Design, modeling and application of electric vehicle charging infrastructure including smart charging management

The depletion of fossil fuels, high oil prices and environmental pollution have led to the development of new, cleaner energy-based technologies in transport electrification. Nowadays, different types of electric vehicles (EVs) available in the market, such as hybrid, plug-in hybrid and battery electric vehicles. Among these types, battery based electric vehicle, plug-in hybrid electric vehicles needs electricity to charge the battery. The charging of EVs has an impact on the power system when great numbers of EVs connected to the grid. Besides, insufficient charging infrastructure and high charging demand in a specific time will cause a challenge. This dissertation has been proposed as a two phase comprehensive solution to tackle this problem. Firstly, EV charging infrastructure planning model and second, smart charging management strategy have been presented. In the first phase, a new planning method for EV charging infrastructure is proposed. Queue theory (QT) and analytic hierarchy process (AHP) optimization methods are used to develop a combined model to optimize the number of charging units and to choose their optimal locations in the selected region. A case study is performed to show the suitability of the proposed model for finding the optimum location and the number of charging stations by evaluating survey data, which reflects driver profiles, parking areas, substation locations and substations loading data. Charging infrastructure planning is not solely sufficient to provide a comprehensive solution for EVs in the long term. Thus, a new smart charging management strategy is needed to meet the demands of power grid, system operator and drivers. Smart charging management strategy is proposed through application of shortest way algorithm, parking space and power capacity control. The developed charging management strategy is ensured by routing EVs to the most suitable charge point with energy efficiently and also it makes the cheapest cost charging schedules of EVs with dynamic pricing mechanism. The proposed strategy considers the substation loading data, power losses, voltage fluctuation, and behavior of drivers and tries to prevent the overloading of transformers. Within the scope of the dissertation, the proposed model and obtained results are discussed in detail. For the performance evaluation of the proposed model, a case study in the Davutpasa campus area of Yildiz Technical University, Istanbul, Turkey is conducted. A case study is performed to show the suitability of the proposed model for finding the optimum location, the number of charging stations and smart charging strategy by evaluating actual data.