Estimating the input–output parameters of thermal power plants using PSO

This paper presents a new and accurate method for estimating the parameters of thermal power plants fuel cost function. Proper and precise estimation of these parameters is very important for optimal economical operations of power systems as they directly impact the economic dispatch calculations. The objective function to be minimized in the economic dispatch is usually the summation of fuel cost functions corresponding to generating units. 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 power plant characteristics is very crucial to improve the overall operational and economical practices. The higher the accuracy of the estimated coefficients, the more accurate the results obtained from the economic dispatch calculations. Different models that describe the input–output curve of thermal generating units are considered. The traditional estimation problem is viewed and formulated as an optimization one. The goal is to minimize the total estimation error. A particle swarm optimization algorithm is employed to minimize the error associated with the estimated parameters. Three different study cases are considered in this work to test the performance of the method. Results obtained are compared to those computed by least error square method. Comparison results are in favor of particle swarm optimization algorithm in all study cases considered.