Power Systems Operation with 100% Renewable Energy Sources combines fundamental concepts of renewable energy integration into power systems with real-world case studies to bridge the gap between theory and implementation. The book examines the challenges and solutions for renewable energy integration into the transmission and distribution grids, and also provides information on design, analysis and operation. Starting with an introduction to renewable energy sources and bulk power systems, including policies and frameworks for grid upgradation, the book then provides forecasting, modeling and analysis techniques for renewable energy sources. Subsequent chapters discuss grid code requirements and compliance, before presenting a detailed break down of solar and wind integration into power systems. Other topics such as voltage control and optimization, power quality enhancement, and stability control are also considered. Filled with case studies, applications and techniques, Power Systems Operation with 100% Renewable Energy Sources is a valuable read to researchers, students and engineers working towards more sustainable power systems.  

Renewable Energy Integration in Utility Grids reviews current challenges and technologically driven solutions to mitigate the significant issues associated with increasing renewable resource penetration in utility grid networks. It provides a detailed framework to address mostly all the significant issues of high renewable energy integration into the utility grid networks using intelligent techniques and advanced power electronics technology. Chapters address current advances in the grid integration of wind technology, solar PV systems, solar thermal plants, reactive power management, grid stability, variability, power quality, power system protection, generation side flexibility, demand-side flexibility, smart monitoring and communication, and regulatory frameworks.

Power Quality in Modern Power Systems presents an overview of power quality problems in electrical power systems, for identifying pitfalls and applying the fundamental concepts for tackling and maintaining the electrical power quality standards in power systems. It covers the recent trends and emerging topics of power quality in large scale renewable energy integration, electric vehicle charging stations, voltage control in active distribution network and solutions to integrate large scale renewable energy into the electric grid with several case studies and real-time examples for power quality assessments and mitigations measures. This book will be a practical guide for graduate and post graduate students of electrical engineering, engineering professionals, researchers and consultants working in the area of power quality.

Electric vehicles (EV), are being hailed as part of the solution to reducing urban air pollution and noise, and staving off climate change. Their success hinges on the availability and reliability of fast and efficient charging facilities, both stationary and in-motion. These in turn depend on appropriate integration with the grid, load and outage management, and on the mitigation of loads using renewable energy and storage. Charging management to preserve the battery will also play a key role. This book covers the latest in charging technology; stationary as well as wireless and in-motion. Grid integration, simulations, fast charging, and battery management are also addressed. The objective of this book is to provide readers with an in-depth knowledge about EV charging infrastructure, and grid integration issues and solutions. The book serves as a reference for researchers in academia and industry, covering almost every aspect of the charging and grid integration of EVs.

The growing share of renewable energies, as well as the rising demand for electricity for transport and heating, are increasing the importance of power converters and the requirements for reliability and control. Intelligent control can increase converter efficiency, reducing size and weight. The application of intelligent control techniques to power converters has therefore recently become a focus of research. Intelligent Control of Medium and High Power Converters summarizes the state of the art in the control of electric power converters. After an overview of the topic, the chapters cover optimization, bi-directional DC-DC converters, high-gain converters, GaN-based synchronous converters, control design, sliding mode control of three-phase inverters and three-level grid-connected inverters, neurological control, low-frequency switching operation, and a comparison and overview chapter. Comparing control methods for different converters helps users find the best solution for each type of converter and application. The book is a valuable resource for researchers and manufacturers involved with converters and power grids, as well as for advanced students.

THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research findings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the field and create a broad knowledge about the most recent research. Series Editor Dr. Siddhartha Bhattacharyya, CHRIST (Deemed to be University), Bangalore, India Editorial Advisory Board Dr. Elizabeth Behrman, Wichita State University, Kansas, USA Dr. Goran Klepac Dr. Leo Mrsic, Algebra University College, Croatia Dr. Aboul Ella Hassanien, Cairo University, Egypt Dr. Jan Platos, VSB-Technical University of Ostrava, Czech Republic Dr. Xiao-Zhi Gao, University of Eastern Finland, Finland Dr. Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil

The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

A smart building is the state-of-art in building with features that facilitates informed decision making based on the available data through smart metering and IoT sensors. This set provides useful information for developing smart buildings including significant improvement of energy efficiency, implementation of operational improvements and targeting sustainable environment to create an effective customer experience. It includes case studies from industrial results which provide cost effective solutions and integrates the digital SCADE solution. Describes complete implication of smart buildings via industrial, commercial and community platforms Systematically defines energy-efficient buildings, employing power consumption optimization techniques with inclusion of renewable energy sources Covers data centre and cyber security with excellent data storage features for smart buildings Includes systematic and detailed strategies for building air conditioning and lighting Details smart building security propulsion. This set is aimed at graduate students, researchers and professionals in building systems, architectural, and electrical engineering.

Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing. Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.

Active Electrical Distribution Network: Issues, Solution Techniques and Applications is a comprehensive reference that addresses the issues and opportunities across one of the most overlooked sectors of the electrical industry, electrical distribution. The book begins with an introduction to electrical distribution networks, and then explores both present and future developments in the areas of smart grids, electric vehicles, micro grids, demand side response and active distribution networks. The ongoing transition of energy systems is also covered, providing recommendations for a higher penetration of renewable energy, utilization of new equipment and new network configurations, as well as development of new design and operation methods, and applications of new incentives and business models. The book closes with a section on optimizing operational issues, featuring guidance on optimal expansion planning of distribution systems in smart grids and optimization of photovoltaic (PV) systems. Active Electrical Distribution Network is an ideal reference for all those interested in the modeling, analysis, control, operation and planning techniques that are key to addressing the knowledge and information needs of the engineering and research audience.

Blockchain-Based Systems for a Paradigm Shift in the Energy Grid explores the technologies and tools to utilize blockchain for energy grids and assists professionals and researchers to find alternative solutions for the future of the energy sector. The focus of this globally edited book is on the application of blockchain technology and the balance between supply and demand for energy and where it is achievable. Looking at the integration of blockchain and how it will make the network resistant to any failure in sub-components, this book has very clearly explores the areas of energy sector that need in-depth study of Blockchain for expanding energy markets. Meeting the demands of energy by local trading, verifying use of green energy certificates and providing a greater understanding of smart energy grids and Blockchain use cases. Exhaustively exploring the use of Blockchain for energy, this reference useful for all those in the energy industry looking to avoid disruption in the grid and sustain and control successful flow of electricity.

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation.

This book covers smart grid applications of various big data analytics, artificial intelligence, and machine learning technologies for demand prediction, decision-making processes, policy, and energy management. It delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems. In the era of propelling traditional energy systems to evolve towards smart energy systems, systems, including power generation energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic and industrial loads. Similarly, with the integration of solid-state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. Technical topics discussed in the book include: Hybrid smart energy system technologies Energy demand forecasting Use of different protocols and communication in smart energy systems Power quality and allied issues and mitigation using AI Intelligent transportation Virtual power plants AI business models

Multi-level Inverters (MLIs) are widely used for conversion of DC to AC power. This book provides various low-switching frequency (LSF) modulation schemes (conventional and improved), which can be implemented on MLIs. The LSF modulation schemes are implemented to three different MLI topologies to demonstrate their working and aimed at their application to reader invented MLI topologies. Highlighting the advantages of LSF over high-switching frequency (HSF) modulation schemes, the simulations are carried out using MATLAB®/Simulink along with hardware experiments. The practical application of MLIs to renewable energy sources and electric vehicles is also provided at the end of the book. Aimed at researchers, graduate students in Electric Power Engineering, Power Electronics, this book: Presents detailed overview of most commonly used multi-level invertor topologies. Covers advantages of low-switching over high-switching frequency scheme. Includes an exclusive section dedicated for an improved low-switching modulation scheme. Dedicated chapter on application of renewable energy sources to multi-level invertors and electric vehicles. Explains all the low-switching frequency modulation schemes.

This book covers smart grid applications of various big data analytics, artificial intelligence, and machine learning technologies for demand prediction, decision-making processes, policy, and energy management. The book delves into new technologies such as the Internet of Things, BlockChain for smart home solutions, and smart city solutions in depth in the context of modern power systems. In the era of propelling traditional energy systems to evolve towards smart energy systems, systems, including power generation energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. Technical topics discussed in the book include: Hybrid smart energy system technologies Smart meters Energy demand forecasting Use of different protocols and communication in smart energy systems Power quality and allied issues and mitigation using AI Intelligent transportation Virtual power plants AI based smart energy business models Smart home solutions Blockchain solutions for smart grids

Residential Microgrids and Rural Electrifications contains an overview of microgrids' architecture, load assessments, designing of microgrids for residential systems, and rural electrifications to help readers understand the fundamentals. Including many new topics in the field of home automation and the application of IoT for microgrids monitoring and control, the book includes sections on the infrastructure necessary for charging Electric Vehicles in residential systems and rural electrifications and how to estimate the energy and cost of various combinations of energy resources. Many examples and practical case studies are included to enhance and reinforce learning objective goals. Those in engineering research and technical professions will be able to perform energy and cost analyses of various combinations of energy sources by using advanced, real simulation tools.

