The global energy landscape is undergoing rapid transformation, driven by increasing electricity demand, decarbonization goals, and the integration of renewable energy resources. Traditional power systems, characterized by centralized operations and limited responsiveness, are increasingly inadequate for managing modern grid complexities. Smart grid systems have emerged as a critical paradigm, embedding advanced sensors, communication infrastructures, and intelligent decision-making mechanisms to enhance grid stability and reliability. However, the scale and unpredictability of dynamic energy flows demand advanced solutions that surpass conventional optimization and monitoring methods. Artificial intelligence (AI) has proven to be a transformative enabler in this context, offering predictive, adaptive, and real-time decision-making capabilities. By leveraging machine learning and deep learning algorithms, AI-enabled smart grids can accurately forecast load demand, detect and isolate faults, and dynamically balance distributed energy resources. Predictive load forecasting allows operators to anticipate fluctuations in consumer demand, ensuring efficiency and stability, while AI-driven fault detection systems improve resilience by rapidly identifying anomalies and preventing cascading failures. Furthermore, AI supports the seamless integration of renewable energy sources, mitigating intermittency challenges by optimizing grid dispatch and storage solutions. This paper explores the convergence of artificial intelligence with smart grid infrastructures, emphasizing its applications in real-time load forecasting, fault detection, renewable energy integration, and system-wide optimization. It also addresses associated challenges such as data privacy, model interpretability, and cybersecurity risks, offering a balanced discussion of opportunities and limitations. Ultimately, AI-enabled smart grids represent a pivotal step toward building resilient, sustainable, and intelligent energy ecosystems capable of supporting the future of decentralized, low-carbon power systems.
Iyaniwura et al. (Wed,) studied this question.