4 papers
Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids
Hadi Nekoei, Alexandre Blondin Massé, Rachid Hassani +2
Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challenge in real-world settings, particularly…
Safety Representations for Safer Policy Learning
Kaustubh Mani, Vincent Mai, Charlie Gauthier +3
Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks assoc…
Accelerating Quasi-Static Time Series Simulations with Foundation Models
Alban Puech, François Mirallès, Jonas Weiss +5
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. Howev…
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage
Vincent Mai, Quang Hung Pham, Arthur Favrel +2
Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evol…