The transition from conventional power systems to intelligent smart grids has become essential due to the increasing demand for sustainable, reliable, and efficient energy systems. Smart grids integrate advanced communication, control, and computational technologies to enable real-time monitoring, bidirectional power flow, and automated decision-making. However, traditional optimization and control techniques are inadequate for handling the complexity, scale, and uncertainty associated with modern power systems. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers for smart grid optimization, offering advanced capabilities such as predictive analytics, adaptive control, and real-time decision-making. These techniques are particularly crucial in addressing challenges introduced by the integration of renewable energy sources, which are inherently intermittent and unpredictable. This paper presents a comprehensive review of AI and ML techniques applied to smart grid optimization, focusing on key application areas including load forecasting, stability assessment, fault detection, and cybersecurity. Furthermore, recent advancements such as digital twins, federated learning, and hybrid AI models are discussed. The paper also identifies critical research gaps related to scalability, data privacy, and real-time deployment, and highlights future directions for achieving fully autonomous and resilient energy systems.
Goel et al. (2026) studied this question.