Implementing Digital Twins in Energy Systems: Meta Analysis of the State of Art and Evolution

EasyChair Preprint 7154

Abstract

Energy System is a critical Infrastructure that is categorized in Cyber-Physical-Social Systems because of the integration of computerized control systems and consumption patterns of the society, with Physical entities of production, transmission, and distribution. Cutting-edge technologies, sustainable development, and Digitization of the essential services of the society disclose emerging issues to the energy systems. Digital Twin(DT) is a state-of-the-art technology that is employed to deal with the complexity of Cyber-Physical-Social systems and brings Energy IoT(EIoT) into action. The current study investigates the implementations of DT in the Energy Systems Domain. The search results from three digital libraries (SCOPUS, WOS, IEEE) unveiled the freshness of the topic, which has been started in this sector since 2018. This article is a Meta Data Analysis, which is the primary phase of a Systematic Literature Review that carries out on DT implementations in Energy Systems. The results of statistical analysis that combine the results of multiple scientific studies shows the most studied features, applications, and gaps in the body of knowledge.

Keyphrases: Artificial Intelligence, Cyber-Physic System, Digital Twins, Smart Grid, data-driven model, energy systems, machine learning, power systems, smart energy system

@booklet{EasyChair:7154,
  author    = {Ali Aghazadeh Ardebili and Antonella Longo and Antonio Ficarella},
  title     = {Implementing Digital Twins in Energy Systems: Meta Analysis of the State of Art and Evolution},
  howpublished = {EasyChair Preprint 7154},
  year      = {EasyChair, 2021}}