Energy Optimization in Catenary-free Railway System and Improved Time-based Model for Train Movements

Electrified railway system has drawn more and more interests to solve the traffic congestion problems in metropolitan cities. The growing concerns about the environment and energy prices have forced the railway operators and suppliers to improve the efficiency in mass transit system. The usage of energy storage devices are considered as way to increase the total system efficiency by recovering the kinetic energy when the trains run in braking mode.;A typical light rail vehicle makes many run-and-brake cycles in short time. Today, the railway operators around world have been utilizing supercapacitors, batteries and flywheels to enhance the usability of the breaking energy in the system. Compared to the batteries, the supercapacitors have high power density, long lifetime, and fast charge/discharge capability. Moreover, according to the development trends in supercapacitors, their energy density is expected to be in a comparable level with batteries in 10 years. Rapid development in supercapacitors makes possible to have a catenary-free operations in the railway system. Catenary-free railway system provides an increased safety, maximized energy recovery, minimized stray currents and related corrosion effects.;In this dissertation, the usage of supercapacitors as an only and primary source is shown. Three-phased interleaved buck/boost converter as an interface between the supercapacitor and traction inverter has been suggested and its model is obtained for train movement simulations. An improved time based model is proposed to calculate train movements. With a proposed model, compared to time based, a better traction performance is achieved without using mechanic brake. In addition to that, the more breaking energy is recuperated. Thus the additional cost for wear and tear of mechanic braking system is reduced. Particle swarm optimization algorithm is used to search the optimum coasting points and the results are verified with Genetic Algorithm and Simulated annealing algorithm.