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Smart Buildings: A Status Quo Check

A Workshop for IEEE CDC'18

Organizers: Rong Su, Christos G. Cassandras
Time/Date: 8:25 AM - 4:00 PM, December 16, 2018
Location: Splash 15-16, Fontainebleau in Miami Beach, Florida, USA

57th IEEE Conference on Decision and Control, Miami Beach, FL, USA, December 16-19, 2018

Abstract: A smart building is a structure that uses automated processes to control the buildings operations including heating, ventilation, air conditioning, lighting, security and other systems. A smart building uses sensors, actuators and microchips, in order to collect data and manage it according to pre- scribed functions and services. This infrastructure helps owners, operators and facility managers improve asset reliability and performance, which reduces energy use, optimizes how space is used and minimizes the environmental impact of buildings. Making a smart building, or making a building smart, begins by linking core systems such as lighting, power meters, water meters, pumps, heating, fire alarms and chiller plants with sensors and control systems. The key feature of a smart building is "integration". In this workshop several active researchers in this field will report their recent technical progresses at both individual and program levels on smart buildings, and some visionary discussions on the roles of IoT and data analytics, aiming to showcase some recent achievements and at the same time identify challenges ahead in order to arouse more interests and efforts at a broader societal level to ensure research sustainability.

Organizers

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Assoc Prof Rong Su, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Email: rsu@ntu.edu.sg


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Prof Christos G. Cassandras, Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Brookline, MA 02446, USA, Email: cgc@bu.edu

This workshop is technically co-sponsored by the Smart Cities Technical Committee and the Discrete Event Systems Technical Committee in IEEE Control Systems Society.

Topics

Topic 1: Load management over multiple buildings

Topic 2: Emergency handling in smart buildings

Topic 3: HVAC control in smart buildings

Topic 4: Indoor positioning

Topic 5: Data analytics for buildings

Topic 6: Integration of buildings technologies and beyond

Speakers

New York
Alberto Sangiovanni Vincentelli

University of California at Berkeley

Data Analytics for the Built Environment


New York
Lihua Xie

Nanyang Technological University

Indoor Positioning Systems: Some Recent Development and Challenges

New York
Rahul Mangharam

University of Pennsylvania

Bridging Machine Learning and Controls for Intelligent Buildings

New York
Hai Lin

University of Notre Dame

Coordinated Robot-assisted Human Crowd Emergency Evacuation

New York
Rong Su

Nanyang Technological University

IoT-based HVAC Scheduling for Smart Buildings

New York
Jianming (Jamie) Lian

Pacifc Northwest National Laboratory

Transactive Control of Smart Buildings for Demand Response

New York
(Samuel) Qing-Shan Jia

Tsinghua University

Artificial Intelligence in Cyber Physical Energy Systems - Event-Based Learning and Optimization

New York
Anuradha Annaswamy

Massachusetts Institute of Technology

Integration of Smart Buildings into Smart Distribution Grids for Voltage Regulation


New York
Costas Spanos

University of California at Berkeley

Designing a Living Laboratory for Building Energy Efficiency in the Tropics


New York
Amro M. Farid

Thayer School of Engineering at Dartmouth College

Smart Building to Smart City Integration: A Hetero-functional Graph Theory Approach

New York
Scott Moura

University of California at
Berkeley

Controlling Aggregations of Flexible Building Loads: A PDE Approach

Workshop Goals

  1. to report and showcase several recent technical progresses related to smart buildings at both individual and program levels, and some visionary discussions on the roles of IoT and formal design methods.

  2. to identify challenges ahead which, although hindering the current research efforts, are critical for developing smart buildings, in order to arouse more interests and efforts at a broader societal level to ensure R&D sustainability.

