AI-DRIVEN INTELLIGENT POWER GRID OPERATION FOR A SMART AND SUSTAINABLE ENERGY FUTURE

Abstract: The rapid evolution of electrical power systems, coupled with the increasing penetration of renewable energy resources, distributed generation, and digital communication technologies, has transformed conventional power grids into highly interconnected and intelligent energy ecosystems. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving grid reliability, operational efficiency, predictive maintenance, energy management, and cybersecurity. This review article synthesizes recent research on AI applications in smart grids and intelligent power systems, integrating findings from contemporary journal articles and technical studies. The paper critically examines the role of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and hybrid AI techniques in load forecasting, fault detection, predictive maintenance, renewable energy integration, demand response, voltage regulation, and energy optimization.
In addition, the study discusses key implementation challenges including data quality, interpretability, computational complexity, scalability, and cybersecurity concerns. Finally, emerging technologies such as Digital Twins, Internet of Energy (IoE), edge computing, and decentralized AI-driven grid architectures are explored as future research directions. The review concludes that AI-driven smart grids will play a critical role in building resilient, sustainable, and adaptive energy infrastructures capable of meeting future global energy demands.

Keywords: Artificial Intelligence, Smart Grid, Machine Learning, Deep Learning, Predictive Maintenance, Renewable Energy Integration, Intelligent Energy Management, Power Systems, Reinforcement Learning, Digital Twin.