Algorithmic Energy Management in Constrained Railway Traction Networks: A Systematic Review

Abstract

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 alternating current 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) offer a potentially viable alternative for preventing these power capacity challenges. This systematic review synthesises the literature on algorithmic energy management for grid-constrained multi-train AC railway networks, classifying the reviewed studies along three axes: algorithm family, operational scope, and constraint coupling. The review documents three consistent findings across the included studies. First, single-train trajectory optimisation, however mathematically refined, cannot represent the coupled electrical interactions that increasingly define network capacity on mixed-traffic networks. Second, while multi-train Train-Track-Power (TTP) simulations correctly capture these interactions, the algorithm families currently used to solve them face well-documented trade-offs between computational tractability and constraint flexibility. Third, the literature increasingly identifies a gap between mathematically optimal speed profiles and operationally executable ones, particularly for networks operated by human drivers rather than Automatic Train Operation systems. The review delineates where current methods succeed, where they fail, and which directions the literature has identified as open.