Reinforcement Learning for the Unit Commitment Problem
This work addresses the unit commitment problem for power system operators, offering a significant performance improvement over prior methods.
The authors tackled the day-ahead unit commitment problem by formulating it as a Markov decision process and using reinforcement learning algorithms, achieving a 27% improvement in operation costs and reducing running time from 2.5 hours to 2.5 minutes compared to existing state-of-the-art methods.
In this work we solve the day-ahead unit commitment (UC) problem, by formulating it as a Markov decision process (MDP) and finding a low-cost policy for generation scheduling. We present two reinforcement learning algorithms, and devise a third one. We compare our results to previous work that uses simulated annealing (SA), and show a 27% improvement in operation costs, with running time of 2.5 minutes (compared to 2.5 hours of existing state-of-the-art).