Multi-Level Asynchronous Robust State Estimation for Distribution Networks Considering Communication Delays

With the hierarchical evolution of distribution network control architectures, distributed state estimation has become a focal point of research. To address communication delays arising from inter-level data exchanges, this paper proposes a multi-level, asynchronous, robust state estimation algorithm that accounts for such delays. First, a multi-level state estimation model is formulated based on the concept of a maximum normal measurement rate, and a hierarchical decoupling modeling approach is developed. Then, an event-driven broadcast transmission strategy is designed to unify boundary information exchanged between levels during iteration. A multi-threaded parallel framework is constructed to decouple receiving, computation, and transmission tasks, thereby enhancing asynchronous scheduling capabilities across threads. Additionally, a round-based synchronization mechanism is proposed to enforce fully synchronized iterations in the initial stages, thereby improving the overall process of asynchronous state estimation. Case study results demonstrate that the proposed algorithm achieves high estimation accuracy and strong robustness, while reducing the average number of iterations by nearly 40% and shortening the runtime by approximately 35% compared to conventional asynchronous methods, exhibiting superior estimation performance and computational efficiency under communication delay conditions.