Edge-Enabled Forecasting-Aided State Estimation for Active Distribution Networks

Managing extensive measurement sets in power system state estimation (SE) often leads to communication burden and delays, compromising real-time performance. To address this challenge, the paper explores the integration of edge computing, which provides robust support for real-time SE by decentralizing computational tasks. The proposed method partitions large-scale networks into clusters, each managed by an edge estimation point (EEP) that performs the cluster’s local SE (LSE), significantly reducing SE runtime. Building on this framework, the paper introduces a modified dimensionality-reduced cubature Kalman filter (CKF) algorithm for a forecasting-aided state estimation (FASE) model. The algorithm addresses computational complexity and accuracy challenges in large-scale dynamic active distribution networks (ADNs), especially under sudden load variations. Simulations on the IEEE 123-bus system with real load profiles demonstrate the effectiveness of the edge-enabled CKF-based FASE methodology in accurately tracking dynamic system states in large-scale ADNs.