Optimization-Based Bound Tightening using a Strengthened QC-Relaxation of the Optimal Power Flow Problem
Not on the map. Why?
Abstract (arXiv)
This article develops a strengthened convex quadratic convex (QC) relaxation of the AC Optimal Power Flow (AC-OPF) problem and presents an optimization-based bound-tightening (OBBT) algorithm to compute tight, feasible bounds on the voltage magnitude variables for each bus and the phase angle difference variables for each branch in the network. Theoretical properties of the strengthened QC relaxation that show its dominance over the other variants of the QC relaxation studied in the literature are also derived. The effectiveness of the strengthened QC relaxation is corroborated via extensive numerical results on benchmark AC-OPF test networks. In particular, the results demonstrate that the proposed relaxation consistently provides the tightest variable bounds and optimality gaps with negligible impacts on runtime performance.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 13
- 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
- 2024LEVIS: Large Exact Verifiable Input Spaces for Neural Networkscited by 1
- 2024LEVIS: Large Exact Verifiable Input Spaces for Neural Networks16 Aug 2024
- 2023Compact Optimization Learning for AC Optimal Power Flow21 Jan 2023
- 2022Unsupervised Optimal Power Flow Using Graph Neural Networks17 Oct 2022