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Journal of Applied Mathematics
Volume 2013, Article ID 643791, 11 pages
Research Article

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

Power Distribution Research Department, China Electric Power Research Institute, No. 15, Xiaoying East Road, Qinghe, Haidian District, Beijing 100192, China

Received 16 January 2013; Revised 19 March 2013; Accepted 21 March 2013

Academic Editor: Ricardo Perera

Copyright © 2013 Wanxing Sheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.