A Novel EP approach for multi-area economic dispatch with multiple fuel options

Abstract

This paper presents a novel approach to multi-area economic dispatch problems with multiple fuel options using a hybrid evolutionary programming method. The objective is to minimize the operation cost of the entire system while satisfying the tie line constraints. In this paper, EP-LMO (Evolutionary Programming with Levenberg-Marquardt Optimization) technique is proposed to solve multi-area economic dispatch problems with multiple fuel options. The EP-LMO is developed in such a way that a simple evolutionary programming (EP) is applied as a base level search to find the direction of the optimal global region. And Levenberg-Marquardt Optimization (LMO) method is used as a fine tuning to determine the optimal solution. The applicability and validity of the proposed approach on multi-area economic dispatch problems are presented in two parts. In Part I, two multi-area bench mark problems without fuel options are considered. In Part II, 10 unit system, with both multi-area and multi-fuel options is considered. The proposed approach is compared with the results of Incremental Network Flow Programming, Spatial Dynamic Programming and Evolutionary Programming approaches. The results show that the EP-LMO gives the optimum generation cost than any other methods.