Journal of Applied Mathematics

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Wanxing Sheng, Ke-yan Liu, Yongmei Liu, Xiaoli Meng, and Xiaohui Song

Full-text: Open access

Abstract

A distribution generation (DG) multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2), a particle swarm optimization (PSO) algorithm, and nondominated sorting genetic algorithm II (NGSA-II). The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 643791, 11 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394807948

Digital Object Identifier
doi:10.1155/2013/643791

Mathematical Reviews number (MathSciNet)
MR3045416

Zentralblatt MATH identifier
1266.90167

Citation

Sheng, Wanxing; Liu, Ke-yan; Liu, Yongmei; Meng, Xiaoli; Song, Xiaohui. A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm. J. Appl. Math. 2013 (2013), Article ID 643791, 11 pages. doi:10.1155/2013/643791. https://projecteuclid.org/euclid.jam/1394807948


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