Bee Hive Algorithm to Optimize Multi Constrained Piecewise Non-Linear Economic Power Dispatch Problem in Thermal Units

Abstract

This paper presents application of a Bee Hive Algorithm to Economic Load Dispatch which considers practical constraints and non linear characteristics. The proposed ED formulation includes ramp rate limits, valve loading effects, multiple fuels, equality and inequality constraints, which usually are found simultaneously in realistic power systems. Conventional methods such as Lambda iteration and Base point participation are not able to obtain optimal solution for units having discontinuous fuel cost functions. Bee Hive Algorithm can overcome the difficulties and provides an almost global optimal solution, since they don't get stuck up at local optimum.Keywords: Economic load Dispatch, Bee Hive Algorithm, Valve-point loading, Ramp rate limits, Multiple Fuels IntroductionECONOMIC Load Dispatch (ELD) seeks "the best" generation for the generating plants to supply the required demand plus transmission losses with the minimum production cost. Improvement in scheduling the units output can lead to significant cost savings. In traditional ELD problems, the cost function of each generator is approximately represented by a simple quadratic function and is solved using mathematical programming based on several optimization techniques such as dynamic programming, Linear programming, homogenous linear programming and quadratic programming methods[2],[3], [4]. However none of these methods may be able to provide an optimal solution and they usually get stuck at a local optimum. Normally the input-output characteristic of modern generating units are highly nonlinear in nature due to valve-point effect [ [19], etc., have been proposed to solve ELD problem. These techniques can be used to search the global optimum with any type of objective function and constraints [22]. In this paper, two ED problem for 3 and 10 thermal units with a non smooth fuel cost function [8] are employed to demonstrate the performance of the proposed method with BHA and the results were compared with GA.The rest of this paper is organized as follows: Section II describes the formulation of an ED problem; while section III explains the standards in BHA. Section IV then details the procedure of handling the BHA. Section V gives the flow chart. Section VI gives the Data and gives the results of the optimization. Section VII outlines our conclusion and future research.