Abstract
Simulation tools allow changes in electrical network, timetabling, and train speed profiles, which is fundamental for the planning and development of electric railways. When simulating electric railways, it may be challenging to adequately represent their nonlinear characteristics resulting from the rectifier substations, overvoltage protection circuits, and the continuous changes of the load according to the train position. The power exchange between trains and the change of the rail resistance between them require high temporal resolution and thus results in high computational complexity. Given these goals and challenges, the main objective of this work is to develop a sufficiently simple, fast to simulate, yet accurate, simulation method based on real-world assumptions. Further, the simulation method should be extendable to energy storage systems (ESSs) and control to allow designing control methods for energy management to maximise the energy efficiency of electric railways. This article describes the development of a framework for modelling the power flow within an electric rail network referenced to a case study example. In this article, a test scenario is presented comprising of eight trains running on a double rail track in order to verify the model against expected observations. The simulation model includes two DC traction substations separated by 2 km and three passenger stations separated by 1 km. The model operation requirements and parameters are based on the Yizhuang Line in Beijing Metro. The simulation methodology developed for this work differs from other methods reported in pertinent literature as it facilitates modifications to the model by avoiding the common approach of forming iterative methods for solving the power flow. The proposed test scenario allows the model to be verified for accuracy. The verification is accomplished via different methods and the obtained results confirm that the proposed modelling approach is accurate as the error between the supplied and consumed energy is only 0.11%.