To address the issues of reduced system inertia and complex dynamic responses caused by the high proportion of renewable energy and power electronic equipment in new power systems, this article develops a transient coupling modeling method for communication networks and source-grid-load-storage based on a unified time scale and event-driven mechanism, aiming to improve system stability. First, a hybrid system modeling framework with a unified time scale is constructed. Leveraging high-precision global clock synchronization, this approach enables the coordinated solution of continuous power system states and discrete communication system events, achieving precise synchronization of information and energy flows on a microscopic time scale. Second, a dynamic modeling method for the communication network is designed, combining graph topology and queuing theory to accurately describe characteristics such as communication link delay, packet loss, and congestion. Furthermore, cyber-physical interface mapping rules are employed, along with timestamps and adaptive sampling mechanisms, to ensure accurate cross-system data encapsulation, transmission, and execution. Finally, a source-grid-load-storage-based coupled simulation framework is established. Through event-driven and unified time scale mechanisms, continuous integration of power system transient equations and discrete processing of communication events are simultaneously performed. Finally, a bidirectional closed-loop coupled simulation of “power-communication-power” is implemented on a co-simulation platform. Experiments show that the proposed method achieves an average stabilization time of 0.375 s, an average recovery rate of 1.711 Hz/second, a bandwidth utilization rate of 93.38%, and a message success rate of 99.08%. It also demonstrates excellent voltage recovery performance under multiple disturbance scenarios, with a short-circuit recovery time of only 0.12 s. This method effectively addresses the mismatch between information transmission delays and power transients, improving system stability and dynamic response capabilities. It also provides a technical path and theoretical support for the modeling of novel power systems and the deep integration and coordinated control of cyber-physical systems.
Jiang et al. (Wed,) studied this question.