Intended Audience

This workshop consists of two types of presentations: (1) reports of recent individual research progresses on specifc topics, e.g., evacuation, power management, sensing, indoor positioning, and data alaytics, and (2) showcases of some major research efforts in Singapore on smart buildings. The first type of presentations may require audience to have some technical background in modeling, control and optimization, thus, suitable for researchers and senior graduate students in relevant felds. The second type of presentations is accessible to all kinds of audeince, e.g., researchers, engineers and undergraduate/graduate students, due to their illustrative nature with minimum technical exposures. To help registered audience better undertand the presented materials, a printout of each presentation will be disseminated during the workshop.

Workshop Schedule (full-day: 8:25 - 16:00)

Presentation Title Speaker Time Slot
Opening Speech Rong Su
Nanyang Technological University
8:25 - 8:30
Controlling Aggregations of Flexible Building Loads: A PDE Approach Scott Moura
University of California, Berkeley
8:30 - 9:00
Bridging Machine Learning and Controls for Intelligent Buildings Rahul Mangharam
University of Pennsylvania
9:00 - 9:30
Integration of Smart Buildings into Smart Distribution Grids for Voltage Regulation Anuradha Annaswamy
Massachusetts Institute of Technology
9:30 - 10:00
Tea Break - 10:00 - 10:30
Indoor Positioning Systems: Some Recent Development and Challenges Lihua Xie
Nanyang Technological University
10:30 - 11:00
Transactive Control of Smart Buildings for Demand Response Jianming (Jamie) Lian
Pacific Northwest National Laboratory
11:00 - 11:30
Smart Building to Smart City Integration: A Hetero-functional Graph Theory Approach Amro M. Farid
Dartmouth College
11:30 - 12:00
Coordinated Robot-assisted Human Crowd Emergency Evacuation Hai Lin
University of Notre Dame
12:00 - 12:30
Lunch - 12:30 - 14:00
Designing a Living Laboratory for Building Energy Efficiency in the Tropics Costas Spanos
University of California, Berkeley
14:00 - 15:00
Data Analytics for the Built Environment Alberto Sangiovanni Vincentelli
University of California, Berkeley
15:00 - 15:30
Artificial Intelligence in Cyber Physical Enery Systems - Even-Based Learning & Optimization (Samual) Qing-Shan Jia
Tsinghua University
15:30 - 16:00
IoT-Based Scalable HVAC Control Rong Su
Nanyang Technological University
16:00 - 16:30
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Integration of Smart Buildings into Smart Distribution Grids for Voltage Regulation

Abstract: One of the important features of a smart grid is a periphery that is becoming increasingly intelligent. High penetration of renewable energy based generation in distribution grid can introduce significant challenges for carrying out efficient Volt/Var Control. One of the main difficulties is the associated uncertainties and intermittencies which can cause voltage regulation to be quite difficult. Demand Response, where consumption is made flexible, can help ease this diifficulty. In this talk, the use of smart buildings, that represent one of the main building blocks of Demand Response for voltage regulation will be explored. In particular, how these units can be integrated in a Distribution Grid for better voltage regulation will be examined.

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Bridging Machine Learning and Controls for Intelligent Buildings

Abstract: In January 2014, the east coast (PJM) electricity grid experienced an 86X increase in the price of electricity from $31/MWh to $2,680/MWh in a matter of 10 minutes. This extreme price volatility has become the new norm in our electric grids. Building additional peak generation capacity is not environmentally or economically sustainable. Thus, the focus has shifted from energy efficiency to energy exibility. Our AI platform provides energy and cost savings by strategically shifting loads, shaving peak demands and automatic climate con- trol while tracking volatility in the price of electricity. It learns from historical energy usage patterns to make recommendations on how to best choose equipment settings across thousands of controllers to reduce power consumption while ensuring custom comfort conditions. Energy Flexibility software-as-a-service enables commercial and industrial customers to reduce their de- mand charges on monthly bills and maximize their financial rewards by participation in Demand Response programs. On the supply side, utilities use it to reduce their financial risk by getting better insights into available energy exibility across their portfolio of clients. At the heart of our solution lie the learning algorithms that capture whole-building and zone-level models from historical operation data with 96-98% accuracy. The low-touch and low-cost approach taps existing building setpoint/thermostats and meter data from the Building Management System and does not require additional sensors or intervention. It enables MPC with machine learning algorithms reducing the cost and time of modeling by three orders of magnitude.

