Multi-modelling for Decarbonisation in Urban Rail Systems

• Main Authors: David Golightly, Carl Gamble, Roberto Palacin, Ken Pierce
• Format: Article
• Language: English
• Published: SpringerOpen 2019-11-01
• Series: Urban Rail Transit
• Subjects: Dynamic modelling; Multi-modelling; Power optimisation; Decarbonisation; Railway
• Online Access: http://link.springer.com/article/10.1007/s40864-019-00114-2

Abstract This paper demonstrates a methodology for flexible, dynamic systems modelling relevant to urban rail decarbonisation. Decarbonisation of urban rail is a vital component of policy and strategy to minimize anthropogenic emissions. Decarbonisation is a systems problem, however, that needs to reflect the interaction between components and processes. Dynamic computer modelling of systems for decarbonisation involves interfacing multiple models together and running them in parallel in order to observe and predict systems-level effects. This is challenging due to the diverse nature of models, achieving parallel model integration and concerns around intellectual property (IP). One solution is the multi-modelling paradigm, which supports integrated, diverse, secure interfacing of models. This paper demonstrates the application of the multi-modelling approach, using the INTO-CPS tool chain. A multi-model was developed comprising key components required for urban rail decarbonisation problems. This multi-model was tested for power consumption in four different scenarios with an example drawn from the Tyne and Wear Metro in Newcastle-upon-Tyne in the United Kingdom. These scenarios compared combinations of decarbonisation intervention (baseline rolling stock versus lightweight, regenerative braking rolling stock and baseline driving style versus energy-efficient defensive driving style), generating different power consumption profiles for each. As such, this serves as a proof of the application of the multi-modelling approach and demonstrates a number of benefits for flexible and rapid systems modelling. This paper fills a knowledge gap by demonstrating a potentially valuable tool for future systems-level decarbonisation challenges in urban rail.