Real-time traction grid modelling for sustainable technologies integration towards smart electric transport systems

Despite advancements in internal combustion engine vehicles, road transport remains the largest emitter in the sector. Urban areas, covering only 2% of Earth's surface, generate 70% of carbon dioxide emissions, a trend expected to worsen with population growth. Electric transport systems are key to achieving net-zero targets, with metropolitan electric traction networks crucial for carbon-neutral urban mobility. Contact line-powered public transport, such as tramways, metros, and trolleybuses, offers a sustainable solution, particularly with full-electric fleets using in-motion charging. However, rising traction grid demand for vehicle charging and increased service frequencies risk overloading infrastructure, necessitating renewable energy and energy storage integration. Traction networks could also facilitate electric vehicle charging, supporting grid-to-vehicle and vehicle-to-grid operations. This thesis develops advanced electric traction grid modelling methodologies, addressing the need for real-time model-based analysis to support modernisation efforts. A modular approach is proposed, enabling the flexible assembly of network segments in a block-diagram environment, enhancing scalability and adaptability. Comparisons with measured substation data and conventional analytical methods validate its accuracy. An enhanced version of the proposed method method improves simulation precision and computational efficiency, overcoming spatial discretisation constraints in prior approaches. The real-time implementation of this method for a Bologna trolleybus network section demonstrates its capacity for assessing network behaviour under future scenarios, particularly for integrating energy storage. Leveraging high-efficiency partial-power converters with optimised power flow management, this study supports informed decision-making for sustainable urban transport infrastructure.