Convex quadratic relaxations for mixed-integer nonlinear programs in power systems

  • This paper presents a set of new convex quadratic relaxations for nonlinear and mixed-integer nonlinear programs arising in power systems. The considered models are motivated by hybrid discrete/continuous applications where existing approximations do not provide optimality guarantees. The new relaxations offer computational efficiency along with minimal optimality gaps, providing an interesting alternative to state-of-the-art semidefinite programming relaxations. Three case studies in optimal power flow, optimal transmission switching and capacitor placement demonstrate the benefits of the new relaxations.

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Metadaten
Author:Hassan Hijazi, Carleton Coffrin, Pascal Van Hentenryck
DOI:https://doi.org/10.1007/s12532-016-0112-z
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2016
Tag:Software; Theoretical Computer Science
Volume:9
Issue:3
Page Number:47
First Page:321
Last Page:367
Mathematical Programming Computation :MPC 2017 - Issue 3
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