enOPTIMAL: Energy, Optimization and Learning

Virtual seminar series at the interface of energy, optimization and machine learning research

enOPTIMAL seminar series brings together scholars and professionals to discuss the latest developments in theory and applications of optimization & machine learning in modern energy systems. Starting mid-April 2021, every second Friday, we invite speakers to give a 40-minute talk on their most recent research, followed by a 20-minute Q&A session. To receive updates on upcoming seminars, please subscribe using the links below.


Organized by Vladimir Dvorkin (MITEI) and Stefanos Delikaraoglou (MIT LIDS)



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Fridays: 8am PT, 11am ET, 5pm CET

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Upcoming talks

Subhonmesh Bose

Subhonmesh Bose

Department of Electrical and Computer Engineering

University of Illinois at Urbana-Champaign

Risk-Sensitive Market Design for the Power Grid

May 14, 2021

Integration of variable renewable and distributed energy resources in the grid makes demand and supply conditions uncertain. In this talk, we explore customized algorithm to tackle risk-sensitive electricity market clearing problems, where power delivery risk is modeled via the conditional value at risk (CVaR) measure. The market clearing formulations are such that they allow a system operator to effectively explore the cost-reliability tradeoff. We discuss algorithmic architectures and their convergence properties to solve these risk-sensitive optimization problems at scale. The first half of this talk will focus on a CVaR-sensitive optimization problem that can be cast as a large linear program. For this problem, we propose and analyze an algorithm that shares parallels and differences with Benders decomposition. The second half of this talk will focus on another CVaR-sensitive problem for which we propose and analyze sample complexity of a stochastic primal-dual algorithm.


Subhonmesh Bose is an Assistant Professor in the Department of Electrical and Computer Engineering at UIUC. His research focuses on facilitating the integration of renewable and distributed energy resources into the grid edge, leveraging tools from optimization, control and game theory. Before joining UIUC, he was a postdoctoral fellow at the Atkinson Center for Sustainability at Cornell University. Prior to that, he received his MS and Ph.D. degrees from Caltech in 2012 and 2014, respectively. He received the NSF CAREER Award in 2021. He has been the co-recipient of best paper awards at the IEEE Power and Energy Society General Meetings in 2013 and 2019. His research projects have been supported by grants from NSF, PSERC, Siebel Energy Institute and C3.ai, among others.

Andrew A. Chien

Andrew A. Chien

Large-scale Sustainable Systems Group

The University of Chicago

Understanding Productive Coupling of Adaptive Loads for the Renewable Power Grid. Opportunities and Challenges

May 28, 2021

For seven years we have studied stranded power (curtailment and negative pricing), a large (TWh) and rapidly growing phenomena (>30% CAGR), characterizing its temporal and spatial structure in power grids with growing renewable generation. Our goal is to exploit this power for productive use, both reducing the associated carbon-emissions for that use (e.g. cloud computing), and increasing grid renewable absorption. Our work shows that dispatchable computing loads that exploit stranded power can improve renewable absorption significantly. High-performance and cloud computing workloads can be supported with high quality-of-service and superior economics (total-cost-of-ownership) can be achieved despite intermittent power. Such loads can now exceed 1 gigawatt per site. In 2021, variations of these ideas are achieving acceptance and early deployment.

The rise of adaptive loads optimized for selfish purpose raises significant questions as to acceptable and constructive behavior for both grid stability and operations (e.g. social welfare and decarbonization). We have explored the impact of adaptive cloud compute load on power prices and carbon emissions, varying coupling model (none, selfish optimization, and grid optimization). The results show that computing adaptive loads can disturb grid dispatch and both increase carbon emissions and inflict financial harm on other customers. At today’s cloud-computing penetration levels, such effects would be significant and increasing.

More generally, these studies expose fundamental challenges with large-scale adaptive load to make it a pillar of increasing renewable absorption. If possible, we will discuss major economic and operational obstacles to creating adaptive load (flexibility) and effective coupling interfaces (e.g. service and markets). These challenges underpin an emerging “struggle for control” between competing spheres of optimization; directly, the tensions between societal aspirations for grid decarbonization and acceptable power cost.


