Abstract
This article discusses how to use optimization-based methods to efficiently operate microgrids with a large share of renewables. We discuss how to apply a frequency-based method to tune the droop parameters in order to stabilize the grid and improve oscillation damping after disturbances. Moreover, we propose a centralized real-time feasible nonlinear model predictive control (NMPC) scheme to achieve efficient frequency and voltage control while considering economic dispatch results. Centralized NMPC for secondary control is a computationaly challenging task. We demonstrate how to reduce the computational burden using the Advanced Step Real-Time Iteration with nonuniform discretization grids. This reduces the computational burden up to 60 % compared to a standard uniform approach, while having only a minor performance loss. All methods are validated on the example of a 9-bus microgrid, which is modeled with a complex differential algebraic equation.
Zusammenfassung
Dieser Artikel behandelt die Verwendung von optimierungsbasierten Methoden zur effektiven Regelung von Microgrids mit einem hohen Anteil an erneuerbaren Energien. Wir diskutieren, wie man eine frequenzbasierte Methode verwenden kann, um die statischen Parameter so anzupassen, dass das Netz stabilisiert wird und Oszillationen nach Störungen besser gedämpft werden. Außerdem wird ein zentralisiertes und in Echtzeit realisierbares Schema zur nichtlinearen modellprädiktiven Regelung (NMPC) vorgestellt, mit dem man sowohl eine effiziente Frequenz- und Spannungskontrolle erreicht als auch den economic dispatch berücksichtigt. Die Anwendung eines zentralisierten NMPC-Schemas erfordert einen hohen Rechenaufwand. Wir zeigen auf, wie man diesen unter Verwendung einer Advanced Step Real-Time Iteration mit einer ungleichmäßigen Diskretisierung reduzieren kann. Im Vergleich zu einem üblichen Ansatz mit einem gleichmäßigen Gitter verringert sich dabei der Rechenaufwand um bis zu 60 %, wobei kaum Performance verloren geht. Alle Methoden werden am Beispiel eines Microgrids, welches aus 9 Bussen besteht und mittels einer komplexen differential-algebraischen Gleichung modelliert wird, validiert.
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 03SFK3U0
Award Identifier / Grant number: 01S18066B
Funding source: Bundesministerium für Wirtschaft und Energie
Award Identifier / Grant number: 0324166B
Funding statement: This research was supported by the German Federal Ministry of Education and Research (BMBF) via the funded Kopernikus project: SynErgie (03SFK3U0) and the AlgoRes project (01S18066B) and by the German Federal Ministry for Economic Affairs and Energy (BMWi) via DyConPV (0324166B), and by the DFG via Research Unit FOR 2401.
About the authors
Armin Nurkanović received the B.Sc. degree from the Faculty of Electrical Engineering, Tuzla, Bosnia and Herzegovina, in 2015, and the M.Sc. degree from the Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany, in 2018. He is currently working toward a Ph.D. degree at the Systems Control and Optimization Laboratory, Department of Microsystems Engineering, University of Freiburg, Germany, and at Siemens Corporate Technology, Munich, Germany. His research interests include numerical methods for model predictive control, nonlinear optimization and nonsmooth dynamic systems.
Amer Mešanović received the B.Sc. degree from the Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina, in 2013, and the M.Sc. degree from the Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany, in 2015. He is currently working toward the Ph.D. degree at the Laboratory for Systems Theory and Automatic Control, Otto von Guericke University Magdeburg, Germany, and at Siemens Corporate Technology, Munich, Germany. His research interests include analysis of and controller design for large scale power systems.
Mario Sperl received the B.Sc. degree from the Faculty of Computer Science and Mathematics, University of Passau, Germany in 2018 and is currently working towards a M.Sc. degree at the same faculty. His focus is on dynamic systems, where his interests include control theory, optimization, semigroup theory and numerical methods for differential equations.
Sebastian Albrecht joined Siemens Technology in 2015 as a Research Scientist addressing topics from robotics, autonomous systems and control in Munich, Germany. Since 2014 he holds a PhD in Mathematics from Technische Universität München (TUM) in Munich, Germany. Main research interests are numerical methods for nonlinear optimization and control and their application to challenging real-world problems.
Ulrich Münz received the Ph.D. degree in automatic control from the University of Stuttgart, Stuttgart, Germany, in 2010, and the M.Sc. degrees in electrical engineering and telecommunications from the Universities of Stuttgart, Germany, and Madrid, Spain, both in 2005. He is the Head of the Autonomous Systems Research Group, Siemens Corporate Technology, Princeton, NJ, USA. Prior to this appointment, he was a senior key expert Research Scientist for power system stability and control at Siemens Corporate Technology, Munich, Germany. From 2010 to 2011, he was a Systems Engineer for Power Electronic Converters, Robert Bosch GmbH. His research interests include the analysis of and controller design for large scale systems like power systems. He received the EECI European Ph.D. Award on Embedded and Networked Control in 2010.
Rolf Findeisen received the M.S. degree from the University of Wisconsin–Madison, Madison, WI, USA, in 1997, and the Ph.D. degree from the University of Stuttgart, Stuttgart, Germany, in 2005. He was a Research Assistant with the Automatic Control Laboratory, ETH Zurich, Zurich, Switzerland, and a Researcher with the Institute for Systems Theory and Automatic Control, University of Stuttgart. He heads the Systems Theory and Automatic Control Laboratory, Otto von Guericke University Magdeburg, Magdeburg, Germany, where he is also a full chaired Professor.
Moritz Diehl studied physics and mathematics at Heidelberg and Cambridge University from 1993–1999 and received his Ph.D. degree from Heidelberg University in 2001, at the Interdisciplinary Center for Scientific Computing. From 2006 to 2013, he was a professor with the Department of Electrical Engineering, KU Leuven University Belgium, and served as the Principal Investigator of KU Leuven’s Optimization in Engineering Center OPTEC. In 2013 he moved to the University of Freiburg, Germany, where he heads the Systems Control and Optimization Laboratory, in the Department of Microsystems Engineering (IMTEK), and is also affiliated to the Department of Mathematics. His research interests are in optimization and control, spanning from numerical method development to applications in different branches of engineering, with a focus on embedded and on renewable energy systems.
Acknowledgment
Armin Nurkanović acknowledges the helpful discussions with Jonathan Frey and Andrea Zanelli from the University of Freiburg, which lead to the efficient implementation of the AS-RTI scheme in acados via its MATLAB interface. We also acknowledge the contributions of two anonymous reviewers, whose close reading and constructive comments led to improvement of this paper.
Author contributions: Armin Nurkanović and Amer Mešanović equally contributed to this article.
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