Advances in AI-powered energy management systems for renewable-integrated smart grids

The accelerating global shift toward renewable energy integration presents both a technical imperative and a systemic challenge to traditional power grid architectures. Variability, decentralization, and real-time balancing requirements have exposed the limitations of conventional control and forecasting strategies. This review critically examines how artificial intelligence (AI) is redefining energy management systems to meet the operational and strategic needs of renewable-integrated smart grids. It explores the state-of-the-art in AI-based load and generation forecasting, real-time grid state estimation, anomaly detection, and predictive maintenance, highlighting how machine learning and deep learning techniques enhance grid observability and fault resilience. Particular attention is given to AI-driven optimization of energy storage dispatch, multi-agent coordination in microgrids, and the deployment of edge intelligence for decentralized control. Furthermore, the review evaluates current barriers—ranging from data sparsity and model interpretability to lack of standardization—and proposes targeted research directions, including explainable AI, quantum-enhanced computing, and AI-powered coordination of distributed storage and vehicle-to-grid (V2G) networks. The convergence of AI, digital infrastructure, and policy innovation emerges as critical to unlocking the full potential of next-generation grids. This article provides researchers, engineers, and policymakers with a rigorous synthesis of current advancements and a forward-looking agenda for achieving intelligent, resilient, and decarbonized energy systems.