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Designing energy-efficient train operation strategies presents a significant computational challenge due to the inherent nonlinearity introduced by factors such as friction forces, motor efficiency variations, and power supply network fluctuations. Furthermore, when considering the utilization of regenerative braking energy (RBE) between trains, the complexity of collaborative train operation increases. To address this challenge while avoiding excessive computational costs, the solution space is explored focusing on the neighborhood of an empirically good initial solution, and potential solutions are assessed using multi-fidelity simulators, including a numerical simulator considering the power supply network and an analytical simulator. In addition, the proposed methodology is applied to a two-train case study where RBE exchange is feasible. The results from collaborative optimization are compared with those from single-train optimization using the Dynamic Programming method. Time efficiency is further analyzed based on single-train and two-train scenarios. The outcomes underscore the potential benefits of collaborative optimization, including reduced energy consumption and enhanced stability of overhead voltage, contributing to more sustainable and cost-effective train operations.
Meng et al. (Thu,) studied this question.