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Group Search Optimizer for Power System Economic Dispatch

  • Huilian Liao
  • Haoyong Chen
  • Qinghua Wu
  • Masoud Bazargan
  • Zhen Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

This paper presents the application of a group search optimizer (GSO) to solve a power system economic dispatch problem, which is to reduce the fuel cost and transmission line loss in the power system. GSO is inspired by animal searching behavior and group living theory. The framework of GSO is mainly based on the cooperation of producer, scroungers and rangers, which play different roles during the search. GSO has been successfully applied to solve a wider range of benchmark functions [1]. This paper investigates the application of GSO to resolve the power system economic dispatch problem with consideration of minimizing the objectives of fuel cost and transmission line loss. The performance of GSO has been compared with that of genetic algorithm (GA) and particle swarming optimizer (PSO), and the simulation results have demonstrated that GSO outperforms the other two algorithms. The application is also extended to determine the optimal locations and control parameters of flexible AC transmission system (FACTS) devices to achieve the objective. Simulation studies have been carried out on a standard test system and better results have been obtained by GSO.

Keywords

group search optimizer animal searching behavior economic dispatch FACTS devices 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Huilian Liao
    • 1
  • Haoyong Chen
    • 1
  • Qinghua Wu
    • 2
  • Masoud Bazargan
    • 3
  • Zhen Ji
    • 4
  1. 1.South China University of TechnologyChina
  2. 2.University of LiverpoolLiverpoolU.K.
  3. 3.ALSTOM Grid UK LimitedUK
  4. 4.Shenzhen UniversityShenzhenChina

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