Enhancing Reliability of Energy Systems With Digital Twins: Challenges and Opportunities

Digital Twins enable real-time monitoring through automatic model extraction of complex systems using data from sensors, meters and IoT devices to facilitate valuable decisions. In energy systems, Digital Twins can be integrated to improve system reliability by enabling adaptability to demand fluctuations and system disturbances. With the increasing share of renewable energy sources, reliability and stability of power grids is challenged by intermittent supply, unexpected disturbances, and demand mismatches. In this paper, we explore the use of modelling and simulation for reliability analysis and investigate automation of reliability model extraction for Digital Twins in the context of energy systems. By reviewing Digital Twin applications in energy systems, including energy optimization and predictive maintenance, the paper contributes to the understanding of trends, challenges, and opportunities in improving reliability of energy systems through a comprehensive review of literature and implementation results.