Model Predictive Control of Microgrids

ISBN-13: 9783030245696 / Angielski / Twarda / 2019 / 266 str.

Carlos Bordons; Felix Garcia-Torres; Miguel A. Ridao

Opis

The authors provide a modular simulator to be run in MATLAB/Simulink®, for readers to create their own microgrids using the blocks supplied, in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids.

Spis treści

1: Microgrid Control Issues.- 1.1: Microgrid as a New Paradigm for the Electrical System.- 1.1.1: Microgrids and Storage.- 1.1.2: Microgrids Around the World.- 1.2: Control of Microgrids.- 1.2.1: Control Goals and Challenges.- 1.2.2: Control Techniques.- 1.2.3: Introduction to Model Predictive Control.- 1.3: Microgrids and the Electrical System.- 1.3.1: Microgrids and Electric Vehicles.- 1.3.2: Networks of Microgrids.- 1.4: Outline of the Chapters.- 2: Overview of Control Methods Applied to Microgrids.- 3: Dynamic Models of Microgrids and Components.- 4: Basic Energy and Power Management Systems in Microgrids.- 5: Hybrid MPC Applied to Economical Dispatch of Microgrids.- 5.1: Management Electricity Markets.- 5.1.1: Day-Ahead Market.- 5.1.2: Intraday Market.- 5.1.3: Ancilliary Services.- 5.2: Energy Storage Systems in the Electrical Energy Market.- 5.3: Tertiary Model Predictive Control-Schedule.- 5.3.1: Mixed Logic Dynamic Systems.- 5.3.2: Model of the Plant.- 5.3.3: Day-Ahead Market MPC.- 5.3.4: Intraday Market MPC.- 5.3.5: Regulation Service Market MPC.- 5.3.6: System Constraints.- 5.4: Tertiary Model Predictive Control-Load.- 5.4.1: Objective Function.- 5.4.2: System Constraints.- 5.5: Experimental Results.- 5.5.1: Day-Ahead Market.- 5.5.2: Intraday Market.- 5.5.3: Regulation Service Market.- 5.5.4: Load Sharing.- 5.6: Comparison with other Control Methods.- 6: Enhancement of Power Quality using Finite-State MPC.- 6.1: Power Quality in the Smart Grid.- 6.1.1: Analysis of Power Quality Issues in the Components.- 6.1.2: Distributed Storage Interface.- 6.1.3: Microgrid Operation Modes.- 6.2: MPC Methods for Power Converters.- 6.2.1: Direct or Finite Control Set MPC.- 6.2.2: MPC with Continuous Control Set.- 6.3: Primary Model Predictive Control Design.- 6.3.1: VSI Current MPC Based on Fourier Transform.- 6.4: Secondary Model Predictive Control Design.- 6.4.1: VSI Voltage MPC Based on Fourier Transform.- 6.4.2: VSI Current MPC Based on Fourier Transform.- 6.5: Simulation Results.- 6.5.1: VSI Current MPC Based on Fourier Transform.- 6.5.2: VSI Voltage MPC Based on Fourier Transform.- 7: Integration of Electric Vehicles in Microgrids.- 8: Stochastic MPC and Failure Management.- 9: Distributed MPC for Networks of Microgrids.- 10: Glossary.- 11: Index.

O autorze

Carlos Bordons has a PhD in Electrical Engineering (1994) and he is Full Professor at the Systems Engineering and Automatic Control Department of the University of Seville, Spain. He has worked in different projects in collaboration with industry in fields such as control of power management in hybrid vehicles, control of microgrids, simulation and optimization of oil pipeline networks, automation of copper furnaces or modelling and control of fuel cell systems. His current research interests include advanced process control, especially Model Predictive Control (MPC). He is the author of three books in the field of MPC published by Springer and around 60 peer-reviewed journal papers and around 80 conference papers. He is currently Associate Editor of Control Engineering Practice and he has also been  Associate Editor of IEEE Transactions on Industrial Electronics and Optimal Control Application and Methods. He was a Council Member of the European Control Association from 2007 to 2015. Between 2008 and 2012 he was the Managing Director of AICIA, which is the main Research and Technology Organization in Andalusia (Southern Spain).

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.

Blurb

The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids.

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. The authors also provide a modular simulator to be run in MATLAB/Simulink®, for readers to create their own microgrids using the blocks supplied, 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.