Bat Search Algorithm for Solving Multi-objective Optimal Power Flow Problem

Abstract

This work presents bat search (BS) algorithm to solve optimal power flow problem. BS algorithm is a population-based random search technique that mimics bats’ behavior. The main motive of solving an optimal power flow (OPF) problem is to obtain the optimal setting of control variables in a power system that minimizes or maximizes one or more objective functions. The power system equality and inequality constraints such as generator constraints, transformer constraints, shunt VAR constraints, line flows, and bus voltage constraints are effectively handled in OPF problem by implementing penalty factor approach. The proposed bat search algorithm is applied to find optimal setting of the power system control variables like generators real power outputs except slack bus, generator bus voltages, transformer tap settings and other sources of reactive power such as shunt capacitor or some shunt FACTS controller. The objective functions to carry out OPF are fuel cost minimization, improvement voltage profile, and enhancement of voltage stability under normal condition as well as during line outage contingency. Effectiveness of the proposed bat search algorithm has been demonstrated by applying BS algorithm to solve OPF problem in the standard IEEE 30-bus system with the above-mentioned objectives. The results obtained using BS algorithm are compared with the results obtained using other evolutionary computing techniques reported in the literature. The comparison of results clearly shows that the proposed bat search algorithm provides better and feasible solution when solving the OPF problem.