Digital Twin Technology for Renewable Energy Systems: Applications, Challenges, and Future Prospects

Abstract

Renewable energy systems are increasingly deployed to support decarbonization and energy security, yet their operation remains challenged by intermittency, equipment degradation, grid instability, storage limitations, and the need for real-time decision-making. Digital twin technology offers a promising pathway for addressing these challenges by linking physical renewable energy assets with virtual models for monitoring, prediction, optimization, and control. This review examines the applications, enabling technologies, benefits, limitations, and future prospects of digital twin technology in renewable energy systems. A structured narrative review approach was adopted, in which 60 papers were initially considered and 44 studies were retained after screening for relevance to the scope of the study. The review shows that digital twins can support solar photovoltaic systems, wind turbines, battery storage units, hybrid microgrids, and smart grids through real-time monitoring, fault detection, power forecasting, predictive maintenance, battery health estimation, energy management, and grid optimization. However, practical deployment remains constrained by poor data quality, high implementation cost, cybersecurity risks, interoperability issues, model-validation challenges, and limited field testing. The review concludes that digital twin technology has strong potential to transform renewable energy systems from passively monitored assets into intelligent, adaptive, and actively managed energy systems. Future research should focus on affordable, scalable, secure, and field-validated digital twin frameworks, especially for developing countries where renewable energy adoption is growing but digital infrastructure remains limited.