Digital Twin Maturity Level Effects on Microgrid Resilience Validated Through Monte Carlo Simulation

Industry 4.0. represents a critical framework for energy transformation, in the context of digital twin (DT) technology advancements. To enhance microgrid resilience towards the current net-zero targets, this paper proposes a predictive DT architecture that alligns with RAMI 4.0. The proposed architecture leverages the component of the Asset Administration Shell (AAS) as a standardized container that unigies communication and lifecycle management allowing interoperability across distributed energy resources. The approach is validated using a Monte Carlo simulation performed in RStudio by comparing three different levels of DT maturity: baseline/passive, reactive, and predictive. Statistically significant improvements are seen across all analysed metrics. Comparing with the baseline DT the reactive DT managed a 68% reduction in energy not served (ENS), cutting detection delays from 0.5s to 0.15 s and transition times from 2.5s to 0.95s. Additionally, predictive DTs showed an improvement of 21% over reactive DT, meeting utility grade standards of 89% overall reduction, 50ms detection delay, and 98% black start success rate. According to the results the reactive DT captures the a significant amount of benefits backing-up immediate smart microgrid investments. Predictive DT capabilities justify advanced DT research. Both DT progressions achieve greater than 98% stability margins, proof that RAMI 4.0-integrated DTs enables the Industry 4.0 transformation for resilient distributed energy resources. Through the RAMI 4.0.-alligned three-tier DT maturity AAS architecture and a quantitative Monte Carlo simulation comparison between the DT configurations, this work offers evidence supporting the role of reactive and predictive DT in enhancing energy system resilience.