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Controlling Aggregations of Flexible Building Loads: A PDE Approach

Abstraction: Energy storage is one of humanitys greatest technological challenges. As societys size and energy appetite grow, we must seek solutions that facilitate penetration of renewable energy and enhance efficiency, particularly in the transportation and power system infrastruc- tures. This talk focuses on modeling, estimation, and control challenges in the demand-side management of exible loads in the smart grid. Aggregations of large-scale distributions of exible loads can be elegantly modeled by partial differential equations (PDEs). This modeling framework enables one to perform analysis, estimation, and control design using recently devel- oped techniques in PDE control theory. We explore this framework for managing populations of building loads, such as thermostatically controlled loads (TCLs), electric water heaters (EWHs), and plug-in electric vehicles (PEVs).

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Artificial Intelligence in Cyber Physical Energy Systems - Event-Based Learning and Optimization

Abstract: Cyber physical energy system (CPES) is where information and energy merges to- gether to improve the overall system performance including economic, comfort, and safety as- pects. Artificial intelligence which are enabled by internet of things, big data, and cloud comput- ing, has a big role in the optimization of CPES. In this talk, we focus on a real problem in smart buildings, in which multiple buildings are connected into a micro grid. The renewable energy such as solar power and wind power are generated locally in the building, stored in the building, and consumed in the building by plug-in loads and electric vehicles. There are models to predict the power generation and consumption in minutes, hours, and days. And there are models to predict the power generation and consumption in individual buildings or a group of buildings. We developed a multi-scale event-based reinforcement learning method which makes decisions only when certain events occur, and uses policy projection and state and action aggregation to connect the models in multiple scales. The performance of this method is demonstrated by numerical examples. We will also discuss extensions of this method to distributed optimization. We hope this work sheds light to the optimization of CPES.

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Transactive Control of Smart Buildings for Demand Response

Abstract: As distributed energy resources (DERs) are becoming prevalent in power distribution systems, various control schemes have been developed to engage various DERs ranging from the small-sized resources (end-use appliances, electric vehicle, energy storage, etc.) to large-sized resources (residential and commercial building) in participating into grid operations. Among these proposed control strategies, transactive coordination and control have attracted consid- erable research attentions. It uses economic or market-like constructs to manage distributed smart grid assets and is amenable to problems where self-interested entities are coordinated to achieve global control objectives. Transactive control framework actually complements the conventional centralized control framework associated with direct load control. It is based on dis- tributed control and has the significant advantages of scalability, exibility and interoperability. Furthermore, it fully respect individual entities preference and privacy. In this presentation, the latest development of behind-the-meter transactive design for smart buildings will be discussed. Field demonstration results will also be presented to illustrate the effectiveness of transactive control design for demand response.

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Smart Building to Smart City Integration: A Hetero-functional Graph Theory Approach

Abstract: Smart building technology has allowed for advancements in the integration and holis- tic control of building services to provide a higher standard of living for its occupants. Addi- tionally, smart building technology provides unparalleled control and data about the occupants behavior. These advancements are essential for numerous societal challenges, such as decar- bonization, reduction of water consumption, and chronic diseases. Individual household level innovation has led to opportunities to further society as a whole. Smart buildings, consequently, dont operate in a vacuum. Infrastructure systems enable smart buildings by providing critical services such as electric power, natural gas, potable water, wastewater collection, and trans- portation. These infrastructures are interdependent, and the interface with a smart building is just one such example. The existing literature has struggled to integrate interdependent in- frastructures and assess them as one, whereas smart buildings prove that there is need for an integrated approach. This workshop presentation expands the system boundary beyond the building level, to look at the interfaces of smart buildings to a smart citys interdependent in- frastructure systems. Hetero-functional graph theory provides a novel mathematical framework to model interdependent smart city infrastructure systems. Rooted in the establishedf fields of Graph Theory, Axiomatic Design, and Systems Engineering, it facilitates the construction of a single mathematical model that incorporates multiple, unlike engineering systems. Recent ap- plications include the energy-water nexus, electrified transportation, and interdependent smart city infrastructures. Smart buildings interact with all smart city infrastructure systems and it is therefore necessary to investigate their integration with the surrounding context.

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Coordinated Robot-assisted Human Crowd Emergency Evacuation

Abstract: Many emergencies require people to evacuate a building quickly. During an emer- gency, evacuees tend to rely on default decision making, such as exiting the way they entered, following a crowd, or sheltering in place, which may put them in danger. When a crowd attempts to exit through a single exit, choke points and crowd congestion may impede the safe ow of evacuees, potentially resulting in a stampede of people and the loss of human lives. Nowadays more and more situations demand a quick, coordinated evacuation of hundreds or thousands of people. In this talk, we will introduce our recent work on using mobile robots to direct evacuees for a rapid and orderly evacuation. Emergency response robots may save human lives by quickly guiding people to open exits. Particularly, we focused on the mathematical modeling of human crowd and investigate how to optimally deploy robots to guide human crowds in an efficient and safe manner during an evacuation process. Inspired by social force model and mean field game theory, we model pedestrians as Brownian agents using stochatsic differential equations. We further incorporate the impact of the robots in the Brownian agent model for the microscopic individual pedestrian and obtain the corresponding Kolmogorov equation to describe the crowd dynamics evolution at the macroscopic level. Then, a two-step hierarchical structure is proposed to solve the robot deployment and command selection problem based on our modeling framework. We are also going to discuss some encouraging simulation results and potential future directions.

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Designing a Living Laboratory for Building Energy Efficiency in the Tropics

Abstract: SinBerBEST (Singapore - Berkeley Building Efficiency and Sustainability in the Tropics) is a large, multidisciplinary program, with participants from UC Berkeley and several Universities in Singapore. We have recently outlined a comprehensive approach for reducing energy consumption in Green Mark 2010 commercial buildings by almost 50%. We are now in the process of launching a living laboratory in collaboration with Singapores Building and Construction Authority (BCA), in order to run a multi-year experiment in an actual, operational office space in Singapore. Our living laboratory is now in its final design stages and will operate for three years in a fully remodelled 700m2 oor of a state of the art low energy building. This presentation will summarise our preliminary results to date and will describe the modelling and design of this facility, as well as the various long term experiments that will take place.

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Data Analytics for the Built Environment

Abstract: Machine learning methods applied to large data sets have transformed many scientific and engineering disciplines. Enormous data corporated from the built environment exist but the value of such information remains largely hidden and consequently, unexploited. In this talk, we will present our research in analytics engines to analyze building data with particular attention to the following issues: (1) Modeling occupant needs and preferences that can be fulfilled by intelligent building control systems, and their behavioral response to energy reduction incentives; (2) Assessing the system state for preventive maintenance scheduling and low-latency fault detection of building components; (3) Identifying characteristics of highly energy efficient buildings at the community-scale; (4) Making aggregate data and data analytics algorithms and software available to the community using widely acceptable open source principles.

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IoT-Based Scalable HVAC Control

Abstract: This talk presents an Internet of Things (IoT) prototype that implements a novel hierarchical control approach called the Token Based Scheduling Algorithm (TBSA) to save energy in heating, ventilation, and air-conditioning (HVAC) systems in commercial buildings. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software and their integration) along with their interactions. A detailed description of these different components, communication among them as well with the cloud and legacy Building Automation System (BAS) is presented. In addition, the simple modifications to the existing HVAC control for translating TBSA from being an abstract optimization application to an active HVAC control strategy is presented. This involves experiments and non-intrusive modifications to the existing BAS control loops. The investigation also enhances the TBSA by including soft-constraints in the optimization model and recursive learning for updating zone thermal models in a large commercial building. Our investigation illustrates that by combining the IoT with the TBSA, an otherwise rigid and centralized BAS control architecture can be transformed to a more exible decentralized one. In addition, desirable features such as better scalability, engineering simplicity, and performance can be achieved by deploying upgrades on lowcost devices over legacy BAS. The IoT prototype and TBSA implementation are illustrated on a test-building in Nanyang Technological University, Singapore having 85 zones with variable air volume controlled HVAC system. Our results shows that the integration of IoT and TBSA meets the envisioned benefits such as scalability in terms of a number of zones, economic upgrade with faster payback times, and less disruptive modifications. The energy savings is around 20% in the test bed with an average payback time of 1-1.5 years.

