Control and Optimization for Integration of Renewables in Smart Grids.
Anuradha Annaswamy 教授
Massachusetts Institute of Technology， 美国
World over, there’s a big push towards a 100% incorporation of wind and solar power for electricity production. Significant changes have occurred over the past decade in the energy landscape, especially in the power sector. Natural gas prices have declined, costs of renewable energy technologies have come down, and large‐scale battery energy storage technologies have advanced rapidly. There are however a host of challenges, most of which are due to the intermittency and unpredictability of the renewable energy resources. Most of the requisite solutions for the deep integration of these renewable resources for electricity production are control‐centric. A distributed optimization approach that judiciously combines renewable generation with storage and flexible loads has the possibility for ensuring power balances. A distributed control approach that enables a coordinated network of millions of controllers, all integrated with solar and wind power generation nodes, storage sites, and flexible consumption can lead to effective frequency regulation and voltage control in real‐time. In this talk, some of these challenges, highlights of the current research in distributed optimization and control, and opportunities for future directions will be discussed. Examples of use cases that illustrate the role of optimization and control in renewable‐rich power grids will be presented.
Dr. Anuradha Annaswamy is Founder and Director of the Active-Adaptive Control Laboratory in the Department of Mechanical Engineering at MIT. Her research interests span adaptive control theory and its applications to aerospace, automotive, and propulsion systems as well as cyber physical systems such as Smart Grids, Smart Cities, and Smart Infrastructures. Her current research team of 15 students and post-docs is supported at present by the US Air-Force Research Laboratory, US Department of Energy, Boeing, Ford-MIT Alliance, and NSF. She has received best paper awards (Axelby; CSM), Distinguished Member and Distinguished Lecturer awards from IEEE CSS, and a PYI award from NSF. She is a Fellow of IEEE and IFAC. She will serve as the CSS President in 2020.
数字视网膜 – 从云视觉计算走向类脑视觉计算之模型框架
IEEE Fellow、ACM Fellow、中国工程院院士。
Rodolphe Sepulchre 教授
University of Cambridge, 英国
This talk will present a novel approach for the analysis and design of systems that switch and oscillate.
While such nonlinear behaviors abound in control engineering of electrical, mechanical, and biological
circuits, it is often considered that they fall outside the scope of control theory. In contrast, the proposed
approach closely mimics linear-quadratic dissipativity theory, a very foundation of modern control theory.
In its classical formulation, dissipativity theory formulates system properties as dissipation inequalities to be satisfied by the storage, an abstraction of the system internal energy. Linear systems admit quadratic storages. When the storage is positive definite, it serves as a Lyapunov function for stability analysis of equilibria. Our generalization rests on two distinct ingredients. First, we apply dissipativity theory differentially: instead of studying the nonlinear system via the nonlinear theory, we apply the linear theory to a family of linearized systems. Second, we relax the positivity constraint of the quadratic storage to a fixed inertia constraint. We allow for one negative eigenvalue in the analysis of switches and two negative eigenvalues in the analysis of clocks.
The talk will illustrate the theory in classical models of switches and clocks and discuss the potential of dissipativity theory for the analysis and design of interconnected systems away from equilibrium.
received the engineering degree and the Ph.D. degree from the Université
catholique de Louvain in 1990 and in 1994, respectively. From 1994-1996, he was as postdoctoral research
associate at the University of California, Santa Barbara. In 1997, he joined the Université de Liege, where he
was professor until 2013. In 2013, he joined Cambridge University and also became a professioral fellow of
Sidney Sussex College. He held visiting positions at Princeton University (2002-2003), the Ecole des Mines de
Paris (2009-2010), Caltech (2018), and part-time positions at the University of Louvain (2000-2011) and at
INRIA Lille Europe (2012-2013).
His research interests are in nonlinear control and optimization theory. He co-authored the monographs "Constructive Nonlinear Control" (Springer-Verlag, 1997) and "Optimization on Matrix Manifolds" (Princeton University Press, 2008). From 2009, his research has been increasingly motivated by control questions from neuroscience. A current focus is his ERC advanced grant "Switchlets", aiming at a multi-scale control theory of excitable systems.
He is currently Editor-in-Chief of the IEEE Control Systems Magazine and an Associate Editor for Annual Review in Control, Robotics, and Autonomous Systems. He was Editor-in-Chief of Systems and Control Letters from 2009-2018 and has also served as an Associate Editor for several journals, including, Transactions on Network Science and Engineering, Automatica, SIAM Journal of Control and Optimization, the Journal of Nonlinear Science, and Mathematics for Control, Signals, and Systems. In 2008, he was awarded the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize. He is a fellow of IEEE, SIAM, and IFAC. He has been IEEE CSS distinguished lecturer between 2010 and 2015. In 2013, he was elected at the Royal Academy of Belgium.
Systems and Control Theory for Advanced Manufacturing.
Richard D. Braatz 教授
Massachusetts Institute of Technology, 美国
The world is seeing renewed interest in advanced manufacturing, which can be seen in a number of initiatives from governments and industry-government partnerships with such names as Industry 4.0 and Smart Manufacturing. After discussing the role of cyber-physical systems, the Internet of Things, and cloud computing, this presentation describes systems and control theories that underpin the actual processes employed in the manufacturing of high-tech products. These manufacturing processes typically have (1) high to infinite state dimension, (2) probabilistic uncertainties, (3) time delays, (4) unstable zero dynamics, (5) actuator, state, and output constraints, (6) stochastic noise and disturbances, and (7) phenomena described by combinations of algebraic, ordinary differential, partial differential, and integral equations (that is, generalizations of descriptor/singular systems). Key points are illustrated for fully automated, advanced, and modular manufacturing systems developed at MIT. Stochastic model predictive control formulations are presented that have the flexibility to handle linear dynamical systems with these characteristics, while employing projections and shifting of the most expensive calculations offline so that the online computational cost is low. Implementation to a detailed mechanistic model of an advanced drug manufacturing plant demonstrates an order-of-magnitude improved robustness of the product quality to model uncertainties while having an online optimization cost of less than 1 second. Some extensions to nonlinear dynamical systems are discussed.
Richard D. Braatz is the Edwin R. Gilliland Professor at the Massachusetts Institute of Technology (MIT) where he does research in applied mathematics and control theory and their application to advanced manufacturing systems. He received MS and PhD from the California Institute of Technology and was on the faculty at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT. He is a Fellow of IEEE and IFAC, and a member of the U.S. National Academy of Engineering.