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arXiv · 1805.04228

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

Mingxi Liu·Stef Peeters·Duncan S. Callaway·Bert J. Claessens
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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.

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Mingxi Liu, Stef Peeters, Duncan S. Callaway, Bert J. Claessens. 2018-05-11. Trajectory tracking with an aggregation of domestic hot water heaters: Combining model-based and model-free control in a commercial deployment. https://doi.org/10.1109/tsg.2018.2890275

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