Reinforcement learning (RL) can transform power grid operations by providing adaptive and scalable controllers essential for grid decarbonization.
RL2Grid is a benchmark designed in collaboration with power system operators to accelerate progress in grid control and foster RL maturity.
RL2Grid standardizes tasks, state and action spaces, and reward structures within a unified interface for systematic evaluation and comparison of RL approaches.
The benchmark results highlight the challenges power grids pose for RL methods, emphasizing the need for novel algorithms capable of handling real-world physical systems.