Multi-objective optimal power flow with DG placement using TLBO and MIPSO: A comparative study

This article presents an approach to multi-objective optimal power flow (OPF) solutions by implementation of a robust and efficient heuristic method on the basis of the swarm behaviours, with teaching learning based optimization (TLBO).To check and demonstrate the performance of this algorithm, multi-objective power flow problems are solved. A study has been done on standard 9-bus and 26-bus systems with application of different objective functions, namely fuel cost minimization, active power loss minimization and voltage deviation minimization. The multi-objective functions are formed by the use of weighted sum method. In multi-objective problem formulation fuel cost itself; fuel cost with voltage deviation; fuel cost, loss and voltage deviation are minimized sequentially. Outcomes achieved by TLBO are compared with mixed integer particle swarm optimization (MIPSO). The demonstration shows that the new TLBO algorithm outperforms the former technique in terms of global search ability and convergence property. Improvement the voltage profile is obtained by the optimal placement of DG.