Historically, centrally computed algorithms have been the primary means of power system
optimization and control. With increasing penetrations of distributed energy resources …

Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

We develop, in this paper, a machine learning approach to optimize the real-time operation
of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow …

In this paper, we explore a new approach to synchronization of coupled oscillators. In
contrast to the celebrated Kuramoto model, we do not work in polar coordinates and do not …

Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

This paper develops an algorithmic framework for real-time optimization of distribution-level
distributed energy resources (DERs). The framework optimizes the operation of DERs that …

Autonomous optimization refers to the design of feedback controllers that steer a physical
system to a steady state that solves a predefined, possibly constrained, optimization …

The implementation of optimal power flow (OPF) methods to perform voltage and power flow
regulation in electric networks is generally believed to require extensive communication. We …

Feedback optimizing control aims to achieve asymptotic optimal operation by directly
manipulating the inputs using feedback controllers, without the need to solve numerical …

This article proposes a unifying design framework for dynamic feedback controllers that track
solution trajectories of time-varying generalized equations, such as local minimizers of …