Convex Relaxations for Quadratic On/Off Constraints and Applications to Optimal Transmission Switching

This paper studies mixed-integer nonlinear programs featuring disjunctive constraints and trigonometric functions and presents a strengthened version of the convex quadratic relaxation of the optimal transmission switching problem. We first characterize the convex hull of univariate quadratic on/off...

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Veröffentlicht in:INFORMS journal on computing 2020-07, Vol.32 (3), p.682-696
Hauptverfasser: Bestuzheva, Ksenia, Hijazi, Hassan, Coffrin, Carleton
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Hijazi, Hassan
Coffrin, Carleton
description This paper studies mixed-integer nonlinear programs featuring disjunctive constraints and trigonometric functions and presents a strengthened version of the convex quadratic relaxation of the optimal transmission switching problem. We first characterize the convex hull of univariate quadratic on/off constraints in the space of original variables using perspective functions. We then introduce new tight quadratic relaxations for trigonometric functions featuring variables with asymmetrical bounds. These results are used to further tighten recent convex relaxations introduced for the optimal transmission switching problem in power systems. Using the proposed improvements, along with bound propagation, on 23 medium-sized test cases in the PGLib benchmark library with a relaxation gap of more than 1%, we reduce the gap to less than 1% on five instances. The tightened model has promising computational results when compared with state-of-the-art formulations.
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subjects Computational geometry
Computer Science
Convex analysis
Convexity
Integer programming
Mathematics
MATHEMATICS AND COMPUTING
Mixed integer
Mixed-Integer Nonlinear Programming
Nonlinear programming
On/Off constraints
Optimal Transmission Switching
Perspective Relaxation
Quadratic functions
Relaxation methods (Mathematics)
Switching
Trigonometric Functions
title Convex Relaxations for Quadratic On/Off Constraints and Applications to Optimal Transmission Switching
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