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This talk will describe some recent research works at the IETR lab on the decentralized smart charging of large fleets of electric vehicles. This work is based on an artificial intelligence method combining adaptive multi-agent systems (AMAS) and bandits (a reinforcement learning method) [Zafar, 2023]. As the training of AI-based energy management methods requires numerous iterations to determine the state of the electrical network (e.g presence/absence of congestion), whose computations may be time-consuming, recent work have focused on the development of fast surrogate models based on artificial intelligence. This talk will present a preliminary work on this topic covering tools such as deep neural network, decision trees and XGBoost, and several training dataset generation techniques including data augmentation techniques such as called Synthetic Minority Oversampling Technique (SMOTE) [Cuenca, 2024]. It is also important to note that currently enforced grid constraints may be considered as quite conservative (seasonal or even static ratings applied to power transmission/distribution components) in a context where the penetration rate of variable renewables (such as wind and PV) increases. This justifies the growing integration of dynamic rating to maximise the utilization of the power system closer to its limits. However, operating electrical networks with dynamic grid constraints presents challenges that works conducted at IETR contributed to address, in particular on the relevant modeling of electrothermal behaviours for their integration in stochastic dynamic programming for storage management under uncertainty [Faye, 2023].



[Zafar, 2023] S. Zafar, “Optimized management of an active distribution network using AMAS combined with the RL bandit method”, PhD thesis, ENS Rennes, France, 2023.

[Cuenca, 2024] J. Cuenca, E. Aldea, E. Le Guern-Dall’o, R. Féraud, G. Camileri, A. Blavette, “Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids », to be presented at the IEEE ISGT Europe conference, Dubrovnik, Croatia, October 2024.

[Faye, 2023] A. Faye-Bédrin, A. Blavette, P. Haessig, S. Bourguet, I. Daminov, “Stochastic Dynamic Programming for Energy Management of an Overplanted Offshore Wind Farm with Dynamic Thermal Rating and Storage”, in Proc. IEEE PowerTech, Belgrade, Serbia, 2023.

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