| With the continuous depletion of traditional fossil energy,renewable energy technology and electric vehicle technology have been greatly developed.Connect the distributed energy to the power grid to achieve environmental protection,energy conservation and sustainable power output has attracted extensive attention in smart grid construction.Smart microgrid is an important technology to solve the problem of distributed energy grid connection in the process of smart grid construction.Compared with the existing power system,the requirements of smart microgrid on information processing technology and microgrid optimal scheduling strategy are further improved.Due to the fact that the smart microgrid contains a large number of distributed renewable energy sources that are random and difficult to control,the frequent interconnection and disconnection of the smart microgrid brings great difficulties to the operation control of the smart microgrid.In order to effectively improve the utilization rate of distributed energy and the quality of power supply,it is necessary to process control information in time and implement specific control strategies to manage the operation and scheduling of microgrid.Therefore,this paper uses intelligent heuristic algorithm to study information processing technology and distributed energy optimization control in smart microgrid system,and the main work includes:(1)A load balancing method based on immune chaos particle swarm algorithm is proposed to solve distributed computing problems of edge/cloud hybrid architecture.An edge computing layer is built between the cloud computing layer and the terminal layer to proactively offload cloud data to the data source side for nearby processing,aiming to reduce the cloud computing burden and task processing delay.Considering the shortage of computing power of edge nodes,an immune chaos particle swarm optimization algorithm based on the hybrid architecture is proposed to reasonably allocate the computing tasks of each node,improve the power system’s massive data processing support for the scheduling and control of subsequent microgrid systems.(2)An economic scheduling strategy for microgrid based on annealing mutation particle swarm optimization algorithm is proposed to solve the microgrid scheduling problem with large-scale electric vehicle access.Through the analysis of existing work,taking into account peak-valley electricity price,environmental management and other factors,combined with energy storage technology and loss of vehicle-to-grid for electric vehicles,a microgrid economic dispatch model with minimum operating cost and environmental protection cost based on large-scale electric vehicles is established.In order to solve the model,an annealing mutation particle swarm optimization algorithm is proposed to improve the economy and reliability of microgrid operation.(3)A multi-microgrid economic scheduling strategy based on adaptive mutation genetic algorithm is proposed for multi-microgrid systems with different load types and power demands.Based on the analysis of industrial,residential and commercial loads,considering the synergy and complementarity among multiple microgrid systems,from the perspective of system environmental protection and economy,an optimal scheduling model for multiple microgrid systems with minimum operating costs and environmental protection costs is established.At the same time,an adaptive mutation genetic algorithm is proposed to optimized the system model and find the optimal economic scheduling scheme. |
