Digital Twin Technology and Smart Grid explores the intersection of these technologies that 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.
1. Introduction by the editors - p. xiii 2. Part I Introduction - p. 1 3. Chapter 1 Introduction to Digital Twins for the Smart Grid - p. 3 4. Chapter 2 Introduction to Digital Twin: Applications - p. 23 5. Chapter 3 Introduction to smart grid - p. 35 6. Chapter 4 Introduction to Digital Twin technology for power grids - p. 55 7. Part II Digital Twin computing for microgrids - p. 77 8. Chapter 5 Using Machine Learning algorithms for prediction and diagnosis problems - p. 79 9. Chapter 6 Machine learning algorithms for forecasting and diagnosis applications in smart grids - p. 103 10. Chapter 7 Intelligent Digital Twins for social acceptance by design in smart grids - p. 125 11. Chapter 8 Fault detection in modern power systems - p. 151 12. Chapter 9 Digital Twin testing platforms for power grids - p. 169 13. Chapter 10 Automatic control and reinforcement learning techniques for microgrids - p. 185 14. Part III Digital Twin computing for power grids - p. 207 15. Chapter 11 Towards digital and smart substations: A Digital Twin approach - p. 209 16. Chapter 12 Machine learning for power system restoration - p. 221 17. Chapter 13 Algorithm and simulation for a Digital Twin–based protection system - p. 233 18. Chapter 14 Cloud computing for Digital Twin power grids - p. 247 19. Chapter 15 Fog computing for Digital Twin power grids - p. 277 20. Chapter 16 Edge computing for Digital Twin-enabled future electrical grid systems - p. 293 21. Chapter 17 Quantum computing with Digital Twins and power grids - p. 313 22. Chapter 18 Integrating generative AI and LLMs with digital twins in smart grids - p. 323 23. Chapter 19 Harnessing generative AI and large language models for Digital Twin-enabled smart grids - p. 359 24. Chapter 20 Integration of generative artificial intelligence and large language models into Digital Twin-based smart grids - p. 375 25. Chapter 21 Artificial intelligence, Digital Twin and blockchain technology for secure smart grids - p. 395 26. Chapter 22 Future perspectives for Digital Twin–based power grids - p. 419 27. Index - p. 429
Digital Twin Technology and Smart Grid explores the intersection of these technologies that 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.
1. Introduction by the editors - p. xiii 2. Part I Introduction - p. 1 3. Chapter 1 Introduction to Digital Twins for the Smart Grid - p. 3 4. Chapter 2 Introduction to Digital Twin: Applications - p. 23 5. Chapter 3 Introduction to smart grid - p. 35 6. Chapter 4 Introduction to Digital Twin technology for power grids - p. 55 7. Part II Digital Twin computing for microgrids - p. 77 8. Chapter 5 Using Machine Learning algorithms for prediction and diagnosis problems - p. 79 9. Chapter 6 Machine learning algorithms for forecasting and diagnosis applications in smart grids - p. 103 10. Chapter 7 Intelligent Digital Twins for social acceptance by design in smart grids - p. 125 11. Chapter 8 Fault detection in modern power systems - p. 151 12. Chapter 9 Digital Twin testing platforms for power grids - p. 169 13. Chapter 10 Automatic control and reinforcement learning techniques for microgrids - p. 185 14. Part III Digital Twin computing for power grids - p. 207 15. Chapter 11 Towards digital and smart substations: A Digital Twin approach - p. 209 16. Chapter 12 Machine learning for power system restoration - p. 221 17. Chapter 13 Algorithm and simulation for a Digital Twin–based protection system - p. 233 18. Chapter 14 Cloud computing for Digital Twin power grids - p. 247 19. Chapter 15 Fog computing for Digital Twin power grids - p. 277 20. Chapter 16 Edge computing for Digital Twin-enabled future electrical grid systems - p. 293 21. Chapter 17 Quantum computing with Digital Twins and power grids - p. 313 22. Chapter 18 Integrating generative AI and LLMs with digital twins in smart grids - p. 323 23. Chapter 19 Harnessing generative AI and large language models for Digital Twin-enabled smart grids - p. 359 24. Chapter 20 Integration of generative artificial intelligence and large language models into Digital Twin-based smart grids - p. 375 25. Chapter 21 Artificial intelligence, Digital Twin and blockchain technology for secure smart grids - p. 395 26. Chapter 22 Future perspectives for Digital Twin–based power grids - p. 419 27. Index - p. 429
