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Digital Twin Technology and Smart Grid

  • 1st Edition - August 1, 2026
  • Latest edition
  • Editors: Iraklis Varlamis, Antoine Bagula, Hossein Hassani
  • Language: English

Digital Twin Technology and Smart Grid explores the intersection of these technologies, which are essential for the evolution of energy systems. The book explains how it utiliz… Read more

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Description

Digital Twin Technology and Smart Grid explores the intersection of these technologies, which are essential for the evolution of energy systems. The book explains how it utilizes intelligent wireless sensor networks, the Internet of Things, artificial intelligence, machine learning, cloud, edge, and fog-computing to monitor power consumption. It discusses how security risks and privacy challenges can be accommodated, and explains the ethical/legal implications of collecting data. As the global energy landscape moves toward greater sustainability and decentralization, digital twins present unprecedented opportunities to enhance grid efficiency, bolster resilience, and support the integration of renewable energy sources.

The integration of Digital Twin (DT) technology with Smart Grids (SG) represents a groundbreaking development in energy management, making this a highly significant and timely topic. As urban areas expand and energy demands rise, the need for more efficient, sustainable, and resilient energy systems becomes critical. DT technology, with its ability to create real-time, virtual replicas of physical systems, offers unprecedented opportunities for enhancing the performance, reliability, and security of smart grids.

Key features

  • Explains how digital twins gather real-world data to create a model of a given situation of real micro grid/power grids
  • Discusses how the model can be utilized in making informed decisions
  • Considers the associated technologies that support digital twins: IoT, AI, ML, and blockchain
  • Explores how digital twins can be used in the real-world to monitor and improve services of the micro grid/power grid

Readership

Academic researchers, and professional engineers/scientists working in the field of advanced sensor networks, smart grid, computer science, electronics, communication, public management, infra-structure management, traffic information management

Table of contents

PART 1. Introduction

1. Introduction to Digital Twin

2. Introduction to Smart Grid

3. Introduction to Digital Twin Technology for Power Grids

PART 2. Digital Twin Computing for Microgrids

4. ML Algorithms for Prediction and Diagnosis Problems

5. Edge Computing for Energy Management and Monitoring

6. Fault Detection System

7. Harmonized Attacks and Cyber-Attack

PART 3. Digital Twin Computing for Power Grids

8. Neural Network for Power System Restoration

9. Algorithm and Simulation for Digital Twin-Based Protection System

10. Cloud Computing for Digital Twin Power Grids

11. Fog-Computing for Digital Twin Power Grids

12. Edge-Computing for Digital Twin Power Grids

13. Quantum Computing Digital Twin Power Grids

14. Integration of Generative AI and LLMs into Digital Twin-Based Smart Grids

15. Artificial Intelligence, Digital Twin, and Blockchain Technology for Secure Smart Grids

16. Regulatory and Policy Implications of Digital Twins in Smart Grids

17. Future Perspectives

Product details

  • Edition: 1
  • Latest edition
  • Published: August 1, 2026
  • Language: English

About the editors

IV

Iraklis Varlamis

Iraklis Varlamis is Associate Professor of Data Management at the Department of Informatics and Telematics at the Harokopio University of Athens. His research interests span data-mining and knowledge extraction from social media, to intelligent systems and machine learning with application in recommender systems and personalization. He has co-authored three books and more than 130 papers concerning graph and text mining, data analytics, intelligent systems and personalization and has more than 2000 citations on his work. He holds a patent from the Greek Patent Office and an application pending from the US patent office. He is been involved on several EU funded projects concerning intelligent systems, machine learning, and data mining on the industrial and automotive domain.
Affiliations and expertise
Associate Professor of Data Management, Department of Informatics and Telematics, Harokopio University of Athens, Greece

AB

Antoine Bagula

Bigomokero Antoine Bagula, holds a Ph.D. in Communication Systems from the Royal Institute of Technology (KTH) in Stockholm, Sweden. He also obtained two MSc degrees: one in Computer Engineering from the Université Catholique de Louvain (UCL) in Belgium, and another in Computer Science from the University of Stellenbosch (SUN) in South Africa. Presently, Dr. Bagula serves as a full professor in the Department of Computer Science at the University of the Western Cape (UWC) in South Africa, where he leads the Intelligent Systems and Advanced Telecommunication (ISAT) laboratory. Additionally, he holds a professorial position at Université Nouveaux Horizons (UNH) in the Democratic Republic of the Congo (DRC), where he is responsible for driving the institution's faculty of Informatics research agenda and enhancing its teaching curriculum. Prof. Bagula's research interests encompass various areas such as Data Engineering with a focus on Big Data Technologies, Cloud/Fog Computing, and Network Softwarization, including concepts like NFV and SDN. He actively explores the potential of the Internet of Things (IoT), covering both the Internet-of-Things and Tactile Internet-of-Things. Moreover, he delves into Data Science, particularly Artificial Intelligence, Machine Learning, and their applications in Big Data Analytics. Dr. Bagula's expertise extends to Next Generation Networks (NGN), including 5G/6G. Through his academic accomplishments and research pursuits, Dr. Bagula significantly contributes to the fields of computer science, data engineering, and telecommunications.
Affiliations and expertise
University of the Western Cape, South Africa

HH

Hossein Hassani

Dr. Hossein Hassani is among the top 1% of scientists worldwide, according to the 2023 Elsevier and Stanford University ranking. He has over 15 years of extensive experience working with national, international, and multicultural organizations, research institutes, and academia. Dr. Hassani has published seven books and more than 200 articles. His research interests include digital twins, time series analysis and forecasting, AI, big data, data mining, as well as theoretical and applied statistics.

Affiliations and expertise
Webster University in Vienna, Austria