central to microgrid optimization. This review critically examines the role of AI, including Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), in enhancing key functions such as load forecasting, energy scheduling, fault detection, and cybersecurity. AI facilitates real-time decision-making and adaptive control through intelligent data-driven approaches, thereby improving microgrid efficiency and resilience. The paper also discusses microgrids' structural and functional design and highlights the need for interdisciplinary collaboration between power system engineers, data scientists, and control experts. It concludes by emphasizing the importance of translating AI models into practical applications to accelerate the deployment of innovative, low carbon microgrid infrastructures.

Date: 2025
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