FEASIBILITY AND CHALLENGES OF AI-POWERED MICROGRIDS FOR RURAL ELECTRIFICATION

Abstract

We discuss the relationship between artificial intelligence (AI) and microgrid systems in the paper to enhance rural electrification- breaking the technical, economical, and social constraints of decentralized energy supply in resource scarce environments. The paper employs a multidisciplinary analytical review approach and provides the synthesis of the available empirical evidence regarding the AI application in deep reinforcement learning, Long Short-Memory networks, and hybrid physical-machine learning algorithms of load prediction, energy dispatch optimization, and power quality management. The findings demonstrate that AI-based control mechanisms increase the effectiveness of the operations, renewable energy utilization, and the resilience of the systems compared to the traditional approaches. It is interesting to mention that the research creates an ongoing gap like deficiency of the long-term field validation, shortage of data, socio-economic problems and implementation mismatches like trafficking in the calculators and privacy concerns. The article provides a unified techno-socio-economic approach to emphasize the need to have powerful, explainable AI frameworks, privacy-enhancing data methods, and participatory governance to facilitate scalable and sustainable AI-based microgrid applications. The contribution to the body of knowledge is that the work provides the development of scholarly knowledge and presents practical implications to researchers, practitioners, and policymakers who need to hasten the process of inclusive energy transitions in rural areas that are under-served.