Design of daily energy optimal timetables for metro lines using metaheuristics

This article intends to present a metaheuristic-based methodology to design energy optimal railway timetables so as to maximize overlapping time between speed up and slow down phases of trains. After a presentation of an electrical modeling of a metro line, a nodal analysis based on Newton-Raphson solving technique is introduced to determine electrical power flows inside the network. Then a particle swarm optimization and a genetic algorithm are implemented to define timetables that will maximize braking energy recovery. Finally, a simulation is performed to compare a nominal timetable and a timetable found by previous metaheuristics. Results show promising energy saving on an operational day.