The AI-Powered Grid: A Systematic Review of Machine Learning for Optimization and Resilience in Smart Energy Systems

Abstract

Traditional energy grids are evolving into smart grids (SGs), which incorporate advanced communications, real-time analytics, and two-way power flows due to the global shift in the direction of sustainable energy.  Complex problems as a result of this paradigm shift include handling large datasets, integrating intermittent renewable energy resources, and ensuring grid security and reliability.  This literature review systematically examines the transformative role of machine learning (ML) and artificial intelligence (AI) in addressing these challenges.  In addition to summarizing their use in key domains including demand forecasting, predictive maintenance, resource allocation, and anomaly detection, it also presents a selection of ML algorithms ranging from simple models such as SVM to state-of-the-art deep learning (DRL). The review also examines new hybrid architectures, including AI integration with blockchain to ensure secure transactions and federated learning to ensure data security. Despite significant improvements in efficiency and sustainability, AI adoption faces barriers related to data quality, model scalability, computational complexity, and cybersecurity. The study concludes by identifying critical research gaps and proposing future directions such as the development of explainable AI (XAI), lightweight models, standardized frameworks, and robust policy reforms to realize the full potential of self-adaptive smart energy grids.