Multi Objective Evolutionary Algorithm Applied to the Optimal Power Flow Problem

Abstract

This work presents the application of a multiobjective evolutionary algorithm (MOEA) for optimal 
power flow (OPF) solution. The OPF is modeled as a constrained nonlinear optimization problem, 
non-convex of large-scale, with continuous and discrete variables. The violated inequality constraints 
are treated as objective function of the problem. This strategy allows attending the physical and 
operational restrictions without compromise the quality of the found solutions. The developed MOEA 
is based on the theory of Pareto and employs a diversity-preserving mechanism to overcome the 
premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to 
extract the best compromises of the Pareto set. Results for the IEEE-30, RTS-96 and IEEE-354 
test systems are presents to validate the efficiency of proposed model and solution technique.