Abstract
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 (Formula presented.) 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.
| Original language | English |
|---|---|
| Pages (from-to) | 2261-2276 |
| Number of pages | 16 |
| Journal | IET Renewable Power Generation |
| Volume | 18 |
| Issue number | 14 |
| DOIs | |
| State | Published - Oct 26 2024 |
Funding
This work is supported by the U.S. Department of Energy Office of Science, Office of Electricity Microgrid R&D Program, and Office of Energy Efficiency and Renewable Energy Solar Energy Technology Office under the EPSCoR Grant Number DE-SC0020281, and the U.S. National Science Foundation (NSF) under Grant Number OIA-2316400. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This work is supported by the U.S. Department of Energy Office of Science, Office of Electricity Microgrid R&D Program, and Office of Energy Efficiency and Renewable Energy Solar Energy Technology Office under the EPSCoR Grant Number DE\u2010SC0020281, and the U.S. National Science Foundation (NSF) under Grant Number OIA\u20102316400. This manuscript has been authored by UT\u2010Battelle, LLC, under contract DE\u2010AC05\u201000OR22725 with the US Department of Energy (DOE). The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid\u2010up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe\u2010public\u2010access\u2010plan ).
Keywords
- DC-AC power convertors
- distribution networks
- power electronics
- power system dynamic stability
