Local energy communities are citizens' associations that allow efficient energy sharing and management among their members. Such organizations play a crucial role in the energy transition, and smart grids represent the core technology for their implementation. In this article, we propose a framework based on hierarchical Digital Twins interconnecting the physical devices of the smart grid. By exploiting this framework, we propose an energy-sharing approach in which users of a local energy community can share the excess local batteries' capacity with each other. In our experiments, we analyze energy savings with respect to battery capacity and percentage of prosumers in the community, showing the advantages of the proposed architecture.
Data-Driven Adaptation of Smart Grids with Hierarchical Digital Twins / Hadjidimitriou, N.; Lippi, M.; Mamei, M.; Nastro, R.; Picone, M.; D'Andreagiovanni, F.. - In: IEEE PERVASIVE COMPUTING. - ISSN 1536-1268. - 24:1(2025), pp. 10-18. [10.1109/MPRV.2025.3551625]
Data-Driven Adaptation of Smart Grids with Hierarchical Digital Twins
Hadjidimitriou N.;Lippi M.;Mamei M.;Picone M.;D'Andreagiovanni F.
2025
Abstract
Local energy communities are citizens' associations that allow efficient energy sharing and management among their members. Such organizations play a crucial role in the energy transition, and smart grids represent the core technology for their implementation. In this article, we propose a framework based on hierarchical Digital Twins interconnecting the physical devices of the smart grid. By exploiting this framework, we propose an energy-sharing approach in which users of a local energy community can share the excess local batteries' capacity with each other. In our experiments, we analyze energy savings with respect to battery capacity and percentage of prosumers in the community, showing the advantages of the proposed architecture.| File | Dimensione | Formato | |
|---|---|---|---|
|
J11_IEEE_Pervasive_Energy.pdf
Accesso riservato
Tipologia:
VOR - Versione pubblicata dall'editore
Licenza:
[IR] closed
Dimensione
364.12 kB
Formato
Adobe PDF
|
364.12 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate

I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris




