Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation

Peter Shaffery, Rui Yang, Yingchen Zhang

Research output: NRELPoster


Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
Original languageAmerican English
StatePublished - 2020

Publication series

NamePresented at the Innovative Smart Grid Technologies (ISGT 2020) North America, 17-20 February 2020, Washington, D.C.

NREL Publication Number

  • NREL/PO-5D00-76108


  • bayesian structural time series
  • behind-the-meter PV
  • disaggregation


Dive into the research topics of 'Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation'. Together they form a unique fingerprint.

Cite this