AI and Blockchain in Smart Grids: Fundamentals, Methods, and Applications examines the cutting-edge solution that combines artificial intelligence (AI), blockchain technology, and digital twin concepts to innovate the management and optimization of electrical power distribution. This innovative approach enhances the resilience, efficiency, and security of electricity grids while providing real-time insights for grid operators and stakeholders. The book covers such key elements as using:
- Digital twins in smart grids to gather real-time data from various grid components
- AI-powered analytics to process the data generated by digital twins and to analyze this information to detect patterns, predict grid failures, and recommend adjustments to enhance a grid's performance
- Blockchain-based security to ensure the secure and transparent management of data within a smart grid, especially a tamper-resistant ledger to store information related to energy production, distribution, and consumption
- Decentralized data sharing to allow grid data to be shared securely among various stakeholders, including utilities, regulators, and consumers
- Grid optimization techniques to improve electricity distribution, reduce energy waste, and balance supply and demand efficiently
Select real-world case studies and practical examples demonstrate how AI and blockchain are currently being applied to enhance grid management, energy distribution, and sustainability. By explaining to researchers, academics, and students how AI and blockchain can revolutionize electricity distribution and make grids smarter, more secure, and environmentally friendly, the book points to a future where grid operators, regulators, and consumers will benefit from real-time data and a resilient, efficient energy ecosystem.
Amit Kumar Tyagi is an assistant professor, Department of Fashion Technology, National Institute of Fashion Technology, New Delhi. He earned a PhD degree from Pondicherry Central University, India. He has worked as an assistant professor and senior researcher at the School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. He is a regular member of the ACM and senior member of IEEE.
Shrikant Tiwari received a PhD degree from the Department of Computer Science & Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Currently, he is an associate professor in the School of Computing Science and Engineering (SCSE), Galgotias University, Greater Noida, India. He has authored or co-authored more than 50 national and international journal publications, book chapters, and conference articles. He has five patents filed to his credit. His research interests include machine learning, deep learning, computer vision, medical image analysis, pattern recognition, and biometrics. Dr. Tiwari is a FIETE, a senior member of the IEEE, and member of ACM, IET, CSI, ISTE, IAENG, and SCIEI.
