Quasi-oppositional group search optimization for hydrothermal power system. (October 2016)

• Record Type: Journal Article
• Authors: Basu, M.
• Is Part Of: International journal of electrical power & energy systems. Volume 81(2016:Oct.)
• Page Start: 324
• Page End: 335
• Publication Date: 2016-10
• Subjects: Quasi-oppositional group search optimization -- Hydrothermal system -- Fixed head -- Variable head -- Prohibited operating zones -- Ramp-rate limits; 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.2016.02.051
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: This paper presents QOGSO for solving hydrothermal power system. The QOGSO is tested on two fixed head and three variable head hydrothermal systems. It is found that QOGSO based approach is able to provide better solution. Abstract: This paper presents quasi-oppositional group search optimization to determine the optimal schedule of power generation in a hydrothermal system. Group search optimization 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 effectiveness of the proposed method has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. Test results of the proposed QOGSO approach are compared with those obtained by other evolutionary methods. It is found that the proposed QOGSO based approach is able to provide better solution.