Load Flexibility Analysis of Residential HVAC and Water Heating and Commercial Refrigeration
The emergence of connected devices and the Internet of Things has opened the door to the concept of widespread use of building equipment for load-side flexibility. This stems from the increased use of distributed and clean energy resources for electric generation and the growing demand for a more resilient and reliable grid. A significant challenge associated with the use of building equipment as a distributed energy resource is quantifying the magnitude of power or energy flexibility. Unlike batteries that have well-defined and relatively constant capacity and maximum charge/discharge rates, building equipment often has characteristics that vary based on external factors (e.g., weather or occupant behavior). This makes quantifying the availability and flexibility of these resources difficult. Controlling a fleet of equipment while minimizing any adverse impacts to the occupants (e.g., discomfort or food spoilage) is a nontrivial matter. Although there are many different control methodologies available, this study focuses on two: priority-based control (PBC) and model-predictive control (MPC). These were selected because they represent two ends of the control spectrum. PBC is a relatively simple control strategy that requires only the current state of the devices to be controlled and an estimate of how much power each device consumes when turned on. MPC is much more complex and requires a model of the system and forecasts of any disturbances that influence the system. Because MPC has a predictive element, it can prepare for a desired response before the event and therefore is expected to provide a more optimal control relative to PBC. Both MPC and PBC are compared with a baseline simulation that uses feedback control typical of the thermostatically controlled devices being evaluated. This report investigates the load flexibility of three building systems: residential air conditioners, residential water heaters, and commercial refrigeration. Air heating and cooling and water heating account for 45% of residential electricity use, making them good candidates for providing load flexibility. Refrigeration in commercial buildings accounts for 16% of commercial building electricity use, making it another good candidate. Simplified models of each system were generated and validated based on measured data from actual equipment. These models were used to simulate the thermal and power response of the systems using PBC and MPC for a fleet of devices trying to achieve a variety of different responses, including hourly load shed, hourly load up, and daily peak demand reduction. Significant findings from each piece of equipment follow.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1564190
- Report Number(s):
- ORNL/SPR-2019/1210
- Country of Publication:
- United States
- Language:
- English
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