Demand Response by Aggregates of Domestic Water Heaters with Adaptive Model Predictive Control

TL;DR

This paper presents an adaptive model predictive control algorithm for managing groups of domestic water heaters to provide demand response services, ensuring user comfort while tracking regulation signals.

Contribution

It introduces a recursive polynomial model estimation technique for adaptive control of water heater aggregates in demand response applications.

Findings

Successfully tracks regulation signals in simulations

Mitigates rebound effect after demand response events

Maintains user comfort during control actions

Abstract

This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This allows the control to be adaptive, i.e., able to adjust its decisions to the system characteristics, which vary over time due to the daily distribution of users hot water consumption. To answer the demand response requirements, aggregated power variations are realized by modifying the temperature set-points of the water heaters without compromising the users comfort. The developed approach allows tracking a regulation signal and mitigating the so-called rebound, i.e., the recovery of energy needed by the aggregate at the end of the service to return to the baseline thermal state. Analyses in a simulation environment allow the validation of the potentialities of the proposed method.