Solution of Economic Power Dispatch using Teaching Learning Algorithm

Abstract

In this paper, a Teaching Learning Algorithm (TLA) method is proposed to solve the non-convex economic power dispatch (NCEPD) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying equality and inequality constraints. The feasibility of the TLA-NCEPD method is demonstrated on thirteen unit power systems. It is compared with the pattern search, improved genetic algorithm, cultural differential evolution and quantum particle swarm optimization in terms of solution quality. The simulation results show that the proposed TLA method is able to obtain higher quality solutions stably and efficiently in the NCEPD problem than any other tested optimization algorithm.
Keywords: Economic power dispatch, Non-convex, Quadratic cost functions, Teaching learning Algorithm, Valve point effects