A Study on the Cloud-Edge-Terminal Framework for Large Computing Models in New Power Systems

With the rapid evolution of a new power system characterized by a high proportion of renewable energy, system operations have become increasingly random, variable, and uncertain. The system model exhibits features such as high dimensionality, multiple time scales, stochastic behavior, and nonlinearity. This paper proposes a large-scale computational power system model architecture based on cloud-edge-terminal collaboration. By defining functional roles within the cloud-edge-terminal structure and implementing a global model coordination mechanism, the approach enables an organic integration of global awareness, local adaptation, dynamic training, and online optimization for power system problem models. At the cloud level, various object models and the power grid topology are constructed. The edge generates typical problem models for the power system, while the terminal devices produce lightweight models adapted to local grids. This architecture supports collaborative modeling for key business scenarios such as power flow analysis, stability assessment, and reactive power optimization. The study focuses on the training methods of distilled parameters within the terminal models to enhance their adaptability for real-world deployment in power systems. Simulation results demonstrate that the cloud-edge-terminal model offers excellent scalability, adaptability, and real-time performance for computations in new power systems, effectively supporting localized, intelligent operations and decision-making within the system.