Improved Group Search Optimization Algorithm for Multi-Objective Optimal Reactive Power Dispatch
AbstractThis paper proposes the improved group search optimization algorithm for optimal reactive power dispatch (ORPD). The ORPD problem is a non-linear, non-convex optimization problem which has various decision variables such as the compensation capacitors proportions, voltages of generators and the tap position of tap changing transformers. In this paper the multi-objective ORPD considering loss and voltage deviation is studied. Due to complicating objectives and also physical and operating constraints, an efficient optimization algorithm is needed. This paper solves the mentioned problem by using the group search optimization algorithm (GSO) which is one of the novel presented optimization algorithms based on group living and especially searching behavior of animals. In order to improve the algorithm efficiencies, the improved group search optimization algorithm (IGSO) is proposed. Accordingly, the algorithm would obtain better result due to its ability to find the global optimal rather than local ones. Additionally, the penalty factor approach is used in order to solve the multi-objective case.
 C.-M. Huang, S.-J. Chen, Y.-C. Huang, and H.-T. Yang, “Comparative study of evolutionary computation methods for active-reactive power dispatch,” Generation, Transmission & Distribution, IET, vol. 6, pp. 636-645, 2012.
 T. Niknam, M. Narimani, J. Aghaei, and R. Azizipanah-Abarghooee, “Improved particle swarm optimization for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index,” IET generation, transmission & distribution, vol. 6, pp. 515-527, 2012.
 Z. Wang, “Optimal Reactive Power Dispatch,” Faculty of Engineering, The Hong Kong Polytechnic University, 2012.
 A. Abbasy and S. H. Hosseini, “Ant colony optimization-based approach to optimal reactive power dispatch: A comparison of various ant systems,” in Power Engineering Society Conference and Exposition in Africa, 2007. PowerAfrica'07. IEEE, 2007, pp. 1-8.
 W. Yan, S. Lu, and D. C. Yu, “A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique,” Power Systems, IEEE Transactions on, vol. 19, pp. 913-918, 2004.
 C. Dai, W. Chen, Y. Zhu, and X. Zhang, “Seeker optimization algorithm for optimal reactive power dispatch,” Power Systems, IEEE Transactions on, vol. 24, pp. 1218-1231, 2009.
 Q. Wu, Y. Cao, and J. Wen, “Optimal reactive power dispatch using an adaptive genetic algorithm,” International Journal of Electrical Power & Energy Systems, vol. 20, pp. 563-569, 1998.
 B. Zhao, C. Guo, and Y. Cao, “A multiagent-based particle swarm optimization approach for optimal reactive power dispatch,” Power Systems, IEEE Transactions on, vol. 20, pp. 1070-1078, 2005.
 C. Zhang, M. Chen, and C. Luo, “A multi-objective optimization method for power system reactive power dispatch,” in Intelligent Control and Automation (WCICA), 2010 8th World Congress on, 2010, pp. 6-10.
 M. A. Abido, “Multiobjective optimal VAR dispatch using strength pareto evolutionary algorithm,” in Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, 2006, pp. 730-736.
 S. Duman, Y. Sönmez, U. Güvenç, and N. Yörükeren, “Optimal reactive power dispatch using a gravitational search algorithm,” IET generation, transmission & distribution, vol. 6, pp. 563-576, 2012
 A. Khazali and M. Kalantar, “Optimal reactive power dispatch based on harmony search algorithm,” International Journal of Electrical Power & Energy Systems, vol. 33, pp. 684-692, 2011.
 W. Zhang and Y. Liu, “Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm,” International Journal of Electrical Power & Energy Systems, vol. 30, pp. 525-532, 2008.
 B. Mandal and P. K. Roy, “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization,” International Journal of Electrical Power & Energy Systems, vol. 53, pp. 123-134, 2013.
 B. Shaw, V. Mukherjee, and S. Ghoshal, “Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 29-40, 2014.
 T. Niknam, M. R. Narimani, R. Azizipanah-Abarghooee, and B. Bahmani-Firouzi, “Multiobjective optimal reactive power dispatch and voltage control: a new opposition-based self-adaptive modified gravitational search algorithm,” IEEE Systems Journal, vol. 7, pp. 742-753, 2013.
 S. He, Q. H. Wu, and J. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior,” Evolutionary Computation, IEEE Transactions on, vol. 13, pp. 973-990, 2009.
 K. Zare, M. T. Haque, and E. Davoodi, “Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method,” Electric Power Systems Research, vol. 84, pp. 83-89, 2012.
 M. Tarafdar Hagh, M. Alipour, and S. Teimourzadeh, “Application of HGSO to security based optimal placement and parameter setting of UPFC,” Energy Conversion and Management, vol. 86, pp. 873-885, 2014.