Trajectory tracking with an aggregation of domestic hot water heaters: Combining model-based and model-free control in a commercial deployment

Abstract

Scalable demand response of residential electric loads has been a timely research topic in recent years. The commercial coming of age or residential demand response requires a scalable control architecture that is both efficient and practical to use. This work presents such a strategy for domestic hot water heaters and present a commercial proof-of-concept deployment. The strategy combines state of the art in aggregate-and-dispatch with a novel dispatch strategy leveraging recent developments in reinforcement learning and is tested in a hardware-in-the-loop simulation environment. The results are promising and present how model-based and model-free control strategies can be merged to obtain a mature and commercially viable control strategy for residential demand response.

arXiv comment8 pages, 8 figures, submitted to IEEE Transactions on Smart Grid

References in corpus (1)

• Deep Reinforcement Learning based Optimal Control of Hot Water Systems

Cited by in corpus (5)

• Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings
• Performance analysis of frequency regulation services provided by aggregates of domestic thermostatically controlled loads
• Flexibility management with virtual batteries of thermostatically controlled loads: real-time control system and potential in Spain
• Flexibility Evaluation of Domestic Electric Water Heater Aggregates
• Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households