@inproceedings{azzolini_pvimpact_2022,
author={Azzolini, Joseph A. and Talkington, Samuel and Reno, Matthew J. and Grijalva, Santiago
and Blakely, Logan and Pinney, David and McHann, Stanley},
booktitle={49th IEEE Photovoltaics Specialists Conference (PVSC)},
title={{Improving Behind-the-Meter PV Impact Studies with Data-Driven Modeling and Analysis}},
year={2022},
volume={},
number={},
pages={204-204},
doi={10.1109/PVSC48317.2022.9938462}}
Frequent changes in penetration levels of distributed energy resources (DERs) and grid control objectives have caused the maintenance of accurate and reliable grid models for behind-the-meter (BTM) photovoltaic (PV) system impact studies to become an increasingly challenging task. At the same time, high adoption rates of advanced metering infrastructure (AMI) devices have improved load modeling techniques and have enabled the application of machine learning algorithms to a wide variety of model calibration tasks. Therefore, we propose that these algorithms can be applied to improve the quality of the input data and grid models used for PV impact studies. In this paper, these potential improvements were assessed for their ability to improve the accuracy of locational BTM PV hosting capacity analysis (HCA). Specifically, the voltage- and thermal-constrained hosting capacities of every customer location on a distribution feeder (1,379 in total) were calculated every 15 minutes for an entire year before and after each calibration algorithm or load modeling technique was applied. Overall, the HCA results were found to be highly sensitive to the various modeling deficiencies under investigation, illustrating the opportunity for more data-centric/model-free approaches to PV impact studies.
Samuel Talkington, Cameron Khanpour, Rahul K. Gupta, Sergio A. Dorado-Rojas, Daniel Turizo, Hyeongon Park, Dmitrii M. Ostrovskii, and Daniel K. Molzahn