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System reduction techniques for storage allocation in large power systems
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0001-6000-9363
2018 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 95, 108-117 p.Article in journal (Refereed) Published
Abstract [en]

Semi-Definite Relaxation (SDR) techniques for AC optimal power flow (OPF) have recently been proposed as a means of obtaining a provably global optimal solution for many IEEE benchmark power systems. Solving the resulting semi-definite programs (SDP) can, however, be computationally intensive. Therefore new algorithms and techniques that enable more efficient computations are needed to extend the applicability of SDP based AC OPF algorithms to very large power networks. This paper proposes a three-stage algorithm for AC OPF based storage placement in large power systems. The first step involves network reduction whereby a small equivalent system that approximates the original power network is obtained. The AC OPF problem for this equivalent system is then solved by applying an SDR to the non-convex problem. Finally, the results from the reduced system are transferred to the original system using a set of repeating optimizations. The efficacy of the algorithm is tested through case studies using two IEEE benchmark systems and comparing the solutions obtained to those of DC OPF based storage allocation. The simulation results demonstrate that the proposed algorithm produces more accurate results than the DC OPF based algorithm.

Place, publisher, year, edition, pages
Elsevier Ltd , 2018. Vol. 95, 108-117 p.
Keyword [en]
Equivalent power system, Large power system planning, Optimal Power Flow (OPF), Semi-Definite Programming (SDP), Storage allocation, Acoustic generators, Electric load flow, Electric network analysis, Electric power transmission networks, Storage allocation (computer), Ac optimal power flows, Global optimal solutions, Large power systems, Optimal power flows, Semi-definite program (SDP), Semi-definite programming, Semidefinite relaxation, Optimization
National Category
Environmental Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216799DOI: 10.1016/j.ijepes.2017.08.007ISI: 000416497900009Scopus ID: 2-s2.0-85027683518OAI: oai:DiVA.org:kth-216799DiVA: diva2:1156868
Note

Export Date: 24 October 2017; Article; CODEN: IEPSD; Correspondence Address: Shayesteh, E.; KTH Royal Institute of Technology, School of Electrical EngineeringSweden; email: ebrahim.shayesteh@ee.kth.se; Funding details: OISE-1243482, NSF, Norsk Sykepleierforbund; Funding text: Partial support by the NSF (grant OISE-1243482, the WindInspire project) is gratefully acknowledged. QC 20171114

Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2017-12-18Bibliographically approved

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Shayesteh, E.Amelin, Mikael

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