Ph.D. Position : Variable Price-Optimal Policies For Urban Electromobility Networks (M/F)

Universities and Institutes of France
November 30, 2022
Offerd Salary:Negotiation
Working address:N/A
Contract Type:Temporary
Working Time:Full time
Working type:N/A
Job Ref.:N/A
  • Organisation/Company: CNRS
  • Research Field: Computer science Engineering Mathematics
  • Researcher Profile: First Stage Researcher (R1)
  • Application Deadline: 30/11/2022 23:59 - Europe/Brussels
  • Location: France › ST MARTIN D HERES
  • Type Of Contract: Temporary
  • Job Status: Full-time
  • Hours Per Week: 35
  • Offer Starting Date: 01/01/2023
  • The Gipsa-lab is a joint research unit CNRS, Grenoble-INP (Grenoble Institute of Technology), University of Grenoble approved by Inria and the Observatory of Sciences of the Universe of Grenoble. With 350 people, including around 130 doctoral students, Gipsa-lab is a multidisciplinary research unit developing both fundamental and applied research on signals and complex systems. Gipsa-lab develops projects in the strategic fields of energy, environment, communication, intelligent systems, life and health and linguistic engineering. Through its research activities, Gipsa-lab maintains a constant link with the economic environment thanks to a strong partnership with companies. Gipsa-lab staff is involved in teaching and training in the various universities and engineering schools in the Grenoble area (Grenoble Alpes University). Gipsa-lab is internationally recognized for the research carried out in Automation and Diagnostics, Information Sciences and Signal Image, Speech and Cognition. The unit develops its research through 16 teams organized into 4 research centers: .Automatic and Diagnosis .Data Science .Geometries, Learning, Information and Algorithms .Speech and Cognition The Gipsa-lab brings together 148 permanent staff and approximately 260 non- permanent staff (doctoral students, post-doctoral students, guest researchers, master's trainees, etc.)

