DocumentCode :
3187571
Title :
Solved Environmental/Economic Dispatch Based on Multi-objective PSO
Author :
Zhang, Libiao ; Xu, Xiangli ; Wang, Sujing ; Ma, Ming ; Zhou, Chunguang ; Sun, Caitang
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Volume :
3
fYear :
2010
fDate :
11-12 May 2010
Firstpage :
352
Lastpage :
355
Abstract :
A new multi-objective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem based on Particle Swarm Optimization (PSO) is proposed in this paper. The new algorithm has adopted the maintenance method of Pareto candidate solution set based on the max-min distance density. The algorithm effectively guarantees the convergence of the algorithm and the diversity solutions. The performance of algorithm has been examined over the standard IEEE 30-bus six-generator test system, and other multi-objective evolutionary algorithm are compared. Testing and comparing results showed this paper algorithm is feasible and efficient.
Keywords :
evolutionary computation; particle swarm optimisation; power system economics; IEEE 30-bus six-generator test system; Pareto candidate solution set; environmental-economic power dispatch problem; max-min distance density; multiobjective PSO; multiobjective evolutionary algorithm; particle swarm optimization; Automation; Cost function; Educational institutions; Environmental economics; Evolutionary computation; Fuels; Particle swarm optimization; Power generation; Power generation economics; Power systems; Particle Swarm Optimization; environmental/economic dispatch; multiobjective evolutionary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
Conference_Location :
Changsha
Print_ISBN :
978-1-4244-7279-6
Electronic_ISBN :
978-1-4244-7280-2
Type :
conf
DOI :
10.1109/ICICTA.2010.470
Filename :
5522453
Link To Document :
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