Discusses various aspects of role of Internet of Things (IoT) and Machine Learning in smart buildings. Explains pertinent system architecture focusing on power generation and distribution. Covers power enabling technologies for smart cities. Includes Photovoltaic System Integrated Smart Buildings.

Systematically defines energy-efficient buildings, employing power consumption optimization techniques with inclusion of renewable energy sources. Covers data centre and cyber security with excellent data storage features for smart buildings. Includes systematic and detailed strategies for building air conditioning and lighting. Details smart building security propulsion.

This new volume, Deep Learning in Visual Computing and Signal Processing, covers the fundamentals and advanced topics in designing and deploying techniques using deep architectures and their application in visual computing and signal processing. The volume first lays out the fundamentals of deep learning as well as deep learning architectures and frameworks. It goes on to discuss deep learning in neural networks and deep learning for object recognition and detection models. It looks at the various specific applications of deep learning in visual and signal processing, such as in biorobotics, for automated brain tumor segmentation in MRI images, in neural networks for use in seizure classification, for digital forensic investigation based on deep learning, and more. Key features: Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text An enlightening amalgamation of deep learning concepts with visual computing and signal processing applications, this valuable resource will serve as a guide for researchers, engineers, and students who want to have a quick start on learning and/or building deep learning systems. It provides a good theoretical and practical understanding and complete information and knowledge required to understand and build deep learning models from scratch.

Presents technologies and algorithms associated with the application of big data for smart cities. Discussion on big data theory modeling and simulation for smart cities Covers applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS). Discussion on concepts including smart education, smart culture, and smart transformation management for social and societal changes.

1) Complete details on IoT in Renewable Energy Systems 2) Analytics and its application in Renewable Energy Systems 3) Applications in renewable energy systems. 4) Real-time Case Studies

1) Complete details on IoT in Renewable Energy Systems 2) Analytics and its application in Renewable Energy Systems 3) Applications in renewable energy systems. 4) Real-time Case Studies

Energy is a key source of economic growth due to its involvement as the primary input. Energy drives economic productivity and industrial growth. It can be considered as the prime requirement for the modern economy. Solar energy is a renewable source of energy that can be used to produce heat or generate electricity. The total amount of solar energy available on Earth’s surface is vastly in excess of the world’s current and anticipated energy requirements. In the 21st century, solar energy is expected to become increasingly attractive as a renewable energy source. An increase in the share of solar energy may destabilize the grid. To overcome the issues of grid instability, specifically in remote areas, BIM and GIS-based microgrid planning based on data can be effectively used. BIM and GIS are used to assess alternative solutions and big data analytics in building solar electrical systems according to planning requirements and managing assets. The integration of BIM and GIS information systems for microgrid planning is appealing due to its potential benefits, such as it decreases the microgrid planning time and cost. The present book is about the advancements in technology for harnessing solar energy and the challenges associated with different modes of utilizing this inexhaustible renewable energy source. This book will be helpful for researchers, academicians, technologists, innovators, and industry experts working in the area of solar energy, artificial intelligence, and smart grids.

Microgrids offers a complete discussion and details about microgrids and their applications, including modeling of AC/DC and hybrid grids in a tied mode with simulation for the solar systems, wind turbines, biomass and fuel cells, and deployment issues. The data communications and control mechanism implementations are analyzed for proper coordination of the AC/DC microgrid. The various real-time applications and future development of the microgrid are also discussed in this book, with MATLAB (R)-based simulations and results. This book: Discusses the fundamentals of microgrids, the components of microgrids, the modeling of renewable energy sources, and the implementation of microgrids. Explores AC and DC microgrid modeling with real-time examples. Examines the effective extraction of energy from renewable energy sources. Covers analysis of data communications and control-mechanism implementations. Includes HOMER/MATLAB (R)-based simulations and results on microgrids. This book would be a welcome addition to the libraries of researchers, senior undergraduate students, and graduate students in power and electrical engineering, especially those working with smart and microgrids.