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2016 Research Grants

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CCG – Cars, Communications, and the Grid

Lead Researcher / Institution:
Marco Ajmone Marsan – Politecnico di Torino

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Project Abstract:
The project will investigate the benefits achievable with the integration of three different technologies and service areas relating to wireless communications, car sharing, and electricity distribution. This will require mathematical and simulation models based on real data for the three domains. The project will thus benefit from the participation of experts in telecommunications, energy, transportation, and economics. The expected project outcome will consist of an assessment of the technological and economic benefits of the proposed approach, and identification of the technologies necessary to obtain them.

About Marco Ajmone Marsan:
Marco Ajmone Marsan is Full Professor at the Department of Electronics and Telecommunications of the Politecnico di Torino (Italy), and Research Professor at IMDEA Networks Institute (Spain). He earned his graduate degree in Electrical Engineering from the Politecnico di Torino in 1974 and completed his MSEE at the University of California, Los Angeles (UCLA) in 1978. In 2002, he was awarded a “Honoris Causa” PhD in Telecommunication Networks from the Budapest University of Technology and Economics. From 2003 to 2009 he was Director of the IEIIT-CNR (Institute for Electronics, Information, and Telecommunication Engineering of the National Research Council of Italy). From 2005 to 2009 he was Vice-Rector for Research, Innovation, and Technology Transfer at Politecnico di Torino. Ajmone Marsan was the Chair of the Italian Group of Telecommunication Professors (GTTI), and the Italian Delegate in the ICT Committee and in the ERC Committee of the EC’s 7th Framework Programme. He is a Fellow of the IEEE and a member of the Academy of Europe, and of the Science Academy of Torino. He is listed by Thomson-ISI amongst the highly-cited researchers in Computer Science. He has been principle investigator for a large number of research contracts with industries, and coordinator of several national and international research projects.

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Smart Grid Resilience: Automatic Real-time Detection and Prediction of Critical Conditions and Preventive Network Management Through Distributed Sensing, Smart Metering, Environmental/Social Info

Lead Researcher / Institution:
Ettore Bompard – Politecnico di Torino

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Project Abstract:
Resilience is the preparedness and ability to adapt to external and operational changing situations in order to assure the continuity and quality of the electricity supply to the end-users. It implies the capability to withstand and recover rapidly from adverse events like deliberate attacks, accidental failures, or natural threats or incidents. In recent years power distribution networks have grown in complexity. Electricity consumers are becoming active producers (prosumers) with the ability to both inject and withdraw energy, in a low predictable and controllable way. Therefore, grid resilience is becoming a key concern and a serious challenge for Distribution System Operators (DSOs), which also have to face changing environmental conditions, especially weather and social behaviors. Advanced metering infrastructures (AMI) provide the means to get almost real-time information (voltage, real and reactive power, etc.) from consumers and prosumers. All together, huge amounts of data from heterogeneous sources, updated in timeframes from seconds to hours, can be gathered and elaborated to extract useful information to predict critical conditions and optimize the network operation. The main focus of the full proposal will be to jointly exploit data from i) distributed sensors in the network, ii) smart meters, iii) environment monitoring tools (with special focus on weather), and iv) social and user generated information, to define big data methods and algorithms to assure resilience of the distribution systems for achieving an assigned level of quality and continuity of electricity supply.

About Ettore Bompard:
Ettore Bompard is Professor of Power Systems at Politecnico di Torino (Polito). He has been Visiting Assistant Professor at the Electrical and Computer Engineering Department of the University of Illinois at Urbana-Champaign (US) (Fulbright Fellowship for research and lecturing) (1999 – 2000), Power Systems and Critical Infrastructures Senior Scientist at the Energy Systems, Security, and Market Unit of the Institute for Energy and Transport of the Joint Research Center of the European Commission in Petten (Netherlands) (2012 – 2014). He has been scientific coordinator of many research projects within EC FP7, Next Generation Infrastructure (Netherlands), and NATO projects. He is Editor of the IEEE Transactions on Sustainable Energy and Associate Editor of IET Generation, Transmission & Distribution. His research interests include electricity markets analysis and simulation, smart grid design and modeling, power system vulnerability assessment, and security management.

