Global optimization for optimal power flow over transmission networks

Shi, Y; Tuan, HD; Tuy, H; Su, S

Global optimization for optimal power flow over transmission networks

Shi, Y

Tuan, HD

Tuy, H Su, S

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Publication Type:: Journal Article
Citation:: Journal of Global Optimization, 2017, 69 (3), pp. 745 - 760
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Shi, Y https://orcid.org/0000-0003-2302-4980	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Tuy, H	en_US
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	Journal of Global Optimization, 2017, 69 (3), pp. 745 - 760	en_US
dc.identifier.issn	0925-5001	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123531
dc.description.abstract	© 2017, Springer Science+Business Media New York. The optimal power flow (OPF) problem for power transmission networks is an NP-hard optimization problem with nonlinear constraints on complex bus voltages. The existing nonlinear solvers may fail in yielding a feasible point. Semi-definite relaxation (SDR) could provide the global solution only when the matrix solution of the relaxed semi-definite program (SDP) is of rank-one, which does not hold in general. Otherwise, the point found by SDR is infeasible. High-order SDR has recently been used to find the global solution, which leads to explosive growth of the matrix variable dimension and semi-definite constraints. Consequently, it is suitable only for OPF over very small networks with a few buses. In this paper, we follow our previously developed nonsmooth optimization approach to address this difficult OPF problem, which is an iterative process to generate a sequence of improved points that converge to a global solution in many cases. Each iteration calls an SDP of moderate dimension. Simulations are provided to demonstrate the efficiency of our approach.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130104617
dc.relation.ispartof	Journal of Global Optimization	en_US
dc.relation.isbasedon	10.1007/s10898-017-0538-5	en_US
dc.subject.classification	Operations Research	en_US
dc.title	Global optimization for optimal power flow over transmission networks	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	69	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	69	en_US

Abstract:

© 2017, Springer Science+Business Media New York. The optimal power flow (OPF) problem for power transmission networks is an NP-hard optimization problem with nonlinear constraints on complex bus voltages. The existing nonlinear solvers may fail in yielding a feasible point. Semi-definite relaxation (SDR) could provide the global solution only when the matrix solution of the relaxed semi-definite program (SDP) is of rank-one, which does not hold in general. Otherwise, the point found by SDR is infeasible. High-order SDR has recently been used to find the global solution, which leads to explosive growth of the matrix variable dimension and semi-definite constraints. Consequently, it is suitable only for OPF over very small networks with a few buses. In this paper, we follow our previously developed nonsmooth optimization approach to address this difficult OPF problem, which is an iterative process to generate a sequence of improved points that converge to a global solution in many cases. Each iteration calls an SDP of moderate dimension. Simulations are provided to demonstrate the efficiency of our approach.

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http://hdl.handle.net/10453/123531