Quasi-oppositional group search optimization for multi-area dynamic economic dispatch. (June 2016)

• Record Type: Journal Article
• Authors: Basu, M.
• Is Part Of: International journal of electrical power & energy systems. Volume 78(2016:Jun.)
• Page Start: 356
• Page End: 367
• Publication Date: 2016-06
• Subjects: Quasi-oppositional group search optimization -- Group search optimization -- Multi-area dynamic economic dispatch -- Tie line constraints; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.11.120
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: This paper presents QOGSO for solving MADED problem. GSO inspired by the animal searching behavior is a biologically realistic algorithm. QOGSO has been used here to improve the effectiveness and quality of the solution. The QOGSO is tested on two multi-area test systems. 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.