Algorithmic Power Optimisation in Constrained Railway Networks: A Systematic Review
This is an incremental review identifying gaps in software-based solutions for decarbonising constrained railway networks.
The paper tackles the problem of power capacity constraints in railway networks by reviewing Energy Management Strategies, finding that current algorithms generate theoretically optimal but overly complex speed profiles that are impractical for human execution.
The decarbonisation of heavy-duty railway networks requires maximising the capacity of existing electrical infrastructure. Integrating heavy freight alongside fast passenger services exposes the hard physical limits of conventional AC traction networks, causing severe localised power quality degradation, phase unbalance, and low-voltage behaviour that triggers protective substation tripping. Because upgrading physical hardware is highly capital-intensive, software-based Energy Management Strategies (EMS) have the potential to offer viable solution for preventing these power capacity challenges. This systematic review demonstrates that traditional, single-train optimisations are fundamentally "grid-blind", necessitating a shift toward multi-train simulations to protect the network's Firm Service Capacity (FSC). However, evaluating this shift reveals a critical tension between the computational bottlenecks of deterministic models and the latency of heuristic approaches. Furthermore, a fundamental operational gap exists: while current algorithms generate theoretically optimal speed profiles to increase efficiency and therefore reduce power consumption from the grid, these profiles are excessively complex and inappropriate for human execution. Consequently, future EMS frameworks must bridge this human-machine interface gap to realise capacity improvements on constrained mixed-traffic networks.