Microgrid optimal scheduling based on improved particle swarm optimization algorithm

Abstract

It is of great significance to study how to use intelligent algorithm to optimize the scheduling of microgrid, so as to improve the operation efficiency of microgrid. In this paper, particle swarm optimization(PSO) algorithm is improved by nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors. Compared with PSO, the global search ability of the improved particle swarm optimization(IPSO) algorithm is improved. IPSO is used to optimize the scheduling of the microgrid grid connection model. The example results show that nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors can obviously improve PSO. IPSO has faster convergence and lower comprehensive cost of microgrid scheduling. This paper provides an effective method for the day ahead economic scheduling of microgrid.