A new method for composite system annualized reliability indices based on genetic algorithms

This paper presents a genetic algorithms (GA) based method for state sampling of composite power system. Sampled states are used to assess annualized reliability indices. In the proposed method GA intelligently searches the enormous state space of a power system to find the most probable states contributing to system failure. Binary encoded GA is used to represent system states. Through its fitness function GA is able to trace failure states in a more intelligent manner than conventional methods. A linearized optimization load flow model is used for evaluation of sampled states. The model takes into consideration importance of load in calculating load curtailment at different buses in order to obtain a unique solution for each state. The full set of composite system adequacy indices and load bus indices is calculated. The proposed method is applied to a sample test system to be validated. Obtained results are compared with other conventional methods.