DocumentCode :
676415
Title :
A new hybrid algorithm for economic dispatch considering the generator constraints
Author :
Menglin Zhang ; Zhijian Hu ; Jianglei Suo ; Ziyong Zhang
Author_Institution :
State Key Lab. of Electr. Insulation & Power Equip., Xi´an Jiaotong Univ., Xi´an, China
fYear :
2013
fDate :
22-25 Oct. 2013
Firstpage :
1
Lastpage :
4
Abstract :
The practical economic dispatch (ED) problems have many non-convex characteristics, which makes the searching of the global optimum difficult when using traditional mathematical methods. This paper presents a novel hybrid algorithm (HA) to the ED problems based on the particle swarm optimization (PSO) technique and differential evolution (DE) algorithm. Since the standard PSO has the adversity of premature convergence, the mutation and crossover operators of the DE, as well as the chaotic sequences, are considered to be integrated into the PSO to improve the global searching ability. Moreover, the dynamic penalty function is applied to deal with the constraints. The proposed method is tested on a 15-unit system considering the ramp rate limits, operating zones, and network losses. Also, the results are compared with those of other optimization methods.
Keywords :
chaos; evolutionary computation; particle swarm optimisation; power generation dispatch; DE algorithm; PSO technique; chaotic sequences; crossover operators; differential evolution algorithm; dynamic penalty function; generator constraints; global searching ability; nonconvex characteristics; novel hybrid algorithm; particle swarm optimization technique; practical economic dispatch problems; Chaos; Convergence; Economics; Generators; Heuristic algorithms; Optimization; Particle swarm optimization; chaotic sequences; differential evolution; dynamic penalty function; economic dispatch; particle swarm optimization; prohibited operating zones;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
Conference_Location :
Xi´an
ISSN :
2159-3442
Print_ISBN :
978-1-4799-2825-5
Type :
conf
DOI :
10.1109/TENCON.2013.6718481
Filename :
6718481
Link To Document :
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