Based on Hybrid Particle Swarm Optimization of the AGC Units Dispatch


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On the premise of ensuring safety and reliability in electricity market environment, the goal of State Grid Corporation is that purchase AGC ancillary service charges of reducing cost. This paper first takes total expense from many AGC units as an objective function, , which synthetically considers total regulation MW amount and total regulation speed constraints. A novel hybrid particle swarm optimization (PSO) algorithm is applied to solve the problem. Numerical simulation results show that the improved PSO algorithm has advantages both in the calculation accuracy and the convergence speed. Therefore, it is concluded that the algorithm is supposed to be an effective way to deal with the optimized issue in the power market.



Edited by:

Zhixiang Hou




X. H. Wang and Y. M. Zhang, "Based on Hybrid Particle Swarm Optimization of the AGC Units Dispatch", Applied Mechanics and Materials, Vols. 48-49, pp. 274-279, 2011

Online since:

February 2011




[1] DING Junwei; SHEN Yu; HUANG Yonghao, etc. Competition models for automatic generation control in deregulated power systems [J]. Journal of Tsinghua University(Science and Technology), 2003. 43(9), 1191-1194.

[2] Xiao Baoling; Zhao Qingbo, etc. Discussion on the Pattern of Competitive Trade in the AGC Ancillary Service Market [J]. Modern Electric Power 2004. 21(2), 81-86.

[3] Wu Yaguang; Li Weidong; Wu Haibo, etc.FRAMEWORK OF AGC UNIT SELECTION ON THE GENERATION-SIDED ELECTRICITY MARKET [J]. Automation of Electric Power Systems, 27(2): 37~40.

[4] Li Weidong; Wu Haibo; Wu Yaguang, etc.APPLICATION OF GENETIC ALGORITHM TO AGC SERVICE DISPATCH IN A DEREGULATED POWER SYSTEM [J].Automation of Electric Power Systems, 2003, 27(15):08~10.

[5] MA Ye-wei; YANG Feng. The AGC Deployment Competition of Adaptive Genetic Algorithm of the Based on Optimal Retention [J]. Central China Electric Power, 2007, 20(02): 9-11, 14.

[6] ZHANG Tao; CAI Jinding. Application of Improved Particle Swarm Optimization in Power Purchase Model Optimization Problem [J]. High Voltage Engineering, 2006. 32(11)131-134.

[7] YUAN Xiao-hui; WANG Cheng; ZHANG Yong-chuan; YUAN Yan-bin, A SURVEY ON APPLICATION OF PARTICLE SWARM OPTIMIZATION TO ELECTRIC POWER SYSTEMS [J]. Power System Technology, 2004. 28(19), 14-19.

[8] Kennedy J, Eberhart R. Particle Swarm Optimization[J]. Proceedings of IEEE International Conference on Neural Networks, 1995, 4: 1942-(1948).

[9] Eberhart R, Shi Y. Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization[A]. Proceedings of the Congress on Evolutionary Computing, 2000. 84-88.


[10] J. Kennedy and R. C. Eberhart. A Discrete Binary Version of the Particle Swarm Algorithm, Proc. of the conference on Systems, Man, and Cybernetics SMC97, pp.4104-4.


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