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

Conference · Mon Dec 24 04:00:00 EST 2018

DOI:https://doi.org/10.1109/PESGM.2018.8586485· OSTI ID:1496049

Garifi, Kaitlyn ^[1]; Baker, Kyri ^[1]; Touri, Behrouz ^[2]; Christensen, Dane T ^[3]

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1. University of Colorado
2. University of California, San Diego
3. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1496049
• Report Number(s): NREL/CP-5500-73330
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
DR
HEMS
demand response
home energy management system