The continuous increase in the penetration ratio of new energy in active distribution network has led to a sharp increase in operational data. While

coupled with the intermittent nature of new energy sources and other factors

has posed significant challenges to dispatching strategies. Therefore

a real-time optimization scheduling model called VMD-BiLSTM-PPO is proposed. Based on edge computing

this model constructs a multi-region energy autonomous framework

adopts the proximal policy optimization (PPO) algorithm of deep reinforcement learning to minimize operating scheduling costs and achieve optimized scheduling of the distribution network with cloud-edge collaboration. It decentralizes a large amount of computing and data storage tasks to the edge

effectively reducing the computing and data transmission loads at the scheduling center. Furthermore

in order to effectively alleviate the impact of new energy fluctuation

it adopts a new energy output prediction framework based on variational mode decomposition (VMD) decomposition and bi-directional long short-term memory (BiLSTM) in the PPO algorithm. Simulation experiments indicate that the model can improve the utilization rate of new energy and enhance the economic performance of real-time distribution network scheduling.