The Open Electrical & Electronic Engineering Journal

2017, 11 : 23-37
Published online 2017 January 25. DOI: 10.2174/1874129001711010023
Publisher ID: TOEEJ-11-23

RESEARCH ARTICLE
 A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen Gonggui, *,1 , Huang Shanwai1 and Sun Zhi2

* Address correspondence to this author at the Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Tel: + 86-15616106539; Fax: +86-23-62461585; E-mail: .chenggpower@126.com

ABSTRACT

This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving short-term hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

Keywords:

Short-term hydrothermal scheduling, Quantum-behaved particle swarm optimization, Chaotic local search, Constrains handling.