An Overview of Digital Twins Application Domains in Smart Energy Grid

The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewab...

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Bibliographic Details
Published inarXiv.org
Main Authors Tudor Cioara, Anghel, Ionut, Antal, Marcel, Salomie, Ioan, Antal, Claudia, Arcas Gabriel Ioan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.04.2021
Subjects
Cloud computing
Data analysis
Domains
Electric power grids
Energy
Energy distribution
Energy management
Internet of Things
Machine learning
Model testing
Optimization
Renewable resources
Smart grid
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Summary:The Digital Twins offer promising solutions for smart grid challenges related to the optimal operation, management, and control of energy assets, for safe and reliable distribution of energy. These challenges are more pressing nowadays than ever due to the large-scale adoption of distributed renewable resources at the edge of the grid. The digital twins are leveraging technologies such as the Internet of Things, big data analytics, machine learning, and cloud computing, to analyze data from different energy sensors, view and verify the status of physical energy assets and extract useful information to predict and optimize the assets performance. In this paper, we will provide an overview of the Digital Twins application domains in the smart grid while analyzing existing the state of the art literature. We have focused on the following application domains: energy asset modeling, fault and security diagnosis, operational optimization, and business models. Most of the relevant literature approaches found are published in the last three years showing that the domain of Digital Twins application in smart grid is hot and gradually developing. Anyway, there is no unified view on the Digital Twins implementation and integration with energy management processes, thus, much work still needs to be done to understand and automatize the smart grid management.
ISSN:2331-8422