TY - GEN
T1 - Stochastic Home Energy Management Systems with Varying Controllable Resources
AU - Garifi, Kaitlyn
AU - Baker, Kyri
AU - Christensen, Dane
AU - Touri, Behrouz
N1 - ACKNOWLEDGMENT This work was supported in parts by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy and the Air Force Office of Scientific Research under the AFOSR-YIP award FA9550-16-1-0400. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308.
PY - 2019/8
Y1 - 2019/8
N2 - This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.
AB - This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.
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U2 - 10.1109/PESGM40551.2019.8973708
DO - 10.1109/PESGM40551.2019.8973708
M3 - Conference contribution
AN - SCOPUS:85079052437
T3 - IEEE Power and Energy Society General Meeting
BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PB - IEEE Computer Society
T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Y2 - 4 August 2019 through 8 August 2019
ER -