This paper gathers experiences and results from several demonstration projects in the field of grid integration of electric vehicles. The analyzed research projects are selected among research institutes and universities that are part of the European Energy Research Alliance Joint Program on Smart Grids. The paper provides an overview of recent trends in the field of electric vehicles integration issues and then dives deeper into specific aspects of each project. Twelve research projects are presented in general terms, while detailed information can be retrieved from the references and the websites. Although each project has its focus, a common element that can be devised is that the charging process can be technically controlled based on different interests and algorithms, but its role in the market is still under development. Particular focus is always given to the behavior of the user, which ultimately determines the possible level of flexibility that the electric vehicle can provide to the grid.

In recent years, the number of electric vehicles (EVs) has increased rapidly. Due to technological advancement, government policies and the focus on reducing greenhouse gas emission, the growth can be expected to continue. Home charging of EVs will often be sufficient for short-distance travel and daily routines. However, EVs still have a limited range. Thus, for longdistance travel, a network of fast charging stations (FCS) is needed. The stochastic nature, high power demand and short duration of EV fast charging, make it in many cases a grid capacity issue rather than an energy issue. Therefore, knowledge about the load profile of FCSs is important. In this paper, a model is developed for the simulation of the aggregated load profile of an FCS. The FCS load model includes a mobility model based on actual traffic flow, EV charging curves and temperaturedependent EV efficiency. Simulations are performed using the Monte Carlo simulation technique, to get a daily load profile for the FCS. Real-world data for the studied FCS in Norway is compared with the results from the simulation to analyze the performance of the FCS load model. The developed load profile for the FCS has a high peak-to-average power ratio, which indicates that the socioeconomic profitability of fast charging stations still is low.

High-power charging stations have recently drawn the attention of many researchers and electric vehicle (EV) infrastructure industries. However, the installation of fast chargers at various corridors of highways and cities can cause high peak loads and voltage deviations in distribution networks. In addition, the usage patterns can vary drastically from one fast charging station to another at any given instant. However, future highpower charging stations could be able to operate in any of the four P-Q quadrants. Thus, high-power charging stations for EVs have the capability of serving as a flexibility resource and minimising voltage deviations. This research introduces a method for mitigating voltage quality problems in distribution network by effective utilisation of the reactive power potential from fast charging stations. The suggested method introduces a secondorder cone programming approach that is validated on the IEEE 69 bus distribution system. The performance of the method is analysed in a case study with measured charging profiles from real high-power charging stations in Norway.

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There is a growing interest in applying machine learning methods on large amounts of data to solve complex problems, such as prediction of events and disturbances in the power system. This paper is a comparative study of the predictive performance of state-of-the-art supervised machine learning methods. The event prediction models are trained and validated using high-resolution power quality data from measuring instruments in the Norwegian power grid. The recorded event categories in the study were voltage dips, ground faults, rapid voltage changes and interruptions. Out of the tested machine learning methods, the Random Forest models indicated a better prediction performance, with an accuracy of 0.602. The results also indicated that rapid voltage changes (accuracy = 0.710) and voltage dips (accuracy = 0.601) are easiest to predict among the tested power quality events.