Research On Optimization Control System Of Virtual Power Plant With Electric Vehicle

Under the development of the new power system,the power grid operation gradually shows the characteristics of intelligence and digitalization.The development of “Source-Grid-Load-Storage” integrated operation is in urgent need of cloud computing,big data,artificial intelligence and other technical means in the "cloud big material transfer intelligent chain edge",so that the power grid system can be equipped with solutions with massive data processing and analysis and highly intelligent decision-making ability.In order to realize the integration of all kinds of energy resources,break through the barriers of multiple links of energy,so that the elements of "source-network-load-storage" really achieve friendly coordination.As a special power plant,virtual power plant technology is a power supply coordination management system which participates in power market and power grid operation.The main work contents are as follows:Firstly,this paper summarizes the domestic and foreign research status of energy Internet,virtual power plant optimization scheduling and energy management system involving large-scale electric vehicles.The functional logic of the virtual power plant aggregation control system was designed in combination with the idea of micro-service architecture,which laid the foundation for the subsequent optimization scheduling model solution and software development.Secondly,the optimal scheduling strategy of virtual power plant,including multiple flexible loads such as large-scale charging piles and renewable energy,is analyzed and modeled.This system realizes the prediction of comprehensive energy load and renewable energy output based on neural network algorithm,which provides basic data support for the optimization of virtual power plant system.A two-layer virtual power plant scheduling model was constructed based on the orderly charge-discharge guidance of electric vehicles.In the scheduling process,a stepped carbon trading mechanism and a demand response strategy based on electricity price were introduced to guide the flexible load distribution.The multi-objective evolutionary algorithm is used to solve the virtual power plant optimal scheduling model.Finally,Java,Python,HTML and other coding languages are used to develop the virtual power plant aggregation control system,which is deployed on Kubernetes platform.The system realizes the visualization of renewable energy output and load prediction,charging pile energy management,virtual power plant optimization scheduling visualization and other services,and designs the corresponding database according to the data structure required by the above services.