Improved genetic algorithm solution to unit commitment problem

As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement. Previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the Unit Commitment (UC) algorithm must be updated. This work deals with the Improved Genetic Algorithm (IGA) Solution to UC problem. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A simple GA implementation using the standard reproduction, cross over and mutation operators has been utilized to get optimal solution. In this paper, we have obtained the satisfactory solutions for the UC problem using the varying quality function technique and by adding problem specific operators. The idea has been implemented using technical simulation package MATLAB and the results for the same were obtained. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach. Numerical results are shown to compare the superiority of the cost solutions obtained using the conventional methods.