  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.
