Reliability Assessment of Power Supply Systems in Electric Roads

During the COP26 conference held in Glasgow in October 2021, the Glasgow Climate Pact was reached, which underscored the critical role of coal consumption as a primary contributor to climate change, catalyzing an urgent global discourse on energy transformation in all industrial sectors. As reported, the total carbon dioxide emissions in the UK reached 331.5 Mt in 2021, but this number decreased by 2.4% in 2022 from 2021, while the total greenhouse gas emissions decreased by 2.2% to 417.1 MtCO2e. Notably, the carbon emissions from the road transport accounted for 78% emissions from the transport sectors, which has became the largest emission source in the UK. This reveals that the road transport emitted more carbon dioxide (CO2) compared to other transport modes, therefore emphasizing the critical need for developing a green system for road vehicles.

In this context, electrification of the road sector needs to be prioritized. It is obvious that, on one hand, the replacement of fuel-controlled vehicles with electric vehicles (EVs) and integrating renewable energy into the road power supply, emerges as a viable strategy for reducing carbon emissions. On the other hand, although these two solutions can meet current decarbonisation requirements, concerns on the travel distance of EV batteries still remain, necessitating further investigations. Currently, with the introduction of dynamic charging concept in electric roads, it brings great opportunities for road electrification to achieve road net-zero, which also gives more challenges to the charging system design, power supply reliability and energy management of the electric road system (ERS). Meanwhile, moving from the traditional fossil-based road power supply system to an intelligent ERS system is also increasingly becoming a dominant trend.

The primary objective of this thesis is to explore various methods which can be used to analyse the power supply reliability in electric road traction power supply systems (TPSSs). This research begins with a comprehensive review of the power supply modes of ERS systems and the reliability assessment methods for electric and traditional transport power supply systems in Chapter 2. In Chapter 3, a novel reliability analysis method based on a three-state Markov Chain is introduced, aimed at evaluating the operational reliability of TPSSs in the context of emerging power supply modes for electric roads. This method accounts for multiple system states in its assessment. Then, in Chapter 4, aiming for the road system structural aspect, a failure mode and effect analysis (FMEA) method has been employed first to summarise all the potential failures and consequences for EVs travelling on electric road sections. Next, the fault tree models of three different types of electric road have been built to analyse its reliability and identify the weak points in the TPSS. In Chapter 5, integrating the renewable energy into ERS power supply systems, this chapter proposed an EV travel pattern model to simulate power transfer in relation to road traffic and power networks. Additionally, a simplified time-series daily trip Monte Carlo simulation method for ERS systems has been proposed to assess the power supply reliability in electric road power supply systems. In Chapter 6, continuing focus on the electric road power supply reliability, an optimized electric truck (ET) travel pattern model has been built, based on which the corresponding ET charging load demand including both static and dynamic charging has been simulated, respectively. Lastly, a daily ET travel-pattern-driven Monte Carlo simulation based reliability assessment method has been presented.

Overall, this research presents a comprehensive study on the reliability assessment of the future green road power supply system to support the practical electric road TPSSs integrated with renewable energies and heavy-duty vehicles. Meantime, it also provides valuable suggestions into constructing sustainable, net-zero electric road transportation systems.