SIMULATION CLONING FOR DIGITAL TWINS: A SCALABLE APPROACH

Digital Twin (DT) represents an essential technology in which an operations model of a physical system uses real-time data to predict, monitor, and improve the physical system’s operations. One of the primary objectives of a DT is to inform the physical system of measures to take in response to one or multiple intervening events that change the physical system’s state. The capability to perform various real-time scenario assessments in readiness for such events is an effective use of simulations as DTs, and here, scalable performance-efficient simulation cloning methods become relevant. However, continuous evaluations of simulation clones, each representing a unique cascade of intervening events, are highly challenging due to the constraints of finite memory and an extensive exploration space. This paper reports a novel simulation cloning-based method to continuously evaluate k-tree probabilistic what-if scenarios under finite resource constraints to realize a DT for the power grid.