Teaching Learning Based Optimization for Different ‎Economic Dispatch Problems

Abstract

This paper presents a teaching learning based algorithm (TLBO) to solve economic load dispatch (ELD) problems involving different linear, non-linear constraints. The problem formulation also consideredthe non-convex objective functions including the effect of valve-point loading, multi-fuel option of large-scale thermal plants.Many difficulties such as multimodality, dimensionality and differentiability are associated with the optimization of large scale non-linear constraints basednon-convex economic load dispatchproblems.TLBO is a population based technique which implements a group of solutions to proceed for the optimum solution. TLBO uses two different phases ‘Teacher Phase’ and ‘Learner Phase’. TLBO uses the mean value of the population to update the solution. Unlike other optimization techniques TLBO does not require any parameters to be tuned, thus making the implementation of TLBO simpler. TLBO uses the best solution of the iteration to change the existing solution in the population thereby increasing the convergence rate. Therefore, in the present paper Teaching–Learning-Based Optimization (TLBO) is applied to solve such type of complicated problems efficiently and effectively in order to achieve superior quality solution in computationally efficient way.Simulation results show that the proposed approach outperforms several existing optimization techniques. Results also proved the robustness of the proposed methodology.