Paper Info
Title
A Primal-Dual Gradient Method for Time-Varying Optimization with Application to Power Systems
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.
Year
DOI
Venue
2018
10.1145/3308897.3308939
ACM SIGMETRICS Performance Evaluation Review
Field
DocType
Volume
Gradient method,Mathematical optimization,Computer science,Real-time computing,Feasible region,Discrete time and continuous time,Karush–Kuhn–Tucker conditions,Optimization problem,Trajectory,Tracking error,Bounded function
Journal
46
Issue
ISSN
Citations 
3
0163-5999
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Yujie Tang1132.85
Emiliano Dall'Anese236038.11
Andrey Bernstein3298.99
S. H. Low45999585.58