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Title: Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics

Abstract

Distributed photovoltaic systems (DPV) can cause adverse grid impacts, including voltage or thermal violations. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current methods for determining hosting capacity tend to be conservative by either only considering infrequent worst-case snapshots in time and/or only capturing coarse time and spatial resolution. Additionally, current hosting capacity methods do not accurately capture the time-dependence making them unable to capture the behavior of voltage regulating equipment and of some advanced controls mitigations. This can trigger delays from unnecessary engineering analysis or deter solar installations in areas that are actually suitable. We propose a quasi-static-time-series (QSTS) based PV hosting capacity methodology to address these issues. With this approach, we conduct power flow analysis over the course of a full year, to capture time-varying parameters and control device actions explicitly. We show that this approach can more fully capture grid impacts of DPV than traditional methods.

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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:
1566041
Report Number(s):
NREL/CP-5D00-74935
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 18-21 February 2019, Washington, D.C.
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; PV hosting capacity; quasi-static time-series simulation; system impact studies

Citation Formats

Jain, Akshay Kumar, Horowitz, Kelsey A, Ding, Fei, Gensollen, Nicolas, Mather, Barry A, and Palmintier, Bryan S. Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics. United States: N. p., 2019. Web. doi:10.1109/ISGT.2019.8791569.
Jain, Akshay Kumar, Horowitz, Kelsey A, Ding, Fei, Gensollen, Nicolas, Mather, Barry A, & Palmintier, Bryan S. Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics. United States. doi:10.1109/ISGT.2019.8791569.
Jain, Akshay Kumar, Horowitz, Kelsey A, Ding, Fei, Gensollen, Nicolas, Mather, Barry A, and Palmintier, Bryan S. Thu . "Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics". United States. doi:10.1109/ISGT.2019.8791569.
@article{osti_1566041,
title = {Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics},
author = {Jain, Akshay Kumar and Horowitz, Kelsey A and Ding, Fei and Gensollen, Nicolas and Mather, Barry A and Palmintier, Bryan S},
abstractNote = {Distributed photovoltaic systems (DPV) can cause adverse grid impacts, including voltage or thermal violations. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current methods for determining hosting capacity tend to be conservative by either only considering infrequent worst-case snapshots in time and/or only capturing coarse time and spatial resolution. Additionally, current hosting capacity methods do not accurately capture the time-dependence making them unable to capture the behavior of voltage regulating equipment and of some advanced controls mitigations. This can trigger delays from unnecessary engineering analysis or deter solar installations in areas that are actually suitable. We propose a quasi-static-time-series (QSTS) based PV hosting capacity methodology to address these issues. With this approach, we conduct power flow analysis over the course of a full year, to capture time-varying parameters and control device actions explicitly. We show that this approach can more fully capture grid impacts of DPV than traditional methods.},
doi = {10.1109/ISGT.2019.8791569},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
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