Evaluation of smart grid in the presence of dynamic market-based pricing and complex network of small and large producers, consumers, and distributers is very difficult task. Not only it involves multiple, interacting, heterogeneous cyber-physical domains, it also requires tight integration of power markets, dynamic pricing and transactions, price-sensitive consumer behavior, who may also be producers of power.
The PIs are developing their novel modeling paradigm, Generalized Synchronization Trees (GSTs), into a rich framework for both describing cyber-physical systems (CPSs) and studying their behavior under interconnection. GSTs were inspired by Milner's use of Synchronization Trees (STs) to model interconnected computing processes, but GSTs generalize the mathematical structure of their forebears in such a way as to encompass many classes of CPSs.
The research objective of this project is to bridge two disparate paths to the control of hybrid dynamical systems--namely, symbolic model-based and Lyapunov analysis-based approaches--via convex programming in order to address major challenges in hybrid control. The primary goal is to establish nonconservative, robust, and scalable control theories and algorithms for verifying/achieving desired stability and performance bounds for hybrid affine systems.
The purpose of this research is to develop optimization and control techniques and integrate them with real-time simulation models to achieve load balancing in complex networks. Our application case is the regional freight system. Freight moves on rail and road networks which are also shared by passengers. These networks today work independently, even though they are highly interdependent, and the result is inefficiencies in the form of congestion, pollution, and excess fuel consumption. These inefficiencies are obse
