Advanced Grid Technologies: Grid Evolution, Smart Grid Fundamentals, and Renewable Energy Integration, Volume One provides a coherent, practical introduction to the innovative, modern techniques required to integrate renewable energy into power grids. The book focuses on the basic history of the grid, fundamental aspects of grid management, and the challenges set by renewable integration. It begins by outlining the history of the modern energy grid before providing a comprehensive overview of renewable energy sources, the challenges their use present for typical energy grids, and the fundamentals of modern smart adaptations.Specific major challenges are broken down into fundamentals, including electric vehicles and vehicle-to-grid, energy storage, intelligent electronic device-based substations, and demand forecasting for energy stability. Including a variety of numerical examples and working from early principles for each topic, this book provides late-stage undergraduate and graduate students in energy systems and power engineering, as well as engineers working to modernize the power grid, with a fundamental resource for renewable energy integration.
Advanced Energy Management: Digitalization and AI for the Internet of Energy presents the latest advances in the fields of distributed renewable systems, advanced controls, and energy management of nonlinear energy behaviours. The book addresses the problems of energy resilience under extreme climate and extreme events and presents new applications for energy-efficient, low-carbon and energy-reliable cities. The book explains how energy management is used in multi-energy systems for improved power dispatch, fast response, dynamic ageing, techno-economic performance. The introduction provides a comprehensive overview of artificial intelligence applications for renewable energy systems. The subsequent sections delve into various aspects, such as the interconnection between climate change and multi-energy systems, the role of big data and digitalization in sustainable energy supply, the integration of renewable energy sources, and the application of machine learning in power forecasting, demand predictions, and energy storage. Following this, the book explains demand-side management and grid-response controls in integrated energy management systems (IEMSs), and energy management systems (EMSs) in integrated energy systems and power grid with artificial intelligence. Dedicated chapters review peer-to-peer and blockchain-based energy sharing and trading, dynamic pricing and decision making in distributed energy markets, as well as city-scale energy resilience and robustness with distributed energy systems. Finally, the ethical, regulatory, and policy considerations of applying artificial intelligence in energy systems and energy management are discussed, and detailed Sustainability and Environmental Impact Analyses are presented. Advanced Energy Management: Digitalization and AI for the Internet of Energy is an invaluable reference for students, researchers, and industry professionals seeking advanced energy management strategies, and will be of interest to anyone involved in carbon neutrality solutions.
Case Studies in Energy Systems: A Practical Approach to Energy System Design and Analysis demonstrates the application of both fundamental and advanced principles by providing a broad range of case studies, examples, and models. Opening with a clear summary of the foundational theory, this book then presents a selection of case studies based on the experience of experts. Examples are analyzed in a holistic context, considering environmental, economic, and technological aspects. Finally, the authors have provided models, codes, and calculation spreadsheets where appropriate, allowing readers to test the tools for themselves.Equipping researchers and advanced students for the transition to industry practice, this book is a guide to real-world problem-solving for the energy future.
Artificial Intelligence for Reliability and Maintainability of Energy Systems prepares students, researchers, and industry engineers to design and maintain reliable, sustainable energy systems using state-of-the-art AI techniques. The book provides a clear foundation in the fundamentals of power systems statistics and reliability, including resilience principles and strategies, practical applications, and real-world solutions. It covers a wide range of renewable sources, including biomass and biomethane, solar, and hybrid-renewable systems. The AI tools presented cover forecasting, the Internet-of-Things, machine learning, digital twin technology, and big data analysis, with a variety of applications to avoid power outages, minimize disruption, and accurately assess system resilience.Including case studies and details methodology for practical techniques, this book helps energy systems engineers and researchers to provide a stable and consistent power supply in the face of climate change challenges and the energy transition.
Development of Electric Vehicles in Smart Grid Concepts presents a guide to the design, development, and deployment of electric vehicles in smart grids. The book examines the current technological progression of power electronic converters for smart grid operation and EV technology as well as providing the international standards, policies, and market needs to ensure readers understand the nuanced requirements of the grid. The book begins by introducing EVs, encompassing their historical evolution, current status, and potential advancements. It delves into the architecture, components, working modes, and technological challenges within EVs.There is a dedicated chapter on international standards, governmental policies, and codes for implementation, followed by the integration of EVs into smart grids, discussing infrastructure requirements, impacts, and integration modes (V2G, G2V, V2B, and V2H). Technical aspects, modeling, and optimization methods for EV scheduling are explored alongside battery modeling, degradation, charging standards, SOC estimation, and battery management systems. Additionally, the book examines EV interaction with smart grids, renewable energy sources, energy storage systems, global market surveys, and future trends.
Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management.
Cyber-Physical Structures of Power Applications looks at the necessary cyber-physical structures for the proper operation of power applications and the challenges and future trends of cyber-physical power applications. This reference considers the coordination and control of power applications necessary for successful implementation of communication networks, as well as the effects on power application physical components, cyber-infrastructures, and controllers. The book also previews challenges (e.g., vulnerability to cyber-attacks) and future trends of cyber structures in control strategies in both large and small power systems.
Enhancing Resilience in Power Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks.Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future.
Internet-of-Things Cybersecurity in Critical Infrastructures: Case Studies in Energy Systems takes readers from the basic principles of cybersecurity in essential public infrastructure to the specific requirements of energy systems connected to the Internet-of-Things. This book provides a holistic approach to the challenges (legal, technological, and human) of AI and IOT integration for renewable, modern energy infrastructure. It outlines the fundamental challenges and technologies of cybersecurity for essential infrastructure and goes on to apply this specifically to energy grids and systems, in the light of the ML, AI, and IOT technologies required for renewable integration.Case studies are provided to ensure the reader is fully equipped to problem-solve and apply the knowledge in the development and planning of secure energy infrastructure. Packed with a wealth of practical experience and guidance, this book is a critical resource for energy system engineers looking for a primer on the cybersecurity needs of connected, renewable-integrated energy infrastructure.
The Agricultural Energy Internet: Theories, Methods, and Future Prospects provides a pioneering guide to the grid integration and impact of agricultural energy systems for a distributed and sustainable power grid. This book begins with an introduction to fundamental concepts that is followed by a comprehensive safety analysis considering crop physiological characteristics. Subsequent chapters delve into enhancing the synergy of agriculture, energy, and the environment through carbon monitoring, optimal management strategies for rural microgrids, and distributed energy planning.Later chapters cover system design insights, load modeling, control strategies, and AI-based tools. Designed for engineers, students, and researchers, this book collates the state of research and provides detailed case studies and practical applications, making it indispensable for those innovating in sustainable energy systems.