Time-of-Use and Demand Charge Battery Controller Using Stochastic Model Predictive Control

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations


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.


Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-76240 for preprint

NREL Publication Number

  • NREL/CP-5D00-79089


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


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