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Title: Optimal Power Flow Sensitivities: Analysis and Applications
Abstract: Sensitivities or partial derivatives of power-flow quantities have long been used for analysis and design of protection and control systems. Acknowledging these in the talk, we will shift our attention to analysis and computation of sensitivities of prevalent optimal power flow (OPF) formulations. Specifically, we will look at how OPF minimizers change with small perturbations in problem parameters, such as, the demand to be served, or transmission line capacities. We will formally answer when these sensitivities exist, and how numerically easy it can be to compute them, once the OPF has been solved. After pointing out how these sensitivities feature in some classical power-system design aspects, we will explore the rich space of applications where efficient sensitivity-computation tools can be a great asset. We will conclude the talk by discussing some contemporary efforts on the subject and promising future research directions.
Bio: Manish K. Singh is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Prior to this, he was a postdoctoral associate at the University of Minnesota, Minneapolis. He received the B.Tech. degree from the Indian Institute of Technology (BHU) in 2013; and the M.S. and Ph.D. degrees in electrical engineering from Virginia Tech in 2018 and 2021, respectively. During 2013-2016, he worked as an Engineer in the Smart Grid department of POWERGRID, the national transmission utility of India. His research interests are focused on modeling, optimization, control, and data-driven methods for energy systems. His doctoral dissertation was awarded the second prize for the 2022 Bill and LaRue Blackwell Graduate Research award at Virginia Tech.
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