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

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  • Wanxing Sheng
  • Ke-yan Liu
  • Yongmei Liu
  • Xiaoli Meng
  • Xiaohui Song

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.

Suggested Citation

  • Wanxing Sheng & Ke-yan Liu & Yongmei Liu & Xiaoli Meng & Xiaohui Song, 2013. "A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-11, April.
    • Handle: RePEc:hin:jnljam:643791
    DOI: 10.1155/2013/643791
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    Cited by:

    1. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.

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