Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy. The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references. This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.

Microgrids: Modeling, Control, and Applications presents a systematic elaboration of different types of microgrids, with a particular focus on new trends and applications. The book includes sections on AC, DC and hybrid AC/DC microgrids and reflects state-of-the-art developments, covering theory, algorithms, simulations, error and uncertainty analysis, as well as novel applications of new control techniques. Offering a valuable resource for students and researchers working on the integration of renewable energy with existing grid and control of microgrids, this book combines recent advances and ongoing research into a single informative resource. The book highlights recent findings while also analyzing modelling and control, thus making it a solid reference for researchers as well as undergraduate and postgraduate students.

Multidimensional Lithium-Ion Battery Status Monitoring focuses on equivalent circuit modeling, parameter identification, and state estimation in lithium-ion battery power applications. It explores the requirements of high-power lithium-ion batteries for new energy vehicles and systematically describes the key technologies in core state estimation based on battery equivalent modeling and parameter identification methods of lithium-ion batteries, providing a technical reference for the design and application of power lithium-ion battery management systems. Reviews Li-ion battery characteristics and applications. Covers battery equivalent modeling, including electrical circuit modeling and parameter identification theory Discusses battery state estimation methods, including state of charge estimation, state of energy prediction, state of power evaluation, state of health estimation, and cycle life estimation Introduces equivalent modeling and state estimation algorithms that can be applied to new energy measurement and control in large-scale energy storage Includes a large number of examples and case studies This book has been developed as a reference for researchers and advanced students in energy and electrical engineering.

State Estimation Strategies in Lithium-ion Battery Management Systems presents key technologies and methodologies in modeling and monitoring charge, energy, power and health of lithium-ion batteries. Sections introduce core state parameters of the lithium-ion battery, reviewing existing research and the significance of the prediction of core state parameters of the lithium-ion battery and analyzing the advantages and disadvantages of prediction methods of core state parameters. Characteristic analysis and aging characteristics are then discussed. Subsequent chapters elaborate, in detail, on modeling and parameter identification methods and advanced estimation techniques in different application scenarios. Offering a systematic approach supported by examples, process diagrams, flowcharts, algorithms, and other visual elements, this book is of interest to researchers, advanced students and scientists in energy storage, control, automation, electrical engineering, power systems, materials science and chemical engineering, as well as to engineers, R&D professionals, and other industry personnel.

Power electronics converters are devices that change parameters of electric power, such as voltage and frequency, as well as between AC and DC. They are essential parts of both advanced drives, for machines and vehicles, and energy systems to meet required flexibility and efficiency criteria. In energy systems both stationary and mobile, control and converters help ensure reliability and quality of electric power supplies. This reference in two volumes is useful reading for scientists and researchers working with power electronics, drives and energy systems; manufacturers developing power electronics for advanced applications; professionals working in the utilities sector; and for advanced students of subjects related to power electronics. Volume 1 covers converters and control for drives, while Volume 2 addresses clean generation and power grids. The chapters enable the reader to directly apply the knowledge gained to their research and designs. Topics include reliability, WBG power semiconductor devices, converter topology and their fast response, matrix and multilevel converters, nonlinear dynamics, AI and machine learning. Robust modern control is covered as well. A coherent chapter structure and step-by-step explanation provide the reader with the understanding to pursue their research.

Batteries are a necessary part of a low-emission energy system, as they can store renewable electricity and assist the grid. Utility-scale batteries, with capacities of several to hundreds of MWh, are particularly important for condominiums, local grid nodes, and EV charging arrays. However, such batteries are expensive and need to be monitored and managed well to maintain capacity and reliability. Artificial intelligence offers a solution for effective monitoring and management of utility-scale batteries. This book systematically describes AI-based technologies for battery state estimation and modeling for utility-scale Li-ion batteries. Chapters cover utility-scale lithium-ion battery system characteristics, AI-based equivalent modeling, parameter identification, state of charge estimation, battery parameter estimation, offer samples and case studies for utility-scale battery operation, and conclude with a summary and prospect for AI-based battery status monitoring. The book provides practical references for the design and application of large-scale lithium-ion battery systems. AI for Status Monitoring of Utility-Scale Batteries is an invaluable resource for researchers in battery R&D, including battery management systems and related power electronics, battery manufacturers, and advanced students.

Distributed generation of electric energy has become part of the current electric power system. In this book, a recent research study is arising on a new scenario in which small energy sources make up a new supply system: The microgrid. The most recent research projects show the technical difficulty of controlling the operation of microgrids, because they are complex systems in which several subsystems interact: energy sources, power electronic converters, energy storage systems, local, linear and non-linear loads and of course, the main grid. In next years, the electric grid will evolve from the current very centralized model toward a more distributed one. Summing up, it is pursued the generation of small quantities of electric power by the users (called, microgeneration in the origin), considering them not only as electric power consumers but also as responsible for the generation, becoming this way an integral and active part of the grid.