Joint Estimation of Behind-the-Meter Solar Generation in a Community

Farzana Kabir, Nanpeng Yu, Weixin Yao, Rui Yang, Yingchen Zhang

Research output: Contribution to journalArticlepeer-review

45 Scopus Citations

Abstract

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

Original languageAmerican English
Article number9167467
Pages (from-to)682-694
Number of pages13
JournalIEEE Transactions on Sustainable Energy
Volume12
Issue number1
DOIs
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-78825

Keywords

  • Behind-the-meter solar generation
  • Mixed hidden Markov model
  • Net load disaggregation

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