...

2017 Research Grants

50Kbadge

Improved Analytics for Urban Energy Distribution Grids with Smart Buildings

Lead Researcher / Institution:
Audun Botterud – Massachusetts Institute of Technology

READ MORE

Project Abstract:
By 2050, two thirds of the world’s population will live in urban areas, with the greatest growth in the developing world. Urban areas already account for a majority of primary energy use and energy-related CO2 emissions (IEA 2016). Hence, finding cost-efficient and sustainable solutions to meet the increasing demand for energy in cities is of critical importance to meet global climate goals. In this project, we will develop advanced analytics to identify good solutions for planning and operation of urban energy distribution infrastructure, considering electricity, heating/cooling, and transportation. In particular, we will account for the need to balance the stochastic nature of distributed energy resources (DERs, e.g. solar PV, demand response, electric vehicle charging) using local resources (e.g., building or community energy storage) or relying on interconnections to the larger electricity or natural gas grids. We will also consider the potential roles of centralized planning and decentralized decision-making among heterogeneous agents, and how to best design incentives through prices and tariff schemes, to achieve future urban energy-environmental goals.

About Audun Botterud
Audun Botterud is a Principal Research Scientist in the Laboratory for Information and Decision Systems at MIT. The main goal of his research is to improve the understanding of the complex interactions between engineering, economics, and policy in electricity markets. He is particularly interested in integration of renewable energy and energy storage into a smarter electricity grid. Towards this end, he uses analytical methods from operations research and decision science combined with fundamental principles of electrical power engineering and energy economics. Dr. Botterud is also a Principal Energy Systems Engineer at Argonne National Laboratory. He received his MSc in Industrial Engineering (1997) and PhD in Electrical Power Engineering (2004) from the Norwegian University of Science and Technology. He is co-chair for the IEEE Task Force on Bulk Power System Operations with Variable Generation.

Other Researchers / Institutions:
Asu Ozdaglar – Massachusetts Institute of Technology

50Kbadge

Developing a Smart Energy Router for Flexible and Efficient DC Power Distribution in Smart Homes and Buildings

Lead Researcher / Institution:
Minjie Chen – Princeton University

READ MORE

Project Abstract:
Residential and commercial buildings account for more than 40% of US electricity consumption and 30% of carbon emissions. The traditional AC power delivery architecture in US buildings has become outdated, owing to major changes in the nature of loads and in power electronics technologies. In recent years, a large amount of electricity in modern residential buildings is consumed by DC loads, such as flat-panel displays and LED lights. Many emerging and important applications, including electrical vehicles and solar panels are natively DC. The ubiquitous need of DC voltage makes DC power delivery highly promising in future buildings. This project aims to develop a smart and flexible DC power delivery architecture to improve the energy efficiency in future buildings, and to open new opportunities for “smart home” and “smart grid” applications by replacing conventional AC-DC adapters attached to each electronic device by a novel centralized multi-input-multi-output (MIMO) bidirectional AC-DC power converter that can efficiently create many adjustable DC voltage levels. These flexible DC voltage levels will be connected to “smart-outlets” with communication and voltage regulation capabilities and a central converter will function as an energy router, distributing power with low loss and high flexibility, and eliminating the need of low-power AC-DC adapters. Efforts will be devoted to greatly increase the switching frequency of these converters to realize high efficiency and high power density. Methods for routing energy will be investigated, together with fundamental studies on cross-regulation of MIMO power converters, distributed control and system stability analysis. This system will become a multi-functional test platform to enable a wide range of innovations under the Cyber Physical Systems and Internet of Things framework.

About Minjie Chen
Dr. Minjie Chen is an assistant professor at the Department of Electrical Engineering and at the Andlinger Center for Energy and the Environment at Princeton University. He received his B.S. degree from Tsinghua University in 2009 and his Ph.D. degree from MIT in 2015. His research interests include high performance power electronics and intelligent energy management systems. He is the recipient of the Dimitris N. Chorafas doctoral thesis award from MIT EECS, the E.E. Landsman Fellowship, and multiple awards from IEEE Power Electronics journals and conferences.

