Abstract
An improved particle swarm optimization based on cultural algorithm is proposed to solve environmental/economic dispatch (EED) problem in power system. Population space evolves with the improved particle swarm optimization strategy. Three kinds of knowledge in belief space, named situational, normative and history knowledge are redefined respectively to accordance with the solution of multi-objective problem. The results of standard test systems demonstrate the superiority of the proposed algorithm in terms of the diversity and uniformity of the Pareto-optimal solutions obtained.
Manuscript received January 2, 2011. This work was supported by Natural Science Foundation of Shaanxi Province (Grant No.2010JQ8006) and Science Research Programs of Education Department of Shaanxi Province (Grant No.2010JK711).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Talaq, J.H., EI-Hawary, F., EI-Hawary, M.E.: A summary of environmental/economic dispatch algorithms. J. IEEE Trans. Power Syst. 9(3), 1508–1516 (1994)
Lingfeng, W., Chanan, S.: Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm. J. Electr. Power Syst. Research 77, 1654–1664 (2007)
Abido, M.A.: A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch. J. Electr. Power Energy Syst. 25(2), 97–105 (2003)
Abido, M.A.: A novel multiobjective evolutionary algorithm for environmental/ economic power dispatch. J. Electr. Power Syst. Research 65, 71–91 (2003)
Abido, M.A.: Multiobjective evolutionary algorithms for electric power dispatch problem. J. IEEE Trans. Evolut. Comput. 10(3), 315–329 (2006)
King, R.T.F., Rughooputh, H.C.S., Deb, K.: Evolutionary multi-objective environmental/Economic dispatch: Stochastic versus deterministic approaches. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 677–691. Springer, Heidelberg (2005)
Basu, M.: Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. J. Electr. Power. Energy Syst. 30(2), 140–210 (2008)
Wang, L.F., Singh, C.: Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm. J. Electr. Power Syst. Res. 77(12), 1654–1664 (2007)
Cai, J.J., Ma, X.Q., Li, Q., Li, L.X., Peng, H.P.: A multi-objective chaotic particle swarm optimization for environmental/economic dispatch. J. Energy Convers Manage. 50(5), 1318–1325 (2009)
Agrawal, S., Panigrahi, B.K., Tiwari, M.K.: Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch. J. IEEE Trans. Evolut. Comput. 12(5), 529–541 (2008)
Daneshyari, W., Yen, G.G.: Cultural MOPSO: A cultural framework to adapt parameters of multiobjective particle swarm optimization. In: C. IEEE Congress. on Evolut. Comput., pp. 1325–1332 (2009)
Farag, A., Al-Baiyat, S., Cheng, T.C.: Economic load dispatch multiobjective optimization procedures using linear programming techniques. J. IEEE Trans. Power Syst. 10(2), 731–738 (1995)
Landa, B., Carlos, A., Coello, C.: Cultured differential evolution for constrained optimization. J. Comput Methods in Applied Mechanics and Engine 195, 4303–4322 (2006)
Yunhe, H., Lijuan, L., Yaowu, W.: Enhanced particle swarm optimization algorithm and its application on economic dispatch of power systems. J. Proc. of CSEE 24(7), 95–100 (2004)
Hemamalini, S., Simon, S.P.: Emission Constrained Economic Dispatch with Valve-Point Effect using Particle Swarm Optimization. In: C. IEEE Region. 10 Confer., pp. 1–6 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Y., Xu, L., Xue, J. (2011). Improved Multiobjective Particle Swarm Optimization for Environmental/Economic Dispatch Problem in Power System. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-21524-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21523-0
Online ISBN: 978-3-642-21524-7
eBook Packages: Computer ScienceComputer Science (R0)