Optimal Dispatching of Microgrid Based on Improved Particle Swarm Optimization

Abstract

In order to enable the microgrid to meet the system load demand while performing economically optimal operation scheduling, this paper establishes an island-type microgrid model, which is optimized by using an improved immune particle swarm algorithm, and the inertia weight and learning the two parameters of the factor are improved. On the basis of the immune particle swarm algorithm, a power exponential function operator is added to the inertia weight to improve the search ability of the algorithm, in order to reduce the computing time, the dynamically adjusted learning factor is introduced to optimize the immune particle swarm algorithm the local search ability is stronger. Two examples are selected to verify the algorithm, the results prove that the method has better global convergence and local search capabilities and the convergence speed has been improved.