50Kbadge

Development of a Novel Scheme for Introducing Distributed Generation Systems Based on Business Continuity Planning Considering Disaster Risks

Lead Researcher / Institution:
Shigehiko Kaneko – The University of Tokyo

READ MORE

Project Abstract:
Business continuity planning (BCP) is one of the important risk management strategies to enhance resilience of business operations and local communities in the case of disaster. In this research, we focus on introducing distributed generation equipment as a system for enhancing the effectiveness of BCP from the viewpoint of energy supply, including electricity and heat. Our research aims to (1) develop a suggestion scheme to optimize planning for energy system configuration including distributed generation equipment and cogeneration (CGS), (2) improve an economical index to judge whether a customer, who is interested in installing BCP equipment, should introduce distributed generation equipment considering the disaster risks, and (3) suggest an evaluation scheme for an appropriate amount of the governmental subsidy for the introduction of distributed generation equipment based on BCP. Introductory planning of the distributed generation equipment for a target building will be optimized by modeling the energy generation equipment and the disaster risks, then the whole energy demand-supply will be analyzed via mixed integer linear programming. A “disaster-risk-weighted damage inequality” as a decision scheme for installing a CGS will be improved from the viewpoint of practical use. Finally, Net Present Value of the energy system will be calculated to evaluate the governmental installing subsidy, thus contributing to the development of a resilient energy infrastructure with a reasonable, appropriate, and quantitative introductory planning and subsidy system.

About Shigehiko Kaneko
Shigehiko Kaneko earned a Bachelor of Engineering degree from the Department of Mechanical Engineering, Faculty of Engineering from the University of Tokyo in 1976, and a Doctor of Engineering in 1981 from the same department. In 1981, he became a Lecturer at the Department of Mechanical Engineering, Faculty of Engineering, the University of Tokyo. In 1982, he became an Associate Professor at the same department. In 2003, he became a Professor at the same department after serving as a Visiting Associate Professor at McGill University from 1985 to 1986. Dr. Kaneko’s works in the wide-field mechanical engineering realizing safe and secure society produced various books and papers. He is a director of Combustion Control, which is one of Cross-ministerial Strategic Innovation Promotion Programs. He is currently a member of the Japan Society of Mechanical Engineering (JSME), the past president of the JSME (2011-2012), the American Society of Mechanical Engineering, and Science Council of Japan.

Other Researchers / Institutions:
Akane Uemichi – The University of Tokyo

50Kbadge

Data-driven Based Low-carbon Operation of Active Distribution Systems Considering Forecasting Uncertainty and Demand Response

Lead Researcher / Institution:
Chongqing Kang – Tsinghua University

READ MORE

Project Abstract:
Active distribution systems (ADS) play a significant role in enabling the integration of distributed renewable energy. This project addresses three challenges to accommodate the stochastic distributed renewable energy. The first is to forecast the load and renewable energy output of each feeder with higher accuracy through data mining techniques. The second is to evaluate the demand response (DR) potential of each consumer to provide flexibility for the ADS. The third is to develop low-carbon optimal operation strategy of ADS based on energy forecasting and DR potential evaluation.

About Chongqing Kang
Chongqing Kang is full professor of electrical engineering in Tsinghua University. He is Chairman of Executive Committee of Department of Electrical Engineering. From 2011 to 2014 he was the Director of Centre for Teaching Excellence, Tsinghua University. His research interest focused on power system planning, power system operation, renewable energy, low carbon electricity technology and load forecasting.

He is Fellow of IEEE and IET. He is the senior member of CSEE. He is the recipient of the National Science Fund for Distinguished Young Scholars. He was supported by Fok Ying Tung foundation and enrolled in Program for New Century Excellent Talents in University by Ministry of Education. He was a visiting scholar at Cambridge University during 2007-2008. He was enrolled in the “Ten Thousand Talent Program” in 2016. He was an invited speaker of the Super Session in IEEE PES General Meeting in July 2015.

