Abstract

This paper investigates AI optimized digital twins for resource allocation and forecasting in smart grids, addressing the challenges of integrating renewable energy and dynamic demand. A systematic literature review synthesizes insights on machine learning, digital twin architectures, and smart grid analytics. A proposed framework integrates AI driven digital twins to optimize resource allocation and enhance forecasting accuracy. Application scenarios in load balancing, renewable integration, and outage management demonstrate the framework’s potential, suggesting up to 15% improvement in forecasting accuracy and 20% reduction in operational costs. The study contributes to smart grid literature and provides practical guidelines for deploying digital twins to enhance grid efficiency and sustainability.