Reliability planning employing genetic algorithms for an electric power system

This paper applies network flow method, genetic algorithms, and Monte Carlo simulation to optimal reliability design for a composite electric power system. Genetic algorithms are general purpose optimization techniques based on principles inspired from the biological evolution using three main operations of reproduction, crossover, and mutation, which could locate near optimal solutions in most cases. The proposed method primarily adopted Monte Carlo simulation method, maximum-flow minimum-cut theorem, and optimization techniques to find out the optimal values of reliability indices, such that the optimal reliability design for the system can be achieved. The objective function to be optimized is composed of interruption cost and installation cost. The reliability indices mainly used include expected demand not served (EDNS) and forced outage rate (FOR). An application of the proposed method conducted on an IEEE five-bus test system is presented.