Sparse Time Series Sampling for Recovery of Behind-the-Meter Inverter Control Models

BibTeX

@inproceedings{talkington_sparsesampling_2022,
  author={Talkington, Samuel and Grijalva, Santiago and Reno, Matthew J.},
  booktitle={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}
}

Abstract

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.

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Sparse Time Series Sampling for Recovery of Behind-the-Meter Inverter Control Models (Apr 2022)
 Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
 IEEE PES Innovative Smart Grid Technologies Conference (ISGT) / bibTeX