@INPROCEEDINGS{talkington_sparse_2022,
author={Talkington, Samuel and Grijalva, Santiago and Reno, Matthew J.},
booktitle={2022 IEEE PES Innovative Smart Grid Technologies Conference (ISGT)},
title={{Sparse Time Series Sampling for Recovery of Behind-the-Meter Inverter Control Models}},
year={2022},
pages={1-5},
doi={10.1109/ISGT50606.2022.9817504}
}Incorrect modeling of control characteristics for inverter-based resources (IBRs) can affect the accuracy of electric power system studies. In many distribution system contexts, the control settings for behind-the-meter (BTM) IBRs are unknown. This paper presents an efficient method for selecting a small number of time series samples from net load meter data that can be used for reconstructing or classifying the control settings of BTM IBRs. Sparse approximation techniques are used to select the time series samples that cause the inversion of a matrix of candidate responses to be as well-conditioned as possible. We verify these methods on 451 actual advanced metering infrastructure (AMI) datasets from loads with BTM IBRs. Selecting 60 15-minute granularity time series samples, we recover BTM control characteristics with a mean error less than 0.2 kVAR.
Strategic Electric Distribution Network Sensing via Spectral Bandits
Samuel Talkington, Rahul Gupta, Richard Asiamah, Paprapee Buason, and Daniel K. Molzahn
IEEE CDC 2024 / arXiv / slides / bibTeX
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
Samuel Talkington, Santiago Grijalva
ACM e-Energy / arXiv / code / slides / bibTeX
Sparse Time-Series Sampling for Recovery of Behind-the-Meter Inverter Control Models
Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
IEEE PES Innovative Smart Grid Technologies Conference (ISGT) / bibTeX