AGENTIC DIGITAL TWINS INTEGRATING LARGE LANGUAGE MODELS FOR AUTONOMOUS SMART GRID MANAGEMENT

Abstract

Smart grid energy management is increasingly shifting from conventional rule-based automation toward intelligent, adaptive, and autonomous decision-making systems. This paper examines the role of agentic digital twins in smart grid energy management by integrating large language models, forecasting techniques, and autonomous control mechanisms. The proposed perspective highlights how digital twins can represent real-time grid behavior, simulate future operating conditions, and support proactive decisions across demand response, renewable energy integration, load balancing, fault detection, and operational resilience. By embedding LLM-based reasoning into digital twin environments, smart grids can move beyond static monitoring toward context-aware coordination between human operators, distributed energy resources, and automated control agents. Forecasting models improve the prediction of electricity demand, renewable generation variability, and grid congestion, while autonomous control modules enable timely corrective actions under uncertain operating conditions. The results indicate that agentic digital twins can improve forecasting accuracy, reduce peak load stress, enhance renewable utilization, lower operational latency, and strengthen grid resilience. However, effective implementation requires careful attention to data quality, cybersecurity, explainability, regulatory compliance, and human oversight. The study concludes that agentic digital twins offer a promising framework for next-generation smart grids, where predictive intelligence and autonomous control work together to support reliable, sustainable, and efficient energy systems.