A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning

Due to the high degree of controllability of the flexible smart traction power supply system (FSTPSS), day-ahead energy management strategy (DAEMS) was developed to optimize the power flow of the FSTPSS. However, the use of DAEMS is not based on real-time information. For FSTPSS, without real-time information, it cannot solve the problem of planning deviation caused by the real-time fluctuation of uncertain loads or sources. Therefore, this paper proposes a real-time energy management strategy (REMS) which is based on the real-time information to address the problem of planning deviation. REMS is implemented by LSTM and deep Q learning algorithm, where LSTM predicts uncertain loads or sources, and the deep Q-learning controls the operation of FSTPSS based on real-time predicted state. The proposed strategy is validated with the power flow simulation model of TPSS and the real measured data. The simulation results verify the necessity and superiority of the proposed method.