Next-Generation Railway Electrification and Operational Strategies for Decarbonisation

The railway sector is undergoing a profound transformation driven by ambitious decarbonisation targets, evolving patterns of passenger and freight demand, and the rapid integration of renewable energy technologies. As railways expand their electrified networks and adopt new traction technologies, there is an urgent need for advanced modelling, optimisation, and control strategies that ensure safe, reliable, and energy-efficient operations. This PhD project aims to develop next-generation methodologies that support a fully decarbonised and resilient railway energy and operations system.

Modern railway electrification presents a complex set of challenges. Existing AC and DC traction power networks were historically designed for predictable demand and centralised fossil-fuel generation. The shift towards intermittent renewable energy sources, the increasing use of energy-storage-equipped rolling stock, and the adoption of smart grid technologies require a step-change in how railway power systems are planned and operated. Operational challenges include voltage instability, harmonic distortion, regenerative-braking energy utilisation, and real-time management of power flows under varying traffic conditions. These issues become even more critical as railways seek to expand high-speed services, increase capacity through digital signalling, and integrate low-carbon technologies such as hydrogen and battery-hybrid trains.

This PhD project will address these challenges by developing an integrated modelling and optimisation framework that links railway operations with electrification infrastructure and low-carbon energy sources. The project will explore several core research themes:

1. Multi-domain modelling of railway electrification systems

Development of high-fidelity simulation models capturing the dynamic interaction between trains, traction power supply, renewable generation, and grid interfaces. This will include AC and DC traction modelling, energy flow analysis, and system-level assessment of voltage stability, power quality, and infrastructure constraints.

2. Decarbonised energy integration and smart grid interaction

Investigation of how railways can interact more intelligently with the wider power system—leveraging renewables, grid-scale and onboard energy storage, and flexible demand capabilities. The research will explore opportunities for railway systems to support grid resilience through ancillary services such as frequency response, peak shaving, and renewable curtailment reduction.

3. Optimisation of railway operations and energy management

Design of optimisation algorithms that coordinate train timetabling, traction power control, energy-storage utilisation, and substation operation. The aim is to minimise energy consumption and carbon emissions while maintaining operational performance. Machine learning and digital-twin approaches may be used to support predictive control and real-time decision making.

4. Resilience and reliability under future operating scenarios

Assessment of system performance under extreme events, demand uncertainties, and infrastructure failures, enabling the development of robust operational strategies and infrastructure-planning models that support long-term resilience.

The project will produce practical methodologies that can be applied by rail operators, infrastructure owners, and energy-system planners. Opportunities may exist to collaborate with industry partners, such as Network Rail, rolling-stock manufacturers, metro operators, and national grid organisations. The outcomes of this research will contribute to the UK and international vision for a zero-carbon, smart-energy railway system and will position the researcher at the forefront of future transport-energy innovation.

The project will be supervised by Dr Zhongbei Tian ([email protected]).

References:

[1] X Liu, Z Tian, Y Gao, L Jiang, RMP Goverde, ‘Data-Driven Substation Energy Minimisation for Train Speed-Profile and Dwell-Time Optimisation’, IEEE Transactions on Transportation Electrification, vol. 11, no. 5, pp. 11320 - 11331, 2025.

[2] H Dong, Z Tian, K Liao, J Yang, JW Spencer, ‘Three-Stage Energy Management of Urban Rail Transit based Micro Grid and EV Charging Station with V2T Technology’, IEEE Transactions on Transportation Electrification, vol. 11, no. 3, pp. 8604-8616, 2025.

[3] Y Ying, Z Tian, M Wu, Q Liu, P Tricoli, ‘A Real-Time Energy Management Strategy of Flexible Smart Traction Power Supply System Based on Deep Q-Learning’, IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 8, pp. 8938-8948, 2024.

[4] H Dong, Z Tian, JW Spencer, D Fletcher, S Hajiabady, ‘Bi-level Optimization of Sizing and Control Strategy of Hybrid Energy Storage System in Urban Rail Transit Considering Substation Operation Stability’, IEEE Transactions on Transportation Electrification, vol. 10, no. 4, pp. 10102-10114, 2024.

[5] P Guo, Z Tian, Z Yuan, XY Zhang, D Sharifi, ‘Research on symmetric bipolar MMC-M2Tdc-based flexible railway traction power supply system’, IEEE Transactions on Transportation Electrification, vol. 10, no. 1, pp. 1043-1055, 2023.

[6] N Kano, Z Tian, N Chinomi, X Wei, S Hillmansen, ‘Renewable Sources and Energy Storage Optimization to Minimize the Global Costs of Railways’, IEEE Transactions on Vehicular Technology, vol. 74, no. 5, pp. 7049 – 7060, 2023.

[7] J Zhang, Z Tian, W Liu, L Jiang, J Zeng, H Qi, Y Yang, ‘Regenerative Braking Energy Utilization Analysis in AC/DC Railway Power Supply System with Energy Feedback Systems’, IEEE Transactions on Transportation Electrification, vol. 10, no. 1, pp. 239-251, 2024.

[8] Y Ying, Z Tian, M Wu, Q Liu, P Tricoli, ‘Capacity configuration method of flexible smart traction power supply system based on double-layer optimization’, IEEE Transactions on Transportation Electrification, vol. 9, no. 3, pp. 4571-4582, Sept. 2023.

[9] Z. Tian, N. Zhao, S. Hillmansen, C. Roberts, T. Dowens, and C. Kerr, "SmartDrive: Traction Energy Optimization and Applications in Rail Systems," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 7, pp. 2764-2773, 2019.