Ensuring reliability of energy systems is critical for maintaining a secure and adequate energy supply, especially as the integration of renewable energy increases systems’ complexity and variability. Digital Twins offer a promising approach for data-driven reliability assessment and decision support in energy systems. Digital Twins provide decision support by dynamically modeling and analyzing system reliability using real-time data to create a digital replica of the physical counterpart. As modern energy systems generate vast amounts of data, it is essential to precisely define the data required for enabling Digital Twins for their reliability assessment. In this paper, we systematically investigate the data requirements for reliability-oriented Digital Twins for energy systems and propose a structured categorization of these requirements. To illustrate our findings, we present a case study demonstrating the link between data and model extraction for enhancing system reliability.
Mostafa et al. (Thu,) studied this question.