Other Researchers / Institutions:
Michela Meo – Politecnico di Torino
Carlo Ratti – Massachusetts Institute of Technology
Marguerite Nyhan – Massachusetts Institute of Technology
Rex Britter – Massachusetts Institute of Technology
Francesco Profumo – Politecnico di Torino
Marcelo Masera – European Commission, DG Joint Research Center, Institute for Energy and Transport
Gianfranco Chicco – Politecnico di Torino

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Novel Physically-driven Approaches for Multiscale Wind-Energy Forecasting

Lead Researcher / Institution:
Elie Bou-Zeid – Princeton University

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Project Abstract:
Reducing greenhouse gas (GHG) emissions will require a fundamental shift in energy production modes, from fossil fuels to renewables with much lower GHG emissions. All future scenarios put wind at the center of this green energy revolution, but the inherent time-variability of this resource, and more importantly our rudimentary ability to forecast it, persist as substantial barriers for increasing the market penetration and reducing the effective cost of wind-energy. Currently, short-term prediction of wind energy production relies on data-driven statistical or physical approaches, while mid-term prediction relies on numerical weather prediction (NWP) models. The two forecasts are then subjectively blended to produce a continuous forecast between 0 and 48 hours. However, the potential benefits from their joint analysis are not realized since the two are not simultaneously used to inform each other and obtain the best forecast. We will develop novel approaches to produce a continuous forecasted time series based on the joint exploration of the data-driven short-term forecasts and the numerically simulated mid-term ones. The optimal blending approach will emerge dynamically from this joint analysis. This innovation will both reduce the financial overhead imposed by poor wind-energy forecasts and increase the total electricity that can be supplied by wind without compromising reliability. Its incorporation into the energy market model will open new possibilities for optimizing grid integration.

About Elie Bou-Zeid:
Elie Bou-Zeid is an Associate Professor in the Department of Civil and Environmental Engineering at Princeton University. After earning a Bachelor’s in Mechanical Engineering and a Master’s in Environmental and Water Resources Engineering at the American University of Beirut, he obtained his PhD in Environmental Engineering from Johns Hopkins University in 2005. Prior to joining Princeton in 2008, he was a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne in Switzerland where he was awarded the prize of the “Fondation Latsis Internationle.” His broad research interests are in developing a fundamental understanding and better predictive tools for flow, and energy and mass transport in the environment. These advances are then applied to various problems ranging from atmospheric flows over complex urban surfaces and the implication for urban sustainability, to wind farm modeling and optimization.

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From Resource to Price: Machine Learning for Italian Electricity Network and Market

Lead Researcher / Institution:
Anna Creti – École Polytechnique & Université Paris Dauphine

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Project Abstract:
The project aims to use machine learning and data analysis to build a simulation tool able to reproduce the Italian electricity market with different network configurations and several possible geographical distributions of renewable generation facilities. We aim to characterize the relationship between the natural resource endowments of a region (solar, wind, and hydro) and the actual production of green electricity. Unveiling the links between the availability of a natural resource, the renewable production offered in the spot market and the equilibrium prices allows one to detect how the regulator can possibly intervene to introduce locational signals in support schemes for renewable and in network tarification, which could improve system efficiency and reliability. Italy is a natural case study: its power market is organized in six-regional sub-markets where transmission rights are assigned through implicit auctions; the resulting electricity price reflects both the cost of generation and the cost of network expansion. Moreover, the ambitious support policies for the development of renewable power sources have generated a significant amount of new investments in renewable power plants.

About Anna Creti:
Anna Creti is Full Professor of Economics at Paris Dauphine University, senior researcher at the Department of Economics (EXCESS) at the École Polytechnique, and associated researcher at UC3E, Berkeley and Santa Barbara. Her research focuses on the links between economics, energy and environment, the prices of raw materials, and the industrial organization applied to the energy sector.

