Leveraging Artificial Intelligence for Enhanced Distributed Control Strategies in Low-Inertia Microgrids_ Current Approaches, Challenges, and Quantitative Assessment Methods

Abstract

With the trend of accelerated integration in power systems having renewable energy sources, low-inertia microgrids have become one of the most significant points of interest in modern energy management. The aim of this paper is to investigate how AI can help improve distributed control methods within these systems. To better analyze this topic, we outline what low-inertia microgrids are, look at distinctive characteristics of these systems, and discuss challenges with stability in frequency control. The paper contributes to several AI methodologies, including machine learning and predictive analytics, which resolve the above mentioned problems through better decision-making processes and optimal energy management. A review of the current AI-driven control frameworks undergoes detailed analysis regarding the indicators for measuring stability and efficiency of operations. Quantitative metrics to explain effectiveness, simulation tools, and comparative analyses are applied to show greater efficacy of approaches based on AI against conventional means. A crucial aspect related to microgrid systems is that there arise technical and practical challenges associated with AI : such urgency requires robust data governance and algorithm reliability. Such emerging trends and possibilities of collaboration across disciplines are the avenues that the authors propose for further work to bridge some gaps in the use of AI for low-inertia microgrids. Ultimately, such a review offers extreme value for researchers and practitioners in contributing to the advancement of sustainable energy management practices.