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

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