Paper Info
Title
Multiobjective Optimal Power Flow Using a Semidefinite Programming-Based Model
Abstract
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 ε-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.
Year
DOI
Venue
2021
10.1109/JSYST.2020.2971838
IEEE Systems Journal
Keywords
DocType
Volume
Convexification,multiobjective OPF (MO-OPF),optimal power flow (OPF),semidefinite programming (SDP),ϵ-constraint method
Journal
15
Issue
ISSN
Citations 
1
1932-8184
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Elnaz Davoodi100.68
Ebrahim Babaei216435.89
Behnam Mohammadi-Ivatloo34119.67
Miadreza Shafie-khah44324.07
Catalao, J.P.S.54122.84