Evaluating Distributed PV Curtailment Using Quasi-Static Time-Series Simulations

By strategically curtailing active power and providing reactive power support, photovoltaic (PV) systems with advanced inverters can mitigate voltage and thermal violations in distribution networks. Quasi-static time-series (QSTS) simulations are increasingly being utilized to study the implementati...

Full description

Saved in:
Bibliographic Details
Published in:IEEE open access journal of power and energy 2021-01, Vol.8, p.365-376
Main Authors: Azzolini, Joseph A., Reno, Matthew J., Gurule, Nicholas S., Horowitz, Kelsey A. W.
Format: Article
Language:English
Subjects:
Advanced inverter
Analytical models
autonomous volt-var
Circuits
curtailment
Data models
distribution system analysis
Integrated circuit modeling
Inverters
Load modeling
Modelling
Photovoltaic cells
Photovoltaics
PV grid integration
quasi-static time-series
Reactive power
Reliability analysis
Sensitivity analysis
Time series
Uncertainty
Voltage control
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:By strategically curtailing active power and providing reactive power support, photovoltaic (PV) systems with advanced inverters can mitigate voltage and thermal violations in distribution networks. Quasi-static time-series (QSTS) simulations are increasingly being utilized to study the implementation of these inverter functions as alternatives to traditional circuit upgrades. However, QSTS analyses can yield significantly different results based on the availability and resolution of input data and other modeling considerations. In this paper, we quantified the uncertainty of QSTS-based curtailment evaluations for two different grid-support functions (autonomous Volt-Var and centralized PV curtailment for preventing reverse power conditions) through extensive sensitivity analyses and hardware testing. We found that Volt-Var curtailment evaluations were most sensitive to poor inverter convergence (−56.4%), PV time-series data (−18.4% to +16.5%), QSTS resolution (−15.7%), and inverter modeling uncertainty (+14.7%), while the centralized control case was most sensitive to load modeling (−26.5% to +21.4%) and PV time-series data (−6.0% to +12.4%). These findings provide valuable insights for improving the reliability and accuracy of QSTS analyses for evaluating curtailment and other PV impact studies.
ISSN:2687-7910
2687-7910
2644-1314
DOI:10.1109/OAJPE.2021.3111821