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

TL;DR

This paper introduces a scalable control strategy for domestic hot water heaters that combines model-based and model-free methods, demonstrated through a commercial proof-of-concept deployment and hardware-in-the-loop testing.

Contribution

It presents a novel hybrid control approach that merges aggregate-and-dispatch with reinforcement learning for residential demand response.

Findings

Promising results in hardware-in-the-loop simulations

Effective merging of model-based and model-free control strategies

Potential for commercial deployment in residential demand response

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.