Integration of electric vehicles, rapid and ultra-rapid chargers into UK distribution networks

This thesis examines the effects of increasing the uptake level of electric vehicles (EVs) in a generic medium-voltage distribution network. On a real low-voltage distribution network, the impact of integrating a dynamic battery charging model is also investigated. When and where EV charging loads are connected in distribution networks has a substantial effect on the severity of grid issues. It is necessary to ensure that the grids have sufficient hosting capacity and are accompanied by robust measures. Simulation results have demonstrated that increasing the number and rating of chargers increases power losses, voltage deviations, and distribution network equipment (cables and transformers) loading. At the transmission level, it has been shown that utilisation of transformer on-load tap changers, optimum placement of distributed generation units, and adequate sizing of static VAr compensator devices eliminate voltage violations. On the distribution level, coordinated smart charging systems, vehicle-to-grid chargers, and battery energy storage systems have proven effectiveness in reducing the peak loads. A stochastic model is developed for estimating the energy consumption of EVs and quantifying the peak demand in a distribution network. Twenty stochastic scenarios are produces, and the worst-case scenario is selected for a detailed network analysis. According to the results of the worst-case scenario, simultaneously charging one Audi and two Tesla EVs between 17:00 and 18:00 would result in a fivefold increase in peak demand, causing the substation transformer to operate 30% above its maximum rated capacity. The results have shown that the substation transformer can accommodate a maximum demand of 432 kW without becoming overloaded in the worst-case scenario's peak period. By supplying the additional demand caused by EV charging, battery energy storage units are used to reduce transformer loading by up to 40%. In conclusion, increasing the rating of the substation transformer from 500 kVA to 660 kVA enabled the secure integration of EVs and high-power charging devices in the worst-case scenario. Using lithium-ion batteries, discharge profiles for battery energy storage units are developed based on the stochastic charging profiles and the magnitude of the network's peak demand. These discharge profiles are then implemented into a battery charger and analyser unit to determine the relationship between the cell voltage and discharged capacity of the batteries. This relationship is used to estimate the usable capacity, state of charge, and depth of charge of lithium-ion batteries under different tests. The end-of�discharge voltage of the batteries (i.e., the voltage at which a battery's discharge stops) has never been reached during the tests. Due to their high energy density capability, lithium-ion batteries maintained over 85% of their capacities when they are used to accommodate the simultaneous charging demand of EVs during the peak periods