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

被引:29
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
相关论文
共 38 条
[1]  
Andersen MS, IEEE T POWER SYST
[2]  
[Anonymous], 2012, MULTIPLE OBJECTIVE D
[3]   Optimal power flow solutions using differential evolution algorithm integrated with effective constraint handling techniques [J].
Biswas, Partha P. ;
Suganthan, P. N. ;
Mallipeddi, R. ;
Amaratunga, Gehan A. J. .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2018, 68 :81-100
[4]  
Boyd L., 2004, CONVEX OPTIMIZATION
[5]   Adaptive group search optimization algorithm for multi-objective optimal power flow problem [J].
Daryani, Narges ;
Hagh, Mehrdad Tarafdar ;
Teimourzadeh, Saeed .
APPLIED SOFT COMPUTING, 2016, 38 :1012-1024
[6]   A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
STRUCTURAL OPTIMIZATION, 1997, 14 (01) :63-69
[7]   Application of modified NSGA-II algorithm to Combined Economic and Emission Dispatch problem [J].
Dhanalakshmi, S. ;
Kannan, S. ;
Mahadevan, K. ;
Baskar, S. .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2011, 33 (04) :992-1002
[8]   ECONOMIC-DISPATCH IN VIEW OF THE CLEAN-AIR ACT OF 1990 [J].
ELKEIB, AA ;
MA, H ;
HART, JL .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1994, 9 (02) :972-978
[9]  
Exposito A.G., 2016, Electric energy systems: analysis and operation
[10]  
Gan L., 2012, ARXIV12084076