Title :
An Improved Particle Swarm Optimization Algorithm for Unit Commitment
Author :
Xiong, Wei ; Li, Mao-Jun ; Cheng, Yuan-lin
Author_Institution :
Sch. of Electr. & Inf. Eng., Changsha Univ. of Sci. & Technol., Changsha
Abstract :
This paper presents a new approach via multi-particle swarm optimization (MPSO) to solve the unit commitment (UC) problem. A new strategy which can generate feasible particles and make the search space narrow within the feasible solutions is presented. Some particle swarms are generated by the new strategy, and location optimum solutions are searched in each particle swarm, then a new particle swarm is made up of location optimum solutions, and the global optimum solution is searching in this new particle swarm. The application of the new generating strategy in PSO can efficiently improve the global searching capability and escape from local minima. The simulation results show that the method is more efficient than genetic algorithm, and could obtain the global optimum solution more probably.
Keywords :
particle swarm optimisation; power generation dispatch; power generation scheduling; search problems; multiparticle swarm optimization algorithm; search space; unit commitment problem; Cost function; Fuel economy; Genetic algorithms; Particle swarm optimization; Power engineering computing; Power generation; Power generation economics; Production; Space technology; Spinning;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2008 International Conference on
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3357-5
DOI :
10.1109/ICICTA.2008.363