Editorial

Authors

  • V. Vinoth Kumar School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, India
  • Lipo Wang Nanyang Technological University, Singapore.
  • Polinpapilinho F. Katina Department of Informatics and Engineering systems, University of South Carolina Upstate, USA.
  • David Asirvatham School of Computer Science, Taylor’s University, Selangor, Malaysia

Keywords:

Editorial

Abstract

We are very glad to introduce you to this Special Issue of the Distributed Generation & Alternative Energy Journal “Digital Twin for Accelerating Sustainability in Energy Automation and Smart Grid”. This Special Issue is published in collaboration with authors from research institutions and universities who have great experience in the development of international research projects on both distributed generation and alternative energy.

The aim of this Special Issue is to present the Digital Twin for Accel- erating Sustainability in Energy Automation and Smart Grid. Therefore, the utilities can harness the power of transparency with a single source of truth for data across their entire utility IT landscape. A digital twin is a digital representation of a physical object, person, or process, contextualized in a digital version of its environment. Digital twins can help an organization simulate real situations and their outcomes, ultimately allowing it to make better decisions [1, 2]. This Digital Twin closely aligns real and virtual worlds by providing utilities with a single source of truth to model data across their entire IT landscape. A common network model is there to facilitate grid simulation across all domains relevant to reliable, efficient, and secure electrical system planning, operation, and maintenance.

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Published

2023-10-30

How to Cite

Kumar, V. V. ., Wang, L. ., Katina, P. F. ., & Asirvatham, D. . (2023). Editorial. Distributed Generation &Amp; Alternative Energy Journal, 39(01), v-viii. Retrieved from https://journals.riverpublishers.com/index.php/DGAEJ/article/view/24189

Issue

Section

Digital twin for Accelerating Sustainability in Energy Automation and Smart Grid