SMRT - Ifsttar

7 juin 2024

14h00	Professor Sharon Di, Associate Professor at Columbia University, New York.
	AI for Urban Transportation Digital Twin.
	Abstract: Transportation digital twins have become increasingly popular tools to improve traffic efficiency and safety. However, the majority of effort nowadays is focused on the “eyes” of the digital twin, which is object detection using computer vision. I believe the key to empowering the intelligence of a transportation digital twin lies in its “brain,” namely, how to utilize the information extracted from various sensors to infer traffic dynamics evolution and devise optimal control and management strategies with real-time feedback to guide the transportation ecosystem toward a social optimum. My research aims to employ tools including machine learning and game theory to develop an urban transportation digital twin, leveraging data collected from the NSF PAWR COSMOS city-scale wireless testbed being deployed in West Harlem next to the Columbia campus. In this talk, I will primarily focus on two solutions: (1) scientific machine learning that leverages both domain knowledge and available data, and (2) mean field game that bridges the gap between micro- and macroscopic behaviors of multi-agent dynamical systems. In the first topic, physics-informed deep learning will be introduced and applied to traffic state estimation and uncertainty quantification. In the second topic, I will introduce how to model behaviors of new actors (e.g., a large number of autonomous vehicles) in a transportation system and their interaction with existing actors (e.g., human-driven vehicles).

Sharon Di, Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University in the City of New York. She also serves on the committee of Columbia's Center for Smart Cities in the Data Science Institute.

20 Fevrier 2024

15h00	Prof. Carlos Canudas-de-Wit, Director of research at CNRS, CNRS/GIPSA-Lab, Grenoble, France.
	EVs and Renewable Energy: Paving the Way for Greener Electromobility Networks.
	Abstract: The simultaneous expansion of electric vehicles (EVs) and intermittent renewable energy sources holds the potential to accelerate the decarbonization of highly emissions-intensive sectors. However, these developments also pose challenges to the stability of power systems, which could impede their widespread adoption. Nevertheless, electric vehicles present a significant opportunity to enhance flexibility, enabling better integration of renewable energy variations and optimization of energy markets. To address these challenges, there is a need to design new models that incorporate both electric vehicle traffic flows and battery charge dynamics. These models represent a crucial step towards utilizing e-flexibility, a concept aimed at predicting the spatial and temporal evolution of EVs' state of charge in relation to the electrical grid and associated electricity market operations. The presentation introduces a graph-based approach to model the mobility of electric vehicles and the evolution of their state of charge in large-scale urban traffic networks. This model combines the vehicles' mobility, described by dynamic equations over a graph that captures origin-destination movements, with the energy consumption associated with their mobility patterns. The model also incorporates power inputs from charging stations. We will showcase the operation of these models using our numerical twin (eMob-Twin) on the open-to-public platform emob-twin.inrialpes.fr. This model can be extended to account for driver behavior in determining when and where to charge, considering factors such as current state of charge, distance to charging stations, and charging costs. [1]. Moreover, it can be used to identify optimal locations for charging stations, maximizing convenience for EV users and profitability for charging station owners. We will also introduce a simplified (averaged) version of the model, which serves as a foundation for optimizing charging station locations while reducing computational complexity [2]. Lastly, we propose an innovative approach that utilizes aggregated EVs for grid-balancing services in the auxiliary market. This is donne by an optimization framework which establishes pricing strategies to maximize profits for aggregators and CSOs while minimizing charging costs for EV users. Our findings demonstrate the effectiveness of this strategy in realistic simulations, integrating EV mobility and the Electricity FCR market.

References:
[1] Martin Rodriguez-Vega, Carlos Canudas-de-Wit, Giovanni De Nunzio, and Bassel Othman “A Graph-Based Mobility Model for Electric Vehicles in Urban Traffic Networks: Application to the Grenoble Metropolitan Area”. European Control Conference, Bucharest, Rumania, 2023.
[2] Remi Mourgues , Martin Rodriguez-Vega, Carlos Canudas-de-Wit . “Optimal location of EVs public charging stations based on a macroscopic urban electromobility model” , CDC 2023, Singapore.
[3] Guillaume Gasnier, Carlos Canudas-de-Wit . “Optimal Pricing Strategies for Charging Stations in the Frequency Containment Reserves Market for Vehicle-to-Grid Integration”. Submitted to the ECC 2024, Stockholm, SE.

Other related references:
- Mladen Cicic and Carlos Canudas-de-Wit ,” Coupled Macroscopic Modelling of Electric Vehicle Traffic and Energy Flows for Electromobility Control”, CDC2022, Cancun Mexico.
- Mladen Čičić Carlos Vivas, Carlos Canudas-de-Wit, Francisco R. Rubio, “Optimal Renewable Energy Curtailment Minimization Control Using a Combined Electromobility and Grid Model”. IFAC WC, Japan 2023.
- Mladen Cicic, Guillaume Gasnier, Carlos Canudas-de-Wit , “Electric Vehicle Charging Station Pricing Control under Balancing Reserve Capacity Commitments”, Submitted to CDC 2023 Singapore.

Short Bio: Canudas-de-Wit, Carlos was born in Villahermosa, Tabasco, Mexico in 1958. He received his B.Sc. degree in electronics and communications from the Technological Institute of Monterrey, Mexico in 1980. In 1984 he received his M.Sc. in the Department of Automatic Control, Grenoble, France. He was visitor researcher in 1985 at Lund Institute of Technology, Sweden. In 1987 he received his Ph.D. in automatic control from the Polytechnic of Grenoble (Department of Automatic Control), France. Since then he has been working at the same department as "Director of Research at the CNRS", where He teaches and conducts research in the area of control systems. He did lead and form the NeCS GIPSA-Lab (CNRS)-INRIA team on Networked Controlled Systems from 2006-2020. He has established several industrial collaboration projects with major French companies (FRAMATOME, EDF, CEA, IFREMER, RENAULT, SCHNEIDER, ILL, IFP, ALSTOM). He has been associate editor of the IEEE-Transaction on Automatic Control, from 1992 to 1997, AUTOMATICA, from 1999 to 2002, and IEEE Transaction on Control of System Networks (2013-18), IEEE Transaction on Control System Technology (Since 2014-18). He is currently Senior editor of the Asian Journal of Control (since 2010), , and the IEEE Transaction on Control of System Networks (since 2021). He holds the presidency of the European Control Association (EUCA) for the period 2013-15, and served at the IEEE Board of Governors of the Control System Society 2011-2014. He holds the ERC Advanced-Grant 2015 Scale-FreeBack for the period 2016-2022, and the PoC (from the EC) eMob-Twin 2023-24. He is IEEE-Fellow of the IEEE Control System Society. He is also IFAC-Fellow. His research publications include: 200 International conference papers, and 90 published papers in international journals, 5 books, 10 Book chapter, and holds 11 Patents. At present, His research focuses on the control of large-scale and complex physical networks with application to transportation networks and electromobility.
https://www.gipsa-lab.grenoble-inp.fr/user/carlos.canudas-de-wit