Data-driven and model-based hybrid reinforcement learning to reduce stress on power systems branches

This work proposes a reinforcement learning (RL) approach to tackle the control problem of branch overload relief in large power systems. Accordingly, a control agent is trained to change generators' real power output in order to relieve the stressed branches. For large power systems, this control problem becomes one whose decision space (i.e., the action space) is both highly-dimensioned and cont...