Journal of Zhejiang University-SCIENCE A

, Volume 7, Issue 4, pp 615–622 | Cite as

A pooled-neighbor swarm intelligence approach to optimal reactive power dispatch

  • Guo Chuang-xin 
  • Zhao Bo 


This paper presents a pooled-neighbor swarm intelligence approach (PNSIA) to optimal reactive power dispatch and voltage control of power systems. The proposed approach uses more particles℉ information to control the mutation operation. The proposed PNSIA algorithm is also extended to handle mixed variables, such as transformer taps and reactive power source installation, using a simple scheme. PNSIA applied for optimal power system reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system in which the control of bus voltages, tap position of transformers and reactive power sources are involved to minimize the transmission loss of the power system. Simulation results showed that the proposed approach is superior to current methods for finding the optimal solution, in terms of both solution quality and algorithm robustness.

Key words

Reactive power dispatch Swarm intelligence Multi-agent systems Global optimization 

CLC number

TM73 TM74 


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  1. Bonabeau, E., Dorigo, M., Theraulaz, G., 1999. Swarm Intelligence: from natural to Artificial Systems. Oxford University Press.Google Scholar
  2. Clerc, M., Kennedy, J., 2002. The Particle Swarm—Explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evol. Comput., 6(1):58–73. [doi: 10.1109/4235.985692]CrossRefGoogle Scholar
  3. Das, D.B., Patvardhan, C., 2002. Reactive power dispatch with a hybrid stochastic search technique. Int. J. Elect. Power Energy Syst., 24(9):731–736. [doi: 10.1016/S0142-0615(01)00085-0]CrossRefGoogle Scholar
  4. Dommel, H.W., Tinney, W.F., 1968. Optimal power flow solutions. IEEE Trans. on Power Apparatus and Systems, 87:1866–1876.CrossRefGoogle Scholar
  5. Dorigo, M., Maniezzo, V., Colorni, A., 1996. The ant system: optimization by a colony of cooperating agents. IEEE Trans. on Systems, Man, and Cybernetics, Part B, 26(1):29–41. [doi: 10.1109/3477.484436]CrossRefGoogle Scholar
  6. Hong, Y.Y., Sun, D.I., Lin, S.Y., Lin, C.J., 1990. Multi-year multi-case optimal AVR planning. IEEE Trans. on Power Systems, 5(4):1294–1301. [doi: 10.1109/59.99380]CrossRefGoogle Scholar
  7. Iba, K., 1994. Reactive power optimization by genetic algorithm. IEEE Trans. on Power System, 9(2):685–692. [doi: 10.1109/59.317674]CrossRefGoogle Scholar
  8. Kennedy, J., Eberhart, R.C., 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, p.1942–1948.Google Scholar
  9. Kennedy, J., Eberhart, R.C., 2001. Swarm Intelligence. Morgan Kaufmann Publishers.Google Scholar
  10. Lai, L. L., Ma, J. T., 1997. Application of evolutionary programming to reactive power planning—Comparison with nonlinear programming approach. IEEE Trans. on Power Systems, 12(1):198–204. [doi: 10.1109/59.574940]CrossRefGoogle Scholar
  11. Lee, K.Y., Bai, X.M., Park, Y.M., 1995. Optimization method for reactive power planning by using a modified genetic algorithm. IEEE Trans. on Power Systems, 10(4):1843–1850. [doi: 10.1109/59.476049]CrossRefGoogle Scholar
  12. Mendes, R., Kennedy, J., Neves, J., 2004. The fully informed particle swarm: simpler, maybe better. IEEE Trans. on Evol. Comput., 8(3):20–211.CrossRefGoogle Scholar
  13. Wong, K.P., Wong, Y.W., 1994. Genetic and genetic/simulated-annealing approaches to economic dispatch. IEE Proc. Pt. C, 141(5):507–513.Google Scholar
  14. Wu, Q.H., Cao, Y.J., Wen, J.Y., 1998. Optimal reactive power dispatch using an adaptive genetic algorithm. Int. J. Elect. Power Energy Syst., 20(8):563–569. [doi: 10.1016/S0142-0615(98)00016-7]CrossRefGoogle Scholar
  15. Yin, X., Germany, N., 1991. Investigations on solving the load flow problem by genetic algorithms. Electric Power System Research Journal, 22(3):151–163. [doi: 10.1016/0378-7796(91)90001-4]CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Guo Chuang-xin 
    • 1
  • Zhao Bo 
    • 1
  1. 1.School of Electrical EngineeringZhejiang UniversityHangzhouChina

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