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

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.

Authors:
 [1];  [1];  [2]; ORCiD logo [3]
  1. University of Colorado
  2. University of California
  3. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1484851
Report Number(s):
NREL/CP-5500-70492
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the IEEE Power and Energy Society General Meeting, 5-10 August 2018, Portland, Oregon
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; demand response; DR; home energy management system; HEMS

Citation Formats

Garifi, Kaitlyn, Baker, Kyri, Touri, Behrouz, and Christensen, Dane T. Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint. United States: N. p., 2018. Web.
Garifi, Kaitlyn, Baker, Kyri, Touri, Behrouz, & Christensen, Dane T. Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint. United States.
Garifi, Kaitlyn, Baker, Kyri, Touri, Behrouz, and Christensen, Dane T. Fri . "Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint". United States. https://www.osti.gov/servlets/purl/1484851.
@article{osti_1484851,
title = {Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint},
author = {Garifi, Kaitlyn and Baker, Kyri and Touri, Behrouz and Christensen, Dane T},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
Other availability
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