Battery Control Using Stochastic Model Predictive Control

Research output: NRELPresentation


Stationary batteries in residential and commercial buildings are often used to smooth customer load profiles and to lower customer electricity bills. Controllers for these battery systems should account for customer energy consumption, rate structures, and high internal battery temperatures, which can lead to reduced performance over the battery lifetime. It is important to consider the uncertainty in forecasting energy consumption and temperature, especially for customers with highly variable and uncertain loads. We propose a novel battery controller using stochastic model predictive control that accounts for these uncertainties and can handle complex rate structures, including demand charges. We show that the controller performs better than standard model predictive control when there is significant uncertainty in the forecast. We also show improvements in the performance with more accurate forecasts and with a more aggressive control strategy.
Original languageAmerican English
Number of pages14
StatePublished - 2020

Publication series

NamePresented at IEEE SmartGridComm 2020, 11-13 November 2020

NREL Publication Number

  • NREL/PR-5D00-79754


  • battery control
  • behind-the-meter
  • stationary batteries
  • stochastic model predictive control


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