The increasing adoption of battery electric vehicles (BEVs) is leading to rising demand for electricity and, thus, leading to new challenges for the energy system and, particularly, the electricity grid. However, there is a broad consensus that the critical factor is not the additional energy demand, but the possible load peaks occurring from many simultaneous charging processes. Hence, sound knowledge about the charging behavior of BEVs and the resulting load profiles is required for a successful and smart integration of BEVs into the energy system. This requires a large amount of empirical data on charging processes and plug-in times, which is still lacking in literature. This paper is based on a comprehensive data set of 2.6 million empirical charging processes and investigates the possibility of identifying different groups of charging processes. For this, a Gaussian mixture model, as well as a k-means clustering approach, are applied and the results validated against synthetic load profiles and the original data. The identified load profiles, the flexibility potential and the charging locations of the clusters are of high relevance for energy system modelers, grid operators, utilities and many more. We identified, in this early market phase of BEVs, a surprisingly high number of opportunity chargers during daytime, as well as switching of users between charging clusters.

Plug-in electric vehicles (PEVs) are a promising option for greenhouse gas (GHG) mitigation in the transport sector - especially when the fast decrease in carbon emissions from electricity provision is considered. The rapid uptake of renewable electricity generation worldwide implies an unprecedented change that affects the carbon content of electricity for battery production as well as charging and thus the GHG mitigation potential of PEV. However, most studies assume fixed carbon content of the electricity in the environmental assessment of PEV and the fast change of the generation mix has not been studied on a global scale yet. Furthermore, the inclusion of up-stream emissions remains an open policy problem. Here, we apply a reduced life cycle assessment approach including the well-to-wheel emissions of PEV and taking into account future changes in the electricity mix. We compare future global energy scenarios and combine them with PEV diffusion scenarios. Our results show that the remaining carbon budget is best used with a very early PEV market diffusion; waiting for cleaner PEV battery production cannot compensate for the lost carbon budget in combustion vehicle usage.

The increasing number of electric vehicles poses new challenges to the power grid. Their charging process stresses the power system, as additional energy has to be supplied, especially during peak load periods. This additional load can result in critical network situations depending on various parameters. These impacts may vary based on market penetration, the energy demand, the plug-in time, the charging rate, and the grid topology and the associated operational equipment. Hence, the impact of electric vehicles (EVs) on the power grid was analysed for twelve typical German low voltage grids by applying power flow calculations. One main result was that thermal and voltage-related network overloads were highly dependent on market penetration and grid topology.

The power grid is a critical infrastructure that is becoming increasingly indispensable due to rising electrification. Depending on their size, failures in the power grid are accompanied by considerable disruption to society. It is imperative to understand the vulnerabilities that exist within this intricate network, as attacks on the power grid have the potential to wreak havoc on a national and even global scale. With the growing trend of smart grid technologies, the attack surface has expanded, making it easier for malicious actors to compromise multiple entry points. Therefore, quantifying the number of devices required for an attack is a critical aspect of grid security. This underlying paper underscores the importance of identifying vulnerabilities within the power grid’s control systems, understanding potential attack vectors, and implementing robust security measures. Our results reveal a concerning reality: malicious actors with a relatively limited degree of control over the technologies mentioned above have the ability to exert a profound influence on the balance of the power grid, especially with regard to the vital frequency containment reserve. By illuminating these potential points of influence, we aim to create a deeper understanding of the multi-layered threats that can compromise the resilience of our power grids.