An Integrated Energy-Efficient Scheduling and Train Control Model with Regenerative Braking for Metro System

Rising energy cost and environmental awareness make energy-efficient operation a key issue for metro management. The speed profile and timetable optimization are two significant ways to reduce total energy consumption for metro systems. This paper proposes an integrated speed profile and timetable optimization model to reduce the net energy consumption while incorporating with complex track conditions like undulate gradients, curves and tunnels. The net energy consumption is minimized by force coefficients and coast control for single train movement and accelerating and braking synchronization for multiple trains. An efficient hybrid particle swarm method based on the particle swarm optimization and genetic algorithm is designed to obtain a satisfactory solution. Finally, numerical case studies based on one metro line in Beijing are conducted to validate the energy-efficient performance of integrated model and the results show that the integrated model can achieve a better tradeoff between traction energy consumption and reused braking energy on comparison with individual speed profile and timetable optimization.