Published Paper Details:

This paper presents an efficient approach to estimate the thermal power plant fuel cost parameter which are usually modeled as a polynomial of desired order. The accuracy of parameters of fuel cost is in turn influences the economic load dispatch (ELD) where cost function of fuel is to be minimized. The fuel cost polynomial parameters in this paper are obtained first by popularly used and industry adopted Least square estimation method. Upon observing the local optimality of error by LSE this paper proposes a simple geometry based, non-stochastic and derivative free optimization, known as Nelder-Mead (NM) to further reduce the absolute error by LSE. This approach LSE-NM is tested on different fuel costs such as coal, oil and gas for obtaining parameters of polynomial which results in global minimum error between practical data points and mathematical model. The global optimality for situation considered is confirmed upon comparison with global optimizers such as Particle swarm optimization(PSO) and Ant Bee colony(ABC). The prime requirements of reliability of estimated parameters and time of computation along with global optimality is possible by LSE-NM for ELD parameter estimation.