Scheduling of futuristic railway microgrids—A FRA-pruned twins-actor DDPG approach

Transport decarbonization including railway electrification has become a global trend in tackling the climate change challenge due to the substantive green-house-gas emissions from the transport sector. However, within the context of the rapidly growing electricity demand due to railway decarbonization, the existing power supply infrastructures are often stretched beyond their rating capacity, thus slowing down the railway electrification pace. To address this challenge, this paper presents a novel railway microgrid solution to economically and efficiently cater for the growing electricity demands of the railway sector. The proposed railway microgrid uses the existing traction network, local renewable generations and local power grid as its energy sources to meet both the traction and non-traction loads in the electrified railway systems. It also employs hydrogen storage systems and aggregated electric vehicle (EV) fleets to support flexible energy scheduling. To address the uncertainty of renewable energy and implement efficient energy scheduling, machine learning tools and agents are introduced in formulating and solving the optimal scheduling problem, including bidirectional long short-term memory (BiLSTM) and an improved twins-actor deep deterministic policy gradient (Twins-Actor DDPG) method. To reduce the overall computational complexity, a fast recursive algorithm (FRA) is adopted to streamline the machine learning agents. Case studies confirm that the proposed optimal microgrid operation can achieve up to 18.7% costs reduction for the daily operation of the railway power supply system while meeting the electricity demand, and FRA can achieve up to 78.6% agent size reduction and 93.1% computational cost savings.