A New Platform for Automatic Bottom-Up Electric Load Aggregation

Alfredo Bartolozzi, Salvatore Favuzza, Mariano Giuseppe Ippolito, Diego La Cascia, Eleonora Riva Sanseverino and Gaetano Zizzo
Additional contact information
Alfredo Bartolozzi: Direzione Territoriale Lazio Abruzzo Molise (DTR LAM)-e-distribuzione SPA, ENEL Group, via della Bufalotta 255, 00139 Rome, Italy
Salvatore Favuzza: Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy
Mariano Giuseppe Ippolito: Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy
Diego La Cascia: Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy
Eleonora Riva Sanseverino: Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy
Gaetano Zizzo: Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy

Energies, 2017, vol. 10, issue 11, 1-24

Abstract: In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and the possibility to meet the target power demand at a given hour according to each customer’s availability.

Keywords: loads aggregation; loads clustering; energy market; active demand (AD) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/11/1682/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/11/1682/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:11:p:1682-:d:116289

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().