Spectrum Allocation Research In Cognitive Smart Grid

With the upgrading of power system,smart power grid as the next generation of new power grid,all-round upgrade the electric power generation,power distribution and power utilization to improve the overall performance of traditional power grid.Research shows that the wireless communication environment of the smart grid is shortage of spectrum,the resources use efficiency is low,so the cognitive radio technology is applied to the smart grid communication,will effectively solve the development of smart grid,wireless spectrum resource bottleneck problem.It can improve the spectrum utilization efficiency and the two-way data transmission rate,promote the development of smart grid,raise the level of the smart grid construction.Under the above research background,the main tasks of this paper are as follows:(1)In the neighborhood network of cognitive smart grid,the spectrum allocation problem is modeled and the signal-to-noise ratio and path loss are used to constrain the system interference.Then,different swarm intelligence algorithms are used to optimize the spectrum allocation scheme,so that network throughput,system energy efficiency and user fairness can be better solved.(2)The improved binary cat swarm algorithm is applied to spectrum allocation optimization.Firstly,in order to prevent the prematurity of binary cat group algorithm,dynamic nonlinear inertia weight is added into the velocity updating formula.Secondly,the breeding operator is introduced to generate the offspring cat population to increase the diversity of the population,so as to obtain a better global optimal solution.(3)The improved binary butterfly algorithm is applied to power control and spectrum allocation optimization.Firstly,the binary butterfly optimization algorithm based on improved adaptive transformation function and perturbation factor is used to allocate the spectrum for cognitive smart grid users,and then the closed-loop power control algorithm based on the received signal-to-noise ratio is used to dynamically adjust the transmitted power of users,thus reducing the interference between cognitive smart grid users and main users.Finally,the system energy efficiency and two user equity indexes are taken as optimization objectives,and the experiments are compared with genetic algorithm and binary particle swarm optimization.The results show that the improved binary cat swarm algorithm and the improved binary butterfly optimization algorithm proposed in this paper have the characteristics of fast convergence speed and strong searching ability in spectrum allocation,and greatly improve the spectrum utilization rate and system energy efficiency.