For a successful market take-up of plug-in electric vehicles, fast-charging stations along the highway network play a significant role. This paper provides results from a first study on estimating the minimum number of fast-charging stations along the European highway network of selected countries (i.e., France, Germany, the Benelux countries, Switzerland, Austria, Denmark, the Czech Republic, and Poland) and gives an estimate on their future profitability. The combination of a comprehensive dataset of passenger car trips in Europe and an efficient arc-cover-path-cover flow-refueling location model allows generating results for such a comprehensive transnational highway network for the first time. Besides the minimum number of required fast-charging stations which results from the applied flow-refueling location model (FRLM), an estimation of their profitability as well as some country-specific results are also identified. According to these results the operation of fast-charging stations along the highway will be attractive in 2030 because the number of customers per day and their willingness to pay for a charge is high compared to inner-city charging stations. Their location-specific workloads as well as revenues differ significantly and a careful selection of locations is decisive for their economic operation.

Electric vehicles (EVs) are used instead of fossil fuel-burning vehicles to reduce greenhouse gases. The increased rate of utilizing EVs has an impact on the electric power system. This is due to the fact that EVs are loads that increase the peak demand. From these perspectives, this thesis studies the load of the electric chargers in a parking house in Uppsala and studies the effect of adding battery storage systems. The cost of used electricity is also calculated at different battery storage system (BSS) capacities, and the electricity bill is evaluated at these different conditions.  The data used in this work was collected from a parking house allocated in Uppsala city in 2021. It is equipped with 30 charging stations and 60 charging points in total. In addition, the parking house is connected to a PV system with two racks of batteries with a capacity of 137kWh. The surplus power generated by the PV system is sold to the grid. The data from the PV production and EVs demand is analyzed using MATLAB. The electricity price used in this study is from a standard contract from Vattenfall, with different prices at day and night (0.144 SEK/kWh at night and 0.48 SEK/kWh during day time). The battery is charged during the night, at a lower price, and discharges during the day. The excess power from the PV is also stored in the battery as self-consumption is more profitable than selling it to the grid.  The results show that the installed BSS units reduce the overall cost of the load as compared to those without BSS units. By studying the system under different battery capacities, the electricity bill is reduced to a certain limit. However, the increase in storage capacity beyond 137 kWh has no effect on the overall electricity cost. This maximum limit of the BSS unit's capacity would be greatly affected by the size of the installed PV system and the number of EVs to be charged. Controlling the charging and discharging periods has a significant impact on reducing the system cost.

In a smart city, it is vital to provide a clean and green environment by curbing air pollution and greenhouse gas emissions (GHGs) from transportation. As a recent action from many governments aiming to minimize transportation’s pollution upon the climate, new plans have been announced to ban cars with gas engines throughout the world. Therefore, it is anticipated that the presence of electric vehicles (EVs) will grow very fast globally. Consequently, the necessity to establish electric vehicle supply equipment (EVSE) in the smart city through public charging stations is growing incrementally year by year. However, the EV charging process via EVSE which is primarily connected to the power grid will put high pressure upon the centralized power grid, especially during peak demand periods. Increasing the power production of power grid will increase the environmental impact. Therefore, it is fundamental for the smart city to be equipped with a modern power grid to cope with the traditional power grid’s drawbacks. In this thesis, we conduct an in-depth analysis of the problem of EVs’ interaction with the modern power grid in a smart city to manage and control EV charging and discharging processes. We also present various approaches and mechanisms toward identifying and investigating these challenges and requirements to manage the power demand. We propose novel solutions, namely Decentralized-EVSE (D-EVSE), for EVs’ charging and discharging processes based on Renewable Energy Sources (RESs) and an energy storage system. We present two algorithms to manage the interaction between EVs and D-EVSE while maximizing EV drivers’ satisfaction in terms of reducing the waiting time for charging or discharging services and minimizing the stress placed on D-EVSE. We propose an optimization model based on Game Theory (GT) to manage the interaction between EVs and D-EVSE. We name this the decentralized-GT (D-GT) model. This model aims to find the optimal solution for EVs and D-EVSE based on the concept of win-win. We design a decentralized profit maximization algorithm to help D-EVSE take profit from the electricity price variation during the day when selling or buying electricity respectively to EVs or from the grid or EVs as discharging processes. We implement different scenarios to these models and show through analytical and simulation results that our proposed models help to minimize the D-EVSE stress level, increase the D-EVSE sustainability, maximize the D-EVSE profit, as well as maximize EV drivers’ satisfaction and reduce EVs’ waiting time.

The presented dissertation deals with the problem of deploying the charging infrastructure for electric vehicles in the Czech Republic. The core of the thesis is a mathematical optimization model, which is implemented in the language of MATLAB computing software. The model consists of several sub-units representing separate models of studied sub-problems. The individual chapters of the work describe successively these sub models. The sub models are: demand model of the charging service, model of charging supply, charging simulator model, optimization model and its resolving optimization method. The optimization model is accelerated by parallelization on the graphics card. The optimization method is designed as a case-specific implementation of genetic algorithms on a population of tree-structured individuals. The final chapter deals with an economic aspect of the problem under consideration, the implications of the findings and the role that the optimization model plays in the context under consideration. The main benefit of the work lies in the formulation of the problem as a mathematical model, the accompanying analyses and the provided justifications. Any user with updated data can then use this work along with the attached scripts to find answers to questions about the relationship between electromobility and the charging infrastructure.

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