A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch


A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Share and Cite:

Wu, C. , Li, H. , Wu, L. and Wu, Z. (2015) A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch. Journal of Computer and Communications, 3, 83-89. doi: 10.4236/jcc.2015.39009.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] David, C.W. and Gerald, B.S. (1993) Genetic Algorithm Solution of Economic Dispatch with Valve Point Loading. IEEE Transactions on Power Systems, 8, 1325-1332.
[2] Wheimin, L., Fusheng, C. and Mingtong, T. (2001) Non-Convex Economic Dispatch by Integrated Artificial Intelligence. IEEE Transactions on Power Systems, 16, 307-311.
[3] Hou, Y.H., Lu, L.J., Xiong, X.Y., et al. (2004) Enhanced Particle Swarm Optimization Algorithm and Its Application on Economic Dispatch of Power Systems. Proceedings of the CSEE, 24, 95-100. (In Chinese)
[4] Wang, S.J., Shahidehpour, S.M. and Kirschen, D.S. (1995) Short-Term Generation Scheduling with Transmission and Environmental Constraints Using an Augmented Lagrangian Relaxation. IEEE Transactions on Power Systems, 10, 1294-1301. http://dx.doi.org/10.1109/59.466524
[5] Fan, J.-Y. and Zhang, L. (1998) Real-Time Economic Dispatch with Line Flow and Emission Constrains Using Quadratic Programming. IEEE Transactions on Power Systems, 13, 320-325.
[6] Zhang, X.W. and Li, Y.J. (2006) Self-Adjusted Particle Swarm Optimization Algorithm Based Economic Load Dispatch of Power System. Power System Technology, 30, 8-13. (In Chinese)
[7] Tang, W. and Li, D.P. (2000) Chaotic Optimization for Economic Dispatch of Power Systems. Proceedings of the CSEE, 20, 36-40. (In Chinese)
[8] Kennedy, J. and Eberhart, R.C. (1995) Particle Swarm Optimization. Proceeding of the 1995 IEEE International Conference on Neural Network. Perth, 27 November-1 December 1995, 1942-1948.
[9] Wooldridge, M. (2002) An Introduction to Multi-Agent System. Wiley, New York.
[10] Zhong, W.C., Liu, J., Xue, M.Z. and Jiao, L.C. (2004) A Multi-Agent Genetic Algorithm for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, 34, 1128-1141.
[11] Wang, X., Wang, X., Li, L.X., et al. (2013) Reactive Power Optimization for Wind Power System Based on Dynamic Cloud Evolutionary Particle Swarm Optimization. Power System Protection and Control, 41, 36-43. (In Chinese)
[12] Liu, H. and Liu, Z.G. (2015) An Improved Particle Swarm Algorithm Study on Optimization Model of Maintenance Schedules for Railway Traction Substations. Power System Protection and Control, 43, 87-94. (In Chinese)
[13] Ling, S.H., Lam, H.K., Leung, F.H.F. and Lee, Y.S. (2003) Improved Genetic Algorithm for Economic Load Dispatch with Valve-Point Loadings. The 29 Annual Conference of the IEEE Industrial Elec-tronics Society, 1, 442-447.
[14] Park, J.-B., Lee, K.-S., Shin, J.-R. and Lee, K.Y. (2003) Economic Load Dispatch for Non-Smooth Cost Functions Using Particle Swarm Optimization. 2003 IEEE Power Engineering Society General Meeting, 2, 938-943.
[15] Sinha, N., Chakrabarti, R. and Chattopadhyay, P.K. (2003) Evolutionary Programming Techniques for Economic Load Dispatch. IEEE Transactions on Evolutionary Computation, 7, 83-94.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.