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Indoor Positioning Systems: Some Recent Development and Challenges

Abstract: The Internet of Things (IoT) envisions a highly networked future where every object is integrated to interact with each other, allowing for communications between objects, as well as between humans and objects. In this talk, we shall focus on indoor positioning and localization of objects and individuals which is essential to IoT. The demands for indoor location based service have increased significantly in recent years. We shall discuss opportunities and challenges of indoor localization such as environmental dynamics, device heterogeneity and tedious calibration requirements, and present solutions to these challenges. Machine learning methods are leveraged to develop algorithms for indoor positioning and human activity recognition based on received signal strength (RSS) and channel state information (CSI). With the WiFi indoor positioning system we have developed in recent years, we shall demonstrate some applications in smart buildings such as localization, navigation, and occupancy based cooling. The talk will be concluded with directions for future research.

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Bio: Alberto Sangiovanni Vincentelli

Alberto Sangiovanni Vincentelli holds the Buttner Chair at the Department of EECS, University of California, Berkeley. He is an IEEE and an ACM Fellow, and a member of the National Academy of Engineering. Recipient of the Kaufman Award of the Electronic Design Automation Council for pioneering contributions to EDA and of the IEEE/RSE Maxwell Medal for groundbreak- ing contributions that have had an exceptional impact on the development of electronics and electrical engineering or related fields. He received an honorary Doctorate from Aalborg University (Denmark) and one from KTH (Sweden). He authored 17 books and over 950 papers. He has co-founded enter- prises in US and Europe including Cadence and Synopsys, the Electronic Design Automation leading companies quoted in NASDAQ. He has advised among others Elettronica, Intel, IBM, HP, General Motors, ATT, GE, Kawasaki Steel, Fujitsu, Hitachi, Mercedes Benz, BMW, Magneti Marelli, Pirelli, Telecom Italia, and sits on the Board of Directors of Cadence, KPIT Technologies, Sonics, Expert System and Cogisen. He is the Chairman of the Board of UltraSoC, and Expert System USA.

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Bio: Dr Lihua Xie

Dr Lihua Xie received the B.E. and M.E. degrees in electrical engineering from Nanjing University of Science and Technology in 1983 and 1986, respectively, and the Ph.D. degree in electrical engineering from the University of Newcastle, Australia, in 1992. He was with the Department of Automatic Control, Nanjing University of Science and Technology from 1986 to 1989. Since 1992, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, where he is currently a professor and the Director, Delta-NTU Corporate Lab for Cyber- Physical Systems. He served as the Head of Division of Control and Instrumentation from July 2011 to June 2014, and the Director, Centre for E-City from July 2011 to June 2013. His current research interests include networked control, multi-agent systems, sensor networks, compressive sensing, local- ization, and unmanned systems. He has authored/co-authored 8 books, over 350 journal papers, and 7 patents. He was listed as a highly cited researcher by Thomson Reuters and Clarivate Analytics in 2014, 2015 and 2016. He is currently an Editor-in-Chief of Unmanned Systems and Associate Editor, IEEE Transactions on Network Control Systems. He has served as Editor for IET Book Series in Control and Associate Editor for Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Circuit and Systems-II, etc. Dr Xie is Fellow of IEEE, Fellow of IFAC, and an elected member of the Board of Governors of IEEE Control System Society (2016-2018).

