# Fuel Cost Reduction for Thermal Power Generator with Economic Load Dispatch Problem using SFLA

 International Journal of Electrical and Electronics Engineering © 2018 by SSRG - IJEEE Journal Volume 5 Issue 5 Year of Publication : 2018 Authors : M.Muthuselvi, M.Kavinkumar, G.Mithun Mr.P.Vignesh 10.14445/23488379/IJEEE-V5I5P105
##### How to Cite?

M.Muthuselvi, M.Kavinkumar, G.Mithun Mr.P.Vignesh, "Fuel Cost Reduction for Thermal Power Generator with Economic Load Dispatch Problem using SFLA," SSRG International Journal of Electrical and Electronics Engineering, vol. 5,  no. 5, pp. 20-25, 2018. Crossref, https://doi.org/10.14445/23488379/IJEEE-V5I5P105

##### Abstract:

Economic load dispatch problem with multiple fuel cost options is one of the major problems in a power system. The thermal plant fuel cost is highly non-linear, because of the load demand and discontinuities for the power generation. This makes the EDP problem a non-linear constrained optimization problem. In the traditional EDP problem, each generator’s cost function is represented by a single quadratic polynomial equation which can be solved by using numerical programming based techniques such as gradient-based method, lambda iteration method. In this paper is to minimize the fuel cost of the power system for the various load conditions by solving the EDP of the real power generation by using SFLA optimization algorithm. This paper compares the optimization techniques such as PSO, MPSO in a 3-unit generating system. The EDP is to determine the optimal combination of power outputs of all generating units to minimize the total fuel cost while satisfying the load demand and operational constraints. The comparisons of results show that the proposed SFLA algorithm provides the less cost of the fuel cost and the quality solution of the power generation. The proposed methodology emerges as robust optimization techniques for solving the ELD problem for different size power system.

##### Keywords:

Optimal power flow (OPF), Economic load dispatch (ELD), Genetic Algorithm (G.A), particle swarm optimization (PSO), Quantum behaved particle swarm optimization (SFLA).

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