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

Abstract

Decarbonising 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 hardware upgrades are highly capital-intensive, software-based Energy Management Strategies (EMS) offer a potentially viable alternative. 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. Three findings emerge consistently. 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 capture these interactions, the algorithm families used to solve them face well-documented trade-offs between computational tractability and constraint flexibility; predictive and distributed methods, including hierarchical model predictive control and decomposition-based schemes, narrow this trade-off substantially, but electrical fidelity and network-scale real-time operation have been demonstrated largely in separate studies rather than together. Third, the literature increasingly identifies a gap between mathematically optimal speed profiles and operationally executable ones, particularly on 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.