Bilateral Event-Triggered Adaptive State Estimator For Demand Response Enabled Distribution Systems

Widespread customer participation in modern-day Active Distribution Networks (ADNs), and co-ordinated dispatch of controllable loads based on time-of-use energy pricing leads to significant intermittencies during Demand Response (DR) events. The accuracy of conventional Distribution System State Estimation (DSSE) methodologies suffers during DR or sudden switching events of these controllable loads. Tuning the noise covariances helps in achieving better estimates considering these aforementioned events. Towards this objective, this work proposes Sage-Husa Adaptive Cubature Kalman Filter (SH-ACKF) for DSSE in the ADNs. To further reduce the computational burden in real-time, Sage-Husa adaptation is conditionally enforced over CKF on violation of Event-Trigger (ET) threshold. These events are detected using Entropy Weighted Independent Component Analysis (EW-ICA), and the results obtained in the IEEE benchmark test feeders validate the superiority of ET-SHACKF over SH-ACKF and CKF in terms of computational effort and estimation accuracy.