Recent updates in digital twin driven smart energy systems for intelligent grid integration

Abstract The rapid expansion of renewable energy (RE) systems, such as solar and wind, has created numerous challenges due to their inherent variability. Real-time adaptations will be needed to predict when maintenance should be undertaken and to enable integration with the operating grid. Traditional methods for monitoring and management may not be adequately responsive to variability, which can degrade performance or reduce operational reliability. Digital twin (DT) technology offers unique solutions by enabling a replica of the physical asset to be built and continuously populated with real-time datasets. This review presents a comprehensive analysis of the various frameworks, integral technologies, and applications of DTs in RE systems, focusing on the period from 2020 to 2025. DTs can assist with accurate forecasting, intelligent control, and fault detection, as well as performance enhancement using digitalization methods that incorporate IoT sensors, cloud or edge computing, artificial intelligence, and simulation. With an emphasis on predictive maintenance, generation scheduling, grid adaptability, and durability, applications in wind farms, solar PV facilities, and smart microgrids are investigated. Important issues, including real-time validation, scalability, compatibility, and cybersecurity, are thoroughly examined. Finally, the review suggests that DT technology will play a crucial role in developing smart, robust, and cost-effective RE systems, thereby advancing reliable and intelligent energy networks.