Application of monarch butterfly optimization algorithm for solving optimal power flow

Abstract

This  paper  proposes  a  highly  flexible, robust,  and  efficient  constraint-handling approach for the solution of the optimal power flow (OPF) problem and  this  solution  lies  in  the  ability  to  solve  the  power  system  problem  and avoid  the  mathematical  traps.  Centralized  control  of  the  power  system  has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives,  generation  cost  minimization,  power  loss  minimization,  and enhancement  of  voltage  stability  index.  These  three  objectives  are  solved separately    by    considering    the    evolutionary-based    monarch    butterfly optimization  (MBO)  algorithm.  This  MBO  algorithm  is  validated  on  the IEEE 30 bus network and the obtained results are compared with differential evolution,   particle   swarm   optimization,   genetic   algorithm,   and   Jaya algorithm.  The  obtained  results  reveal  that  among  the  various  optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies.