Benjamin Donnot / RTE

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  • benjamin.donnot@rte-france.com

  • Title: Learning to run a power network in a sustainable world Part II: The RTE competition tutorial


    Date: August 19, 2020


    Bio: After a master degree in applied mathematics (statistics) at ENSAE, Benjamin obtained a PhD in computer science at Universite Paris Saclay under the direction of Isabelle Guyon. Benjamin joined RTE as an R&D researcher. One of his roles is to close the gap between Artificial Intelligence academic community and industry: studying and implementing state of the art AI research into power grid as a possible tool to allow energy transition. His interests include open science, power system and machine learning. He has been the lead software developer of the Grid2Op platform and co-organizer of the L2RPN set of competitions.



    Abstract: On the way towards a sustainable future, this competition aims at unleashing the power of reinforcement learning for a real-world industrial application: controlling electricity power transmission and moving closer to truly “smart” grids using underutilized flexibilities. In track 1, develop your agent to be robust to unexpected events and keep delivering reliable electricity everywhere even in difficult circumstances. In track 2, develop your agent to adapt to new energy productions in the grid with an increasing share of less controllable renewable energies over years.


    Learning Materials: Talk Flyer Talk Slides


    Interacting Materials:

    1. Papers
      1. Review of Power System Distribution Network Architecture
      2. Defining power network zones from measures of electrical distance
      3. Power Networks: The Digital Approach
      4. Energy network: towards an interconnected energy infrastructure for the future
      5. Electricity Networks: Technology, Future Role and Economic Incentives for Innovation
    2. Videos
      1. You aren't my electricity supplier - who are you?
      2. Aircraft Power Network
      3. What is AI (Artificial Intelligence)?
      4. A.I. Experiments: Making it easier for anyone to explore A.I.
      5. What is Natural Language Processing? | Accenture
    3. Data



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