LAPSE:2023.12134
Published Article
LAPSE:2023.12134
Load Flow and Short-Circuit Methods for Grids Dominated by Inverter-Based Distributed Generation
February 28, 2023
Abstract
The use of power-electronics-based devices in distribution generation seeks to improve energy quality and reduce costs. The inverter-based distributed generator, that works in different operation modes, has emerged as a promising technology. In a high distributed generation penetration scenario it is important to know the voltage profile and fault information due to the uncertainty in the generator operation and the impact that have on the network. This study aims to use two proposed methods of analysis: for power flow, based on backward/forward sweep method, and short-circuit, based on hybrid impedance matrix, that considers the inverter operation modes and represents each generator as a voltage-controlled current source. The chosen network is the IEEE 34-Node Test Feeder with a generator on each load per phase. The voltage profiles obtained will be validated with a Simulink/Matlab phasorial model. The results show an average error of 2.39% and a gain in voltage profile processing time of 2185.24%, making its use consistent for larger systems.
Keywords
distribution systems, inverter-based generation, operation modes, power flow, short-circuit
Suggested Citation
Tonini LGR, Ferraz RSF, Batista OE. Load Flow and Short-Circuit Methods for Grids Dominated by Inverter-Based Distributed Generation. (2023). LAPSE:2023.12134
Author Affiliations
Tonini LGR: Electrical Engineering Department, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil [ORCID]
Ferraz RSF: Electrical Engineering Department, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil [ORCID]
Batista OE: Electrical Engineering Department, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil [ORCID]
Journal Name
Energies
Volume
15
Issue
13
First Page
4723
Year
2022
Publication Date
2022-06-28
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15134723, Publication Type: Journal Article
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LAPSE:2023.12134
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https://doi.org/10.3390/en15134723
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