@inproceedings{13073ce1e3314ffa847a75cbe669cc54,
title = "Strengthening convex relaxations with bound tightening for power network optimization",
abstract = "Convexification is a fundamental technique in (mixedinteger) nonlinear optimization and many convex relaxations are parametrized by variable bounds, i.e., the tighter the bounds, the stronger the relaxations. This paper studies how bound tightening can improve convex relaxations for power network optimization. It adapts traditional constraint-programming concepts (e.g., minimal network and bound consistency) to a relaxation framework and shows how bound tightening can dramatically improve power network optimization. In particular, the paper shows that the Quadratic Convex relaxation of power flows, enhanced by bound tightening, almost always outperforms the state-of-the-art Semi-Definite Programming relaxation on the optimal power flow problem.",
keywords = "AC optimal power flow, AC power flow, Bound consistency, Continuous constraint networks, Convex relaxation, Minimal network, QC relaxations",
author = "Carleton Coffrin and Hijazi, {Hassan L.} and {Van Hentenryck}, Pascal",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 21st International Conference on the Principles and Practice of Constraint Programming, CP 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
doi = "10.1007/978-3-319-23219-5_4",
language = "English",
isbn = "9783319232188",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "39--57",
editor = "Gilles Pesant",
booktitle = "Principles and Practice of Constraint Programming - 21st International Conference, CP 2015, Proceedings",
address = "Germany",
}