Electricity supply operators offer financial incentives to encourage large energy users to reduce their power demand during declared periods of increased demand from energy users such as residential homes. This demand flexibility enables electricity system operators to ensure adequate power supply and avoid the construction of peaking power plants.

Railway operators can sometimes reduce their power demand during specified peak demand periods without disrupting the train schedules. For trains with infrequent stops, such as intercity trains, it is possible to speed up trains prior to the peak demand period, slow down during the peak demand period, then speed up again after the peak demand period. We use simple train models to develop an optimal strategy that minimizes energy use for a fleet of trains subject to energy-use constraints during specified peak demand intervals. The strategy uses two sets of interacting parameters to find an optimal solution—a Lagrange multiplier for each energy-constrained time interval to control the speed of trains during each interval, and a Lagrange multiplier for each train to control the relative train speeds and ensure each train completes its journey on time.

In this paper, we show that the cost of an optimal train journey on level track over a fixed distance is a strictly decreasing and strictly convex function of journey time. The precise structure of the cost–time curves for individual trains is an important consideration in the design of energy-efficient timetables on complex rail networks. The development of optimal timetables for busy metropolitan lines can be considered as a two-stage process. The first stage seeks to find optimal transit times for each individual journey segment subject to the usual trip-time, dwell-time, headway and connection constraints in such a way that the total energy consumption over all proposed journeys is minimized. The second stage adjusts the arrival and departure times for each journey while preserving the individual segment times and the overall journey times, in order to best synchronize the collective movement of trains through the network and thereby maximize recovery of energy from regenerative braking. The precise nature of the cost–time curve is a critical component in the first stage of the optimization.