Miguel Anjos

Professor and Chair of Operational Research



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Miguel Anjos

Professor and Chair of Operational Research




Miguel Anjos

Professor and Chair of Operational Research



Tight-and-cheap conic relaxation for the AC optimal power flow problem


Journal article


C. Bingane, M.F. Anjos, S. Le Digabel
IEEE Transactions on Power Systems, vol. 33(6), 2018, pp. 7181-7188


Semantic Scholar ArXiv DOI
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Cite

APA   Click to copy
Bingane, C., Anjos, M. F., & Digabel, S. L. (2018). Tight-and-cheap conic relaxation for the AC optimal power flow problem. IEEE Transactions on Power Systems, 33(6), 7181–7188. https://doi.org/10.1109/TPWRS.2018.2848965


Chicago/Turabian   Click to copy
Bingane, C., M.F. Anjos, and S. Le Digabel. “Tight-and-Cheap Conic Relaxation for the AC Optimal Power Flow Problem.” IEEE Transactions on Power Systems 33, no. 6 (2018): 7181–7188.


MLA   Click to copy
Bingane, C., et al. “Tight-and-Cheap Conic Relaxation for the AC Optimal Power Flow Problem.” IEEE Transactions on Power Systems, vol. 33, no. 6, 2018, pp. 7181–88, doi:10.1109/TPWRS.2018.2848965.


BibTeX   Click to copy

@article{c2018a,
  title = {Tight-and-cheap conic relaxation for the AC optimal power flow problem},
  year = {2018},
  issue = {6},
  journal = {IEEE Transactions on Power Systems},
  pages = {7181-7188},
  volume = {33},
  doi = {10.1109/TPWRS.2018.2848965},
  author = {Bingane, C. and Anjos, M.F. and Digabel, S. Le}
}

Abstract

The classical alternating current optimal power flow problem is highly nonconvex and generally hard to solve. Convex relaxations, in particular semidefinite, second-order cone, convex quadratic, and linear relaxations, have recently attracted significant interest. The semidefinite relaxation is the strongest among them and is exact for many cases. However, the computational efficiency for solving large-scale semidefinite optimization is lower than for second-order cone optimization. We propose a conic relaxation obtained by combining semidefinite optimization with the reformulation-linearization technique, commonly known as RLT. The proposed relaxation is stronger than the second-order cone relaxation and nearly as tight as the standard semidefinite relaxation. Computational experiments using standard test cases with up to 6515 buses show that the time to solve the new conic relaxation is up to one order of magnitude lower than for the chordal relaxation, a semidefinite relaxation technique that exploits the sparsity of power networks.





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