He has been the PI for nine grants supported by NSFC, three of which were supported to collaborate with the UK (Royal Society), South Korea (NRF-National Research Foundation), and United States (TAMU). He lead one of the Major State Basic Research Development Programs “Smart Grid Technology and Equipment” in 2016. He has been on the editorial board of five international journals, including IEEE Transactions on Power Systems and Electric Power Systems Research, and four Chinese journals.

He won the second prize of National Teaching Achievement Award in 2014. He and his team were granted the Institute Prize in Global Energy Forecasting Competition in 2014. He was granted one gold award and one silver award in the 44th International Exhibition of Inventions in Geneva in 2016. He was granted two silver awards in the 10th Beijing Invention and Innovation Contest in 2016.

Other Researchers / Institutions:
Duncan Callaway – University of California, Berkeley

50Kbadge

Beyond Discrete Choice and Prices in Route Choices – Towards Efficient Revealed Preferences Identification and Nudging in the Multi-utility Paradigm

Lead Researcher / Institution:
Cedric Langbort – University of Illinois at Urbana-Champaign

READ MORE

Project Abstract:
Our goal is to create, implement, and validate an en-route online personalized informational nudging system, which actively exploits users’ altruism and personalized information (as opposed to monetary incentives and undifferentiated information procurement), to help drive traffic conditions to system optimum, be it in terms of congestion or energy use reduction. Towards this goal, we will investigate the possibility of modeling and identifying commuter preferences without the traditional restrictive assumptions of discrete choice theory (e.g., substitutability, attribute transitivity), so as to preserve the unique features of the pro-sociality and informational attributes.

About Cedric Langbort
Cedric Langbort is an associate professor in the department of Aerospace Engineering, with joint appointments in the Department of Electrical Engineering and the Coordinated Science Lab (CSL) at the University of Illinois at Urbana-Champaign. Within CSL, he co-founded and co-directs the Center for People & Infrastructures (CPI), which brings together engineers, social scientists, and artists to study and design the tenuous interactions between modern infrastructures and their users. He works on applications of control, game, and information and optimization theory to a variety of fields; most recently to smart infrastructures, and especially transportation systems. He is a recipient of the NSF CAREER Award, and an active member of the IFAC Technical Committee on Networked Control Systems and the IEEE CSS Technical Committee on Smart Cities. He is currently serving as an associate editor of Systems & Control Letters.

Other Researchers / Institutions:
Hari Sundaram & Venkata Sriram Siddhardh Nadendla – University of Illinois at Urbana-Champaign

50Kbadge

Quantile Regression in Large Energy Datasets

Lead Researcher / Institution:
Leo Liberti – École Polytechnique

READ MORE

Project Abstract:
Linear regression is one of the basics of statistics and data science: fitting a hyperplane to a set of m points in Rn so as to minimize the errors (i.e., distances) between the points and the hyperplane, where “fitting” means “deciding the coefficients”: in this sense, classical linear regression is a regression towards the mean. A variant of linear regression can also be carried out with respect to the median and every r-quantile. Statistically speaking, quantile regression amounts to computing an r-quantile of the n-th column sample (of size m) conditioned to the belief that the corresponding random variable Xn depends, by way of a linear model, on the other n – 1 samples (corresponding to random variables X1…Xn -1). If historical data are available, interpolation on the r-quantile values yields a visualization of the historical data which is very suggestive and useful for gaining insights. It is well known that linear regression reduces to solving a Linear Program (LP). It is also known that random projections can (in theory) help speed up quantile regression. We recently discovered that random projections can also help solve very large dense LPs efficiently: our project consists of applying this discovery to the quantile regression LP.

About Leo Liberti:
Leo Liberti is a research director at CNRS, part-time professor at École Polytechnique (X), where he is also campus lead for the Siebel Energy Institute, and founder of the SYSMO team at LIX (the Computer Science Department at at X). He obtained his Ph.D. at Imperial College London, was a postdoctoral fellow at Politecnico di Milano, was recruited as a professor at X, and spent two years as a Research Staff Member at IBM Research at Yorktown Heights, prior to joining CNRS. His main interests are Mathematical Programming (MP) as well as Distance Geometry (DG). While at IBM, he was exposed to ideas in Machine Learning (ML), which he is currently using on both MP and DG. He serves on many editorial boards and program committees.

