AMPS: Model Reduction for Analysis, Identification, and Optimal Design of Power Networks

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Power networks are a rich source of dynamical systems that play a critical role in the infrastructure of modern society. Costly computer simulations are needed to model the impact of virtually any planning, monitoring, control and stability analysis task of the power grid, due to its scale and complexity. Such simulations become only more complicated with the growth of renewable energy generation such as solar and wind. To expedite dynamic simulation and to aid the design and optimization of power grids, this project pursues a suite of new model reduction algorithms tailored to the special considerations of power network modeling. The proposed methods will take advantage of the large volume of observation data that is available, moving toward algorithms for reliably estimating the state of the grid. This project supports 2 graduate students each year of the 3 year project.This project will develop and analyze new approaches to model reduction that are specially adapted to the needs of power grid operation and analysis. The project will pursue both projection-based methods and data-driven algorithms, considering nonlinear power network equations and their linearization. The resulting methods seek reliable, high-fidelity, low-order models that preserve critical physics-based structural features and parametric dependence germane to power grid dynamics. Data-driven modeling frameworks will be refined to allow for rapid model updating using near real-time observation streams from phasor measurement units. Reduced models will also contribute to the development of cost-effective power grid optimization tools.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.