Title :
Unified bare bone particle swarm for economic dispatch with multiple fuel cost functions
Author :
Chen, Chang-Huang ; Sheu, Jia-Shing
Author_Institution :
Dept. of Electr. Eng., Tungnan Univ., Taipei, Taiwan
Abstract :
Economic generating electric power is a very important issue for power utilities, especially in current state of fuel cost booming. In this paper, the unified bare bone particle swarm algorithm (UBPSO), which integrates local and global learning strategies, is proposed to solve economic dispatch problems with multiple fuel options. Tested on three systems with different number of units has verified that the proposed method can obtain better solution compared with other methods found in literature.
Keywords :
fuel economy; learning (artificial intelligence); particle swarm optimisation; power generation dispatch; power generation economics; UBPSO; economic dispatch problem; electric power generation economics; global learning strategy; local learning strategy; multiple fuel cost functions; power utilities; unified bare bone particle swarm algorithm; Bones; Cost function; Economics; Fuels; Particle swarm optimization; Power systems; Propagation losses; bare bone particle swarm optimization; economic dispatch; multiple fuel options; particle swarm optimization;
Conference_Titel :
Lightning (APL), 2011 7th Asia-Pacific International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4577-1467-2
DOI :
10.1109/APL.2011.6111106
