Josep M. Guerrero - Böcker

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.

Covers different types of microgrids and their architecture and control in a single book Includes original, state-of-the-art research contributions by international experts Features global case studies for better understanding and real-life examples

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. Sections introduce 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. Other sections provide detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, optimization, and more.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.Explores cutting-edge intelligent techniques and their implications for future energy systems developmentOpens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and moreIncludes a range of case studies that provide insights into the challenges and solutions in real-world applications

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.

Introduces lithium-ion batteries, characteristics and core state parameters Examines battery equivalent modeling and provides advanced methods for battery state estimation Analyzes current technology and future opportunities

A Microgrid Cluster Management and Internet-of-Things Framework: Fundamentals, Protocols, and Case Studies presents a cutting-edge guide on leveraging IoT technologies to optimize renewable energy integration through microgrid clusters. The book begins by detailing the fundamental aspects of microgrid clusters, encompassing design, control, and optimization. This comprehensive grounding prepares readers for the exploration of IoT integration to harness big data analytics for energy efficiency, power management, fault monitoring, and cybersecurity. The book further delves into the meticulous analysis of protocol selection for effective operation and control.It covers wired protocols such as Modbus, Profibus, and Ethernet, wireless protocols like Zigbee, Wi-Fi, and Bluetooth, and IoT protocols including CoAP, MQTT, and HTTP. By outlining an adaptable framework for interdisciplinary opportunities, this book guides researchers and engineers in creating the flexible, renewable grid of the future.Includes clearly structured steps from the fundamentals of microgrid clusters to complex applicationsProvides technical protocols for data exchange, cybersecurity, and signal conditioningPresents dedicated case studies to support readers taking the next steps in their own work

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.

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.

This book aims to evaluate and improve the state of charge (SOC) and state of health (SOH) of automotive lithium-ion batteries.The authors first introduce the basic working principle and dynamic test characteristics of lithium-ion batteries. They present the dynamic transfer model, compare it with the traditional second-order reserve capacity (RC) model, and demonstrate the advantages of the proposed new model. In addition, they propose the chaotic firefly optimization algorithm and demonstrate its effectiveness in improving the accuracy of SOC and SOH estimation through theoretical and experimental analysis.The book will benefit researchers and engineers in the new energy industry and provide students of science and engineering with some innovative aspects of battery modeling.

ACTIVE ELECTRICAL DISTRIBUTION NETWORKDiscover the major issues, solutions, techniques, and applications of active electrical distribution networks with this edited resourceActive Electrical Distribution Network: A Smart Approach delivers a comprehensive and insightful guide dedicated to addressing the major issues affecting an often-overlooked sector of the electrical industry: electrical distribution. The book discusses in detail a variety of challenges facing the smart electrical distribution network and presents a detailed framework to address these challenges with renewable energy integration.The book offers readers fulsome analyses of active distribution networks for smart grids, as well as active control approached for distributed generation, electric vehicle technology, smart metering systems, smart monitoring devices, smart management systems, and various storage systems. It provides a treatment of the analysis, modeling, and implementation of active electrical distribution systems and an exploration of the ways professionals and researchers from academia and industry attempt to meet the significant challenges facing them.From smart home energy management systems to approaches for the reconfiguration of active distribution networks with renewable energy integration, readers will also enjoy: A thorough introduction to electrical distribution networks, including conventional and smart networksAn exploration of various existing issues related to the electrical distribution networkAn examination of the importance of harmonics mitigation in smart distribution networks, including active filtersA treatment of reactive power compensation under smart distribution networks, including techniques like capacitor banks and smart devicesAn analysis of smart distribution network reliability assessment and enhancementPerfect for professionals, scientists, technologists, developers, designers, and researchers in smart grid technologies, security, and information technology, Active Electrical Distribution Network: A Smart Approach will also earn a place in the libraries of policy and administration professionals, as well as those involved with electric utilities, electric policy development, and regulating authorities.

Presents a comprehensive guide to transforming power systems through data Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning. Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book’s accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges. Integrating rigorous analysis with application-oriented strategies, this book: Presents advanced techniques in machine learning, predictive modeling, and pattern recognition tailored to energy management and tariff designProvides accessible explanations of complex algorithms through a didactic and visual teaching style, including informative tables and illustrationsHighlights tools for grid stability, demand forecasting, and peak load management using high-resolution energy dataAddresses the integration of renewable energy sources into existing infrastructures through data-driven optimizationDesigned for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.

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.

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.

