ABSTRACT: I will begin with a summary of the current state, expected trends, and future needs over the coming decades by employing major roadmaps in decarbonization of the energy system. In addition to the deep integration of variable renewable sources such as solar and wind, I will discuss the much harder challenges in decarbonization of transportation, manufacturing, and industry. After this, I will identify specific engineering and technological approaches where tools and techniques from a mix of machine learning and control can make critical contributions and thereby accelerate energy system decarbonization. I will highlight the important role of multidisciplinary convergent research paradigm for engaging in this research and making progress on this crucial societal challenge.

BIO: Pramod Khargonekar received B. Tech. Degree in electrical engineering in 1977 from the Indian Institute of Technology, Bombay, India, and M.S. degree in mathematics in 1980 and Ph.D. degree in electrical engineering in 1981 from the University of Florida, respectively. He has been on faculty at the University of Florida, University of Minnesota, The University of Michigan, and the University of California, Irvine. He was Chairman of the Department of Electrical Engineering and Computer Science from 1997 to 2001 and also held the position of Claude E. Shannon Professor of Engineering Science at The University of Michigan. From 2001 to 2009, he was Dean of the College of Engineering and Eckis Professor of Electrical and Computer Engineering at the University of Florida till 2016. He also served briefly as Deputy Director of Technology at ARPA-E, U. S. Department of Energy in 2012-13. He was appointed by the National Science Foundation (NSF) to serve as Assistant Director for the Directorate of Engineering (ENG) in March 2013, a position he held till June 2016. In June 2016, he assumed his current position as Vice Chancellor for Research and Distinguished Professor of Electrical Engineering and Computer Science at the University of California, Irvine. He has been recognized as a Web of Science Highly Cited Researcher. He is a recipient of the IEEE Control Systems Award, IEEE Control Systems Society Bode Lecture Prize, NSF Presidential Young Investigator Award, the American Automatic Control Council’s Donald Eckman Award, the Japan Society for Promotion of Science fellowships, World Automation Congress Honor, the IEEE W. R. G. Baker Prize Award, the IEEE CSS George Axelby Best Paper Award, the Hugo Schuck ACC Best Paper Award, and the Distinguished Alumnus and Distinguished Service Awards from the Indian Institute of Technology, Bombay. He is a Fellow of IEEE, IFAC, and AAAS. At the University of Michigan, he received the Claude Shannon Chair and Arthur F. Thurnau Professorship. His recent research and teaching interests are centered on the confluence of machine learning and control, energy system decarbonization and climate change mitigation, adaptation, and resilience.

ABSTRACT: The proliferation of distributed energy resources (DERs) such as solar PV in the distribution grid introduces several challenges in terms of both physical grid operation as well as market design. In this talk, we will present a hierarchical framework for decision-making in DERrich distribution networks, addressing grid voltage regulation as well as market structures for smooth integration of DERs. In the first part of the talk, we will address the problem of maintaining grid voltages under intermittent PV generation. Traditional voltage regulation methods which use electro-mechnical devices are insufficient in addressing the intermittency of PV generation, and result in loss of life of voltage regulation equipment such as load tap changers (LTCs). We propose the use of a hierachical voltage regulation strategy which leverages PV units alongside LTCs, to extend equipment life and improve grid voltages. The resulting mixed integer voltage regulation problem can be reformulated as a bi-level optimization with minimal data exchange between centralized LTC decisions over discrete variables, and distributed PV decisions over continuous setpoints. This minimal data exchange is extended to an intelligent coordination mechanism between the substation LTC and solar PV units, which restricts PV injections in orde to maintain grid voltages. In the second part of the talk, we will discuss a hierarchical local electricity market (LEM) structure in order to effectively use DERs to increase grid efficiency and resilience, with a secondary market (SM) at the lower level representing secondary feeders anda primary market (PM) at the upper level, representing primary feeders. The lower level SM enforces budget, power balance, and flexibility constraints and accounts for costs related to consumers, such as their disutility, flexibility limits, and commitment reliability, while the upper level PM enforces grid physics constraints such as power balance and capacity limits, and also minimizes line losses.

