Presented at the 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington DC, USA
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.
@INPROCEEDINGS{8791569,
author={A. K. {Jain} and K. {Horowitz} and F. {Ding} and N. {Gensollen} and B. {Mather} and B. {Palmintier}},
booktitle={2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
title={Quasi-Static Time-Series PV Hosting Capacity Methodology and Metrics},
year={2019},
volume={},
number={},
pages={1-5},
keywords={distributed power generation;load flow;photovoltaic power systems;power grids;time series;system upgrades;coarse time;current hosting capacity methods;time-dependence;voltage regulating equipment;time-varying parameters;DPV;quasistatic time-series PV hosting capacity methodology;distributed photovoltaic systems;adverse grid impacts;thermal violations;worst-case snapshots;power flow analysis;Measurement;Loading;Load modeling;Voltage control;Data models;Computational
modeling;Photovoltaic systems;PV Hosting Capacity;Quasi-Static Time-Series Simulation;System Impact Studies},
doi={10.1109/ISGT.2019.8791569},
ISSN={2167-9665},
month={Feb},}