Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks

In this paper, we solve the optimal power flow problem in alternating current networks to reduce losses. For that purpose, propose a master–slave methodology combines multiverse optimization algorithm (master stage) and method for based on successive approximation (slave stage). The master stage determines level of active be injected by each distributed generator network, slave evaluates impact proposed solution terms objective function constraints. simulations, used 10-, 33-, 69-node radial test systems 10-node mesh system with three levels generation penetration: 20%, 40%, 60% provided slack scenario without DGs. order validate robustness convergence algorithm, compared it four other methods have been reported specialized literature addressed here: Particle Swarm Optimization, Continuous Genetic Algorithm, Black Hole Optimization Ant Lion algorithm. results obtained demonstrate can find best (in loss reduction, repeatability, technical conditions) any size while offering excellent performance computation time.