- ISSN: 2287-4844 (Print), 2287-4852 (Online)
- Abbreviated Title: Prog. Intell. Comput. Appl.
- Frequency: Annually
- Editor-in-Chief: Dr. William Guo
- Executive Editor: Xian Zhang
- Published by: Australasian Professional Development and Academic Services (APDAS)(registered from Feb 2013)
- Indexed by: Google Scholar, Engineering & Technology Digital Library, Crossref, Proquest and DOAJ
- E-mail: pica@etpub.com
PICA 2012 Vol.1(1): 37-49 ISSN: 2287-4844 (Print); 2287-4852 (Online)
doi: 10.4156/pica.vol1.issue1.3
doi: 10.4156/pica.vol1.issue1.3
Evolutionary Optimisation for Power Generation Unit Loading Application
1Lily D Li and 2Jiping Zhou
Abstract: Power generation unit loading optimisation is a practically viable tool for efficiency improvement. The objectives for the coal-fired power generation loading optimisation are to minimize fuel consumption and to minimize emissions for a given load demand. This paper presents two models for this significant industrial application. Depending on the environmental regulation, either a single objective constrained model or a multi-objective constrained model can be chosen in practice. A multi-objective constraint-handling method incorporating the constraint dominance concept via Particle Swarm Optimisation (PSO) algorithm has been adopted for problem solving. The simulation results based on a coal-fired power plant demonstrates the capability, effectiveness and efficiency of using the proposed approach in a large scale industrial application.
Keywords: Power Generation; Evolutionary Optimisation; PSO Algorithm; Load Distribution
1School of Information Communication Technology, Central Queensland University, Australia l.li@cqu.edu.au
2Stanwell Cooperation Limited, Australia jiping.zhou@stanwell.com
Cite: Lily D Li and Jiping Zhou, "Evolutionary Optimisation for Power Generation Unit Loading Application," Progress in Intelligent Computing and Applications , vol. 1, no. 1, pp. 37-49, October 2012.