    Context. As efforts for environmental protection become a major priority, Electric Vehicles (EVs) have started to emerge as one of the main components of sustainable traffic systems in cities worldwide. By 2030, however, EVs will account for 70% of all the vehicles sold, according to the EU roadmap (the Fit for 55 plan), which envisions a ban on the sale of new petrol and diesel cars as early as 2035. Integration of the electrical vehicles (EV) with the city infrastructure (charging stations), and the electrical power supply network (power grid) possess unsolved critical problems that will become critical with the massive adoption of EV by the population. Contrary to what is generally thought to be the case, electromobility (e-mobility), i.e., the electrification of the transport system, will not necessarily be a hurdle to the development of future electric power systems, where most synchronous generators will be replaced by utility-scale and behind-the-meter renewable energy sources (RES). Massive amounts of daily and seasonal storage capacity will be required in the medium-term future in order to properly compensate for the lack of RES dispatchability. On the daily horizon, a large portion of this storage capacity could be provided by EVs (here named e-flexibility), if the future vehicle-to-grid (V2G) technology is duly developed and managed to meet the needs of the moment. The electrification of transport could therefore help to integrate much larger amounts of renewables at a lower cost, which is crucial to achieving Europe's long-awaited energy independence Ref.5. One of the main potential barriers to fully exploit the e-flexibility potential available (by optimizing the infrastructure, and developing new EV services and business strategies), is the lack of tools and methods for forecasting EV fleets' e-flexibility in both time and space. This means being able to forecast when and where EVs are going to move, how their State of Charge (SoC) is evolving, and how they going to interact with the infrastructure and the power grid. Besides, combining EVs mobility models with grid power models will allow to use the EVs e-flexibility potential to minimize the energy curtailment of renewables and the better use of the existing power transmission network. It will be also possible to use the electricity price of EVs charging stations to control the EVS mobility profiles, and hence the way that the e-flexibility evolves in time and space. This will allow to, improve the grid operation, to balance the grid power demand/supply, to maximize the energy use from renewables by minimizing the amount of energy curtailment Ref. 6, but also provide better and more fair electricity price for the EVs users. Work program. The PhD will include topics on modeling and optimal game design. The PhD will contribute to improve the model obtained in the ERC AdG Scale- FreeBack Ref.1, 2, 3, which recently developed a large-scale mobility model describing the daily movements of people in an urban network between their place of residence and destinations of five kinds (work, schools, etc.) This model generates a dynamic graph with nodes (origins and destinations) and their interconnections via an origin/destination matrix specifying the directions, arc weights and temporal profiles of the connections between nodes Ref.1. A set of nonlinear ordinary differential equations (ODEs) describes the movements of people at an aggregated anonymized level. This model has been recently extended to include EV mobility with battery charging/discharging dynamics, i.e. its State-of-Charge (SoC) Ref. 2. It will be completed with charging station models of several kinds (with low, medium, and high-power levels). Several extensions to reach a richer and more repressive model will be worked out in the team: • This large-scale ODEs model Ref. 3 can be transform by applying the continuation method developed in the context of the ERC AdG Scale-FreeBack Ref. 4 into a partial differential equation (PDE) to obtain an aggregated and distributed representations of the energy and traffic density evolution in time and space, • Extension to multi-class models where different type of vehicles will be modelled (thermic, electrical) and with different characteristics (low, medium, high charge profiles), • Introducing “incentives driver functions” that will push the EVS drivers to look for charging station s with lower electricity charging price, • Finally, the model will be coupled with a grid network using available open- source existing software. A first direct use of the model will be in solving problems of controlling the e-flexibility, (i.e. the EVs mobility) so as to optimize the grid operation, to balance the grid power demand/supply, and to maximize the energy use from renewables by minimizing the amount of energy curtailment Ref. 6. Using price-incentives for the EVs drivers, we will formulate a set of game-optimal problems to trade between drivers interests versus benefit for the grid and electricity markets. Simulations will be conducted using our digital twin under development Collaborations. It is envisioned a collaboration in this topic with Prof. Marta Gonzalez at the department of civil & Environmental Eng. UC-Berkeley, USA, to extend these models using real-time mobility data. References 1. Where, when and how people move in large-scale urban networks: the Grenoble saga. Ujjwal Pratap, Carlos Canudas-de-Wit, Federica Garin. https: // 2. Coupled Macroscopic Modelling of Electric Vehicle Traffic and Energy Flows for Electromobility Control Mladen Čičić, Carlos Canudas-de-Wit, CDC 2022 - 61st IEEE Conference on Decision and Control, Dec 2022, Cancún, Mexico. https: // 3. A new model for electric vehicle mobility and energy consumption in urban traffic networks. Carlos Canudas-de-Wit, Martin Rodriguez-Vega, Giovanni de Nunzio. 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022 (MFTS2022), Nov 2022, Dresden, Germany. https: // hal.archives- 4. A Continuation Method for Large-Scale Modeling and Control: from ODEs to PDE, a Round Trip, Denis Nikitin, Carlos Canudas-de-Wit, Paolo Frasca, IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers. https: // 5. Gym-ANM: Reinforcement learning environments for active network management tasks in electricity distribution systems. Robin Henry, and Damien Ernst. Energy and AI, Vol. 11, Jan. 2023. 6. https: // www. 7. Scale-FreeBAck, ERC AdG . Scale-Free Control for Complex Physical Network Systems. C. Canudas de Wit. https://

    Additional comments

    the workplace will also be at INRIA on Montbonnot Saint Martin

    Web site for additional job details

    https: //

    Required Research Experiences
  • Engineering

  • None

  • Computer science

  • None

  • Mathematics

  • None

    Offer Requirements
  • Engineering: Master Degree or equivalent

    Computer science: Master Degree or equivalent

    Mathematics: Master Degree or equivalent

  • FRENCH: Basic

    Contact Information
  • Organisation/Company: CNRS
  • Department: Grenoble Images Parole Signal Automatique
  • Organisation Type: Public Research Institution
  • Website: https:// www.
  • Country: France
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