Other Researchers / Institutions:
Philippe Drobinski – École Polytechnique
Claudia D’Ambrosio – École Polytechnique
Silvia Concettini – École Polytechnique

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Learning to Solve Hydro Unit Commitment Problems in France

Lead Researcher / Institution:
Claudia D’Ambrosio – École Polytechnique

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Project Abstract:
It is well known that electricity cannot be stored easily and that one of the most efficient methods for storing it is to turn it into potential energy by pumping water up mountain valleys into water basins. However, a system of interconnected valleys presents several issues when storing and re-using stored electricity. Specifically, scheduling the turbine/pump units in valleys is known as the Hydro Unit Commitment (HUC) problem. In France, hydro plants represent the first source of renewable energy and HUC corresponds to hundreds of difficult Mixed-Integer Nonlinear Programs to solve daily. Even when they are approximated to Mixed-Integer Linear Programming (MILP), they pose formidable challenges. Currently, MILP solution technology cannot even find a feasible solution to these MILPs. A feature of current solvers, namely their extensive configuration possibilities, is not usually exploited to the fullest. We believe, instead, that supervised machine learning techniques can be used to learn a good solver configuration for the numerical and structural properties of the instance being solved. Due to its structure and characteristics, we believe that restricting this idea to the HUC problem could have a strong impact, both as a methodology and practically, towards finding locally optimal solutions of the difficult HUC instances.

About Claudia D’Ambrosio:
Claudia D’Ambrosio is a permanent researcher at CNRS and part-time Assistant Professor at École Polytechnique. She obtained her PhD from the University of Bologna, interned at IBM Research, held a postdoctoral position at the University of Wisconsin-Madison, then was offered her position at CNRS, ranked 1st over the ten open positions. Her main field of interest is Mixed-Integer Nonlinear Programming (MINLP), as well as Mixed-Integer Linear Programming (MILP) and Mathematical Programming (MP) in general. She received the Euro Doctoral Dissertation Award in 2010, the highest European recognition for a PhD thesis in OR, and the 2nd Robert Faure ROADEF prize 2015, a triennial prize of the French OR society, with three laureates at each edition.

Other Researchers / Institutions:
Leo Liberti – École Polytechnique
Juan-Pablo Vielma – Massachusetts Institute of Technology

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Detecting Abnormal Activity on the Internet of Things With Real-time Outlier Detection

Lead Researcher / Institution:
Nick Feamster – Princeton University

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Project Abstract:
As we deploy an increasing number of Internet-connected embedded “Internet of Things” (IoT) devices to help us manage the energy usage of infrastructure from our homes to office buildings to critical infrastructure, securing these connected devices is increasingly important. Unfortunately, we cannot rely solely on device manufacturers to secure these devices; a complete solution requires network traffic analysis systems that can detect abnormal behavior as evident in the traffic itself. This project will deliver new outlier-detection algorithms and software systems that can monitor IoT installations, such as smart energy systems for abnormal activity that may suggest device compromise and general security breaches. The unique nature of the IoT domain will lead to both new machine learning algorithms for outlier detection and systems that can incorporate these algorithms to perform fast and accurate attack detection and remediation.

About Nick Feamster:
Nick Feamster is a Professor in the Computer Science Department at Princeton University and the Acting Director of the Princeton University Center for Information Technology Policy (CITP). He received his Ph.D. in Computer Science from MIT, and his S.B. and M.Eng. degrees in Electrical Engineering and Computer Science from MIT. His research focuses on computer networking and networked systems, with a focus on network operations, network security, and censorship-resistant communication systems. In December 2008, he received the Presidential Early Career Award for Scientists and Engineers (PECASE) for his contributions to cybersecurity, notably spam filtering. His honors include the Technology Review 35 “Top Young Innovators Under 35” award, the ACM SIGCOMM Rising Star Award, a Sloan Research Fellowship, the NSF CAREER award, the IBM Faculty Fellowship, the IRTF Applied Networking Research Prize, and award papers at the SIGCOMM Internet Measurement Conference (measuring Web performance bottlenecks), SIGCOMM (network-level behavior of spammers), the NSDI conference (fault detection in router configuration), Usenix Security (circumventing web censorship using Infranet), and Usenix Security (web cookie analysis).

Other Researchers / Institutions:
Samory Kpotufe – Princeton University

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Data Analytics and Stochastic Control for Optimal Management of Microgrid Generation and Storage Resources

