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Electrical Engineering Colloquium: Feedback-Based Real-Time Optimization of Multiphase Distribution Networks
October 24, 2017 @ 3:30 pm - 4:30 pm
Dr. Andrey Bernstein, Senior Scientist, National Renewable Energy Laboratory
We outline an algorithmic framework for real-time optimization and control of multiphase distribution grids that leverages a feedback-based online optimization methodology. The goal of the online algorithm is to maximize operational objectives of distribution-level distributed energy resources (DERs), while satisfying the network-wide constraints and adjusting the aggregate power generated (or consumed) in response to services requested by grid operators. The design of the online algorithm is based on a primal-dual method, suitably modified to accommodate appropriate measurements from the distribution network and the DERs. By virtue of this approach, the resultant algorithm can cope with inaccuracies in the representation of the AC power flows, it avoids pervasive metering to gather the state of noncontrollable resources, and it naturally lends itself to a distributed implementation. Optimality claims are established in terms of tracking of the solution of a well-posed time-varying convex optimization problem.
Andrey Bernstein (M’15) received the B.Sc. and M.Sc. degrees in Electrical Engineering from the Technion – Israel Institute of Technology in 2002 and 2007 respectively, both summa cum laude. He received the Ph.D. degree in Electrical Engineering from the Technion in 2013. Between 2010 and 2011, he was a visiting researcher at Columbia University. During 2011-2012, he was a visiting Assistant Professor at the Stony Brook University. From 2013 to 2016, he was a postdoctoral researcher at the Laboratory for Communications and Applications of Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. Since October 2016 he has been a Senior Scientist at the National Renewable Energy Laboratory, Golden, CO, USA. His research interests are in the decision and control problems in complex environments and related optimization and machine learning methods, with particular application to intelligent power and energy systems. Current research is focused on real-time control of power distribution systems with high penetration of renewables by using online optimization methodology, and machine learning methods for grid data analytics.