Strong SOCP Relaxations for the Optimal Power Flow Problem
By Burak Kocuk, Santanu S. Dey, Xu Andy Sun. Operational Research, 2015
326 citations recorded by the source corpus.
This paper proposes three strong second order cone programming (SOCP) relaxations for the AC optimal power flow (OPF) problem. These three relaxations are incomparable to each other and two of them are incomparable to the standard SDP relaxation of OPF. Extensive computational experiments show that these relaxations have numerous advantages over existing convex relaxations in the literature: (i) their solution quality is extremely close to that of the standard SDP relaxation (the best one is within 99.96% of the SDP relaxation on average for all the IEEE test cases) and consistently outperforms previously proposed convex quadratic relaxations of the OPF problem, (ii) the solutions from the strong SOCP relaxations can be directly used as a warm start in a local solver such as IPOPT to obtain a high quality feasible OPF solution, and (iii) in terms of computation times, the strong SOCP relaxations can be solved an order of magnitude faster than the standard SDP relaxation. For example, one of the proposed S...
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/c47a373335fb47a63fbe58b1162362a72e1a8310
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In Electrical and Electronic Engineering
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Citations in the corpus
- Non-Iterative Enhanced SDP Relaxations for Optimal Scheduling of Distributed Energy Storage in Distribution Systems (2017, methodology): “As shown in [10], the SOCP relaxation of the branch AC power flow equalities (2d) (as shown in (7)) is tighter than the BSDP relaxation ((6a) and (6d)).”
- A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems (2017, background): “See also [29]–[31] for generalizations and convex relaxation approaches as well as [30] for a comparison of various relaxations.”
- A Linearized OPF Model With Reactive Power and Voltage Magnitude: A Pathway to Improve the MW-Only DC OPF (2018, background): “Approaches include semidefinite programming (SDP) relaxation [3], [6], quadratic programming (QP) relaxation [7], and second-order cone programming (SOCP) relaxation [8], [20].”