Other Researchers / Institutions:
Pierre-Louis Poirion – École Polytechnique
Vu Khac Ky – Chinese University of Hong Kong

50Kbadge

Big Data Platform for FFCS Design: From Gas to Electric

Lead Researcher / Institution:
Marco Mellia – Politecnico di Torino

READ MORE

Project Abstract:
Free-floating car sharing (FFCS) is a relatively new car sharing system that allows customers to pick and drop cars wherever in a geo-fence area. Users check on a Web platform which car is available in their neighborhood, and with a simple click start the rental. The same platform offers (often involuntarily) information (e.g., car availability, position, fuel level) that can be harvested by means of big data techniques to extract further knowledge and then understand potentialities and limits of FFCS systems. One goal of this project is to study the feasibility of creating a platform that harvests data offered by the platforms of different FFCS companies, and (2) to design big data analytics to extract higher level information such as mobility patterns, customer habits, gas consumption, typical areas of usage, and more. All these big data analyses are fundamental to highlight and to understand many aspects about FFCS, and to design a future and more sustainable transport. Another goal of this project is to demonstrate the potentials of big data in this field, we will design an electric-based FFCS systems (e-FFCS) leveraging actual data coming from current gas based FFCS platforms. Answers to questions such as what is the e-car mile range requirements to cope with current usage pattern and what is the impact of recharging station placement policies are based on actual (big) data obtained from the usage patterns of (large amount of) actual users. The key idea of our project is to use data to drive decisions about future development of FFCS systems, and e-FFCS systems in particular. This big data approach could be later extended to integrate and harvest other information sources (e.g., public transport and traffic status, maps details) to scale to a platform for next generation urban traffic management based on big data.

About Marco Mellia:
Marco Mellia is Associate Professor at Politecnico di Torino. In 2002 he visited the Sprint Advanced Technology Laboratories working at the IP Monitoring Project (IPMON). In 2011, 2012, 2013 he collaborated with Narus Inc, CA, working on Internet monitoring and applying big data techniques to complex systems. He has been scientific coordinator of research projects within EC FP7, and EC H2020. He is editor of IEEE Transactions on Networking, IEEE Transactions on Network and Service Management, and ACM Computer Communication.

He has co-authored over 250 papers published in international journals and presented in leading international conferences. His research interests are in the areas of Internet monitoring and big data analysis. He is currently the coordinator of the SmartData@PoliTo Centre at Politecnico di Torino, an interdisciplinary laboratory focusing on big data and data science.

Other Researchers / Institutions:
Danilo Giordano – Politecnico di Torino
Cristina Pronello – Université Technologique de Compiègne
Maria Lapietra – City of Torino

50Kbadge

Informing Occupants and Modifying Their Behavior Through Energy and Air Quality Sensing

Lead Researcher / Institution:
Jovan Pantelic – University of California, Berkeley

READ MORE

Project Abstract:
The objective of this project is to develop a tool that joins building performance and user perceptions and behaviors to support energy savings and occupant wellbeing. To do this, we will use data from two sources to construct this tool: (1) data collected from a mass deployment of Internet-of- Things (IoT) sensing platforms that measure energy and air quality both outdoors and indoors and (2) from building occupants themselves. The ultimate aim of the tool will be to influence an occupant’s awareness of his or her personal energy consumption and air quality of their space in order to influence their behavior towards actions that will save energy while improving indoor air quality.

About Jovan Pantelic:
Dr. Jovan Pantelic, Assistant Professional Researcher, Center for the Built Environment, University of California, Berkeley. Pantelic has a bachelor in mechanical engineering, a masters in thermal and fluids engineering, and a PhD in architectural engineering from the National University of Singapore. Pantelic leads projects that bridge from fundamental science and engineering to design and realization. Pantelic has done a number of projects in the energy and indoor air quality field. His current focus is exploring the potential for energy savings with radiant floor systems. He recently received the 2016 ASHRAE Ralph Nevins Award for his work on human responses to the environment.

Other Researchers / Institutions:
Lindsay T. Graham – University of California, Berkeley

50Kbadge

Resilience of the Integrated Urban Infrastructure Under Extreme Events: A Sequential Decision-making Approach

Lead Researcher / Institution:
Matteo Pozzi – Carnegie Mellon University

READ MORE

Project Abstract:
The energy system is exposed to extreme events, which can induce damages and disruption costs. In the future, the vulnerability of network components is expected to increase due to aging, climate change, and possibly security attacks. The energy system is part of a set of interconnected urban systems that work together to provide communities with the necessary services to thrive. Modernization and improvement of this interconnected ensemble is paramount from environmental, economic, and social viewpoints and the integration of physical components with internet-connected devices, also known as the Internet of Things (IoT), offers a key opportunity for this improvement by affecting design, operation, and control of the urban infrastructure. This project will focus on integrating data analysis and decision-making optimization to improving the resilience of interconnected systems, with a focus on the energy infrastructure. To achieve this goal, we will define resilience in relation with the overall long-term cost and efficiency of the integrated set of systems. Availability of data collected at fine temporal and spatial granularity will allow us to optimize real-time response during recovery from disruptions and, therefore, improve the overall system’s resilience.

About Matteo Pozzi:
Matteo Pozzi is an Assistant Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University. His research deals with risk analysis for civil infrastructure systems, using engineering models and sensor data. He received a Ph.D. in structural engineering from the University of Trento (Italy) and, prior to Carnegie Mellon, he was a postdoctoral researcher at the University of California, Berkeley.

Other Researchers / Institutions:
Bruno Sinopoli – Carnegie Mellon University

50Kbadge

Multi-modal Crowd Sensing to Monitor Buildings in Smart Cities

Lead Researcher / Institution:
Alessandro Rizzo – Politecnico di Torino

READ MORE

Project Abstract:
Monitoring of buildings, including monuments subject to environmental effects and archeological sites, through non-destructing and non-intruding technologies is a fundamental element of smart cities. Much effort has been devoted to seamlessly assess relevant indicators, such as thermal leaks, structural integrity of facades, building occupancy, and wall humidity. Thermal infrared (TIR) measurements have proven to be effective in quantifying several of these phenomena. However, main limitations of TIR are related to the sensor carrier. Two main carriers are being tested in urban areas: cars and aerial vehicles, possibly unmanned aerial vehicles (UAVs). Cars exhibit long mission endurance, provide measurements at a good rate, and perform signal processing on board, due to the high payload and energy availability. They can be safely operated in most weather conditions; and in extreme cases, when the mission should be aborted, they can be easily stopped and parked. However, cars contribute to and suffer from traffic congestion. In building inspections, they do not have the shooting angle necessary to limit the effect of reflected temperature on the captured images, making measurements on highest floors not reliable. UAVs can overcome the latter limitation by inspecting buildings with the right angle, even at the highest floors. However, UAVs have a reduced endurance, a low payload, and cannot be safely operated in harsh conditions. Moreover, the insertion of UAVs in urban congested areas is limited by the risk assessment required by aviation certification authorities that may lead to no-fly limitations for safety considerations. Recently, much effort has been put forward to reduce risk margins for semi-autonomous flight in urban areas, yet more work is needed toward certified, risk-free unmanned flight. In this project, we will put forward a monitoring system for urban areas using TIR, based on the synergic use of UAVs and cars, to minimize the downsides of both approaches and improving the quality and reliability of measurements. Cars will be used as ground measurement and processing units, as well as ground stations and relays for UAVs. UAVs collaborative clusters will be used to perform agile and accurate measurements at the highest levels above the ground. The collected multi-rate, multi-resolution measurement sets will be interpreted and fused on board of the cars in order to produce accurate analyses of the urban area, especially energetic analyses.

