A Clique Merging Algorithm to Solve Semidefinite Relaxations of Optimal Power Flow Problems

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 many different decompositions the same SDP problem and they not equivalent in terms of computation time. In this paper, we propose new strategy efficient with merging heuristic. This heuristic based on two estimates computational burden an problem: size estimation per-iteration cost state-of-the-art interior-point algorithm. We compare our other algorithms MATPOWER instances show significant decrease solver