Fuel Cost Function Estimation for Economic Load Dispatch Using Evolutionary Programming

Abstract

The input–output characteristics of thermal power plants are affected by many factors such as the ambient operating temperature and aging of generating units. Thus, periodical estimation of fuel cost function is very crucial to improve the overall operational and economical practices. The higher the accuracy of the estimated coefficients of the cost function, the more accurate the results obtained from the economic dispatch and optimal power flow calculations. Different models that describe the input–output relationship of thermal units are considered, including the one that accounts for the valve loading point. The goal is to minimize the total estimation error such that the selected model follows the actual data measurements as closely as possible. Evolutionary Programming (EP) is employed to minimize the error associated with the estimated parameters. It relieves some of the mathematical restrictions typically imposed on system modelling, since it does not require convexity or differentiability, as in the case of many conventional estimation techniques. Results obtained are compared to those computed by the least error square method and particle swarm optimization.