About Alessandro Rizzo:
Alessandro Rizzo joined Politecnico di Torino (Italy) in November 2015 as a tenured associate professor at the Department of Control and Computer Engineering (DAUIN), where he conducts and supervises research on complex and networked systems, cooperative robotics, distributed and data-driven estimation, modeling and control. Since 2012, Dr. Rizzo has been a visiting professor at the New York University Tandon School of Engineering, Brooklyn NY, USA. Past affiliations of Dr. Rizzo were at the Politecnico di Bari, Italy; JET Joint Undertaking, UK; University of Messina, Italy; ST Microelectronics, Italy; University of Catania, Italy; IRISA/INRIA, Rennes, France. Dr. Rizzo is the author of one book, more than 100 journal and conference proceedings papers, and two international patents. In 2002, Dr. Rizzo received the best application paper award from the International Federation of Automatic Control (IFAC). Dr. Rizzo is a senior member and a distinguished lecturer of the IEEE.

Other Researchers / Institutions:
Giorgio Guglieri – Politecnico di Torino
Carlo Ratti – Massachusetts Institute of Technology

50Kbadge

Incorporating Real-time Thermal Comfort and Indoor Occupancy into Building Management Systems

Lead Researcher / Institution:
Stefano Schiavon – University of California, Berkeley

READ MORE

Project Abstract:
This project focuses on using wearable sensors to incorporate real-time indoor occupancy and occupant thermal comfort in the loop of building management systems (BMS). Existing BMS receive little occupant thermal feedback, resulting in unnecessary cooling/heating. The lack of occupancy information also yields energy waste via unnecessary lighting and plug load. Occupant feedback based on people answering surveys has a high risk of overburdening already busy occupants. In this project, we propose to develop, test, and deploy a wearable system consisting of a modified identification badge and an optional wristband. The badge integrates an accelerometer and Bluetooth with building access functions. The accelerometer detects occupant activity level, the most important parameter for thermal comfort. Bluetooth is used for occupancy proximity to control computers and lighting. As an option, the wristband measures skin temperature to predict occupant thermal comfort without asking for active voting. We will develop a new control system with occupants in the loops of BMS using the open source platform sMAP (Simple Measurement and Actuation Profile). The system will also be open to other mobile computing units, such as smartphones, to further expand the pool of users within the buildings.

About Stefano Schiavon:
Stefano Schiavon, PhD, is Assistant Professor of Architecture (Sustainability, Energy and Environment) at UC Berkeley. Stefano’s research is focused on finding ways to reduce energy consumption in buildings and, at the same time, increase indoor environmental quality. Stefano has worked on personal environmental control system, occupant satisfaction, underfloor air distribution (UFAD), radiant systems, building energy simulation, air movement, LEED, thermal comfort and statistical modeling. He has experience in laboratory measurements, post occupancy evaluation and building performance simulation and is involved in the Center for the Built Environment and a PI in the SinBerBEST project. Stefano received a PhD in Energy Engineering (2008), and a MS in Mechanical Engineering (2005) with honor from the University of Padova, Italy. Stefano has been a visiting scholar at Tsinghua University and Technical University of Denmark and received the 2010 REHVA Young Scientist Award and 2013 ASHRAE Ralph Nevins Physiology and Human Environment Award.

Other Researchers / Institutions:
Lin Zhang – Tsinghua University

50Kbadge

Blockchain-based Smart Metering and Electricity Trading

Lead Researcher / Institution:
Kenji Tanaka – The University of Tokyo

READ MORE

Project Abstract:
The future electricity grid will be a two directional system with billions of consumers and prosumers interacting with each other. Micro-grids, including batteries, solar, or wind generation modules, will need to be interconnected using distributed energy management software. Being able to conceive a secure and decentralized control and billing system adapted to these autonomous, peer-to-peer exchanges is one of the biggest challenges of this century. We propose to use Blockchain as an underlying platform because could perfectly fit the requirements of a decentralized digital currency that provides bidirectional and transparent rewards to prosumers and that is independent from Feed-In tariffs by guaranteeing a given amount of energy and then converting it into fiat money on an open electricity market. Blockchain 2.0 also enables smart contracts that can further transform the electricity sector as it enables micro-transactions not only between micro-grids but also appliances. It provides effective incentives for subsystems to adapt to changing conditions that will contribute in cutting peak demands and contribute in solving the demand-response matching issue.

