Optimization and Scheduling of Virtual Power Plant Based on Cloud-edge Collaboration

In order to cope with the timeliness problem caused by the excessive amount of basic data in the highly digital and intelligent environment, the recently optimized scheduling strategy of virtual power plants considering distributed power supply, energy storage and ev access based on cloud-side collaboration is proposed, so as to improve the efficiency of data processing and utilization. First of all, establish a "cloud-edge" collaborative virtual power plant scheduling architecture model: clouds are responsible for the edge of the information, to profit maximization as a target function set internal price, the edge layer with the lowest operating cost as the goal function, with power balance and the distributed resources output upper and lower limits, responsible for the internal scheduling management of the virtual power plant. Secondly, the cloud-edge main-slave game model is established, and the model is solved with the improved particle swarm algorithm, and calculated iteratively until convergence is reached. Finally, in the Matlab simulation environment, the three virtual power plant models based on simulation verify the rationality of the front-ahead optimized scheduling model based on cloud-edge collaboration. By comparison, we obtained the superiority of the cloud-edge collaboration mode over the directly participating market mode in terms of solving speed and economy.