Improvement Of Particle Swarm Optimization Algorithm And Its Application On Economic Dispatching Of Smart Grid

The safe and stable operation of electric power system is the heart and power of the orderly development of society,which promotes the steady and rapid development of China’s economy and plays a huge role in promoting the development of human society.Therefore,the research in the field of power system has important value and significance for the society and the country.In this paper,particle swarm optimization algorithm is studied,which is easy to fall into the local optimal value early and difficult to find the optimal solution for some multidimensional problems.It is improved and applied to the economic scheduling problem of power system.First of all,this paper makes an in-depth study of the fundamental particle swarm optimization algorithm,from the algorithm principle,cognitive analysis,algorithm topology.Particle swarm optimization algorithm with inertia weight and particle swarm optimization algorithm with contraction factor are introduced.The parameters of the standard particle swarm optimization algorithm are analyzed so as to understand the application significance of each parameter and its influence on the algorithm.Secondly,an improved particle swarm optimization algorithm is proposed because the elementary particle swarm optimization algorithm is easy to fall into local optimal.Three standard test functions were used to test the performance of the improved particle swarm optimization algorithm,and the results were compared with the test results of the particle swarm optimization algorithm,which proved that the improved particle swarm optimization algorithm has better performance in the optimization ability and convergence.In the economic dispatching model of power system,the improved particle swarm optimization algorithm is compared with the particle swarm optimization algorithm,and the results show that the improved algorithm has lower economic cost.Finally,an multi-population coevolution multi-objective particle swarm optimization algorithm is proposed to overcome the shortcoming of mul-tiobjective particle swarm optimization algorithm,such as low search efficiency and non-dominated solution to approximate the real Pareto frontier.The multi-population coevolution multi-objective particle swarm optimization algorithm is simulated on four standard test functions,and its solutions can be evenly distributed in the real Pareto front,and it has certain advantages in convergence degree comparison with other five algorithms.The economic environment dispatching model of power system for five generating units is established,and the multi-population coevolution multi-objective particle swarm optimization algorithm and multiobjective particle swarm optimization algorithm are respectively used to solve the model.The final result shows that the results obtained by multi-population coevolution multi-objective particle swarm optimization algorithm have lower economic cost and less pollution emission.