Reinforcement Learning for Control and Protection in Power Distribution Systems
This project studies reinforcement learning (RL) methods for control and coordination tasks in power distribution systems, with a focus on reproducible, paper-driven implementation. A representative RL approach from the literature is re-implemented, including the design of state/action representations, reward formulation, and constraint handling.
The implementation is systematically validated on a controlled benchmark to ensure correctness, reproducibility, and consistent evaluation. Subsequently, the approach is assessed on simulation data from a realistic medium-voltage grid model to analyze performance under practical conditions.
Depending on progress, extensions towards more advanced RL formulations and protection-related applications are considered. The work emphasizes structured evaluation and reproducibility in safety-critical learning-based control.