Group Search Optimization for Solution of Different Optimal Power Flow Problems

This article presents group search optimization for the solution of different optimal power flow problems of a power system with generators that may have either convex or non-convex fuel cost characteristics. Different operational constraints, such as generator capacity limits, power balance constraints, line flow, and bus voltages limits, have been considered. Settings of transformer tap ratio and reactive power compensating devices have also been included as the control variables in the problem formulation. Group search optimization, inspired by the animal searching behavior, is a biologically realistic algorithm. Group search optimization has been implemented to solve four different objectives such as fuel cost minimization, emission minimization, voltage profile improvement, and voltage stability enhancement with the optimal power flow embedded on IEEE 30-bus, 57-bus, and 118-bus test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed group search optimization based approach is able to provide a better solution.