Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint

Dane Christensen, Kaitlyn Garifi, Kyri Baker, Behrouz Touri

Research output: Contribution to conferencePaper


This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.
Original languageAmerican English
Number of pages8
StatePublished - 2018
EventIEEE Power and Energy Society General Meeting - Portland, Oregon
Duration: 5 Aug 201810 Aug 2018


ConferenceIEEE Power and Energy Society General Meeting
CityPortland, Oregon

Bibliographical note

See NREL/CP-5500-73330 for paper as published in IEEE proceedings

NREL Publication Number

  • NREL/CP-5500-70492


  • demand response
  • DR
  • HEMS
  • home energy management system


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