The purpose of the “AI and Energy / Smart Grids” chair is to address the scientific challenges associated with integrating renewable energy sources and new applications into energy systems using artificial intelligence techniques. This area is part of the 3D energy transition: Decarbonization, Decentralization, and Digitization. In this context, some data are generated in large quantities by the various sensors distributed throughout the electrical system or at end-user locations (such as the Linky smart meter), while other data are largely missing. This latter issue complicates the full observability of the electrical system, which is necessary for precise and efficient management and control—especially since these systems involve critical infrastructure (vital systems). Traditional methods are limited and cannot keep pace with the rapid evolution of the changing energy paradigm. Therefore, the contribution of artificial intelligence techniques to overcome these obstacles is essential. However, the key challenge lies in coupling data with physical models (hybrid models). Thus, the data gathered from these systems, combined with machine learning processes, should help improve various aspects of smart grids, including preventive maintenance models, real-time system diagnosis and control in uncertain contexts, cybersecurity for SCADA/ EMS/DMS systems, the stability of microgrids with a high proportion of renewable energy systems, the optimization of field interventions for fault management in electrical networks, accurate forecasting models (energy production and consumption with minimal aggregation effects), and the distribution of intelligence across smart grids to address new variable uses and energy sources. Furthermore, the decision-making process in an uncertain context requires support tools for the network operator. These tools often require the integration of deep learning and reinforcement learning techniques. This also makes it possible to incorporate the grid operator’s expertise into these decision models.
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Aleksandr Petrusev, Rebecca Bauer, Remy Rigo-Mariani, Vincent Debusschere, Patrick Reignier et al. “Comparing Time Series Classification and Forecasting to Automatically Detect Distributed Generation.” IEEE PowerTech 2021, June 2021, Madrid, Spain.
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"A Distributed Model Predictive Control Framework for Grid-Friendly Distributed Energy Resources." Subramanian, L.; Debusschere, V.; Gooi, H. B.; and Hadjsaid, N. IEEE Transactions on Sustainable Energy.
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Lalitha Subramanian, Mamadou Goundiam, Vincent Debusschere, Hoay Gooi, Raphael Caire et al. Impact of the Interaction of Synchronous Machines and Virtual Inertia Provisions on the Small-Signal Stability of Microgrids. CIRED 2021, Sep 2021, Online, France.
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Audrey Moulichon, Mazen Alamir, Lauric Garbuio, Vincent Debusschere, Mustapha Rahmani et al. CIRED 2021—26th International Conference & Exhibition on Electricity Distribution, September 2021, Geneva, Switzerland.
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Coignard, J.; Janvier, M.; Debusschere, V.; Moreau, G.; Chollet, S., & Caire, R. “Evaluating forecasting methods in the context of local energy communities.” International Journal of Electrical Power & Energy Systems. Vol. 131, October 2021, 106956.
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Not in HAL_conference paper_ Subramanian, L.; Debusschere, V.; Gooi, H. B. & Hadjsaid, N. A Cooperative Rate-based Model Predictive Framework for Flexibility Management of DERs. IEEE Transactions on Energy Conversion _ August 2021IEEE Transactions on Energy Conversion PP(99):1-1.
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Aleksandr Petrusev, Remy Rigo-Mariani, Vincent Debusschere, Patrick Reignier, Nouredine Hadjsaid. AUTOMATIC DETECTION OF DISTRIBUTED SOLAR GENERATION BASED ON EXOGENOUS INFORMATION. CIRED 2021, September 2021, Geneva, Switzerland.
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Z. Fang, N. Crimier, L. Scanu, A. Midelet, A. Alyafi, B. Delinchant. “Multi-zone indoor temperature prediction with an LSTM-based sequence-to-sequence model.” *Energy and Buildings*, Vol. 245, August 15, 2021, 111053.
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Moulichon, A.; Alamir, M.; Garbuio, L.; Debusschere, V.; Rahmani, M. A.; Boudinet, C.; Noris, W., and Hadjsaid, N., “State Observer to Improve the Stability of VSG Control,” IEEE Annual Conference of the Industrial Electronics Society (IECON), 2020.
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Moulichon, A.; Debusschere, V.; Garbuio, L.; Rahmani, M. A.; Alamir, M. & Hadjsaid, N., “Standardization Tests for the Industrialization of Grid-Friendly Virtual Synchronous Generators,” Bulletin of the Polish Academy of Sciences—Technical Sciences, 2020, 1–9.
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Potel, B.; Cadoux, F.; Debusschere, V., and de Alvaro-Garcia, L., “Impact of the Periodicity of Feeder Reallocation on the Efficiency of Under-Frequency Load Shedding,” IEEE PowerTech, 2019, 1–5.
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Potel, B.; Debusschere, V.; Cadoux, F., & de Alvaro-Garcia, L., “Scales and objectives for under-frequency load shedding.” International Conference on Electricity Distribution (CIRED), 2019, 1–5.
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Zgolli, A., Collet, C., & Bobineau, C. (June 2019). DWS: a data placement approach for smart grid ecosystems. In Proceedings of the 23rd International Database Applications & Engineering Symposium (pp. 1–5).
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Zgolli, A., Collet, C., & Bobineau, C. (June 2019). DWS: a data placement approach for smart grid ecosystems. In Proceedings of the 23rd International Database Applications & Engineering Symposium (pp. 1–5).
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Hamdan, A., Cadoux, F., & Collet, C. (2019). Designing a Laboratory Setup to Experiment With Smart Metering for Smart Low-Voltage Grid Applications, CIRED 2019.
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Moulichon, A.; Garbuio, L.; Debusschere, V.; Rahmani, M. A. & Hadjsaid, N., “A Simplified Synchronous Machine Model for Virtual Synchronous Generator Implementation,” IEEE Power and Energy Society General Meeting, 2019, 1–5.
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Potel, B.; Debusschere, V.; Cadoux, F., & Rudez, U. “A real-time adjustment of conventional under-frequency load shedding thresholds,” IEEE Transactions on Power Delivery, 2019, 34, 2272–2274.
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Potel, B.; Cadoux, F.; de Alvaro-Garcia, L., & Debusschere, V., “A Clustering-based Method for Feeder Selection to Improve the Characteristics of Load Shedding,” IET Smart Grid, 2019.
