System energy optimisation strategies for DC railway traction power networks

Tian, Zhongbei (2017). System energy optimisation strategies for DC railway traction power networks. University of Birmingham. Ph.D.

Preview

Tian17PhD.pdf
PDF - Accepted Version
Download (3MB)

Abstract

Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 32% of the final energy consumption. Electrified railway systems play an important role in contributing to the reduction of energy usage and C0\(_2\) emissions compared with other transport modes. Previous studies have investigated train driving strategies for traction energy saving. However, few of them consider the overall system energy optimisation.

This thesis analyses the energy consumption of urban systems with regenerating trains, including the energy supplied by substations, used in power transmission networks, consumed by monitoring trains, and regenerated by braking trains. This thesis proposes an approach to searching energy-efficient driving strategies with coasting controls. A Driver Advisory System is designed and implemented in a field test on Beijing Yizhuang Subway Line. The driver guided by the DAS achieves 16% of traction energy savings, compared with normal driving. This thesis also proposes an approach to global system energy consumption optimisation, based on a Monte Carlo Algorithm. The case study indicates that the substation energy is reduced by around 38.6% with the system optimised operations. The efficiency of using regenerative braking energy is improved to from 80.6 to 95.5%.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):