Reinsertion-Aware Parameter Estimation of the WECC Composite Load Model via Closed-Loop Equivalent-Network Co-Estimation

Abstract

Measurement-based parameter estimation of the WECC composite load model (CMLD) commonly relies on voltage replay, where measured voltage is imposed on an isolated CMLD and its parameters are optimized to reproduce measured active and reactive power. However, this open-loop formulation neglects the two-way dynamic interaction between the load and grid, potentially causing large errors when the estimated model is reinserted into dynamic grid simulations. To address this limitation, we propose a closed-loop parameter estimation framework that alternately estimates the CMLD parameters and a five-parameter equivalent network representing the external grid. This framework restores load-grid interaction during estimation and enables voltage, active power, and reactive power to jointly constrain the parameter estimation. Compared with four baseline methods, the proposed approach achieves the highest reinsertion accuracy in both the CMLD benchmark and transmission and distribution (T&D) co-simulation environments. In the T&D case, it reduces active-power, reactive-power, and voltage errors by 73%, 64%, and 62%, respectively. The identified model also maintains improved reinsertion accuracy under disturbances not used for estimation, demonstrating transferability beyond the identification event.