The concept of demand response or active demand has recently been introduced in several European projects. The key idea is that end-users play an active role in the electricity distribution process, adjusting their consumption patterns according to dynamic energy pricing policies. This chapter illustrates the functional and software architecture of the energy box following the scheme developed in the ADDRESS project. Moreover, an optimization model is presented for the scheduling of distributed energy resources (DERs) at consumers' premises. The chapter first provides descriptions of the EB functional and software architecture. Next, a classification of appliances, small generation devices and storage systems located at the end-user premises is given. The chapter also presents a mathematical model and a heuristic algorithm for the load scheduling problem solved by the EB. Finally, it illustrates the numerical results obtained from simulation experiments.

Agnetis, A., Brown, C., Detti, P., Huarte, J.J., Vicino, A. (2015). Distributed Intelligence at the Consumer’s Premises. In A. Losi, P. Mancarella, A. Vicino (a cura di), Integration of Demand Response into the Electricity Chain: Challenges, Opportunities and Smart Grid Solutions (pp. 41-64). Wiley-ISTE [10.1002/9781119245636.ch3].

Distributed Intelligence at the Consumer’s Premises

Agnetis, A.
;
Detti, P.;Vicino, A.
2015-01-01

Abstract

The concept of demand response or active demand has recently been introduced in several European projects. The key idea is that end-users play an active role in the electricity distribution process, adjusting their consumption patterns according to dynamic energy pricing policies. This chapter illustrates the functional and software architecture of the energy box following the scheme developed in the ADDRESS project. Moreover, an optimization model is presented for the scheduling of distributed energy resources (DERs) at consumers' premises. The chapter first provides descriptions of the EB functional and software architecture. Next, a classification of appliances, small generation devices and storage systems located at the end-user premises is given. The chapter also presents a mathematical model and a heuristic algorithm for the load scheduling problem solved by the EB. Finally, it illustrates the numerical results obtained from simulation experiments.
2015
978-1-84821-854-3
Agnetis, A., Brown, C., Detti, P., Huarte, J.J., Vicino, A. (2015). Distributed Intelligence at the Consumer’s Premises. In A. Losi, P. Mancarella, A. Vicino (a cura di), Integration of Demand Response into the Electricity Chain: Challenges, Opportunities and Smart Grid Solutions (pp. 41-64). Wiley-ISTE [10.1002/9781119245636.ch3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1010034