Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems

EasyChair Preprint 2546

8 pages•Date: February 4, 2020

Julie Sliwak, Miguel Anjos, Lucas Létocart, Jean Maeght and Emiliano Traversi

Abstract

Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, we experiment several clique decomposition algorithms that lead to different reformulations and we show that the resolution is highly sensitive to the clique decomposition procedure. Our main contribution is to demonstrate that minimizing the number of additional edges in the chordal extension is not always appropriate to get a good clique decomposition.

Keyphrases: Clique decomposition, Optimal Power Flow, chordal extension, optimal power flow problem, rank relaxation, semidefinite programming

Links:

https://easychair.org/publications/preprint/H2LQ

BibTeX entry

BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:

@booklet{EasyChair:2546,
  author    = {Julie Sliwak and Miguel Anjos and Lucas Létocart and Jean Maeght and Emiliano Traversi},
  title     = {Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems},
  howpublished = {EasyChair Preprint 2546},
  year      = {EasyChair, 2020}}

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