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Title: Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters

Abstract

Power-electronics inverters are expected to satisfy a significant fraction of system load in next-generation power networks with the growing integration of renewable resources and flexible loads. Typical dynamical models for grid-tied inverters are nonlinear and composed of a large number of states; therefore it is impractical to study systems with many inverters when their full dynamics are retained. In our previous work, we have shown that a system of parallel-connected grid-tied three-phase inverters can be modeled as one aggregated inverter unit with the same structure and state-space dimension as any individual inverter in the system. Here, we extend this result to networks with arbitrary topologies by leveraging a classical aggregation method for coherent synchronous generators in transmission networks, and a linear approximation of the AC power-flow equations to ease computational burden. Numerical simulation results for a prototypical distribution feeder demonstrate the accuracy and computational benefits of the approach.

Authors:
 [1];  [2];  [2];  [3];  [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Minnesota
  3. University of California at Santa Barbara
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1457655
Report Number(s):
NREL/CP-5D00-70310
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 3-6 October 2017, Monticello, Illinois
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; inverters; phase locked loops; reduced order systems; mathematical model; aggregates; electronic mail; load modeling

Citation Formats

Johnson, Brian B, Purba, Victor, Dhople, Sairaj V., Jafarpour, Saber, and Bullo, Francesco. Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters. United States: N. p., 2018. Web. doi:10.1109/ALLERTON.2017.8262732.
Johnson, Brian B, Purba, Victor, Dhople, Sairaj V., Jafarpour, Saber, & Bullo, Francesco. Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters. United States. doi:10.1109/ALLERTON.2017.8262732.
Johnson, Brian B, Purba, Victor, Dhople, Sairaj V., Jafarpour, Saber, and Bullo, Francesco. Thu . "Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters". United States. doi:10.1109/ALLERTON.2017.8262732.
@article{osti_1457655,
title = {Network-Cognizant Model Reduction of Grid-Tied Three-Phase Inverters},
author = {Johnson, Brian B and Purba, Victor and Dhople, Sairaj V. and Jafarpour, Saber and Bullo, Francesco},
abstractNote = {Power-electronics inverters are expected to satisfy a significant fraction of system load in next-generation power networks with the growing integration of renewable resources and flexible loads. Typical dynamical models for grid-tied inverters are nonlinear and composed of a large number of states; therefore it is impractical to study systems with many inverters when their full dynamics are retained. In our previous work, we have shown that a system of parallel-connected grid-tied three-phase inverters can be modeled as one aggregated inverter unit with the same structure and state-space dimension as any individual inverter in the system. Here, we extend this result to networks with arbitrary topologies by leveraging a classical aggregation method for coherent synchronous generators in transmission networks, and a linear approximation of the AC power-flow equations to ease computational burden. Numerical simulation results for a prototypical distribution feeder demonstrate the accuracy and computational benefits of the approach.},
doi = {10.1109/ALLERTON.2017.8262732},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 18 00:00:00 EST 2018},
month = {Thu Jan 18 00:00:00 EST 2018}
}

Conference:
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