An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

Energies

Qun Niu¹  Zhuo Zhou¹  Hong-Yun Zhang¹ 

[1] Shanghai Key Laboratory of Power Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China; E-Mails:

关键词: economic dispatch;    quantum-behaved particle swarm optimization;    valve-point effects;    multiple fuel options;   

DOI  :  10.3390/en5093655

来源: mdpi

【 摘 要 】

Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE), particle swarm optimization (PSO) and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.