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Bio: Rahul Mangharam

Rahul Mangharam is the CEO of Flexergy focused on cost-efficient energy controls and analytics for volatile energy markets. He is also an Associate Professor in the Dept. of Electrical & Systems Engineering and Dept. of Computer & Information Science at the University of Penn- sylvania. His interests are in cyber-physical systems at the intersection of formal methods, machine learning and controls. Rahul received the 2016 US Presidential Early Career Award (PECASE) from President Obama for his work on Cyber-Physical Systems. He also received the 2016 De- partment of Energys CleanTech Prize (Regional), the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the Na- tional Academy of Engineering for the 2012 and 2017 Frontiers of Engineering. For the work on bridging machine learning and control his team received the Best Paper Award at the International Conference on Cyber-Physical Systems (ICCPS) 2018, the Energy Systems Best Paper Award at the American Control Conference (ACC) 2017 and the Best Presentation Award at BuildSys 2016. Website: http://www.seas.upenn.edu/~rahulm/

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Bio: Hai Lin

Hai Lin is currently an associate professor at the Department of Electrical Engineering, University of Notre Dame, where he got his Ph.D. in 2005. Before returning to his alma mater, Hai has been working as an assistant professor in the National University of Singapore from 2006 to 2011. Dr. Lin's teaching and research interests are in the multidisciplinary study of the problems at the intersections of control, communication, computation, machine learning and computational verification. His current research thrust is on cyber-physical systems, multi-robot cooperative tasking, and human-machine collaboration. Hai has been served in several committees and editorial board, including IEEE Transactions on Automatic Control. He is currently serving as the Chair for the IEEE CSS Technical Committee on Discrete Event Systems. He served as the Program Chair for IEEE ICCA 2011, IEEE CIS 2011 and the Chair for IEEE Systems, Man and Cybernetics Singapore Chapter for 2009 and 2010. He is a senior member of IEEE and a recipient of 2013 NSF CAREER award.

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Bio: Rong Su

Rong Su (NTU) obtained his Bachelor of Engineering degree from University of Science and Technology of China in 1997, and Master of Applied Science degree and PhD degree from Uni- versity of Toronto, in 2000 and 2004, respectively. He was affiliated with University of Waterloo and Technical University of Eindhoven before he joined Nanyang Technological University in 2010. Currently, he is an associate professor with tenure in the School of Electrical and Electronic En- gineering. Dr. Su's research interests include multi-agent systems, discrete-event system theory, model-based fault diagnosis, operation planning and scheduling with applications in exible manu- facturing, intelligent transportation, human-robot interface, power management and green building. In the aforementioned areas he has more than 140 journal and conference publications (including more than 30 publications in the prestigious IEEE Transactions journals and Automatica), 1 granted US patent, 1 filed Singapore patent and 3 technical disclosures, and has been involved as PI or Co- PI in several projects worth more than 6 million SGDs, sponsored by Singapore National Research Foundation (NRF), Singapore Agency of Science, Technology and Research (A*STAR), Singapore Ministry of Education (MoE), Singapore Civil Aviation Authority (CAAS) and Singapore Economic Development Board (EDB). Dr. Su is a senior member of IEEE, and an associate editor for Auto- matica, Journal of Discrete Event Dynamic Systems: Theory and Applications, Transactions of the Institute of Measurement and Control, and Journal of Control and Decision. He is also the Chair of the Technical Committee on Smart Cities in the IEEE Control Systems Society.

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Bio: Jianming (Jamie) Lian

Jianming (Jamie) Lian is currently a senior staff engineer in the Optimization and Control group at Pacific Northwest National Laboratory. He received the B.S. degree with the highest honor from University of Science and Technology of China in 2004. After that, he received the M.S. and the Ph.D. degree in Electrical Engineering from Purdue University, West Lafayette, IN, in 2007 and 2009, respectively. From 2010 to 2011, he worked as a postdoctoral research associate at Center for Advanced Power Systems in Florida State University, Tallahassee, FL, where he was involved in various projects related to the development of future all-electric ship supported by ONR. Since joining in PNNL, he has been serving as a project manager, PI/Co-PI and key technical contributor for many research projects in the areas of power grid, building system, and transportation system. In particular, he has been extensively working on the development of the theoretical foundation for the new market-based (aka, transactive) coordination and control to engage and integrate various distributed energy resources (DERs) into the future distribution management system.

