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Title: Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint

Abstract

As advanced grid-support functions (AGF) become more widely used in grid-connected photovoltaic (PV) inverters, utilities are increasingly interested in their impacts when implemented in the field. These effects can be understood by modeling feeders in real-time systems and testing PV inverters using power hardware-in-the-loop (PHIL) techniques. This paper presents a novel feeder model reduction algorithm using a Monte Carlo method that enables large feeders to be solved and operated on real-time computing platforms. Two Hawaiian Electric feeder models in Synergi Electric's load flow software were converted to reduced order models in OpenDSS, and subsequently implemented in the OPAL-RT real-time digital testing platform. Smart PV inverters were added to the real-time model with AGF responses modeled after characterizing commercially available hardware inverters. Finally, hardware inverters were tested in conjunction with the real-time model using PHIL techniques so that the effects of AGFs on the choice feeders could be analyzed.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Grid Modernization Laboratory Consortium
OSTI Identifier:
1364143
Report Number(s):
NREL/CP-5D00-67400
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: To be presented at the 2017 IEEE Power and Energy Society General Meeting, Chicago, Illinois, 16-20 July 2017
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; advanced grid-support functions; power hardware-in-the-loop simulation; network reduction; real-time simulator; smart PV inverter

Citation Formats

Nagarajan, Adarsh, Nelson, Austin, Prabakar, Kumaraguru, Hoke, Andy, Asano, Marc, Ueda, Reid, and Nepal, Shaili. Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint. United States: N. p., 2017. Web.
Nagarajan, Adarsh, Nelson, Austin, Prabakar, Kumaraguru, Hoke, Andy, Asano, Marc, Ueda, Reid, & Nepal, Shaili. Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint. United States.
Nagarajan, Adarsh, Nelson, Austin, Prabakar, Kumaraguru, Hoke, Andy, Asano, Marc, Ueda, Reid, and Nepal, Shaili. Thu . "Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1364143.
@article{osti_1364143,
title = {Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint},
author = {Nagarajan, Adarsh and Nelson, Austin and Prabakar, Kumaraguru and Hoke, Andy and Asano, Marc and Ueda, Reid and Nepal, Shaili},
abstractNote = {As advanced grid-support functions (AGF) become more widely used in grid-connected photovoltaic (PV) inverters, utilities are increasingly interested in their impacts when implemented in the field. These effects can be understood by modeling feeders in real-time systems and testing PV inverters using power hardware-in-the-loop (PHIL) techniques. This paper presents a novel feeder model reduction algorithm using a Monte Carlo method that enables large feeders to be solved and operated on real-time computing platforms. Two Hawaiian Electric feeder models in Synergi Electric's load flow software were converted to reduced order models in OpenDSS, and subsequently implemented in the OPAL-RT real-time digital testing platform. Smart PV inverters were added to the real-time model with AGF responses modeled after characterizing commercially available hardware inverters. Finally, hardware inverters were tested in conjunction with the real-time model using PHIL techniques so that the effects of AGFs on the choice feeders could be analyzed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 15 00:00:00 EDT 2017},
month = {Thu Jun 15 00:00:00 EDT 2017}
}

Conference:
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