Connected and Coordinated Train Operation and Traction Power Supply Systems (COOPS)
Lead Research Organisation:
University of Birmingham
Department Name: Electronic, Electrical and Computer Eng
Abstract
The British railway transport demand is forecast to increase by around 40% by 2040, as a result of population growth, socio-economic globalisation and sustainable mobility decarbonisation. The enhancement of capacity and efficiency is the major challenge to the railway network, which is already near saturation conditions. Automatic Train Operation (ATO) with advanced signalling systems, such as Moving Block and Virtual Coupling, have been investigated to reduce train separation distances and increase the infrastructure capacity. However, more trains with advanced operation systems affect the performance of traction power supply systems, For example, the synchronisation of train acceleration and braking operation increases the peak power and reduces the energy efficiency due to regenerative braking energy loss. The current technological capabilities do not permit accurate and real-time interaction assessment between the train operation and power networks. Therefore, it is important to develop a holistic approach to improving railway capacity and efficiency.
This collaborative project will exchange the international partners' knowledge in train operation and traction power systems and investigate the flow mechanism between these two distinct systems. A digital twin with adaptive timescales and real-time data feeding will be developed to describe the interactions of the connected and coordinated systems. The outputs from the digital twin replicate the characteristics of real-world railway networks precisely. The multi-scenario simulation studies analyse the impact of various system design and control variables on performance, such as infrastructure capacity, efficiency and cost. The system performance will be evaluated and compared with the existing system. This project will build international partnerships through bilateral visits, and engagement workshops with global academic and industry partners. The project will also provide a roadmap for future collaboration on optimising the railway capacity and efficiency for decarbonisation.
This collaborative project will exchange the international partners' knowledge in train operation and traction power systems and investigate the flow mechanism between these two distinct systems. A digital twin with adaptive timescales and real-time data feeding will be developed to describe the interactions of the connected and coordinated systems. The outputs from the digital twin replicate the characteristics of real-world railway networks precisely. The multi-scenario simulation studies analyse the impact of various system design and control variables on performance, such as infrastructure capacity, efficiency and cost. The system performance will be evaluated and compared with the existing system. This project will build international partnerships through bilateral visits, and engagement workshops with global academic and industry partners. The project will also provide a roadmap for future collaboration on optimising the railway capacity and efficiency for decarbonisation.
Publications
Chinomi N
(2024)
Analysis of Energy Efficiency and Resilience for AC Railways With Solar PV and Energy Storage Systems
in IEEE Transactions on Industrial Cyber-Physical Systems
Dong H
(2024)
Bilevel Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability
in IEEE Transactions on Transportation Electrification
Guo P
(2024)
Future-proofing city power grids: FID-based efficient interconnection strategies for major load-centred environments
in IET Renewable Power Generation
Jiang K
(2024)
Cost modelling-based route applicability analysis of United Kingdom passenger railway decarbonization options
in International Journal of Electrical Power & Energy Systems
| Description | One of the key findings is that we developed a data-driven model to calculate the energy consumption of railway substations with less computing time. This enables the optimisation of multi-train operations for system energy saving. - As more trains start using regenerative braking-a system that recovers and reuses energy-there is growing interest in coordinating train schedules to maximize this energy efficiency. However, calculating how energy moves through the railway power system is complex and takes a lot of computing power, especially when multiple trains are involved. Many existing methods simplify these calculations, but this reduces accuracy. - To overcome this issue, we developed a data-driven approach that significantly speeds up energy calculations while maintaining accuracy. We first gathered data from train simulations, including train locations, power use, and power station demand. Using this data, we trained a neural network to predict power station behavior based on train movements. - Testing this model with real metro data showed that it runs 300 times faster than traditional methods while maintaining over 99% accuracy. By using this model to fine-tune train speeds and stop times, we found that power station energy use could be reduced by up to 13%, improving railway efficiency and sustainability. |
| Exploitation Route | - Train operating companies and railway infrastructure managers could use the data-driven model to optimize train schedules and energy usage, reducing electricity costs and carbon emissions. - Metro and urban rail networks could apply the model to improve regenerative braking efficiency, making their systems more sustainable. - Companies developing railway traffic management systems could integrate this approach into their optimization tools. - AI researchers could refine and expand the neural network model to cover different rail systems or integrate with smart grid technologies. - Future research could extend this work to high-speed rail, freight operations, or even multi-modal transport systems, where energy efficiency is a key challenge. - The methodology could inspire new machine learning applications in transport energy management beyond railways. |
| Sectors | Energy Transport |
| Description | Transport for London (TfL) |
| Organisation | Transport for London |
| Department | London Underground |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The COOPS team is collaborating with TfL for energy data analysis for sustainability. The PI has given presentations of the research outcomes at TfL events. |
| Collaborator Contribution | TfL has contributed the user case studies and the technical support on the traction power supply modelling and data. |
| Impact | Joint publications |
| Start Year | 2024 |
