This project will tackle the algorithmic challenges underlying the transformation of the power grid. Society is at the cusp of a historic transformation of our energy systems, driven by sustainability. Daunting challenges arise in the stable, reliable, secure, and efficient operation of the future grid that will be much more distributed, dynamic, and open. This project will push the boundaries of control, optimization, and learning to develop practical solutions to some of these difficulties. It will advance state of the art in both the science of general cyber-physical systems and its application to smart grids. It will support education and diversity through a tight integration of the research with educational courses and the training of female and minority students.
The theory and algorithms to be developed in this project will contribute directly towards the historic transformation of energy systems to a more sustainable future. Specifically, the project will focus on three core algorithmic challenges facing cyber-physical networks such as a smart grid: control, optimization, and learning. First, this project will develop an optimization-based approach to the design of feedback controllers for cyber-physical systems so that the closed-loop system is asymptotically stable, and every equilibrium point of the closed-loop system is an optimal solution of a given optimization problem. Second, this project will develop a new hierarchy of convex relaxations for exponential programs based on relative entropy optimization. This will immediately yield a fundamentally new approach for solving Optimal Power Flow (OPF) problems, which underlie numerous power system applications and are non-convex and NP-hard in general. Third, this project will develop methods to learn a policy that is near-optimal efficiently, despite not having access to the objective function at run time. This will allow power systems to "learn to optimize" in real time, addressing one of the biggest challenges in power systems -- that data about the system is too expensive or impossible to obtain in real time.
