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.
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Liao, H., Chen, H., Wu, Q., Bazargan, M., Ji, Z. (2012). Group Search Optimizer for Power System Economic Dispatch. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_30
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DOI: https://doi.org/10.1007/978-3-642-30976-2_30
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