Real-time Decision Making for Power System via Imitation Learning and Reinforcement Learning

With the development of intermittent renewable energy sources, modern power systems are facing significant uncertainties as well as voltage deviations. In order to respond quickly to power fluctuations and contingencies such as transmission line tripping due to faults, real-time optimal grid control is required. A real-time alternating current (AC) optimal power flow (OPF) method through deep reinforcement learning ( DRL) combined with imitation learning ( IL) in continuous action space is proposed in this paper. A Markov decision process (MDP) is constructed to describe the real-time AC OPF problem. This proposed method is tested on the IEEE 30-bus system and the results show the significant potential for the optimal online control for power systems compared with the state-of-the-art techniques.