Overview
- Treats both theoretical and practical aspects of control strategies on a pilot-scale microgrid
- Reviews key recent research of conception to implementation of optimal microgrid management
- Provides a reliable simulation tool for simulating microgrids and testing controllers
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Industrial Control (AIC)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.
Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management.
Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Authors and Affiliations
About the authors
Felix Garcia-Torres was born in Cordoba, Spain, 1977. He received the Ph.D. degree in electrical engineering from the University of Seville, Spain, in 2015. In 2009, he joined the Centro Nacional del Hidrogeno, Puertollano, Spain, where he is currently responsible for the Microgrids Laboratory. Prior to this, he worked at several research centers and companies such as the Instituto de Automatica Industrial-Consejo Superior de Investigaciones Cientificas (Spain), the University of Seville spin-off GreenPower Technologies (Spain), and the Universite Catholique de l’Ouest (Angers, France). His current research interests include advanced power electronics and control to introduce energy storage technologies in transport and smart grids applications.
Miguel A. Ridao got his PhD in Electrical Engineering in 1996 and he is currently Full Professor of Systems Engineering and Automation at Engineering School of University of Seville (Spain). His teaching activities are related to Automatic Control and Industrial Automation. His current research interests include distributed control, control of water systems, microgrids and hybrid vehicles including fuel cells. In these areas, he has worked in different projects with public and private funding. He is the main researcher in several projects, including “HDMPC Project”, funded by the European Commission (7th Framework Programme) and coordinator of “Agerar Project” (Interreg POCTEP). He was the Head of the Department of System Engineering and Automation of University of Seville from 2007 to 2011.
Bibliographic Information
Book Title: Model Predictive Control of Microgrids
Authors: Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/978-3-030-24570-2
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-24569-6Published: 24 September 2019
Softcover ISBN: 978-3-030-24572-6Published: 24 September 2020
eBook ISBN: 978-3-030-24570-2Published: 12 September 2019
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XIX, 266
Number of Illustrations: 37 b/w illustrations, 65 illustrations in colour
Topics: Energy Systems, Control and Systems Theory, Power Electronics, Electrical Machines and Networks, Environmental Engineering/Biotechnology