Lead Researcher / Institution:
Emmanuel Gobet – École Polytechnique

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Project Abstract:
Microgrids are a localized and small-scale group of interconnected loads and distributed energy generation that operate either isolated from, or connected to, the main grid. Given the inherent intermittency of local production from renewable energy sources, coupled with the unpredictability of building energy loads, optimal management and device-level coordination of the generation and energy storage elements of a microgrid is difficult yet crucial for reliability and efficiency. One recent trend is the emergence of novel Internet of Things (IoT) sensor modalities, which provide previously unachievable measurement and communication capabilities. Use of IoT devices in microgrids holds great promise, and it is an active research question as to how to best utilize these devices to improve the operation of microgrids. This research will study how to use data from novel IoT sensors to model the impacts of behavioral, economic, and other consumption activity on electricity demand, and study how to use such models to predict future electricity demand and optimize distributed management of microgrid generation and storage resources. The research team includes industry collaborations with eLum and the Electricity of France (EDF) Public Utility. The anticipated outcomes include (i) data analytics algorithms for forecasting electricity demand based on models and measurements of behavioral, economic, and other consumption activity; (ii) stochastic control algorithms to optimize scheduling and operation of microgrid resources; and (iii) deployment of a microgrid testbed in an office building. The microgrid testbed will generate a real-world dataset for validation of developed algorithms.

About Emmanuel Gobet:
Emmanuel Gobet is a Professor of Applied Mathematics at École Polytechnique, a Researcher at CMAP (Applied Mathematics lab), and the scientific leader of the research team SIMPAS on “Statistical learning, numerical probability.” He has worked in several projects in the energy field and focused on developing decision model based on the stochastic energy data. He is coordinating a Research Initiative with EDF and several universities in the fields of stochastic control equations applied to electric systems. Emmanuel received his Bachelor degree at École Polytechnique in Applied Mathematics and a PhD at Université Paris VII in Statistics. His past work includes more than 50 papers in international journals on applied mathematics, focusing on optimization with stochastic processes, applied probabilities and numerical analysis, and two books.

Other Researchers / Institutions:
Anil Aswani – University of California, Berkeley
Philippe Drobinski – École Polytechnique

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Predictive Maintenance Planning for the Power Distribution Grid Using Machine Learning on Heterogeneous Condition Records

Lead Researcher / Institution:
Xuesong (Pine) Liu – Carnegie Mellon University

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Project Abstract:
Historical maintenance events and performance assessment data represent the correlations between the working condition of the grid and component/system faults. Systematically analyzing the correlations provides insights for planning predictive maintenance and avoids failures and shutdown. However, historical condition data (e.g. maintenance work orders, inspection reports, service requests, voltage and current measurements, load requirements, weather conditions) are stored in heterogeneous formats and scattered across different systems, which makes it impractical to perform manual integration and analysis. Our research proposes a fault prediction framework that leverages machine learning (ML) and natural language processing (NLP) technologies to integrate and process these records, producing a model that can be used to assess future conditions of electricity grid and support predictive maintenance.

About Xuesong (Pine) Liu:
Xuesong (Pine) Liu is a Research Assistant Professor in the Civil and Environmental Engineering department at Carnegie Mellon University. Liu focuses on using building information to improve facilities management’s decision making in the operation and maintenance of high performance buildings. He worked as the Asset Manager at CMU and managed building automation systems in more than 100 buildings on the campus. He led research projects on building information integration and automated fault detection and diagnosis for building automation systems.

Other Researchers / Institutions:
Mario Bergés – Carnegie Mellon University
Burcu Akinci – Carnegie Mellon University

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URBE – Understanding the Relationship Between Urban Form and Energy Consumption Through Behavioral Patterns

Lead Researcher / Institution:
Patrizia Lombardi – Politecnico di Torino

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Project Abstract:
Cities are characterized by a large consumption of energy resources (for heating & electricity, food, and mobility needs), environmental degradation (with related health issues due to bad air, water, and soil conditions), and social inequalities. Energy, environmental, and social aspects are all woven together and inseparable, making sustainable urban planning and management a complex and multi-objective task. Hence, to tackle sustainability challenges of post-carbon cities, urban planning policies must be identified that not only look at technology-related issues, but also at citizens’ behavior and how they respond to building performances and available urban services (e.g. mobility services). Furthermore, citizens and all private consumers should be more aware, active, and energy sufficient, as well as being prosumers—consumers that produce their own energy—whenever possible. There is substantial literature on sustainable urban districts and energy efficiency approaches. However, these studies tend to be either engineering-led—without understanding socio-economic complexities of both the building market and the urban forms—or simply qualitative, based on small surveys and case studies. This gap influences negatively sustainable urban planning policies. The proposed research aims to fill this gap by studying the consumption profiles of urban citizens in terms of energy use in buildings (heat, gas, and electricity) and mobility within the city area, in relation to both urban morphology (e.g. average distance from home to workplace, inter-distance among services, inter-distance among streets, building type) and behavioral patterns (e.g. work and home activities schedule, preferred means of mobility). Two major university campuses – one located in Italy (POLITO) and the other in the United States (MIT) – will be used as demonstrators. The broader objective is to define urban planning policies for post-carbon cities.