In the last decade the development and control of Unmanned Aerial Vehicles (UAVs) has attracted a lot of interest. Both researchers and companies have a growing interest in improving this type of vehicle given their many civilian and military applications.This book presents the state of the art in the area of UAV Flight Formation. The coordination and robust consensus approaches are presented in detail as well as formation flight control strategies which are validated in experimental platforms. It aims at helping students and academics alike to better understand what coordination and flight formation control can make possible.Several novel methods are presented:- controllability and observability of multi-agent systems;- robust consensus;- flight formation control;- stability of formations over noisy networks;which generate solutions of guaranteed performance for UAV Flight Formation.Contents1. Introduction, J.A. Guerrero.2. Theoretical Preliminaries, J.A. Guerrero.3. Multiagent Coordination Strategies, J.A. Guerrero, R. Lozano, M.W. Spong, N. Chopra.4. Robust Control Design for Multiagent Systems with Parametric Uncertainty, J.A. Guerrero, G. Romero.5. On Adaptive and Robust Controlled Synchronization of Networked Robotic Systems on Strongly Connected Graphs, Y.-C. Liu, N. Chopra.6. Modeling and Control of Mini UAV, G. Flores Colunga, J.A. Guerrero, J. Escareño, R. Lozano.7. Flight Formation Control Strategies for Mini UAVs, J.A. Guerrero.8. Formation Based on Potential Functions, L. García, A. Dzul.9. Quadrotor Vision-Based Control, J.E. Gomez-Balderas, J.A. Guerrero, S. SALAZAR, R. Lozano, P. Castillo.10. Toward Vision-Based Coordination of Quadrotor Platoons, L.R. García Carrillo, J.A. Guerrero, R. Lozano.11. Optimal Guidance for Rotorcraft Platoon Formation Flying in Wind Fields, J.A. Guerrero, Y. Bestaoui, R. Lozano.12. Impact of Wireless Medium Access Protocol on the Quadrotor Formation Control, J.A. Guerrero, Y. Challal, P. Castillo.13. MAC Protocol for Wireless Communications, A. Mendez, M. Panduro, O. Elizarraras, D. Covarrubias.14. Optimization of a Scannable Pattern for Bidimensional Antenna Arrays to Provide Maximum Performance, A. Reyna, M.A. Panduro, A. Mendez.

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

This book presents volume 3 of selected research papers presented at the fifth International Conference on Digital Technologies and Applications (ICDTA’25). Highlighting the latest innovations in digital technologies as: Artificial Intelligence, Internet of things, Embedded systems, generative AI, Network Technology, digital transformation and their applications in several areas as Industry 4.0, sustainability, Energy transition, Economy, Healthcare, etc. The respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.

This book presents volume 1 of selected research papers presented at the fifth International Conference on Digital Technologies and Applications (ICDTA’25). Highlighting the latest innovations in digital technologies as: Artificial Intelligence, Internet of things, Embedded systems, generative AI, Network Technology, digital transformation and their applications in several areas as Industry 4.0, sustainability, Energy transition, Economy, Healthcare, etc. The respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.

This book presents volume 2 of selected research papers presented at the fifth International Conference on Digital Technologies and Applications (ICDTA’25). Highlighting the latest innovations in digital technologies as: Artificial Intelligence, Internet of things, Embedded systems, generative AI, Network Technology, digital transformation and their applications in several areas as Industry 4.0, sustainability, Energy transition, Economy, Healthcare, etc. The respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.

This book presents volume 4 of selected research papers presented at the fifth International Conference on Digital Technologies and Applications (ICDTA’25). Highlighting the latest innovations in digital technologies as: Artificial Intelligence, Internet of things, Embedded systems, generative AI, Network Technology, digital transformation and their applications in several areas as Industry 4.0, sustainability, Energy transition, Economy, Healthcare, etc. The respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.

This book presents novel idea and concepts developed by the researchers/academia and practicing engineers working in the domain of the power sector infrastructures where power electronics infrastructures are used for improving the system reliability and efficiency in on-grid and off-grid systems.

This book presents novel idea and concepts developed by the researchers/academia and practicing engineers working in the domain of the power sector infrastructures where power electronics infrastructures are used for improving the system reliability and efficiency in on-grid and off-grid systems.

This book offers insights into the 3rd International Conference on Innovations in Energy Management and Renewable Resources, IEMRE 2023. Additionally, it covers energy storage and management, smart grid technologies, renewable energy expansion, and policymaking.

This book offers insights into the 3rd International Conference on Innovations in Energy Management and Renewable Resources, IEMRE 2023. It focuses on various aspects of renewable energy, including solar, wind, hydro, biomass, and other renewable-based energy systems. Additionally, it covers energy storage and management, smart grid technologies, renewable energy expansion, and policymaking. The book is a valuable resource for anyone interested in energy management and renewable resources.

This book highlights recent research advancements in the area of microgrids and virtual power plants. Microgrids and virtual power plants are the future of power generation and delivery systems, and there has been significant research interest in this area over the past decade.