CAREER: Modeling and Quantification of the Interdependent Power Grid Uncertainties

Syracuse University, Syracuse NY

Investigators

Abstract

This NSF CAREER project aims to address the challenges introduced by the increasing power grid uncertainties. The expected increase in intermittent sustainable energy resources will introduce inevitable uncertainties to the power grids. At the same time, the severe weather patterns in recent years have significantly increased the grid outages and component failures. There is a critical need for new techniques that model and quantify these highly interdependent uncertainties. Such techniques will better prepare future grids for the impending surge in uncertainties and facilitate grid reliability. This project will bring transformative changes to real-time tools that model and quantify the impacts of interdependent grid uncertainties, namely, renewable generation and outages. The intellectual merits of the project include the introduction of new knowledge on modeling and quantification of power grid uncertainties and their causal impacts on grid operations. The broader impacts of the project include more efficient and reliable grid operations under uncertainties and tackling barriers for the integration of sustainable energy resources. The integrated research and education objectives of the project will train students, including undergraduate and high school students. The education and outreach activities include the participation and retention of a broad range of groups in engineering through collaborations with industry experts, public libraries, and local schools. The project will develop efficient and adaptive techniques to model and quantify interdependent grid uncertainties while considering their fast-evolving nature. The existing efforts on grid uncertainty quantification have been limited due to the complexity of analyzing the nonlinear dynamics of power systems and large-scale historical data, particularly in near real-time. This project will fuse tools from cascading failure modeling, machine learning, and statistics to develop a scalable and efficient quantification framework to understand the interdependencies among generation and outage uncertainties and allow for near real-time analysis. For a more realistic quantification of the grid uncertainties, the data-driven models and physical power flow equations will inform each other to yield hybrid stochastic models. Building upon these models, new causal models will be developed to demonstrate how individual uncertainties impact grid operations. 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.