About Patrizia Lombardi:
Patrizia Lombardi (PhD, MSc, BA/MA) is Full Professor of Urban Planning Evaluation and Project Appraisal of Politecnico di Torino and Head of the Interuniversity Department of Regional and Urban Studies and Planning (DIST). She is Scientific Coordinator of the UNESCO Master “World Heritage and Cultural Projects for Development” managed by ITC-ILO and of the S3+ Lab on Urban Sustainability & Security Laboratory for Social challenges. She is an established figure in the field of evaluating smart and sustainable urban development for over 20 years, publishing widely in the subject area and coordinating, or serving as lead partner, in several Pan-European Projects related to Smart Cities, Post carbon society and Cultural heritage: BEQUEST, INTELCITY, INTELCITIES, ISAAC; SURPRISE; UNI-metrics; MILESECURE-2050; POCACITO; DIMMER; KIC InnoEnergy/ EIT ICT Lab; EEB Cluster/MIUR.

Other Researchers / Institutions:
Carlo Ratti – Massachusetts Institute of Technology
Isabella Lami – Politecnico di Torino
Romano Borchiellini – Politecnico di Torino
Pierluigi Leone – Politecnico di Torino
Andrea Lanzini – Politecnico di Torino
Elena Baralis – Politecnico di Torino
Tania Cerquitelli – Politecnico di Torino

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Adaptive and Intelligent System for Energy Consumption Optimization Using IoT-based Mobile Sensor Networks and Structural Health Monitoring Systems

Lead Researcher / Institution:
Stephen Mahin – University of California, Berkeley

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Project Abstract:
Recent developments in combining sensors, communication systems, and other fields like cloud computing and big data analysis have provided perfect tools for researchers, developers, and industries to develop cutting edge systems for improving energy efficiency and consumption. Smart homes, smart sensors, and Internet of Things (IoT) are just few examples of these technologies that will lead to more sustainable and resilient energy systems. Unfortunately, these technologies have not always been effective and efficient enough. For example, current industrial solutions that deal with energy consumption optimization and energy efficiency in residential buildings do not consider the inhabitant’s role and their level of comfort effectively. This project will present a new network of smart sensors that makes energy consumption optimization more intelligent, adaptive, and efficient. The network will include wearable devices, customized energy sensors, and monitoring systems. The system will provide the highest level of comfort for people and improve energy efficiency in large-scale areas. Feedback from people (e.g. location, body temperature, humidity) and data from other monitoring sensors integrated inside the building will be processed, to minimize energy consumption based on the current status of the system. Since the data sets obtained from these integrated systems are large and complex, they are considered big data and some methods like cloud computing will be used to process them.

About Stephen Mahin:
Stephen Mahin is the Byron and Elvira Nishkian Professor of Structural Engineering at the University of California, Berkeley and is currently the Director of the Pacific Earthquake Engineering Research Center. His research focuses on improving understanding of the seismic behavior of systems by integrating high performance numerical and experimental simulation methods to improve earthquake performance. He was awarded the Norman Medal by the American Society of Civil Engineers (ASCE) in 1987 and was elected to the ASCE Offshore Technology Council’s Hall of Fame in 2011 for his pioneering work on large steel offshore platforms. He was a finalist for the Charles Pankow Civil Engineering Innovation Prize in 1998 for the development of practical methods of seismic isolation. The Federal Highway Administration honored him with the James Cooper Best Paper Award in 2007 for his innovative research on self-centering bridges.

Other Researchers / Institutions:
Gian Paolo Cimellaro – Politecnico di Torino

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Data-driven Methods to Thwart Attacks on Microgrids

Lead Researcher / Institution:
David Nicol – University of Illinois at Urbana-Champaign

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Project Abstract:
Microgrids offer better reliability, resilience, and ease of management over the traditional centralized grid. However, any autonomous mechanism that maintains reliability of a microgrid relies on the communication among distributed entities, such as generation sources, loads, and controllers. These entities form the nodes in the communication network of a microgrid. In a smart microgrid, these nodes have computational capabilities and are susceptible to attacks. By taking control of these nodes, an attacker can alter control messages and thus undermine the reliability of the microgrid. We propose to develop a monitoring system that uses data analytics to detect such attacks in real-time.

