Data-driven online optimization of dynamical systems: algorithms and applications to power grids

Emiliano Dall'Anese

Assistant Professor in Electrical, Computer, & Energy Engineering

University of Colorado Boulder

Seminar Information

Seminar Series
Energy: Joint Mechanical & Aerospace Engineering Dept & Center for Energy Research

Seminar Date - Time
November 10, 2021, 11:00 am
-
12:15

Seminar Location
~ Topic: MAE+CER Energy Webinar (11/10) w/ Prof. Emiliano Dall'Anese (CU)
~ Meeting ID: 998 5029 2274
~ Seminar Recording Available: Please contact seminar coordinator, Jake Blair at (j1blair@eng.ucsd.edu)


Abstract

The talk focuses on the synthesis of online algorithms to optimize the behavior of networked and dynamical systems operating in uncertain, dynamically changing environments. Motivating examples are drawn from power systems, with specific applications that include management of distributed energy resources and electric vehicles. The main strategy revolves around the synthesis of online optimization algorithms that effectively act as feedback controllers to drive dynamical systems towards well-defined equilibria. In particular, the desired equilibrium points coincide with optimal solution trajectories of optimization problems formalizing performance metrics and operational constraints that may evolve over time. The first part of the talk considers the case where the dynamics of the plant are fast and algorithms are synthetized based on the algebraic representation of the system; the design of the algorithms capitalizes on an online implementation of data-driven first-order methods, suitably modified to accommodate actionable feedback in the form of measurements from the systems and functional evaluations of the cost. Leveraging contraction arguments, the performance of the closed-loop online algorithm is analyzed in terms of tracking of an optimal solution trajectory; in particular, results in terms of convergence in expectation and in high-probability are presented, with the latter leveraging a sub-Weibull model for the gradient error. The talk then considers the case where the time scales of online algorithms and dynamical systems are comparable; in this case, sufficient conditions on the tunable parameters of the online algorithm are presented to guarantee exponential and input-to-state stability of the interconnection between the online algorithm and the dynamical system. Algorithms are applied to solve problems in power systems such as demand response, optimal power flows, and real-time economic dispatch.

Speaker Bio

Emiliano Dall’Anese is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder, where he is also an affiliate faculty with the Department of Applied Mathematics. He received the Ph.D. degree from the Department of Information Engineering, University of Padova, Italy, in 2011. His research interests span the areas of optimization, control, and learning, with current emphasis on online optimization and learning, stochastic optimization, and optimization of dynamical systems; tools and methods are applied to problems in energy and healthcare. He received the National Science Foundation CAREER Award in 2020, and the IEEEPES Prize Paper Award in 2021.