Global insights into machine learning-enabled smart grids: Challenges and solutions

Abstract

This paper reviews machine learning (ML)-enabled smart grids (SGs), with emphasis on practical power-system implementation, current learning paradigms, global practices, challenges, and deployment-oriented solutions. A two-route structured review-and-synthesis methodology identified 167 records, removed 20 duplicates, screened 147 records, and retained 113 sources for the primary thematic evidence synthesis. The final evidence base comprised 67 core technical studies and 46 supporting standards, policy documents, technical reports, books, and foundational sources; its field-oriented subset comprised one direct utility distribution implementation, one utility-connected community DER pilot, and five building, residential, campus, or district-energy demonstrations. The review distinguishes real-world-data applications from conceptual or benchmark-only studies and synthesizes the roles of unsupervised learning, supervised and hybrid learning, and reinforcement learning in anomaly detection, forecasting, stability assessment, fault diagnosis, cybersecurity, energy management, and control. The synthesis identifies four recurrent deployment gaps: fragmented task-specific modelling, accuracy-centred evaluation, limited field validation, and insufficient trust and governance. Based on these gaps, this paper proposes a Central AI-Orchestrated Smart Grid Intelligence Framework that formalizes operational-state-triggered routing across heterogeneous analytical modules, standardized evidence exchange, safety-first admissibility and conflict handling, consequence-dependent authority, and explicit abstention and fallback to conventional protection and control. A six-phase roadmap is proposed for transitioning from conventional grids to AI-enabled smart grids through assessment, digital infrastructure, interoperability, pilot deployment, safe scaling, and continuous improvement. The review concludes that future smart-grid ML research should move beyond accuracy-focused experiments toward interoperable, explainable, secure, and deployable intelligence systems.