Time-Varying ADN Load Modeling Considering the Suppression of the Plateau Phenomenon and Continuous Low-Quality Data

The time-varying parameter identification of load models has attracted broad attention when large amounts of intermittent distributed generations (DGs) and stochastic loads are integrated into the active distribution network (ADN). However, traditional time-varying load modeling approaches usually develop the load model with the composite load model (CLM) or the synthesis load model (SLM), which are unsuitable for representing the dynamics of the DGs. Moreover, the plateau phenomenon and the continuous low-quality data also reduce the performance of traditional load models with time-varying parameters. Therefore, to address these issues, a novel time-varying load modeling approach with an improved load model and time-varying parameter identification scheme is designed in the paper. Firstly, an improved load model with a parallel SLM and voltage source converter (VSC) is developed to represent the time-varying behavior of modern loads. Then, the target parameters, whose changes produce more considerable variations in model output responses, are selected with trajectory sensitivity to avoid the plateau phenomenon. Also, the Chi-square test and the proposed weighted suppression strategy are utilized to suppress the continuous low-quality data. The simulation results on a system-level ADN model show that the load models developed by the proposed approach could accurately reflect the time-varying properties of modern ADN.