@ARTICLE{talkington_disaggregation_2022,
author={Talkington, Samuel and Grijalva, Santiago and Reno, Matthew J. and Azzolini, Joseph A.},
journal={IEEE Transactions on Power Systems},
title={{Solar PV Inverter Reactive Power Disaggregation and Control Setting Estimation}},
year={2022},
volume={37},
number={6},
pages={4773-4784},
doi={10.1109/TPWRS.2022.3144676}
}The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR.
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arXiv / code / bibTeX
Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks
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arXiv / bibTeX
Differentiating Through Power Flow Solutions for Admittance and Topology Control
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arXiv / bibTeX
Optimizing State Estimation Error in the LinDist3Flow Model
Jeslyn Ero, Kieran Slattery, Xianhe Qin, Samuel Talkington, and Daniel K. Molzahn
IEEE Opportunity Research Scholars Symposium (ORSS) / bibTeX
Error Bounds for Radial Network Topology Learning from Quantized Measurements
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arXiv / bibTeX
VArsity: Can Large Language Models Keep Power Engineering Students in Phase?
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57th North American Power Symposium (NAPS 2025) / bibTeX
Covert Distribution Load Tripping Attacks
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57th North American Power Symposium (NAPS 2025) / code / bibTeX
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Kansas Power and Energy Conference (KPEC) / bibTeX
Strategic Electric Distribution Network Sensing via Spectral Bandits
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IEEE CDC 2024 / arXiv / slides / bibTeX
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Paprapee Buason, Sidhant Misra, Samuel Talkington, and Daniel K. Molzahn
Electric Power Systems Research / arXiv / bibTeX
Managing Vehicle Charging During Emergencies via Conservative Distribution System Modeling
Alejandro D. Owen Aquino, Samuel Talkington, and Daniel K. Molzahn
2024 IEEE Texas Power and Energy Conference (TPEC) / arXiv / bibTeX
Localized Structure in Secondary Distribution System Voltage Sensitivity Matrices
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini, and Jouni Peppanen
Electric Power Systems Research / bibTeX
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Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini, David Pinney
Electric Power Systems Research / bibTeX
Phase Retrieval via Model-Free Power Flow Jacobian Recovery
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ACM e-Energy / arXiv / code / slides / bibTeX
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
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IEEE Transactions on Power Systems / arXiv / code / bibTeX
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IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) / code / bibTeX
Improving Behind-the-Meter PV Impact Studies with Data-Driven Modeling and Analysis
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IEEE 49th Photovoltaics Specialists Conference (PVSC) / code / bibTeX
Sparse Time-Series Sampling for Recovery of Behind-the-Meter Inverter Control Models
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IEEE PES Innovative Smart Grid Technologies Conference (ISGT) / bibTeX
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Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini
IEEE Transactions on Power Systems / bibTeX
Recovering Power Factor Control Settings of Solar PV Inverters from Net Load Data
Samuel Talkington, Santiago Grijalva, Matthew J. Reno, Joseph A. Azzolini
North American Power Symposium (NAPS) / slides / bibTeX
Power Factor Estimation of Distributed Energy Resources Using Voltage Magnitude Measurements
Samuel Talkington, Santiago Grijalva, and Matthew J. Reno
Journal of Modern Power Systems and Clean Energy / bibTeX
Rail Transit Regenerative Braking Energy Recovery Optimization to Provide Grid Services
Samuel Talkington, Santiago Grijalva
IEEE Power and Energy Conference at Illinois (PECI) / bibTeX