Data-Driven Adaptation of Smart Grids With Hierarchical Digital Twins

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.

Authors

Institutions

Publication Details

Journal
IEEE Pervasive Computing
Published
2025-01-01
DOI
https://doi.org/10.1109/mprv.2025.3551625
Citations
5
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
3.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data-Driven Adaptation of Smart Grids With Hierarchical Digital Twins

Marco Mamei, Natalia Selini Hadjidimitriou, Marco Picone, Marco Lippi et al.
5 citations
IEEE Pervasive Computing
Smart Grid Energy Management
3.65
article

Data-Driven Adaptation of Smart Grids With Hierarchical Digital Twins

Marco Mamei, Natalia Selini Hadjidimitriou, Marco Picone, Marco Lippi, Fabio D’Andreagiovanni, Raffaele Nastro
article en
5 citations

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.

IEEE Pervasive ComputingVol. 24(1)
University of Modena and Reggio Emilia (IT), SAES Group (Italy) (IT), University of Florence (IT)
Affordable and clean energy
Openalex Percentile: Top 7%
Smart Grid Energy Management
3.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.