Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. (December 2015)

• Record Type: Journal Article
• Authors: Banerjee, Sumit; Maity, Deblina; Chanda, Chandan Kumar
• Is Part Of: International journal of electrical power & energy systems. Volume 73(2015:Dec.)
• Page Start: 456
• Page End: 464
• Publication Date: 2015-12
• Subjects: Economic load dispatch -- Genetic algorithm -- Particle swarm optimization -- Teaching learning based optimization -- Valve point loading effect; 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.2015.05.036
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: TLBO is a new population based optimization technique to solve ELD problem. Effect of valve point loading in the fuel cost function of the thermal plants is examined. Results obtained from TLBO are better as compared to PSO, SA, GA-SA, NN-EPSO, GA, etc. Said method is excellent in terms of convergence characteristic, computational time and minimum cost. Abstract: This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.