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Unified Modeling for Emulating Electric Energy Systems: Toward Digital Twin That Might Work
Abstract
Large-scale computing, including machine learning (MI) and AI, offer a great promise in enabling sustainability and resiliency of electric energy systems. At present, however, there is no standardized framework for systematic modeling and simulation of system response over time to different continuous- and discrete-time events and/or changes in equipment status. As a result, there is generally a poor understanding of the effects of candidate technologies on the quality and cost of electric energy services. In this chapter, the authors discuss a unified, physically intuitive multi-layered modeling of system components and their mutual dynamic interactions. The fundamental concept underlying this modeling is the notion of interaction variables whose definition directly lends itself to capturing modular structure needed to manage complexity. As a direct result, the same modeling approach defines an information exchange structure between different system layers, and hence can be used to establish structure for the design of a dedicated computational architecture, including AI methods.
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