Visualising Energy Consumption and Storage For a DC Electrified Rail System

Abstract

Researchers see an essential need to take the railway industry to the next level since it has been significantly affecting human lives for many years already. Like any other development, simulation has been useful in modelling real-life scenarios. Since there is a big data source in modelling the network of an electrical direct current (DC) railway, computer simulation is a great tool to handle such an immense amount of information. In this study, Python is seen as the most reliable programming language for big data analysis. To ensure the integrity of the model used in this work, the model extracted from the study of Fletcher et al. (2020) was configured to the actual data. Consequently, both the Substations 1 and 8 were consistent with the work of Fletcher et al. (2020), confirming the validity of the model used for this work. On the other hand, results show that utilising a regenerative braking system has significantly improved the train's performance by condensing its energy consumption to as much as 30%. Furthermore, power peak and energy-use peak were greatly lessened with the use of energy storage. The dependence of the train on the grid was observed to be minimised to about 45% based on the overall energy catered to the substations by utilising batteries. Visual representation of the big data became beneficial since it provides truncated information that is simple and more perceivable. It also helped construct a map to illustrate the dispersion of the substations and trains within the vicinity. Furthermore, it provides a picture of the train's motion, energy consumption, and timetable. Python programming language was able to analyse and illustrate the tremendous information on energy consumption and storage based on the model produced for the electrical DC railway system. Exceptional technological advancement was seen in this work since the behaviour and functionality of multiple trains in a full day operation have been observed. Moreover, the survey has provided the actual perception of people regarding the effectiveness of the map produced. The response would be beneficial to further developments in the program.