BIO: Dr. Annaswamy is the Director of the Active-Adaptive Control Laboratory at MIT and a Senior Research Scientist in the Department of Mechanical Engineering. Her research interests pertain to adaptive control theory and applications to aerospace, automotive, and propulsion systems, cyber physical systems science, and CPS applications to Smart Grids, Smart Cities, and Smart Infrastructures. Dr. Annaswamy received her PhD in Electrical Engineering from Yale in 1985. Her research is supported by NSF RIPS, NSF Eager awards, NSF CPS Synergy, NSF CPS Breakthrough, Boeing, Ford-MIT Alliance, Department of Energy, and Air-Force Research Laboratory. Dr. Annaswamy is the author of a hundred journal publications and numerous conference publications, co-author of a graduate textbook on adaptive control (2004), co-editor of several reports including “Systems & Control for the future of humanity, research agenda: Current and future roles, impact and grand challenges,” (Elsevier) “IEEE Vision for Smart Grid Control: 2030 and Beyond,” (IEEE Xplore) and Impact of Control Technology, (ieeecss.org/main/IoCT-report, ieeecss.org/general/IoCT2-report). She has received several awards including the George Axelby and Control Systems Magazine best paper awards from the IEEE Control Systems Society (CSS), the Presidential Young Investigator award from NSF, the Hans Fisher Senior Fellowship from the Institute for Advanced Study at the Technische Universität München, the Donald Groen Julius Prize from the Institute of Mechanical Engineers, a Distinguished Member Award, and a Distinguished Lecturer Award from IEEE CSS. Dr. Annaswamy is a Fellow of the IEEE and IFAC. She has served as the Vice President for Conference Activities (2014-15), and is currently serving as the VP for Technical Activities (2017- 18) in the Executive Committee of the IEEE CSS. She is the Deputy Editor of the Elsevier publication Annual Reviews in Control (2016-present).

ABSTRACT: 

Electric energy systems are experiencing profound transformations, including a considerable degree of uncertainty driven by renewables, new forms of dynamics due to inverter-based resources (IBRs), and large-scale integration of electric vehicles (EVs) that integrate various energy sectors and transportation.

In the first part of the talk, I will propose an “integer-clustering” approach to model a large number of EVs that manage vehicle charging and energy at the fleet level yet maintain individual trip dispatch. The model is then used to develop a spatially and temporally-resolved decision-making tool for optimally planning and operating EV fleets and energy infrastructure (e.g., fast-charging, hydrogen-fueling, and distributed energy resources). The tool comprises a two-stage framework where a tractable disaggregation step follows the integer-clustering problem to recover an individually feasible solution. We establish theoretical lower and upper bounds on the true individual formulation which underpins a guaranteed performance of the proposed method. The optimality accuracy and computational efficiency of the integer-clustering formulation are numerically validated on a real-world case study of Boston’s public transit network. Substantial speedups with minimal loss in solution quality are demonstrated. By using a real geospatial timetable dataset for bus schedules and renewable generation, we provide insights into different pathways for decarbonizing heavy-duty EV fleets and their impacts on energy systems.

In the second part of the talk, I will focus on control and stability of future power systems that host a diverse mix of inverter-based resources (IBRs) and synchronous machines. A novel inverter control scheme that unifies grid-forming and following controllers is presented. The proposed controller incorporates both a phase-locked loop (PLL) for voltage synchronization and a power frequency droop for load sharing. It possesses important practical features such as black-start, low voltage ride-through, and autonomous islanding/reconnecting of microgrids. Both small- and large-disturbance performance are demonstrated, and improved robustness is achieved along with favorable interoperability between various inverters and synchronous machines. I will present a recent result on a decentralized framework for the stability guarantee of inverter-based power systems. The proposed stability criteria have a scalable and robust nature and only need to be checked by individual grid-edge devices to ensure system-wide stability.

BIO: Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University and a Core Faculty with Ralph O' Connor Sustainable Energy Institute (ROSEI). Before joining JHU in January 2023, she was a Postdoctoral Associate at MIT in the Laboratory for Information & Decision Systems (LIDS) in 2022. She received her Ph.D. in Electrical and Computer Engineering from the University of Michigan, Ann Arbor, where she also received an M.S. in Mathematics and an M.S. in ECE. At JHU, she directs the Power and Energy Network System Analysis (PENSA) Laboratory. Her research aims to drive the widespread utilization of renewable energy resources while enhancing the resiliency and efficiency of energy systems through developing rigorous theory and scalable computational tools. Her most recent interests include dynamics, control, and stability of low-inertia smart grids and optimization of electrified transportation systems. She is the recipient of the Best Paper Award at the MIT/Harvard Applied Energy Symposium in 2022 and was named a Barbour Scholar and Rising Star in EECS (MIT) in 2021.