About Kenji Tanaka:
Kenji Tanaka is a Project Associate Professor in the Department of Systems Innovations at Graduate School of Engineering at the University of Tokyo. He also holds the Presidential endowed chair for Electric Power Network Innovation by Digitalgrid at the University of Tokyo. He received the B.E. degree in Naval Architecture, the M.E. degree in Information Engineering in 2000, and the Ph.D. degree in Systems Innovation in 2009 from the University of Tokyo. After obtaining his M.E. degree, he started his business career at McKinsey (2000-2003) and Japan Industrial Partners (2003-2006), then re-joined the University of Tokyo as an assistant professor. He started his academic research in Distributed Energy Network Systems. He is a founder member of Digitalgrid Consortium (since 2011) and Secondary Battery Research Group (since 2008). In 2011, he engaged in Japanese policy making as a policy advisor at Ministry of Land, Transportation and Tourism for energy-efficient city. His research interests include digital-grid, energy storage systems, battery life-evaluation, electric vehicle installation, data mining, demand forecasting, and logistics.

Other Researchers / Institutions:
Annette Werth & Hiroshi Chin – The University of Tokyo
Yasuhiro Takeda – OPT Incubate

50Kbadge

Incentives, Choices, and Analytics for Electric Vehicle Fleets in Jointly Managing Urban Traffic and the Smart Grid

Lead Researcher / Institution:
Lav Varshney – University of Illinois at Urbana-Champaign

READ MORE

Project Abstract:
In April 2016, Téo Taxi launched an all-electric taxi fleet in Montreal; in August, Uber leased out a fleet of plug-in electric vehicles (PEVs) in London. Electrification of urban transportation is on the rise. Centrally managed PEV fleets provide unique opportunities to both offer electric grid support and manage traffic. When driven, a PEV enables personal transportation but adds to the road traffic. When parked, it can act as a controllable storage device to either consume surplus or return energy to the grid. This project aims to explore how traffic and energy data analytics can enable companies such as Téo Taxi and Uber to sell demand flexibility and storage support to the grid, by acting as retail aggregators of mobile distributed energy resources (DERs), while also reducing traffic congestion. The basic idea is to dynamically align incentives of transportation authorities, energy utilities, taxi/rideshare companies, and individual drivers making choices in the presence of their bounded rationality.

About Lav Varshney:
Lav R. Varshney is an assistant professor in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign where his research interests include the fundamental limits and practical designs of data analytics systems in sociotechnical contexts, crowdsourcing and social computing, information theory, and network science. He received the S. M. and Ph. D. degrees from the Massachusetts Institute of Technology in 2006 and 2010, respectively, earning the E. A. Guillemin Thesis Award for best electrical engineering S. M. thesis and the J.-A. Kong Award Honorable Mention for best electrical engineering doctoral thesis. His doctoral work developed new models of human decision-making. He was a research staff member at the IBM T. J. Watson Research Center, Yorktown Heights, NY from 2010 until 2013, where his work on crowdsourcing, computational creativity, and data analytics for city infrastructure improvement was recognized by numerous awards and global media attention.

Other Researchers / Institutions:
Subhonmesh Bose & Tamer Başar – University of Illinois at Urbana-Champaign

50Kbadge

Quantifying the Predictability of City-scale Urban Traffic

Lead Researcher / Institution:
Daniel Work – University of Illinois at Urbana-Champaign

READ MORE

Project Abstract:
This project aims to shape the next generation of traffic prediction and management. Primary methods of traffic control will soon shift from agency-managed traffic signal infrastructure to the real-time and mass routing of flows by private network users (e.g., Uber, Lyft, and autonomous / connected vehicles). Using a combination of structural data reduction and machine learning, we seek to understand what predictability means in urban traffic, and lay foundations for extracting actionable knowledge from complex traffic data.

About Daniel Work:
Daniel Work is an assistant professor in the Departments of Civil and Environmental Engineering and Electrical and Computer Engineering (courtesy), and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His interests are in control, estimation, and optimization of transportation cyber physical systems. Professor Work is a 2014 recipient of an NSF CAREER Award.

Other Researchers / Institutions:
Richard Sowers – University of Illinois at Urbana-Champaign