About David Nicol:
David Nicol is the Franklin W. Woeltge Professor of Electrical and Computer Engineering at the University of Illinois at Urbana‐Champaign, and Director of the Information Trust Institute. He is the PI for two recently awarded national centers for infrastructure resilience: the DHS‐funded Critical Infrastructure Reliance Institute and the DoE funded Cyber Resilient Energy Delivery Consortium. His research interests include trust analysis of networks and software, analytic modeling, and parallelized discrete‐event simulation, research which has lead to the founding of startup company Network Perception, and election as Fellow of the IEEE and Fellow of the ACM. He is the inaugural recipient of the ACM SIGSIM Outstanding Contributions award. He received the M.S. (1983) and Ph.D. (1985) degrees in Computer Science from the University of Virginia, and the B.A. degree in Mathematics (1979) from Carleton College.

Other Researchers / Institutions:
Hoang Hai Nguyen – University of Illinois at Urbana-Champaign
Kartik Palani – University of Illinois at Urbana-Champaign
Rakesh Kumar – University of Illinois at Urbana-Champaign
Vignesh Babu – University of Illinois at Urbana-Champaign

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An Industry Equilibrium Approach to Reducing Reliance on Fossil Fuels in the Power Grid

Lead Researcher / Institution:
Asuman Ozdaglar – Massachusetts Institute of Technology

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Project Abstract:
We will develop an industrial equilibrium approach in which several energy producers make dynamic decisions on entry, exit, production, and upgrading of their technologies (e.g. improving the capacity of generators or switching to different energy sources). We use this model to study how different carbon policies (carbon taxes, Cap-and-Trade, and R&D subsidies for green technology) impact the feasible and efficient transition to cleaner technologies in the context of the power grid. After a theoretical analysis advancing recent work on theoretical characterization in dynamic economies, which gives us qualitative characterizations, we intend to undertake a full quantitative-numerical analysis of these class of models. We also plan to use available data from California energy market to validate our qualitative and quantitative predictions.

About Asuman Ozdaglar:
Asuman Ozdaglar received a B.S. degree in Electrical Engineering from the Middle East Technical University, Ankara, Turkey in 1996, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. She is the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering at the Massachusetts Institute of Technology. She is also the Director of the Laboratory for Information and Decision Systems and Associate Director of the Institute for Data, Systems, and Society. Her research expertise includes optimization theory, with emphasis on non-linear programming and convex analysis; game theory, with applications in infrastructure, social, and economic networks; distributed optimization and control; and network analysis, with special emphasis on contagious processes, systemic risk, and dynamic control. Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the Class of 1943 Career Development Chair, the inaugural Steven and Renee Innovation Fellowship, and the 2014 Spira teaching award.

Other Researchers / Institutions:
Daron Acemoglu – Massachusetts Institute of Technology
Insoon Yang – Massachusetts Institute of Technology

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Power Aware – Lights Off, Brains On

Lead Researcher / Institution:
Antonio Vetrò – Politecnico di Torino

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Project Abstract:
Power Aware is a web platform that compares energy consumption patterns for citizens with similar characteristics (e.g. house size, family composition, number and type of appliances) and recommends saving strategies to reduce power consumption in homes. The comparison is made by means of interactive data visualizations that show citizens their power consumption related to clusters of similar users. The web platform will offer additional features aimed at building a smart community, such as social tools and consumption forecasting. The mission of Power Aware is to enhance citizens’ awareness on key factors of energy costs, thus reducing energy use by changing consumption habits.

About Antonio Vetrò:
Antonio Vetrò is Director of Research at the Nexa Center for Internet and Society at Politecnico di Torino (Italy). Formerly, he has been research fellow in the Software and System Engineering Department at Technische Universität München (Germany) and junior scientist at Fraunhofer Center for Experimental Software Engineering (MD, USA). He holds a PhD in Information and System Engineering from Politecnico di Torino (Italy). He specializes in empirical methodologies and statistical analyses, applying empirical epistemological approaches to study the impact of technology on society. After working for a few years on methodologies to improve the quality of software and data, he recently steered his research on how to transfer technological innovations to industry and public institutions. His mission is to critically understand the benefits of IT innovations and digital culture and deliver them to societies. Vetrò has also volunteered on several development projects.

