Feedback-based Online Optimization of Large-Scale Networked Energy Systems

Date: 9:30am, March 20, 2018
Location: CEPSR 750
Speaker:  Dr. Emiliano Dall’Anese, Senior Researcher, National Renewable Energy Laboratory (NREL)

Abstract:Existing networked energy systems – including smart power grids, electric transportation systems, and the emerging concept of coupled critical infrastructures – are experiencing a rapid growth in size, heterogeneity, and information flows. Considering this transformation coupled with the formidable complexity of the underlying physical, computational, and societal interdependencies among sub-systems, it is apparent that existing optimization and control tools lack the computational and communication flexibility to enable optimal distributed decision making on time scales that match the dynamics of future large-scale networked energy systems. In this context, this talk outlines a new system-theoretic framework that enables a systematic design and analysis of computationally-affordable, distributed, and scalable optimization and control strategies for large-scale networked energy systems. The emerging time-varying optimization formalism is leveraged to model operational trajectories of the networked system, as well as explicit local and network-level operational constraints. Departing from existing feedback control and feed-forward optimization approaches, the design of the algorithms capitalizes on an online implementation of primal-dual projected-gradient methods; the gradient steps are, however, suitably modified to accommodate actionable feedback from the network system – hence, the term “online optimization with feedback.” By virtue of this approach, the resultant control algorithms can cope with model mismatches in the algebraic representation of the energy network, it avoids pervasive measurements of exogenous inputs, and it naturally lends itself to a distributed implementation. Analytical convergence claims are established in terms of tracking of the solution of the postulated time-varying optimization problem. As a prime application, this talk focuses on power distribution systems with high integration of distributed energy resources (DERs). In this domain, the proposed framework enables high integrations of (renewable-based) DERs at scale with reliability and optimality guarantees. An overview of other applications areas, including wind farms, water systems, and transportation systems, as well as future research directions will be offered.

Biography: Emiliano Dall’Anese received the Ph.D. in Information Engineering from the Department of Information Engineering, University of Padova, Italy, in 2011. From January 2009 to September 2010, he was a visiting scholar at the Department of Electrical and Computer Engineering, University of Minnesota, USA. From January 2011 to November 2014 he was a Postdoctoral Associate at the Department of Electrical and Computer Engineering of the University of Minnesota, within the group directed by Prof. Georgios B. Giannakis. Since December 2014 he has been a Senior Researcher at the National Renewable Energy Laboratory, Golden, CO, USA. His research interests lie in the broad areas of Optimization, Control, and Signal Processing. The objective of current research efforts is to advance theory, algorithms, and analysis for optimization, control, and monitoring of large-scale cyber-physical systems. Application domains include, but are not limited to, large-scale power systems, water networks, and (electric) transportation systems.

Host: Professor Matthias Preindl

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