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

Dane Christensen, Kaitlyn Garifi, Kyri Baker, Behrouz Touri

Research output: Contribution to conferencePaperpeer-review

49 Scopus Citations

Abstract

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 pages5
DOIs
StatePublished - 21 Dec 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: 5 Aug 201810 Aug 2018

Conference

Conference2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/TerritoryUnited States
CityPortland
Period5/08/1810/08/18

Bibliographical note

See NREL/CP-5500-70492 for preprint

NREL Publication Number

  • NREL/CP-5500-73330

Keywords

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

Fingerprint

Dive into the research topics of 'Stochastic Model Predictive Control for Demand Response in a Home Energy Management System'. Together they form a unique fingerprint.

Cite this