Digital Twin Integration With Data Fusion for Enhanced Photovoltaic System Management: A Systematic Literature Review

Abstract

The integration of Digital Twin (DT) technology into the photovoltaic (PV) sector represents a significant advancement in energy management, optimization, servicing, and maintenance. This comprehensive literature review aims to enhance understanding, categorization, and adoption of DT and data fusion technologies within the PV industry to guide future research endeavors. The review categorizes PV models into three types: digital models, digital shadows, and digital twins, based on their data connection and integration attributes. It recognizes data fusion as the critical enabling technology for the development of complex DT models and proposes a framework for integrating data fusion with DT systems. A detailed examination of prevalent PV modeling methodologies is conducted to delineate their advantages and limitations, serving as a valuable resource for industry practitioners. The paper concludes that digital models and digital shadows are effective for initial PV system forecast and monitoring, while fully integrated DT models offer significant advantages, including real-time analysis, predictive capabilities, and active system optimization. However, implementing and maintaining DT models require advanced data analytics, high computational costs, and robust system security, presenting important challenges to be addressed in future research endeavors.