Andrew A. Chien is the William Eckhardt Professor at the University of Chicago, Director of the CERES Center for Unstoppable Computing, and a Senior Scientist at Argonne National Laboratory. Since 2017, he has served as Editor-in-Chief of the Communications of the ACM, and he currently serves on the National Science Foundation CISE Advisory Committee. Chien leads the Zero-carbon Cloud project, exploring synergies between cloud computing and the renewable power grid. Chien is a global research leader in parallel computing, computer architecture, clusters, and cloud computing, and has received numerous awards for his research. Dr. Chien served as Vice President of Research at Intel Corporation from 2005-2010, leading long-range and “disruptive technologies” research as well as worldwide academic and government partnership. From 1998-2005, he was the SAIC Chair Professor at UCSD, and prior to that a Professor at the University of Illinois. Dr. Chien is an ACM, IEEE, and AAAS Fellow, and earned his PhD, MS, and BS from the Massachusetts Institute of Technology.

Previous talks

Kyri Baker

Kyri Baker

Civil, Environmental, and Architectural Engineering Department

University of Colorado Boulder

Emulating AC OPF Solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions

April 30, 2021       Recording

Optimization of electric power grids is a challenging, large-scale, non-convex problem. In order to optimize assets across these networks on fast operational timescales, the problem is typically simplified using linear models or other heuristics - resulting in increased cost of operation and potentially decreased reliability. Much work has been performed on improving these models through convexifications and other approximations, but here we take an alternate approach. We use the abundance of data from grid operators or generated offline to train machine learning models that can calculate optimal grid setpoints without even solving an optimization problem. By using an interesting application of a neural network and post-processing procedure, feasible, near-optimal solutions to the AC Optimal Power Flow problem can be obtained in milliseconds.


Dr. Kyri Baker received her B.S., M.S., and Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2009, 2010, and 2014, respectively. From 2015 to 2017, she worked at the National Renewable Energy Laboratory. Since Fall 2017, she has been an Assistant Professor at the University of Colorado Boulder, and is a Fellow of the Renewable and Sustainable Energy Institute (RASEI). She received the NSF CAREER award in 2021. She develops computationally efficient optimization and learning algorithms for energy systems ranging from building-level assets to transmission grids.

Vassilis Kekatos

Vassilis Kekatos

Department of Electrical and Computer Engineering

Virginia Tech

Physics-Aware Deep Learning for Optimal Power Flow

April 16, 2021       Recording

Distribution grids are currently challenged by the rampant integration of distributed energy resources (DER). Scheduling DERs via an optimal power flow problem (OPF) in real time and at scale under uncertainty is a formidable task. Prompted by the success of deep neural networks (DNNs) in other fields, this talk presents two learning-based schemes for near-optimal DER operation. The first solution engages a DNN to predict the solutions of an OPF given the anticipated demands and renewable generation. Different from the generic learning setup, the training dataset here (namely past OPF solutions) features rich yet largely unexploited structure. To leverage prior information, we train a DNN to match not only the OPF solutions, but also their partial derivatives with respect to the input parameters of the OPF. Sensitivity analyses for non-convex and convexified renditions of the OPF show how such derivatives can be readily computed from the OPF solutions. The proposed sensitivity-informed DNN features sample efficiency improvements at a modest computational increase. Nonetheless, this two-stage OPF-then-learn approach may not be suitable for DER operation when the OPF input parameters are changing rapidly. To deal with such setups, we put forth an alternative OPF-and-learn scheme. Here the DNN is not trained to mimic the OPF minimizers but is rather organically integrated into a stochastic OPF formulation. A key advantage of this second DNN is that it can be driven by partial OPF inputs or proxies being the measurements the utility can collect from the grid in real time.


Vassilis Kekatos is an Assistant Professor with the power systems group in the Bradley Dept. of ECE at Virginia Tech. He obtained his Ph.D. from the Univ. of Patras, Greece in 2007. He is a recipient of the US National Science Foundation CAREER Award in 2018 and the Marie Curie Fellowship during 2009-2012. He has been a postdoctoral research associate with the ECE Dept. at the Univ. of Minnesota, and a visiting researcher with the Univ. of Texas at Austin and the Ohio State Univ. His current research focus is on optimization and learning for future energy systems. He is currently serving on the editorial board of the IEEE Trans. on Smart Grid.