Swarm Intelligence And Its Application In Smart Grid

Due to the depletion of traditional fossil energy and its adverse impact on the environment,people’s demand for clean and renewable energy is increasing day by day.With the rapid development of new energy industry,a large number of new energy generation equipment needs to access to the grid.The traditional grid has been unable to meet the demand,the smart grid has become the ultimate solution to maintain the continuous and rapid development of new energy and meet the users’ demand for clean,safe and high-quality electricity.However,the increasing scale of smart grid and the access of more and more distributed generation bring new challenges to the security,stability and plannability of smart grid.As one of the most important directions of artificial intelligence,swarm intelligence can realize the emergence of complex intelligence behavior through simple local interactions between agents.The smart grid as a large multi-agent system,traditional power grid analysis method has received the limits of high computational complexity,complex modeling and so on,therefore this paper provides theoretical basis for the further development of smart grid and research tools by studying swarm intelligence research methods,including collective dynamics and swarm intelligence algorithm.In this paper,the pattern control and phase transition analysis strategies of collective dynamical systems are proposed based on a minimal model of variable interaction distribution.By adjusting the vision angle and the attraction distribution,the phase transitions among four patterns of “torus","dumbbell","twist" and "worm"are obtained and we expand the pattern optimization method to the smart grid planning problem.This work provides a new thinking to predict the state phase transition of smart grid system and solve the optimal planning problem.An improved particle swarm optimization(PSO)algorithm is proposed for location planning of distributed generations.By performing mutation operation when the particle swarm is trapped in the local optimal solution,the algorithm can effectively help the population jump out of the local solution and enhance the global search ability of traditional PSO.Finally,through the example of power grid,the improved PSO and the traditional PSO are compared and analyzed,and the superiority of the improved PSO is proved.This work shows that rational planning of the access location and capacity of distributed generation can effectively reduce network losses,improve economic benefits and reduce the impact on the environment.