ABSTRACT:  Coordinated control for a large group of inverters has long been a challenging control problem. In this talk, we study this problem from a multi-agent reinforcement learning (MARL) perspective, where we model the networked inverter control problem as networked MDP in which a network of agents interact in a local manner. Typical MARL adopts the centralized training decentralized execution (CTDE), but a fundamental challenge in this setting is that the state space size scales exponentially in the number of agents, rendering CTDE intractable for large networks. In this talk, we present a framework that exploits the network structure to conduct reinforcement learning in a scalable manner. Experiment results show that the proposed framework exhibits superior scalability when compared with CTDE-based benchmarks.

BIO: Guannan Qu has been an assistant professor in the Electrical and Computer Engineering Department of Carnegie Mellon University since September 2021. He received his B.S. degree in electrical engineering from Tsinghua University in Beijing, China in 2014, and his Ph.D. in applied mathematics from Harvard University in Cambridge, Massachusetts in 2019. He was a CMI and Resnick postdoctoral scholar in the Department of Computing and Mathematical Sciences at California Institute of Technology from 2019 to 2021. He is the recipient of Caltech Simoudis Discovery Award, PIMCO Fellowship, Amazon AI4Science Fellowship, and IEEE SmartGridComm Best Student Paper Award. His research interest lies in control, optimization, and machine/reinforcement learning with applications to power systems, multi-agent systems, Internet of things, and smart cities.

ABSTRACT: This talk will introduce a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that increasing marginal pollution cost can dramatically reduce inefficiency of equilibria. 

BIO: Henrik Sandberg is Professor at the Division of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden. He received the M.Sc. degree in engineering physics and the Ph.D. degree in automatic control from Lund University, Lund, Sweden, in 1999 and 2004, respectively. From 2005 to 2007, he was a Post-Doctoral Scholar at the California Institute of Technology, Pasadena, USA. In 2013, he was a Visiting Scholar at the Laboratory for Information and Decision Systems (LIDS) at MIT, Cambridge, USA. He has also held visiting appointments at the Australian National University and the University of Melbourne, Australia. His current research interests include security of cyber-physical systems, power systems, model reduction, and fundamental limitations in control. Dr. Sandberg was a recipient of the Best Student Paper Award from the IEEE Conference on Decision and Control in 2004, an Ingvar Carlsson Award from the Swedish Foundation for Strategic Research in 2007, and a Consolidator Grant from the Swedish Research Council in 2016. He has served on the editorial boards of IEEE Transactions on Automatic Control and the IFAC Journal Automatica. He is Fellow of the IEEE.

ABSTRACT: Significant amount of research has been done on how charging of EVs should be priced in a cost-effective way to facilitate grid operations. However, one question that is still unclear is: how does massive-scale EV charging impact the dynamics of the distribution grid, and can pricing help in smoothing of EV loads so that both small-signal and voltage stability margins of the grid can be improved? In this talk, I will present an optimal control design to answer the above question in light of understanding how high charging demands from EV customers may cause dynamical instability, and how price incentivization, charging setpoints, and optimal controllers for the EV converters can all be co-designed to minimize the risk of these instabilities.  

BIO: Aranya Chakrabortty received the PhD degree in electrical engineering from Rensselaer Polytechnic Institute, Troy, NY in 2008. From 2008 to 2009 he was a postdoctoral research associate at University of Washington Seattle. From 2009 to 2010 he was an assistant professorof of electrical engineering at Texas Tech University. Since 2010 Aranya has joined the electrical and computer engineering department at North Carolina State University where he is currently a professor. His research interests are in applying control, optimization and learning theories for solving various problems on power system modeling, dynamics and controls. He received the NSF CAREER award in 2011, and was named a university faculty scholar by NC State Provost office in 2019. Since 2020, Aranya has also been serving as a program director at NSF where he currently manages the research portfolio on power and energy systems.

Abstract: Demand response (DR) has emerged as a promising paradigm to enhance grid reliability under uncertain renewable generation in future decarbonized power grids by aggregating and shaping the power consumption of consumers’ flexible loads such as heating, cooling, and electric vehicle charging at a large-scale. In this talk, we will present a privay-preserving DR mechanism and establish its privacy and utility guarantees. 

