Communications and Signal Processing Seminar
Risk-Sensitive Market Clearing for the Modern Power System
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Abstract: 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.
- CVaR_ArXiv.pdf : This discusses the stochastic primal-dual method for CVaR-sensitive optimization.
- Robust tie-line scheduling.pdf: This discusses the algorithm for solving a large linear program.
- Risk_SCED.pdf: This paper formulates the problem that is solved using the algorithm in (2).
Speaker Bio: 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 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.
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