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Application of NSGA-II in Solving Multiobjective Optimal Power Flow


Affiliations
1 National Institute of Technology Silchar, India
2 Jadavpur University, Kolkata, India
     

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This paper is an application of NSGA-II for solving multiobjective optimal power flow problems in power systems. Objective functions considered in this work are conventional quadratic cost and emission along with highly non-linear features like cost curve with valve point loading and cubic emission function etc. In addition, more than two objectives are optimized simultaneously. The problem is formulated as mixed integer one with both continuous and discrete control variables. The performance of the proposed algorithm has been tested on three different IEEE test systems. Results for the test system-1 have been validated with the reported works. The comparison is done with the classical weighted sum method for IEEE-30 bus system and further experimentation is done on two other test cases such as IEEE-57 bus and IEEE-118 bus systems. The results demonstrate the effectiveness of the proposed approach for finding the Power System optimal solutions even when more than two confl icting objectives are considered simultaneously.

Keywords

Multiobjective Optimization, Optimal Power Flow, Nondominated Sorting, Genetic aAlgorithm.
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  • Application of NSGA-II in Solving Multiobjective Optimal Power Flow

Abstract Views: 267  |  PDF Views: 0

Authors

T. Malakar
National Institute of Technology Silchar, India
N. Sinha
National Institute of Technology Silchar, India
S. K. Goswami
Jadavpur University, Kolkata, India
A. K. Sinha
National Institute of Technology Silchar, India

Abstract


This paper is an application of NSGA-II for solving multiobjective optimal power flow problems in power systems. Objective functions considered in this work are conventional quadratic cost and emission along with highly non-linear features like cost curve with valve point loading and cubic emission function etc. In addition, more than two objectives are optimized simultaneously. The problem is formulated as mixed integer one with both continuous and discrete control variables. The performance of the proposed algorithm has been tested on three different IEEE test systems. Results for the test system-1 have been validated with the reported works. The comparison is done with the classical weighted sum method for IEEE-30 bus system and further experimentation is done on two other test cases such as IEEE-57 bus and IEEE-118 bus systems. The results demonstrate the effectiveness of the proposed approach for finding the Power System optimal solutions even when more than two confl icting objectives are considered simultaneously.

Keywords


Multiobjective Optimization, Optimal Power Flow, Nondominated Sorting, Genetic aAlgorithm.



DOI: https://doi.org/10.33686/prj.v7i2.189819