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Bio: (Samuel) Qing-Shan Jia

(Samuel) Qing-Shan Jia received the B.E. degree in automation in July 2002 and the Ph.D. degree in control science and engineering in July 2006, both from Tsinghua University, Beijing, China. He is an Associate Professor in the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a visiting scholar at Harvard University in 2006, at the Hong Kong University of Science and Technology in 2010, and at Laboratory for Information and Decision Systems, Massachusetts Institute of Technology in 2013. His research interest is to develop an integrated data-driven, statistical, and computational approach to find designs and decision-making policies which have simple structures and guaranteed good performance. His work relies on strong collaborations with experts in manufacturing systems, energy systems, autonomous systems, and smart cities. He is an associate editor (AE) of IEEE Transactions on Automatic Control, and was an AE of IEEE Transactions on Automation Science and Engineering (2012-2017) and Discrete Event Dynamic Systems Theory and Applications (2012-2016). He served the Discrete Event Systems Technical Committee chair in IEEE Control Systems Society (2012-2015), and now serves the Control for Smart Cities Technical Committee chair in International Federation of Automatic Control, the Smart Buildings Technical Committee co-chair in IEEE Robotics and Automation Society, and the Beijing Chapter Chair of IEEE Control Systems Society. He is a member of the 11th Chinese Automation Association Technical Committee on Control Theory (2018- 2022) and the 1st Chinese Automation Association Technical Committee on Information Security of Industrial Systems (2016-2020).

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Bio: Anuradha Annaswamy

Dr. Anuradha Annaswamy received the Ph.D. degree in Electrical Engineering from Yale University in 1985. She has been a member of the faculty at Yale, Boston University, and MIT where currently she is the director of the Active-Adaptive Control Laboratory and a Senior Research Scientist in the Department of Mechanical Engineering. Her research interests pertain to adaptive control theory and applications to aerospace and automotive control, active control of noise in thermo- uid systems, control of autonomous systems, decision and control in smart grids, smart cities, and critical infrastructures, and co-design of control and platform architectures in cyber physical systems. Dr. Annaswamy has received several awards including the George Axelby and Control Systems Magazine best paper awards from the IEEE Control Systems Society, the Presidential Young Investigator award from the National Science Foundation, the Hans Fisher Senior Fellowship from the Institute for Advanced Study at the Technische Universitt Mnchen in 2008, the Donald Groen Julius Prize for 2008 from the Institute of Mechanical Engineers, and the Distinguished Member award from the IEEE Control Systems Society in 2016. Dr. Annaswamy is a Fellow of the IEEE and IFAC. Dr. Annaswamy is an active member of the IEEE Control Systems Society (CSS) and the American Automatic Control Council. She was a nominated and elected member of the CSS Board of Governors for 1993 and 2010 2012, respectively. She was a Program Chair of the American Control Conference (ACC) during 2003, General Chair of the 2008 ACC, and Program Chair for the 2nd Virtual Control Conference on Smart Grid Technology. She served as the Vice-President for Conference Activities in the IEEE CSS Executive Committee for 2014-15. Dr. Annaswamy is a co-editor of the IEEE CSS report on Impact of Control Technology: Overview, Success Stories, and Research Challenges,2011 (1st Edition) and 2014 (2nd Edition) along with Tariq Samad. She is the project lead on the publication, Vision for Smart Grid Controls: 2030 and Beyond, (Eds: A.M. Annaswamy, M. Amin, C. DeMarco and T. Samad), 2013.

