Home and Building Energy Management Systems
NREL researchers are developing tools to understand the impact of changes in home and building energy use and how building assets and energy management systems can provide value to the grid.
The nature of building loads is changing with increased use of smart appliances and building automation that enables consumers to tailor their load in response to price or control signals. In addition, building owners are installing more distributed generation and storage on their premises. These changes make it imperative that building assets (such as distributed generation, storage, and controllable loads) and building-level controls be incorporated into power system simulations.
NREL is developing advanced building-level controllers to meet owner and occupant needs while supporting grid operations.
- Modeling and simulation
NREL has extensive modeling and simulation capabilities to evaluate the impact of emerging technologies on the electric power system. Researchers can co-simulate a distribution feeder, buildings (including thermal performance and building appliances), distributed energy resources (including PV and battery systems), and controllers such as home energy management systems. In addition, they can simulate various tariff structures to evaluate how financial incentives influence power system operation. Our Integrated transmission and distribution Grid Modeling System (IGMS) also includes buildings, distributed energy resources, and price-responsive controllers.
- Development of home energy management system algorithms
- Model-predictive control
NREL has developed a multi-objective optimization using a model predictive control framework that determines the optimal operational schedules of residential appliances—including heating, ventilation and air conditioning systems, electric water heaters, refrigerators, residential batteries, electric vehicles, dishwashers, and pool pumps—to meet objectives such as reductions in cost, energy use, and grid export. It accounts for consumer preferences, electricity price, weather forecasts, and forecasts of rooftop PV power generation. The optimization takes into account forecasting errors by leveraging stochastic optimization approaches.
- Fuzzy-logic subsumption controller
NREL has also developed a home energy management system design based on behavioral control methods, which do not require accurate models or predictions and are responsive to changing conditions.
- Model-predictive control
The objective of this project is to connect an entire smart home—including appliances, distributed energy resources (for example, rooftop PV and home energy storage systems), electric vehicles, and a home energy management system—to a power system simulation running on NREL’s high-performance computing environment. In short, the aim is to develop smart home hardware-in-the-loop capability. This will allow experiments that stress the hardware and controls in a simulation environment, allowing for evaluation of the reliability of a technology.
Effects of Home Energy Management Systems on Distribution Utilities and Feeders under Various Market Structures, 23rd International Conference on Electricity Distribution (2015)
A System-of-Systems Approach for Integrating Energy Modeling and Simulation, Society for Modeling and Simulation International Summer Simulation Multi-Conference (2015)
Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources, IEEE Power and Energy Society General Meeting (2015)
A Fuzzy-Logic Subsumption Controller for Home Energy Management Systems, North American Power Symposium (2015)
View all NREL publications about home and building energy management systems.