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C3.ai Digital Transformation Institute Colloquium on Digital Transformation Science Webinar

Event Type
Lecture
Sponsor
C3.ai Digital Transformation Institute
Virtual
wifi event
Date
Jun 17, 2021   3:00 pm  
Speaker
Alejandro D. Dominguez-Garcia, Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Registration
Registration
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NCSA-related events

The next C3.ai Digital Transformation Institute Colloquium on Digital Transformation Science Webinar will be on Thursday, June 17 at 3:00 p.m. via Zoom. Presenting "Data-Driven Coordination of Distributed Energy Resources" will be Alejandro D. Dominguez-Garcia from the University of Illinois at Urbana-Champaign.

Registration is required to attend this event.

Abstract: The 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 services. 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 (TCUL) transformers and switched capacitors) and DERs with reactive power provision capability. In the second part of the 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.

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