Multidisciplinary Institute in Artificial intelligence

DEBUSSCHERE Vincent,
Associate Professor at Grenoble INP UGA,
vincent.debusschere@grenoble-inp.fr
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DESCRIPTION

The transition to decarbonized energy systems is critical in mitigating climate change. Renewable energy integration and distributed resources, like electric vehicles, accompany current changes in energy systems, participating in their increased complexity and variability. Artificial intelligence offers complementary solutions to optimize energy distribution, storage, and consumption, enhancing system efficiency and reliability. However, energy systems' digitalization should accompany their decarbonization and support self-sufficiency. As such, data-driven techniques, intelligence decentralization, notably relying on edge computing, and the interconnection with the information and control systems should be developed, considering the human in the loop first, but above all ensuring global multi-criteria-based improvements to energy systems, minimizing rebound effects, and benefiting end-users in the appropriation of their energy consumption. This constitutes the scope of the proposed Chair on complex multi-scale energy systems.

ACTIVITIES

• One PhD just started on how generative artificial intelligence could help monitor weaknesses in electrical networks between the G2ELab, the GIPSA-Lab, and the start-up Altrans Energy.
• Ongoing discussion should lead to another action on data generation for better distribution grid modeling, planning, and automatic generation between the G2Elab and INRIA, probably anticipated with a Master’s Thesis.
• A Master’s thesis should work on the effective implementation of flexibility in a domestic energy system, in this case a refrigerator, with the creation of a real-life demonstrator at the G2Elab Monitoring and Intelligent Habitat experimental platform.
• An industry PhD will start with Enedis on low-cost PV control, with edge computing and optimized onboard capabilities with scalability in mind. This PhD will be jointly supervised by the G2Elab and the LIG.
• Two post-docs will start between UGA and Univ of Adelaide on decentralized control and end-users’ behavior modeling with grid-related constraints (one in France and the other in Australia).

EVENTS

• Round table on AI for smart Grids at the Assemblée Générale of the Smart Grids Institute in Lyon (March 21, 2025).
• Presentation at the MIAI meeting on Energy with Tenerrdis (April 10, 2025).
• Organization of a round table with industry key actors on AI and Energy during the MIAI days (June 19, 2025).
• Research workshop with KIT to identify collaboration topics in relation to energy systems and various techniques, notably AI (June 23, 2025).
• Chair of a session on “Multi-sources, multi-energy Microgrids” relying on AI-based techniques at the Symposium de Génie Electrique, SGE, Toulouse (July 1, 2025).
• Organization of a Seminar on AI for energy systems at the G2ELab’s Scientific Council (in front of ~100 researchers from the lab), with Benoit Delinchant and Rémy Rigo-Marinai (July 10, 2025).
• Organization of a special session on grids during the FACET symposium (October 8 2025, afternoon).
• Presentation of a going research project in collaboration with the University of Adelaide and Monash University on better understanding energy consumption through end-users’ reaction to non-complex incentives for flexibility (October 8, 2025, morning).
• Representing the CIMES Chair, the Grenoble INP ENSE3 welcomed the kick-off meeting of a European project whose goal is to create a new international Master's on “Advanced Energy Systems and AI”. Partners include, among others, KU Leuven, UPC Barcelona, KTH, and ESADE (October 9-10, 2025).
• Participation to the creation of a sequence on AI for distribution grids in a MOOC on grids and energy systems, to be released by mid-2026, Romain Rombourg.
• Participation in a round table with Schneider Electric and the UGA on the topic of AI-based solutions for Energy Systems at the Rencontres Nationales des Entreprises Locales de Distribution (October 16, 2025, afternoon).
   

SELECTED LIST OF PUBLICATIONS

• Model-free safe deep reinforcement learning for grid-to-vehicle management considering grid constraints and transformer thermal stress, Z Zhang, R Rigo-Mariani, N Hadjsaid, Y Xu, Engineering Applications of Artificial Intelligence 162, 112529
• Comparative of control strategies on electrical vehicle fleet charging management strategies under uncertainties, Z Zhang, R Rigo-Mariani, N Hadjsaid, Energy and AI, 100522
• Time-series clustering: A benchmark study on energy data with insights into demand response, RK Ahir, B Delinchant, A Easwaran, Engineering Applications of Artificial Intelligence 163, 112892
• Hybrid model of convolutional auto-encoder and ellipse characteristic for unsupervised high impedance fault detection, J Yang, B Delinchant, D Niyato, N Hadjsaid, Electric Power Systems Research 238, 111166
  
  Conference papers
   
• Reinforcement Learning for the Management of an Electrical Vehicle Fleet in A Distribution Grid, Z Zhang, R Rigo-Mariani, N Hadjsaid, 2025 IEEE Kiel PowerTech, 1-6
• Kumar, S. R., Easwaran, A., Delinchant, B., & Rigo-Mariani, R. (2024, June). Real-time Retail Electricity Pricing Using Offline Reinforcement Learning. In Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems (pp. 454-458).
• Modelling local electricity consumption by incorporating data of social media using natural language processing, Y Huang, MS Shahid, A De Moliner, B Delinchant, P Cauchois, IET Conference Proceedings CP922 2025 (14), 263-267
• Ram Kumar, S., Easwaran, A., Delinchant, B., & Rigo-Mariani, R. (2025, June). Improving Demand Response Programs Using Override Signals with Reinforcement Learning. In Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems (pp. 603-611).
• Sanchez, F., Mohamed, A., Rigo-Mariani, R., & Debusschere, V. (2025, June). Supervised Learning for the Bidding of Grid-Connected Batteries in the Day-Ahead Market. In 2025 IEEE Kiel PowerTech (pp. 1-7). IEEE.
• Sanchez, F., Debusschere, V., Rigo-Mariani, R., & Labonne, A. (2025, June). Versatile and self-adapting smart home control. In IET Conference Proceedings CP922 (Vol. 2025, No. 14, pp. 308-312).