Bio: Apurv is a Postdoctoral Associate at Texas A&M working with Prof. Le Xie and Prof. PR Kumar. His research interest lies in systems and control. He obtained the PhD from Columbia University in 2022 and his bachelor’s degree from IIT Kharagpur, India, in 2016.

ABSTRACT: Disruptive technological advances in computing, sensing and communication technologies over the last decade have opened up opportunities for smart cities where large-scale cyber-physical infrastructure networks like the power grid can be efficiently monitored, controlled and managed in real-time. However, traditional control techniques do not scale well to such large-scale networks due to complexities arising from the network size, granularity of real-time measurement data, and multi-layered dynamical interactions between interdependent physical infrastructures, computing, communication, and human participants. On the other hand, purely data-driven and learning-based approaches to operating these networks do not provide guarantees on stability, safety, and robustness that are crucial in such safety-critical systems. In this talk, I will present frameworks that integrate data-driven models and learning-based control algorithms with domain-specific properties drawn from network physics, to design provably safe, robust, and efficient power grids of the future. Specifically, I will (i) discuss approaches to learn control-oriented models of power grids from data while capturing special domain-specific properties such as dissipativity and conservation laws, and (ii) demonstrate how these properties can be leveraged to achieve scalable learning-based control designs with provable guarantees.

BIO: Sivaranjani Seetharaman is an Assistant Professor in the School of Industrial Engineering at Purdue University. Previously, she was a postdoctoral researcher in the Department of Electrical Engineering at Texas A&M University, and the Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS). She received her PhD, M.S., and B.E., all in Electrical Engineering, from the University of Notre Dame, the Indian Institute of Science, and PES Institute of Technology, respectively. She is a recipient of the Schlumberger Foundation Faculty for the Future fellowship, the Zonta International Amelia Earhart fellowship, and the Notre Dame Ethical Leaders in STEM fellowship, and was named among the MIT Rising Stars in EECS in 2018. Her research interests lie at the intersection of control and machine learning in large-scale networked systems with applications to power grids, transportation networks, and interdependent infrastructures.

ABSTRACT: Our energy systems are experiencing unprecedented transformations with increasing share of renewables and emerging participants such as electric vehicles (EVs) and distributed energy resources (DERs). The urgency of climate change also calls for utilization of prevailing energy and climate data along with advanced artificial intelligence tools to develop modeling, optimization, and decision-making paradigms for large scale and complex energy systems. Yet for tasks learned with machine learning in which system performance do approach or surmount model-based counterparts, the underlying data-driven approaches often lack performance (e.g., reliability, robustness, or optimality) guarantees. In this talk, I will present some of our recent efforts in designing learning-enabled modeling and decision-making frameworks tailored for power and energy systems. I will start from discussing how we can leverage differentiable optimization techniques for better modeling and scheduling stochastic EV charging demands to minimize carbon emissions. Then I will generalize such techniques, and delve into the learningbased, robust and generalizable solver design for optimal power flow (OPF) problem, which is one of the fundamental tasks in power grid operations. The talk then explains how such learned representations of physical constraints can be fused into a data-driven optimization paradigm, opening the door for reliably accelerating the large-scale mixed integer optimization solution procedure, which are prevalently used for clean energy systems planning tasks.

BIO:  Yize Chen is an assistant professor in Artificial Intelligence Thrust at Hong Kong University of Science and Technology Guangzhou (HKUST-Guangzhou). He was a postdoc at Berkeley Lab from 2021 to 2022. He got his PhD degree in Electrical and Computer Engineering from University of Washington in 2021, and his bachelor degree from Department of Control Science and Engineering Chu Kochen College at Zhejiang University in 2016. He also held research positions in multiple institutions including Microsoft Research, Los Alamos National Laboratory and Harvard Medical School. His research interests lie at the intersection of machine learning, optimization, and control theory, with applications in energy systems and cyber-physical systems. He has received several best paper and prize paper awards including PES General Meeting, ACM e- Energy and Power Systems Computation Conference (PSCC). He also served as technical program committee member in IEEE SmartGridComm (2021, 2022) and chaired INFORMS

Annual Meeting Sessions (2022, 2023).