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Bio: Costas Spanos

Costas J. Spanos received the EE Diploma from the National Technical University of Athens, Greece in 1980 and the M.S. and Ph.D. degrees in ECE from Carnegie Mellon University in 1981 and 1985, respectively. In 1988 he joined the Faculty at the department of Electrical Engineer- ing and Computer Sciences of the University of California at Berkeley. He has served as the Director of the Berkeley Microlab, the Associate Dean for Research in the College of Engineering and as the Chair of the Department of EECS. He works in statistical analysis in the design and fabrication of integrated circuits, and on novel sensors and computer-aided techniques in semiconductor manufac- turing. He also works on statistical data mining techniques for energy efficiency applications. He has participated in two successful startup companies, Timbre Tech, (acquired by Tokyo Electron) and OnWafer Technologies (acquired by KLA-Tencor). He is presently the Director of the Center of Information Technology Research in the Interest of Society (CITRIS) and the Chief Technical Officer for the Berkeley Educational Alliance for Research in Singapore (BEARS).

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Bio: Amro M. Farid

Amro M. Farid is currently an Associate Professor of Engineering at the Thayer School of Engineering at Dartmouth with a principal expertise in the application of control, automation & information technology to intelligent energy systems. He received his Sc. B. in 2000 and his Sc. M. 2002 from the MIT Mechanical Engineering Department. He went onto complete his Ph.D. degree at the Institute for Manufacturing within the University of Cambridge (UK) Engineering Department in 2007. He has varied industrial experiences from the automotive, semiconductor, defense, chemical, and manufacturing sectors. In 2010, he began his academic career at the Engineering Systems & Management department at the Masdar Institute of Science & Technology in Abu Dhabi, UAE. He currently leads the Laboratory for Intelligent Integrated Networks of Engineering Systems (LIINES) which maintains an active research program in Smart Power Grids, Energy-Water Nexus, Electrified Transportation Systems, Industrial Energy Management, and Intelligent Energy Systems. He is also a Research Affiliate at the MIT Mechanical Engineering Department and at the U. of Massachusetts Transportation Center. He has made active contributions to the MIT-Masdar Institute Collaborative Initiative, the MIT Future of the Electricity Grid Study, and the IEEE Vision for Smart Grid Controls. He currently serves on the Executive Committee for the Council of Engineering Systems Universities (CESUN) and the Executive Committee for Axiomatic Design. He is a senior member of the IEEE and holds leadership positions in the IEEE Control Systems Society (CSS) Technical Committee on Smart Grids, and the IEEE Systems, Man & Cybernetics (SMC) Technical Committee on Cybernetics for Intelligent Industrial Systems. He is also a member of the IEEE SMC Technical Committee on Distributed Intelligent Systems, the IEEE Industrial Electronics Society Technical Committee on Industrial Agents, and the ASME Dynamics Systems & Control Division.

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Bio: Scott Moura

Scott Moura is an Assistant Professor at the University of California, Berkeley in Civil & Environmental Engineering and Director of eCAL. He received the Ph.D. degree from the University of Michigan in 2011, the M.S. degree from the University of Michigan in 2008, and the B.S. degree from the UC Berkeley, in 2006 - all in Mechanical Engineering. He was a postdoctoral scholar at UC San Diego in the Cymer Center for Control Systems and Dynamics, and a visiting researcher in the Centre Automatique et Systmes at MINES ParisTech in Paris, France. He is a recipient of the O. Hugo Shuck Best Paper Award, Carol D. Soc Distinguished Graduate Student Mentoring Award, Hellman Faculty Fellows Award, UC Presidential Postdoctoral Fellowship, National Science Foundation Graduate Research Fellowship, University of Michigan Distinguished ProQuest Disser- tation Honorable Mention, University of Michigan Rackham Merit Fellowship, and Distinguished Leadership Award. He has received multiple conference best paper awards, as an advisor & student. His research interests include control & estimation theory for PDEs, optimization, machine learning, batteries, electric vehicles, and the smart grid.

CONTACT

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Assoc Prof Rong Su, School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Email: rsu@ntu.edu.sg


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Prof Christos G. Cassandras, Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Brookline, MA 02446, USA, Email: cgc@bu.edu