Optimum power flow using flexible genetic algorithm model in practical power systems

Abstract

This thesis aims at providing a solution to Optimum Power Flow (OPF) in practical power systems by using a flexible genetic algorithm (GA) model. The proposed approach finds the optimal setting of OPF control variables which include generator active power output, generator bus voltages, transformer tap-setting and shunt devices with the objective function of minimising the fuel cost. The proposed GA is modelled to be flexible for implementation to any practical power systems with the given system line, bus data, generator fuel cost parameter and forecasted load demand. The GA model has been analysed and tested on the standard IEEE 30-bus system and two real practical power systems which are an industrial park power system and a gold-copper mining power system both located in Indonesia. These case studies of real power systems have been performed using actual data and demand pattern. The results obtained outperform other approaches from the literature which was recently applied to the IEEE 30-bus system with the same control variable limits and system data. Better results are also found when compared against the configurations used in the two real power systems which are heuristic based on the practical expertise of power plant engineers. These superior results are achieved due to the robust and reliable algorithm of the proposed GA which utilises the elitism and non-uniform mutation rate.