Stochastic Home Energy Management Systems with Varying Controllable Resources
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1602665
- Report Number(s):
- NREL/CP-5500-72783
- Resource Relation:
- Conference: Presented at the 2019 IEEE Power & Energy Society General Meeting (PESGM), 4-8 August 2019, Atlanta, Georgia
- Country of Publication:
- United States
- Language:
- English
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