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Colloquium on Digital Transformation Science

  • June 1724, 3 pm CT

    Data-Driven Coordination of Distributed Energy Resources

    Alejandro Dominguez-Garcia,

    Closing the Loop on Machine Learning: Data Markets, Domain Expertise, and Human Behavior

    Roy Dong, Research Assistant Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign


    integration of distributed energy resources (DERs), such as rooftop photovoltaics installations, electric energy storage devices, and flexible loads, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which DERs are connected, but also creates new opportunities for provision of grid servicesAs machine learning and data analytics are increasingly deployed in practice, it becomes more and more pressing to consider the ecosystem created by such methods. In recent years, issues of data provenance, the veracity of available data, vulnerabilities to data manipulation, and human perceptions/behavior have had a growing effect on the overall performance of our intelligent systems. In the first part of the talk, we discuss one such operational challenge — ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem, we propose a Volt/VAR control architecture that relies on the proper coordination of conventional voltage regulation devices (e.g., tap changing under load, or TCUL, transformers and switched capacitors) and DERs with reactive power provision capabilitythis talk, I consider a game-theoretic model for data markets, and demonstrate that whenever multiple data purchasers compete for data sources without exclusivity contracts, there is a fundamental degeneracy in the equilibria, independent of each data purchaser's learning capabilities. In the second part of the this talk, we discuss one such opportunity — utilizing DERs to provide regulation services to the bulk power grid. To leverage this opportunity, we propose a scheme for coordinating the response of the DERs so that the power injected into the distribution network (to which the DERs are connected) follows some regulation signal provided by the bulk power system operator. Throughout the talk, we assume limited knowledge of the particular power system models and develop data-driven methods to learn them. We then utilize these models to design appropriate controls for determining the set-points of DERs (and other assets such as TCULs) in an optimal or nearly-optimal fashion.Alejandro D. Dominguez-Garcia is a professor, William L Everitt Scholar, and Grainger Associate issues of causal inference, which are essential when our learning algorithms are used to make decisions. We analyze how passively observed data can be efficiently combined with actively collected trial data to most efficiently recover causal structures. In the last section of this talk, I will discuss some of our recent experiments with human participants in the context of intelligent building control, and show that commonly designed mechanisms assuming utility-maximizing behavior may fall short of theoretical performance in practice.

    Roy Dong is a Research Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. His research program aims to develop technologies for providing a reliable and efficient supply of electricity — a key to ensuring societal welfare and sustainable economic growth. He received the NSF CAREER Award in 2010, and the Young Engineer Award from the IEEE Power and Energy Society in 2012. He was selected by the UIUC Provost to receive a Distinguished Promotion Award in 2014, and he received the UIUC College of Engineering Dean’s Award for Excellence in Research in 2015He received a BS Honors in Computer Engineering and a BS Honors in Economics from Michigan State University in 2010 and a PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley in 2017, where he was funded in part by the NSF Graduate Research Fellowship. From 2017 to 2018, he was a postdoctoral researcher in the Berkeley Energy and Climate Institute (BECI) and a visiting lecturer in the Department of Industrial Engineering and Operations Research at UC Berkeley. His research uses tools from control theory, economics, statistics, and optimization to understand the closed-loop effects of machine learning, with applications in cyber-physical systems such as the smart grid, modern transportation networks, and autonomous vehicles.

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