AMPS: Compositional Data-Driven Modeling, Prediction and Control for Reconfigurable Renewable Energy Systems

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The modern power grid is rapidly evolving towards a distributed and reconfigurable system dominated by renewable energy resources, represented by distributed generation, plug-in electric vehicles, energy storage, and demand-response resources. The goal of this project is to develop computational tools to address new challenges arising in modeling and control of the distributed and reconfigurable power systems subject to heterogeneous disturbances. This objective will be addressed by the development of new mathematical algorithms and theory that will be deployed in power system applications, leveraging the fundamental knowledge from machine learning, dynamical systems, and control theory. This project will contribute to the NSF mission of advancing STEM through the training of two graduate students and curricular development through the design of courses on the topics of cyber-physical microgrids and machine learning for dynamical systems. This project aims to devise compositional data-driven modeling, prediction, and control methods to ensure the transient stability of the distributed and reconfigurable renewable-energy-dominant power systems, which are inherently nonlinear, high dimensional, partially observed, and subject to heterogeneous uncertainties. This project will illuminate the machine learning advances for developing scalable and cohesive approaches to solve the fundamental challenge of in system’s operation. Specifically, the principal investigators (PIs) will (1) develop a noise-resilient compositional bilinear operator theoretic method to identify a control-amenable model for the transient dynamics of reconfigurable renewable energy systems; (2) devise a stochastic dynamics model for the partially-observed system by integrating a rigorous statistical closure formulation and a physics-informed topology-aware data-driven model; and (3) integrate the developed models with the optimal control algorithms to improve the transient stability of the distributed and reconfigurable system in a predictive manner towards a real-time autonomous operation capability. The PIs anticipate that these outcomes will substantially enrich and expand the current research on dynamic modeling and control of large-scale interconnected systems and support the development of these techniques for applications of the next-generation distribution grids.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.