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.
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 the fundamental concept, followed by a comprehensive safety analysis considering crop physiological characteristics. Subsequent chapters delve into enhancing the synergy of agriculture, energy, and environment through carbon monitoring, optimal management strategies for rural microgrids, and distributed energy planning. Later chapters cover system design insights, load modeling and control strategies, and AI-based tools. Designed for engineers, students, and researchers, The Agricultural Energy Internet collates the state of the research and provides detailed case studies and practical applications, making it indispensable for those innovating in sustainable energy systems.
Towards Future Smart Power Systems with High Penetration of Renewables: Emerging Technologies, New Tools, and Case Studies explores the latest tools and approaches for smart power systems with high-scale integration of renewable energy, covering technology, optimization, control, forecasting, and market structures. The first section of the book on emerging technologies and energy vectors identifies some of the most prominent energy carriers in future power systems, and discusses the implications of different energy technologies as well as their advantages and disadvantages. This is followed by a section focusing on new markets, businesses and structures, discussing how such energy carriers should be managed within existing or future market structures, and discussing the different opportunities and challenges bought by renewable technologies. The third part of the book analyzes real projects and case studies, offering steps forward in the large-scale integration of renewable energy in existing power systems. Finally, the fourth section examines optimization and control for power systems with renewables. covering the needs of future power systems regarding their optimization and control and describing some of the new tools required, with an emphasis on planning, management and forecasting in future power systems. Drawing on real examples, case studies, computational tools, and analysis, this book is a valuable resource for all those with an interest in renewable energy integration and smart power systems, including students, researchers, faculty, engineers, R&D, industry personnel, and policy makers.
Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of “green” machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.
The Sustainable Power Grid provides a breakdown of the different challenges faced by power grid modernization and presents practical approaches to tackle them. The technologies, case studies, and applications are presented from the perspective of engineering consultants who participate in major grid-related disasters and perform detailed forensic investigations that support the evolution of sustainable power quality.Chapters discuss key issues surrounding extreme weather, power quality, new technologies, and power converters. This book also outlines a quantitative risk-based framework for asset health assessment of overhead lines, along with engineering and environmental considerations. Concluding with a deep dive into energy storage, topics consist of energy storage system protection, condition monitoring, and emerging technologies.Completely practical in nature, this is a valuable resource for engineers in the electrical power industry and offers students and researchers applied content in the latest power grid technologies.
Cyber Security Solutions for Protecting and Building the Future Smart Grid guides the reader from the fundamentals of grid security to practical techniques necessary for grid defense. Through its triple structure, readers can expect pragmatic, detailed recommendations on the design of solutions and real-world problems. The book begins with a supportive grounding in the security needs and challenges of renewable-integrated modern grids. Next, industry professionals provide a wide range of case studies and examples for practical implementation.Finally, cutting-edge researchers and industry practitioners guide readers through regulatory requirements and develop a clear framework for identifying best practices. Providing a unique blend of theory and practice, this comprehensive resource will help readers safeguard the sustainable grids of the future.
Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply.An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy.
Future Power System Elements, Challenges, and Solutions synthesizes essential knowledge of power system challenges into a single volume. Ideal for researchers, engineers, and students in power systems, this book supports readers from initial understanding to design and implementation.This book begins with the fundamental history, policies, and long-term needs of a sustainable energy system. A detailed analysis helps evaluate the challenges specific to distribution, generation, and transmission systems, preparing readers to understand the criteria for strong solutions. The final chapters break down potential solutions for each area in turn, offering a chance to develop your own approach. Readers can build understanding of generation technologies from distributed generation to fuel cells, transmission systems including HVDC systems and FACTS devices, and distribution solutions from microgrids to Energy Storage Solutions (ESS).Providing in-depth analysis of the biggest challenges currently facing the industry, Future Power System Elements, Challenges, and Solutions enables researchers, industry engineers and students to generate solutions for the power systems of the future.
Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies.Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering.Readers will develop new strategies and techniques for leveraging data towards real-world outcomes.
Solar Thermal Systems and Applications: New Design Techniques for Improved Thermal Performance brings together the latest advances for the improved performance, efficiency, and integration of solar thermal energy (STE) technology.This book begins by introducing solar energy and solar thermal energy as a viable option in terms of green energy for industrial, commercial, and residential applications, as well as its role and potential within hybrid energy systems. This is followed by detailed chapters that focus on key innovations in solar thermal energy systems, covering novel approaches and techniques in areas such as flat plate solar collectors, modified evacuated tube solar collectors, solar parabolic trough collectors, linear Fresnel reflectors, photovoltaic thermal systems (PVT), phase change materials (PCMs), nanotechnology, combined PVT-PCM systems, solar thermal systems and Trombe wall design, solar still units, and solar dish systems. Throughout this book, the coverage is supported by experimental and numerical modeling methods, and techniques are discussed and assessed with a view to improved electrical and thermal efficiency and performance.Solar Thermal Systems and Applications is a valuable resource for researchers and advanced students in solar energy, thermal engineering, hybrid energy systems, renewable energy, mechanical engineering, nanotechnology, and materials science. This is also of interest to engineers, R&D professionals, scientists, and policy makers with an interest in solar thermal energy in an industrial, residential, or commercial setting.