학술논문
A Clique Merging Algorithm to Solve Semidefinite Relaxations of Optimal Power Flow Problems
Document Type
Periodical
Author
Source
IEEE Transactions on Power Systems IEEE Trans. Power Syst. Power Systems, IEEE Transactions on. 36(2):1641-1644 Mar, 2021
Subject
Language
ISSN
0885-8950
1558-0679
1558-0679
Abstract
Semidefinite Programming (SDP) is a powerful technique to compute tight lower bounds for Optimal Power Flow (OPF) problems. Even using clique decomposition techniques, semidefinite relaxations are still computationally demanding. However, there are many different clique decompositions for the same SDP problem and they are not equivalent in terms of computation time. In this paper, we propose a new strategy to compute efficient clique decompositions with a clique merging heuristic. This heuristic is based on two different estimates of the computational burden of an SDP problem: the size of the problem and an estimation of a per-iteration cost for a state-of-the-art interior-point algorithm. We compare our strategy with other algorithms on MATPOWER instances and we show a significant decrease in solver time.