  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.
