Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment
Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.
- 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:
- 1974026
- Report Number(s):
- NREL/CP-5600-86283; MainId:87056; UUID:c9494324-585a-41a9-831d-181aadd58922; MainAdminID:69518
- Resource Relation:
- Conference: Presented at the 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 16-19 January 2023, Washington, D.C.
- Country of Publication:
- United States
- Language:
- English
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