The Learning, Information, Optimization, Networks, and Statistics (LIONS) seminar series is hosting Subhonmesh Bose from the University of Illinois Urbana-Champaign.
Risk-Sensitive Optimization for Electricity Markets
Presented by Subhonmesh Bose, assistant professor, University of Illinois Urbana-Champaign
Friday, October 1, 2021
1–2:30 p.m. MST
Attend on Zoom
For a recording of the seminar, contact Parth Thaker at firstname.lastname@example.org.
See a list of all School of Electrical, Computer and Energy Engineering LIONS seminar invited talks and lectures.
Power system operation is fraught with uncertainties. Electricity markets must evolve to model such uncertainties and optimize available resources against them. In this talk, I will explore algorithm design motivated to tackle risk-sensitive electricity market clearing formulations, where power delivery risk is modeled via the conditional value at risk (CVaR) measure. I will discuss algorithmic architectures and their convergence properties to solve these risk-sensitive optimization problems. The first half of the talk will focus on an optimization problem that can be cast as a large linear program. For this problem, I will discuss an algorithm that shares parallels and differences with Benders’ decomposition. In the second half of this talk, I will consider another risk-sensitive problem for which I will present sample complexity guarantees of a stochastic primal-dual algorithm.
About the speaker
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. His research projects have been supported by grants from NSF, PSERC, Siebel Energy Institute and C3.ai, among others.