Skip to main content
SpringerLink
Log in

Multiple objective particle swarm optimization technique for economic load dispatch

  • Computer & Information Science
  • Published:
Journal of Zhejiang University-SCIENCE A Aims and scope Submit manuscript

Abstract

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Coello, C.A.C., 1999. A comprehensive survey of evolutionary-based multiobjective optimization techniques.Knowledge and Information Systems,1(3):129–156.

    Google Scholar 

  • Coello, C.A.C., Lechuga, M.S., 2003. Mopso: A proposal for multiple objective particle swarm optimization.IEEE Proceedings World Congress on Computational Intelligence,2:1051–1056.

    Google Scholar 

  • Das, D.B., Patvardhan, C., 1998. New multi-objective stochastic search technique for economic load dispatch.IEE Proceedings—Generation, Transmission and Distribution,145(6):747–752.

    Article  Google Scholar 

  • Dhillon, J.S., Parti, S.C., Kothari, D.P., 1993. Stochastic economic emission load dispatch.Electrical Power Systems Research,26:179–186.

    Article  Google Scholar 

  • El-Keib, A.A., Ma, H., Hart, J.L., 1994. Economic dispatch in view of the clean air act of 1990.IEEE Transactions on Power Systems,9(2):972–978.

    Article  Google Scholar 

  • Fieldsend, J.E., Singh, S., 2002. A Multi-Objective Algorithm Based upon Particle Swarm Optimization. An Efficient Data Structure and Turbulence. The 2002 U.K. Workshop on Computational Intelligence, p. 34–44.

  • Fieldsend, J.E., Everson, R.M., Singh, S., 2003. Using unconstrained elite archives for multi-objective optimization.IEEE Transactions on Evolutionary Computation,7(3):305–323.

    Article  Google Scholar 

  • Goldberg, D.E., 1989. Genetic Algorithms for Search, Optimization, and Machine Learning. Addison-Wesley Professional/Pearson, New York.

    MATH  Google Scholar 

  • Hsiao, Y.T., Chiange, H.D., Liu, C.C., Chen, Y.L., 1994. A computer package for optimal multi-objective VAR planning in large scale power systems.IEEE Transactions on Power Systems,9(2):668–676.

    Article  Google Scholar 

  • Huang, C.M., Yang, H.T., Huang, C.L., 1997. Bi-objective power dispatch using fuzzy satisfaction-maximizing decision approach.IEEE Transactions on Power Systems,12(4):1715–1721.

    Article  Google Scholar 

  • Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, p. 1942–1948.

  • Knowles, J.D., Corne, D., 2000. Approximating the nondominated front using the pareto archived evolution strategy.Evolutionary Computation,8(2):149–172.

    Article  Google Scholar 

  • Niimura, T., Nakashima, T., 2003. Multiobjective tradeoff analysis of deregulated electricity transactions.International Journal of Electrical Power & Energy Systems,25(3):179–185.

    Article  Google Scholar 

  • Parsopoulos, K.E., Vrahatis, M.N., 2002. Particle Swarm Optimization Method in Multi-Objective Problems. Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain, p. 603–607.

  • Srinivasan, D., Chang, C.S., Liew, A.C. 1994. Multiobjective Generation schedule using fuzzy optimal search technique.IEE Proceedings-Generation. Transmission and Distribution,141(3):233–242.

    Article  Google Scholar 

  • Zimmerman, R., Gan, D., 1997. MATPOWER: A Matlab Power System Simulation Package. Available: http://www.pserc.cornell.edu/matpower.

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Zhejiang University, 310027, Hangzhou, China

    Zhao Bo & Cao Yi-jia

Authors
  1. Zhao Bo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cao Yi-jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Project (Nos. 60074040 and 6022506) supported by the National Natural Science Foundation of China

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bo, Z., Yi-jia, C. Multiple objective particle swarm optimization technique for economic load dispatch. J. Zheijang Univ.-Sci. A 6, 420–427 (2005). https://doi.org/10.1631/jzus.2005.A0420

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1631/jzus.2005.A0420

Key words

Document code

CLC number

Search

Navigation