Other Researchers / Institutions:
Giuseppe Rizzo – Istituto Superiore Mario Boella
Enzo Lavolta – City of Torino

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Data-driven Discovery of Resilient Energy Storage for Grid Applications

Lead Researcher / Institution:
Venkat Viswanathan – Carnegie Mellon University

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Project Abstract:
A common trait of several renewable energy sources (e.g. wind, solar, tidal) is their intermittency and geographically distributed nature. The efficient utilization of renewable energy sources requires energy-storage systems. At present, we only have the capacity to store 1% of the energy consumed worldwide, most of which (98%) is through pumped-storage hydroelectricity, which is not a scalable storage approach. Hence, the shift to an economy based on renewable energy necessitates significant improvements in the ability to store energy. Batteries are at the forefront of possible storage technologies that could address this energy storage gap. However, the cost of batteries is much higher than that required to meet the grid storage market (<$200/kWh). In fact, the majority of the challenge involves the identification of a stable, low-cost electrolyte that can be functional in the electrochemical environment of the battery. The goal of this proposal is to develop a tool, titled SEED, for the data-driven discovery of electrolytes, aimed at cataloging, generating, and simulating a range of datasets containing parameters and attributes essential for battery electrolyte design. This will be coupled with state-of-the-art discovery tools aimed at fully leveraging the datasets while eliminating the burden on the part of the battery scientists. We will partner with battery companies developing energy storage solutions for the grid to utilize the tool to identify promising electrolyte candidates for next-generation Li-ion batteries that can provide reliable and robust grid storage.

About Venkat Viswanathan:
Venkat Viswanathan is an Assistant Professor in the Mechanical Engineering Department at Carnegie Mellon University. He has worked on theoretical aspects of advanced batteries, Li-air and Na-air batteries. He is a recipient of National Science Foundation CAREER award, American Chemical Society PRF New Investigator Award, Electrochemical Society Daniel Cubicciotti Award, and the Herbert Uhlig Summer Fellow.

Other Researchers / Institutions:
Aditya Parameswaran – University of Illinois at Urbana-Champaign

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A Data-driven Network Tomography Approach for Evaluating and Improving the Resilience of Power Grids

Lead Researcher / Institution:
Hao Zhu – University of Illinois at Urbana-Champaign

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Project Abstract:
Extreme disturbances such as cyber-attacks or loss of mega generation propagate throughout the electrical grid in the form of electromechanical (EM) waves. The most challenging type of EM waves is the inter-area oscillation that would impact the whole foot-print of an interconnected grid, such as the one that led to the 1996 mega blackout in the Western United States. Thanks to recent deployments of synchrophasor units that provide fast samples of the grid dynamics, the potential arises to infer the propagation model for the grid EM waves as a network tomography problem. However, existing approaches for this problem are either model-based or limited to post-disturbance analysis. Interestingly, a breakthrough in the field of Earth Sciences came with an inspiring observation that it is possible to recover the Green’s function of a medium between two points by cross-correlating the ambient noise fields recorded at these points. This ambient data correlation approach has been successfully validated by real seismology data and spurred a scientific revolution in exploratory geophysics and seismology in the last decade. Our proposed work will leverage the team expertise on both power systems and seismology to explore the potential of extracting the Green’s function from ambient synchrophasor data. Success of this project will lead to a revolutionary framework for the power industry in areas of synchrophasor data analytics and resilience improvements against cyber-physical attacks.

About Hao Zhu:
Hao Zhu is an Assistant Professor of Electrical and Computer Engineering at UIUC. She received a BE degree from Tsinghua University in 2006, and MSc and PhD degrees from the University of Minnesota in 2009 and 2012, all in Electrical Engineering. She worked as a research associate on power grid modeling and validation at the UIUC Information Trust Institute before joining the ECE faculty in 2014. Her current research interests include power grid monitoring, power system operations and control, and energy data analytics. She is currently a member of the steering committee for the IEEE Smart Grid.

Other Researchers / Institutions:
Ahmed Elbanna – University of Illinois at Urbana-Champaign