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. 2017.
"Network Reduction Algorithm for Developing Distribution Feeders for Real-Time Simulators: Preprint". United States. 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 = {},
url = {https://www.osti.gov/biblio/1364143},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jun 15 00:00:00 EDT 2017},
month = {Thu Jun 15 00:00:00 EDT 2017}
}