Improving the Computational Efficiency of the Unit Commitment Problem in Hydrothermal Systems by Using Multi-Agent Deep Reinforcement Learning

In power systems with a significant hydroelectric component, instances of the Unit Commitment (UC) problem may be much more computationally intensive due to the longer decision horizons and the additional hydro constraints. Therefore, this paper presents