A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

Abstract

The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.