An efficient covexified SDP model for multi-objective optimal power flow

被引:28
作者
Davoodi, Elnaz [1 ]
Babaei, Ebrahim [1 ,2 ]
Mohammadi-ivatloo, Behnam [1 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz, Iran
[2] Near East Univ, Engn Fac, Mersin 10, CY-99138 Nicosia, North Cyprus, Turkey
关键词
Multi-objective optimization; Convexification; Optimal power flow; Semidefinite programming; epsilon-constraint; EPSILON-CONSTRAINT METHOD; ECONOMIC-DISPATCH; CONVEX RELAXATION; OPTIMIZATION; ALGORITHM; NETWORKS; SECURITY; WIND;
D O I
10.1016/j.ijepes.2018.04.034
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a convexified multi-objective model for optimal power flow (OPF) that simultaneously minimizes the operational cost and total emission. The proposed multi-objective OPF (MO-OPF) is modeled based on semidefinite programming (SDP) and epsilon-constraint method and employed to generate Pareto optimal solutions. This work extends the existing OPF based on SDP by presenting a general model that contains all security constraints along with operational constraints, extending the convex OPF framework to a multi-objective form, and implementing epsilon-constraint method in the context of SDP. To corroborate the performance of the proposed model, simulations are conducted on the standard IEEE 30, 57, and 118-bus test systems and the obtained results are compared with those of a well-known multi-objective optimization algorithm, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II). The numerical results show that (i) the required zero duality gap and rank condition of all Pareto solutions are satisfied, (ii) SDP is capable of effectively producing a more accurate Pareto-optimal solutions and better distribution of non-dominated solutions, and (iii) better convergence characteristics, especially in dealing with the OPF problem of large scale systems with multiple objective functions.
引用
收藏
页码:254 / 264
页数:11
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