A study of evolutionary based optimal power flow techniques

This paper explores the use of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving multi objective optimal power flow problems. The algorithms are tested on the IEEE 30 bus system by simultaneously minimizing cost, power losses and voltage deviation under operational constraints. The Pareto optimal and fuzzy methods are employed to identify the best compromise for conflicting objectives; the solutions provided by GA and PSO are compared with each other, as well as against other optimization methods.