Real-Time Location-Aware Demand-Shaping for Power-Constrained AC Railway Corridors
For railway operators, this provides a practical method to manage power constraints with minimal timetable disruption, though results are demonstrated on a single representative corridor.
This paper presents a real-time, location-aware controller for power-constrained AC railway corridors that selectively curtails only the trains causing binding constraints, restoring feasibility at one thirtieth of the journey-time cost of the incumbent static limit and matching an offline genetic-algorithm optimum in 1.5 seconds versus a minute.
Power-constrained 25kV AC railway sections, particularly under degraded feeding, are protected today by blunt, section-wide power limits that penalise every train irrespective of whether it contributes to the binding condition. This paper presents a real-time, location-aware controller that restores the electrical feasibility of a feeding section with minimal impact on the timetable: it curtails only the trains that bind, where and when they bind, evaluating feasibility and per-train available power online with a solver-free estimate as an in-loop surrogate for the full power flow. Because the estimate is accurate on average but slightly optimistic at the binding instants, the controller screens with a small voltage margin, and a full multi-conductor power-flow solver confirms the restored feasibility. The resulting selective-curtailment policy is delivered through a cloud-to-edge connected driver advisory system. On a representative GB 25kV corridor under outage feeding, solver-selected to be infeasible uncontrolled yet restorable, the controller is compared against the uncontrolled case, the incumbent static limit, and an offline genetic-algorithm optimum, with every feasibility figure solver-validated. The static limit restores feasibility at a large journey-time cost by throttling the whole section; the location-aware controller restores the same feasibility at one thirtieth of that cost by advising a single train, and matches the offline optimum's solution in about a second and a half against the optimiser's minute. Aggregate peak demand is unmoved, because the active constraint is local far-field voltage rather than gross demand. All claims are relative to the baselines on a representative corridor; a specific-route deployment study is future work.