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A Primal-Dual Gradient Method for Time-Varying Optimization with Application to Power Systems

Tang, Yujie and Dall’Anese, Emiliano and Bernstein, Andrey and Low, S. H. (2018) A Primal-Dual Gradient Method for Time-Varying Optimization with Application to Power Systems. Performance Evaluation Review, 46 (3). p. 92. ISSN 0163-5999. https://resolver.caltech.edu/CaltechAUTHORS:20190128-132503883

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Abstract

We consider time-varying nonconvex optimization problems where the objective function and the feasible set vary over discrete time. This sequence of optimization problems induces a trajectory of Karush-Kuhn-Tucker (KKT) points. We present a class of regularized primal-dual gradient algorithms that track the KKT trajectory. These algorithms are feedback-based algorithms, where analytical models for system state or constraints are replaced with actual measurements. We present conditions for the proposed algorithms to achieve bounded tracking error when the cost and constraint functions are twice continuously differentiable. We discuss their practical implications and illustrate their applications in power systems through numerical simulations.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3308897.3308939DOIArticle
ORCID:
AuthorORCID
Tang, Yujie0000-0002-4921-8372
Low, S. H.0000-0001-6476-3048
Additional Information:© 2018 is held by author/owner(s).
Issue or Number:3
Record Number:CaltechAUTHORS:20190128-132503883
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190128-132503883
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92493
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Jan 2019 17:54
Last Modified:03 Oct 2019 20:45

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