Synopsis
Modern Control of DC-Based Power Systems: A Problem-Based Approach addresses the future challenges of DC Grids in a problem-based context for practicing power engineers who are challenged with integrating DC grids in their existing architecture. This reference uses control theory to address the main concerns affecting these systems, things like generation capacity, limited maximum load demands and low installed inertia which are all set to increase as we move towards a full renewable model. Offering a new approach for a problem-based, practical approach, the book provides a coordinated view of the topic with MATLAB®, Simulink® files and additional ancillary material provided.
- Includes Simulink® Files (of examples and for lab training classes) and MATLAB® files
- Presents video slides to support the problem-based approach to understanding DC Power System control and application
- Provides stability analysis of DC networks and examples of common stability problems
About the Authors
Marco Cupelli is the Division Head of Power Systems Control and Automation at E.ON Energy Research Center (EON ERC), RWTH Aachen University. He is passionate in leading and conduction research activities in the areas of power system control and automation, developing innovative control strategies for medium and low voltage distribution networks and integrating smart metering devices to enable cloud automation solutions. He is managing and mentoring a cross-functional team of more than 20 researchers. He is an IEEE Senior Member where he is involved in two P2030 working. Furthermore, he is an active member of the European Union H2020 Bridge Initiative.
He received his Doctoral degree in Electrical Engineering from RWTH Aachen University and his Diploma degree in electrical engineering and business administration from Technische Universität Darmstadt.
Antonino Riccobono earned his PhD degree in Electrical Engineering from University of South Carolina (USA) in 2013 and BSc and MSc from University of Palermo (Italy) in 2006 and 2009, respectively.
In September 2013, he joined RWTH Aachen University (Germany) where he worked as Postdoctoral Research Associate at the Institute for Automation of Complex Power Systems (ACS) – E.ON Energy Research Center. Within ACS, he also covered the charge of Leader of the Team called “Real Time Simulation and Hardware in the Loop”. He managed R&D projects in the area of modeling, control, stability, and automation of Power Electronics Power Systems using MATLAB/Simulink and real-time digital simulators.
Since July 2017, he has been with MathWorks as Senior Training Engineer, Turin, Italy.
Markus Mirz received the M.Sc. degree in electrical power engineering from RWTH Aachen University, Aachen, Germany, in 2014. Since 2015 he is research associate and Ph.D. student at the Institute for Automation of Complex Power Systems, E.ON Energy Research Center, RWTH Aachen University. His current research interests are modeling and simulation of power systems as well as co-simulation.
Mohsen Ferdowsi was born in Esfahan, Iran in 1984. He received his B.Sc. and M.Sc. degrees in electrical engineering from University of Tehran, Iran in 2007 and 2009, respectively. From 2009 to 2011, he was a research associate at Technical University of Berlin, Germany. From 2012 to 2016, he worked as a research associate at RWTH Aachen University, Germany where he received his Ph.D. degree (summa cum laude) in electrical power engineering. In January 2017, he joined Siemens AG, where works on advanced applications for power system control centers.
Antonello Monti is currently the Director of the Institute for Automation of Complex Power Systems at the E.ON Energy Research Center at RWTH Aachen University and Scientist at Fraunhofer FIT as part of the Center for Digital Energy in Aachen. He has previously held positions at Politecnico di Milano, Italy, and at the University of South Carolina, USA. Antonello has been and is Associate Editor of several international journals edited by IEEE, Elsevier, and Springer. He has been the recipient of the 2017 IEEE Innovation in Societal Infrastructure Award.
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