Real-Time Dynamic Train Dispatching for Sustainable and Energy-Efficient Operations: An Automata-Based Receding Horizon Control Framework

Improving energy efficiency is critical for the sustainable development of urban public transportation. Regenerative braking is widely employed in urban rail transit to recycle braking kinetic energy into the traction network, thereby enhancing system efficiency. However, without effective scheduling, excessive feedback energy can induce instantaneous voltage spikes, leading to line overheating and accelerated equipment aging. Existing studies often fail to fully address these challenges due to simplified physical models and limited adaptability to real-time environments. To overcome these limitations, this study proposes a dynamic scheduling method for the efficient utilization of regenerative energy within a train fleet. A physical simulation system featuring a “Network-Train-Control” three-layer architecture is constructed. By formally describing the physical coupling among network topology, operational rules, and train kinematics, the system enables accurate energy profiling under realistic impedance and signaling constraints. Furthermore, a finite state automaton (FSA) is utilized to abstract continuous train dynamics into discrete states, facilitating a braking-event-triggered Model Predictive Control (MPC) framework. This framework predicts and dynamically adjusts fleet operations within a receding horizon to maximize the immediate absorption of regenerative energy. Experimental results demonstrate that the proposed method achieves active energy cooperation among trains, increasing the regenerative energy utilization rate by approximately 11%, thereby offering a viable technical solution for low-carbon urban transit.