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Quasi-oppositional group search optimization for multi-area dynamic economic dispatch
- Source :
- International Journal of Electrical Power & Energy Systems. 78:356-367
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Multi-area dynamic economic dispatch determines the optimal scheduling of online generator outputs and interchange power between areas with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators, tie line constraints, and transmission losses. This paper presents quasi-oppositional group search optimization for solving multi-area dynamic economic dispatch problem with multiple fuels and valve-point loading. Group search optimization (GSO) inspired by the animal searching behavior is a biologically realistic algorithm. Quasi-oppositional group search optimization (QOGSO) has been used here to improve the effectiveness and quality of the solution. The proposed QOGSO employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. The QOGSO is tested on two multi-area test systems having valve point loading and mult-fuel option. Results of the proposed QOGSO approach are compared with those obtained from group search optimization (GSO), biogeography-based optimization (BBO), gravitational search algorithm (GSA), differential evolution (DE) and particle swarm optimization (PSO). It is found that the proposed QOGSO based approach is able to provide better solution.
- Subjects :
- Mathematical optimization
education.field_of_study
Engineering
business.industry
020209 energy
Population
Economic dispatch
Energy Engineering and Power Technology
Initialization
Particle swarm optimization
02 engineering and technology
Differential evolution
Derivative-free optimization
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
Multi-swarm optimization
education
business
Metaheuristic
Subjects
Details
- ISSN :
- 01420615
- Volume :
- 78
- Database :
- OpenAIRE
- Journal :
- International Journal of Electrical Power & Energy Systems
- Accession number :
- edsair.doi...........951751059fba212554ca54dc2c224641