
Edge Computing for Smart Grid
Description
This book offers a comprehensive, interdisciplinary exploration of how edge computing is transforming smart grids from real-time data processing and microgrid energy management to intelligent algorithms for predictive demand response and energy optimization. It bridges cutting-edge technologies such as blockchain, federated learning, mobile crowd computing, and 5G-enabled MEC, while addressing crucial challenges in cybersecurity, anomaly detection, and resource allocation. It delves into the core principles and architectures of edge-enabled smart grids, illustrating how real-time data processing, decentralized resource management, and localized control are transforming traditional energy infrastructures. With a special emphasis on microgrid energy optimization, predictive demand response, and AI-driven decision-making, this book highlights the pivotal role of intelligent algorithms deployed at the network edge in ensuring efficiency, scalability, and low-latency performance. It offers an insightful analysis of cybersecurity vulnerabilities, anomaly detection frameworks, and real-time threat mitigation strategies from the edge computing perspective. Whether you're a researcher, engineer, or energy professional, this book equips you with the insights and tools needed to design, implement, and secure next-generation smart grid systems driven by edge intelligence.
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Persons
Dr. Amitkumar V. Jha is currently working as an Associate Professor at the Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, India. He has authored more than 80 articles in international journals and conference proceedings. He has also authored 4 book chapters published by Springer and Elsevier. He has also edited 3 books. To his credit, he has 4 patents. He also serves as an editor for the Scientific Reports journal of the Springer Nature portfolio. He is an active reviewer of many international journals from the portfolio of IEEE, Springer, Elsevier, Wiley, Frontier, IOP, etc. Dr. Jha has vast teaching experience for many core engineering subjects, such as data communication and networking, computer networks, wireless sensor networks, digital system design, etc. He is a member of various professional societies including a life-time member of Indian Science Congress (ISC), International Association of Engineers (IAENG), and World Leadership Academy.
Dr. Bhargav Appasani is currently an Associate Professor with the School of Electronics Engineering, KIIT University, Bhubaneswar, India. He has published more than 200 articles in international journals and conference proceedings. He has also published six book chapters with Springer and Elsevier. He has authored one book and edited three. He also has several patents filed to his credit. He is an academic editor for the Journal of Electrical and Computer Engineering (Wiley), Scientific Reports (Springer), Measurement and Control (Sage), and others.
Nicu Bizon (IEEE M'06; SM'16), was born in Albesti de Muscel, Arges county, Romania, 1961. He received the B.S. degree in electronic engineering from the University "Polytechnic" of Bucharest, Romania, in 1986, and the PhD degree in Automatic Systems and Control from the same university, in 1996. From 1996 to 1989, he was in hardware design with the Dacia Renault SA, Romania. He is currently professor with the National University of Science and Technology POLITEHNICA Bucharest, Pite?ti University Centre, Romania. He received two awards from Romanian Academy, in 2013 and 2016. He is editor of 19 books and more than 700 papers in scientific fields related to Energy. His current research interests include power electronic converters, fuel cell and electric vehicles, renewable energy, energy storage system, microgrids, and control and optimization of these systems.
Content
Edge Computing in Smart Grids: Applications, Technologies, and Challenges.- The Role of SGs and the IoT in Enabling Eco-friendly Automation Solutions.- Multi-tier edge computing for IoT-based smart grids: Latency-aware data processing and resource management.- Prosumers' Advantages in Smart Grid using Edge Computing.- Smart Microgrid Energy Management Using Edge Computing.- Decentralized Intelligence in Smart Grids: The Role of Edge Computing and Federated Learning.- Machine Learning on a Power Budget: Energy-Efficient Techniques for the Smart Grid Edge.- Digital Twins Based on Edge Computing for Smart Grid Applications.- Edge Computing and Machine Learning for Anomaly Detection in Smart Grids.- Edge-Centric Architectures for Secure and Sustainable Hybrid Energy Management.- Potential Benefits of Edge Computing for Smart Grid and Distributed Systems.