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.

Given the increasing number of battery electric vehicles, the availability of suitable fast-charging infrastructure is crucial. However, designing such sites requires enough capacity in the electric power grid. A major influencing factor on the effect of fast-charging sites on the power grid is the simultaneity factor, i.e. the share of installed power related to the theoretical maximum power. The aim of this work is to investigate optimal simultaneity factors for fast-charging sites depending on various influencing factors. Real-world charging data from the biggest German operator is used in a stochastic approach via Monte-Carlo Simulation. It was found that in most cases, fast-charging sites can be designed with a simultaneity factor of 0.5 to satisfy demand. Applying this would reduce the effect on the power grid as well as reduce costs and time to build charging infrastructure. In consequence, the demand of the rising electric vehicle number can be met more efficiently.

In this study, we investigate the effect of vehicle-to-grid (V2G) flexibility potential on solving transmission grid congestion in Germany using congestion management measures. We extend existing work on effects of V2G on transmission grid congestion by determining the flexibility provided for improving grid operation based on mobility behavior and findings on V2G user requirements from real-world electric vehicle users. Furthermore, the impact on transmission grid operation is analyzed using an optimal congestion management model with high temporal and spatial resolution. Using a scenario for the year 2030 with ambitious targets for European renewable generation development and electrification of private vehicles, our findings show that by enabling the available fleet of V2G vehicles to participate in congestion management, cost and amount can be reduced by up to 11%. However, the required capacity is shown to be lower than installed capacities in ambitious future scenarios, implying that a limited number of vehicles close to congestion centers will be utilized for transmission grid operation. Our results further suggest that high numbers of vehicles with low availability of V2G for grid operation purposes can lead to an increase in congestion management measures, while V2G proves beneficial for congestion management emissions and cost in all scenarios.

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.

This paper seeks to identify bottlenecks in the energy grid supply regarding different market penetration of battery electric vehicles in Stuttgart, Germany. First, medium-term forecasts of electric and hybrid vehicles and the corresponding charging infrastructure are issued from 2017 to 2030, resulting in a share of 27% electric vehicles by 2030 in the Stuttgart region. Next, interactions between electric vehicles and the local energy system in Stuttgart were examined, comparing different development scenarios in the mobility sector. Further, a travel demand model was used to generate charging profiles of electric vehicles under consideration of mobility patterns. The charging demand was combined with standard household load profiles and a load flow analysis of the peak hour was carried out for a quarter comprising 349 households. The simulation shows that a higher charging capacity can lead to a lower transformer utilization, as charging and household peak load may fall temporally apart. Finally, it was examined whether the existing infrastructure is suitable to meet future demand focusing on the transformer reserve capacity. Overall, the need for action is limited; only 10% of the approximately 560 sub-grids were identified as potential weak points.

This paper analyzes the interplay of transmission and storage investments in a multistage game that we translate into a bilevel market model. In particular, on the first level we assume that a transmission system operator chooses optimal line investments and a corresponding optimal network fee. On the second level we model competitive firms that trade energy on a zonal market with limited transmission capacities and decide on their optimal storage facility investments. To the best of our knowledge, we are the first to analyze interdependent transmission and storage facility investments in a zonal market environment that accounts for the described hierarchical decision structure. As a first best benchmark, we also present an integrated, single-level problem that may be interpreted as a long-run nodal pricing model. Our numerical results show that adequate storage facility investments of firms may in general have the potential to reduce the amount of line investments of the transmission system operator. However, our bilevel zonal pricing model may yield inefficient investments in storages, which may be accompanied by suboptimal network facility extensions as compared to the nodal pricing benchmark. In this context, the chosen zonal configuration of the network will highly influence the equilibrium investment outcomes including the size and location of the newly invested facilities. As zonal pricing is used for instance in Australia or Europe, our models may be seen as valuable tools for evaluating different regulatory policy options in the context of long-run investments in storage and network facilities.

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.

Modern electricity markets are characterized by an increasing share of renewable electricity generation. This growth in the share of renewables yields a highly intermittent generation structure. To efficiently integrate renewable electricity generation into our energy system, flexibility options ranging from storage facilities to demand-side management will play a major role in the low-carbon energy transformation. Keeping up with the growing demand for flexibility, energy law must reduce current investment obstacles for flexibility options and establish a climate for future investments with sufficient incentives for private investors. By taking up perspectives from different disciplines, this paper summarizes current investment barriers, presents an overview of the existing legal energy investment framework of the EU and Germany, and elaborates on challenges of the presented legislation. As we argue, a well-designed energy market legislation will be one of the keys to a successful energy transition. However, policy makers will have to (i) lower investment uncertainty for private investors, (ii) avoid a distortion of energy investment law towards specific flexibility options and technologies, and (iii) reduce the complexity of the current legislation.

In this paper, an open-source Tool for the calculation of simultaneity factors of Electric Vehicles (EV) (i.e. battery electric vehicles (BEV) and plug-in hybrid electric vehicles (PHEV)) charging processes is presented. In addition, the peak loads of EV and households can also be displayed, taking into account the EV and household specific simultaneities. In the following, the underlying input parameters and calculations of the Tool are explained. Based on this, different results are generated and discussed in detail.

This paper analyzes the effects of storage facilities on optimal zonal pricing in competitive electricity markets. In particular, we analyze a zonal pricing model that comprises consumers, producers, and storage facilities on a network with constrained transmission capacities. In its two limit cases, our zonal pricing model includes the reference nodal pricing model as well as the uniform pricing model with storage. To the best of our knowledge we are the first to analyze zonal pricing in the presence of storage. As our numerical results show, storage facilities do not only reduce the inter-temporal price volatility of a market, but may considerably change the inter-regional price structure. In particular, the inter-regional price volatility may increase in the presence of storage, which may imply a complete reconfiguration of optimal zonal boundaries as compared to the no-storage case. However, market participants may have an incentive to keep or implement a sub-optimal zonal design. Thus, storage facilities will in general challenge optimal congestion management with common heuristic approaches to configure optimal price zones (e.g., the use of congested transmission lines of a nodal pricing system) not always suggesting optimal zonal configurations. Therefore, we propose a model extension that allows policy makers to determine welfare maximizing zonal configurations, which account for the complex inter-regional price effects of storage facilities. Especially with regard to increasing storage investments, such a model may help to (at least partially) handle the described inefficiency problems regarding sub-optimal zonal designs that may challenge European or Australian zonal electricity markets in the near future.