Economic Dispatch of Microgrid Based on Adaptive Mutation Particle Swarm Optimization

In order to overcome the disadvantages of traditional Particle Swarm Optimization (PSO), which is easy to form local optimum and has low solving accuracy, a method of microgrid scheduling based on Adaptive Mutation Particle Swarm Optimization was proposed. The inertia weight of AMPSO is decreased by an adaptive normal distribution, and the movement strategy of the particle position is updated with the increase of the number of iterations, and the mutation link is introduced in the late stage of the strategy. In order to verify the effectiveness of the algorithm, this paper compares the convergence performance with other improved algorithms, and solves the simulation of the operation cost model of wind-solar storage under four typical weather conditions to obtain the optimal scheduling strategy. The results of numerical examples show that AMPSO can search and optimize the global optimum of particles, and is better than other algorithms in solving the economic problem of microgrid scheduling. It can reasonably allocate the output time of distributed power supply, and has a good feasibility.