Optimal power flow based upon genetic algorithm deploying optimum mutation and elitism

The aim of optimal power flow is to discover an operating point that minimizes the generation cost while satisfying multiple operating constraints. Over the years, several techniques have been introduced to solve this non-linear optimization problem. In this paper, genetic algorithm deploying optimum non-uniform mutation rate and elitism has been used to solve this problem. After implementation of this algorithm in MATLAB, the data of IEEE 30-bus practical power system and NTDC 32-bus test system of Pakistan have been solved for optimal power flow and results have been compared with the previously used techniques such as; simple genetic algorithm (SGA), linear programming (LP), ant colony optimization (ACO), differential evolution (DE) and artificial bee colony algorithm (ABC). It has been established that the proposed solution proves to be more cost effective than previously used techniques. The proposed technique offers annual cost saving of $6061630.92 for NTDC 32-bus test system. The capital thus saved can be utilized to pay back circular debt and hence the problem of load shedding in Pakistan can be alleviated.