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

Hijazi, Hassan Lionel; Coffrin, Carleton James; Hentenryck, Pascal Van

doi:10.1007/s12532-016-0112-z

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

Journal Article · Sat Oct 15 00:00:00 EDT 2016 · Mathematical Programming Computation

DOI:https://doi.org/10.1007/s12532-016-0112-z· OSTI ID:1469547

^[1];

^[2]; Hentenryck, Pascal Van ^[3]

The Australian National University, Canberra (Australia)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Michigan, Ann Arbor, MI (United States)

Here, 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. Finally, three case studies in optimal power flow, optimal transmission switching and capacitor placement demonstrate the benefits of the new relaxations.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1469547

Report Number(s):: LA-UR-18-21065

Journal Information:: Mathematical Programming Computation, Vol. 9, Issue 3; ISSN 1867-2949

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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

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