Optimal Power Flow Using Differential Evolution Algorithm

With the increasing scale of electric power system,the Optimal Power Flow(OPF)has become a complicated large scales mathematic programming problem.Cause the optimization objective has to consider both of power loss and voltage quality and other constrains.In recent years,DE has provided a new approach to solve power system problems.As an evolutionary-based optimization technique,DE has successfully been studied and applied to many artificial and real problems.Similar to Genetic Algorithm(GA),DE algorithm is a population based algorithm,which uses crossover,mutation and selection operators.And the best solution of the objective function will be calculated by iterating many times.Because voltage collapse accident can cause great loss and ill effects to society,so power system voltage instability has become an important factor in power system instability.In this thesis,DE algorithm is tested on the standard IEEE 30-bus test system with several objective functions that reflect voltage stability enhancement,fuel cost minimization,active power loss,based on a conclusion of OPF algorithms.In addition,a multi-objective optimization function is considered,which makes voltage stability a priority.The results show the effectiveness and superiority.Finally,the main work is summarized,the future research directions is presented.