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Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model

Title
Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model
Type
Article in International Scientific Journal
Year
2021
Authors
Elnaz Davoodi
(Author)
Other
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Ebrahim Babaei
(Author)
Other
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Behnam Mohammadi-Ivatloo
(Author)
Other
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Miadreza Shafie-Khah
(Author)
Other
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Journal
Title: IEEE Systems JournalImported from Authenticus Search for Journal Publications
Vol. 15
Pages: 158-169
ISSN: 1932-8184
Publisher: IEEE
Other information
Authenticus ID: P-00T-KFZ
Abstract (EN): In spite of the significant advance achieved in the development of optimal power flow (OPF) programs, most of the solution methods reported in the literature have considerable difficulties in dealing with different-nature objective functions simultaneously. By leveraging recent progress on the semidefinite programming (SDP) relaxations of OPF, in the present article, attention is focused on modeling a new SDP-based multiobjective OPF (MO-OPF) problem. The proposed OPF model incorporates the classical epsilon-constraint approach through a parameterization strategy to handle the multiple objective functions and produce Pareto front. This article emphasizes the extension of the SDP-based model for MO-OPF problems to generate globally nondominated Pareto optimal solutions with uniform distribution. Numerical results on IEEE 30-, 57-, 118-bus, and Indian utility 62-bus test systems with all security and operating constraints show that the proposed convex model can produce the nondominated solutions with no duality gap in polynomial time, generate efficient Pareto set, and outperform the well-known heuristic methods generally used for the solution of MO-OPF. For instance, in comparison with the obtained results of NSGA-II for the 57-bus test system, the best compromise solution obtained by SDP has 1.55% and 7.42% less fuel cost and transmission losses, respectively.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 12
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