Path and Cycle Decoupled Deterministic Routing for Wide-Area Precision Load Control Services in New Power Systems

With the development of new power systems, new services in the power data network are constantly emerging and the demand for deterministic transmission is on the rise. The transformation from "best - effort" to "punctual and accurate" has become an urgent issue to be addressed in precision load control services. This paper presents a deterministic routing approach which decouples paths and cycles for wide - area precision load control services within new power systems. The aim is to enhance the scheduling success rate and resource utilization rate of wide area deterministic load control services in new power systems. The method formulates the scheduling success rate and resource utilization rate as optimization objectives, transforms the deterministic routing problem in the wide area network into a Markov decision process (MDP), designs the action space, state space, and reward function. Additionally, a Path and Cycle Decoupled Proximal Policy Optimization Algorithm (PCDPPO) based on deep reinforcement learning (DRL) is proposed to optimize the wide-area network routing problem. Simulation results demonstrate that compared with existing methods, the Integrated Scheduling Efficiency (ISE) of this algorithm is increased by more than 4.87%, and the convergence time is less than 30.83% of the non decoupled method. This research offers an effective solution for the efficient, stable, and reliable network transmission of specific services in new power systems.