This paper presents application of Big Bang and Big crunch(BB-BC) a nature inspired optimization method which is developed from the concepts of universal evolution to solve complex static optimal power flow (OPF) with an aim to obtain minimum cost of thermal power generating units whose cost functions are non-convex due to valve point loading effects. Control variables to optimize cost functions by satisfying usual constraints of OPF are of continuous and discrete type (mixed- integer control variables). Mathematical programming approaches presents problem in solving non-convex OPF.Nature inspired heuristic methods can be applied to solve such non-convex optimization problems. One of the requirements of heuristic methods are numerical simplicity without trial parameters in update equation of optimization along with reliability and ease in developing computer code for implementation. Most of the nature inspired methods search efficiency and reliability depends on choice of trial parameters to update control variables as optimization advances in search of optimal control variables.BB-BC optimization has search ability on par with other popular heuristic methods but free from choice of trial parameters is applied to obtain OPF solutions on two typical power systems networks and results are compared with MATLAB-7.0 pattern random search optimization tool box .Digital simulation results indicates a promising nature of the BB-BC to deal with non-convex optimization requirements of power system situations .
Chatti venkata Gopala krishna Rao, MVSR Engineering college ,Hyderabad