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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References
He, S., Wu, Q.H., Saunders, J.R.: Group search optimizer - an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation (2009)
Bounds, D.G.: New optimization methods from physics and biology. Nature 329(6136), 215–219 (1987)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Australia, pp. 1942–1948 (November 1995)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kang, F., Li, J.J., Ma, Z.Y.: Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Information Sciences 181, 3508–3531 (2011)
He, S., Wu, Q.H., Saunders, J.R.: A novel group search optimizer inspired by animal behavioral ecology. In: 2006 IEEE Congress on Evolutionary Computation, pp. 16–21 (July 2006)
Barnard, C.J., Sibly, R.M.: Producers and scroungers: a general model and its application to captive flocks of house sparrows. Animal Behaviour 29, 543–550 (1981)
Wu, Q.H., Ma, J.: Power system optimal reactive dispatch using evolutionary programming. IEEE Transactions on Power Systems 10(3), 1243–1249 (1995)
Zare, K., Haque, M.T., Davoodi, E.: Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method. Electric Power Systems Research 84(1), 83–89 (2011)
Acha, E., Fuerte-Esquivel, C.R., Ambriz-Perez, H., Angeles-Camacho, C.: FACTS: Modelling and Simulation in Power Networks. Wiley, West Sussex (2005)
Haque, M.H.: Evaluation of first swing stability of a large power system with various FACTS devices. IEEE Transactions on Power Systems 23(3), 1144–1152 (2008)
Shao, W., Vittal, V.: LP-based OPF for corrective FACTS control to relieve overloads and voltage violations. IEEE Transactions on Power Systems 21(4), 1832–1839 (2006)
Wu, Q.H., Lu, Z., Li, M.S., Ji, T.Y.: Optimal placement of FACTS devices by a group search optimizer with multiple producer. In: IEEE World Congress on Computational Intelligence, Hong Kong, June 1-6, pp. 1033–1039 (2008)
O’Brien, W.J., Evans, B.I., Howick, G.L.: A new view of the predation cycle of a planktivorous fish, white cappie. Canadian Journal of Fisheries and Aquatic Sciences 43, 1894–1899 (1986)
Dusenbery, D.B.: Ranging strategies. Journal of Theoretical Biology 136, 309–316 (1989)
Higgins, C.L., Strauss, R.E.: Discrimination and classification of foraging path produced by search-tactic models. Behavior Ecology 15, 248–254 (2003)
Wu, Q.H., Cao, Y.J., Wen, J.Y.: Optimal reactive power dispatch using an adaptive genetic algorithm. International Jounal of Electrical Power Energy System 20(8), 563–569 (1998)
Zhao, B., Guo, C.X., Cao, Y.J.: A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems 20(2), 1070–1078 (2005)
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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
Publisher Name: Springer, Berlin, Heidelberg
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