Aggregate data-driven dynamic modeling of active distribution networks with DERs for voltage stability studies

Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model-free, data-driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC-based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state-of-the-art DER_A model with CMLD. The data-driven DPM achieved a fitpercent${\it fitpercent}$ of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model. Design of an aggregate data-driven DPM to represent the dynamic behavior of a dynamic LVDN. Developed of a new detailed single-phase and three-phase LVDN benchmark model that consist PECs-based DERs with IEEE 1547-2018 standard GSFs and CMLD and demonstrated the effectiveness of the proposed DPM approach through comparison with the state-of-the-art DER_A with CMLD model. image