Electric Vehicle Charging Profiles Research Articles

Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.

Effect of solar panel orientation and EV charging profile on grid design

The implementation of solar coupled with daytime electric vehicle (EV) charging, aligns seamlessly with the broader goal of transitioning to a decarbonized grid and clean energy future. This paper explores how fixed-tilt solar arrays coupled with daytime EV charging would affect a future renewable-energy-driven grid in California. We use capacity-expansion modeling to identify the lowest-cost grid design for 40 scenarios that compare two solar panel mounting configurations (east–west versus south-facing), two EV charging profiles, five tilt angles ranging from 15°to 35°, and two assumptions about a long-duration energy storage (LDES) candidate resource. We then evaluate and discuss the mix of generators selected by the model for the various scenarios, the associated curtailment, and the implications about the best approaches for adding fixed-tilt solar arrays as part of transitioning to a new energy system. Although the east–west-facing solar orientation is expected to reduce the need for diurnal storage by being able to charge EVs early and late in the day, it appears that the better matching between seasonal supply and demand for the south-facing orientation is more important.

Model‐based adaptive control of modular DAB converter for EV chargers

AbstractThis paper presents the discrete‐time modelling and control of modular input‐parallel–output‐parallel (IPOP) dual‐active‐bridge (DAB) converters for electric vehicle (EV) charging. The proposed adaptive control system ensures adequate current‐sharing among parallel modules while minimizing DAB current stress by adopting dual phase‐shift modulation. Driven by the growing need for fast EV charging options, the paper highlights the importance of achieving top‐notch control performance, especially with varying load conditions. Specifically, it introduces a discrete‐time model for adjusting controller parameters adaptively, which simplifies the typically cumbersome manual tuning process associated with these systems. The proposed PI formulae are derived to satisfy specifications on the frequency domain as phase margin and the gain crossover frequency of the open loop gain transfer function, ensuring stability and robustness in operation. Moreover, the implementation of these formulae in discrete microcontrollers facilitates seamless PI autotuning for precise current, voltage, or power control. Notably, the proposed control strategy effectively mitigates current overshot issues commonly encountered during module engagement and shedding operations in modular EV chargers. To validate its efficacy, the proposed controller is evaluated through extensive testing and comparisons within the PLECS environment, particularly focusing on a two‐module IPOP‐DAB converter scenario, and including comparisons with classical offline model‐based pole placement methodology. Furthermore, real‐time hardware‐in‐the‐loop experiments are conducted to confirm the feasibility and performance of the proposed controller under realistic EV charging profiles.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

Challenges and Opportunities in Electric Vehicle Charging: Harnessing Solar Photovoltaic Surpluses for Demand-Side Management

The recharging of electric vehicles will undoubtedly entail an increase in demand. Traditionally, efforts have been made to shift their recharging to off-peak hours of the consumption curve, where energy demand is lower, typically during nighttime hours. However, the introduction of photovoltaic solar energy presents a new scenario to consider when synchronizing generation and demand curves. High-generation surpluses are expected during the central day hours, due to the significant contribution of this generation; these surpluses could be utilized for electric vehicle recharging. Hence, these demand-side management analyses present important challenges for electricity systems and markets. This research explores this overdemand avenue and presents a method for determining the ideal recharge curve of the electric vehicle. Consequently, with this objective of maximizing photovoltaic generation to cover as much of the foreseeable demand for electric vehicles as possible in future scenarios of the electrification of the economy, the six fundamental electric vehicle charging profiles have been analyzed. A practical scenario for 2040 is projected for the Canary Islands, estimating the potential levels of demand-side management and associated coverage. The coverage ranges from less than 20% to over 40%, considering the absence of demand-side management measures and the maximum displacement achievable through such measures.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

Real-time vehicle relocation and staff rebalancing problem for electric and shared vehicle systems

ABSTRACT This study addresses the challenging real-time vehicle relocation and staff rebalancing (RT-VR&SR) problem in electric carsharing services. The complexity arises from ad-hoc demand, charging requirements of electric vehicles (EVs), and staff scheduling constraints. The problem aims to maximize the profit of carsharing operators by determining strategies for vehicle relocation, vehicle charging, and staff rebalancing in real-time. It considers the uncertainty of demand and the practical nonlinear charging profile of EVs. We formulate this problem as a Markov Decision Process (MDP), and propose an efficient concurrent-scheduler-based policy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed policy and methodology. The results show that the proposed policy significantly improves service level and profitability compared to a benchmark policy. It is also found that ignoring staff rebalancing in decision making can lead to overestimation of service level and profitability. In conclusion, this study presents a real-time solution for vehicle relocation and staff rebalancing in one-way electric carsharing services. The proposed policy and methodology improve performance and highlight the importance of considering staff rebalancing in decision making.

Hosting capacity of distribution networks for controlled and uncontrolled residential EV charging with static and dynamic thermal ratings of network components

AbstractThe ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board (“slow”) EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV “hosting capacity”, HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected (“pre‐EV demands”), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively.

Evaluating the role of solar photovoltaic and battery storage in supporting electric aviation and vehicle infrastructure at Visby Airport

Following the societal electrification trend, airports face an inevitable transition of increased electric demand, driven by electric vehicles (EVs) and the potential rise of electric aviation (EA). For aviation, short-haul flights are first in line for fuel exchange to electrified transportation. This work studies the airport of Visby, Sweden and the effect on the electrical power system from EA and EV charging. It uses the measured airport load demand from one year’s operation and simulated EA and EV charging profiles. Solar photovoltaic (PV) and electrical battery energy storage systems (BESS) are modelled to analyse the potential techno-economical gains. The BESS charge and discharge control are modelled in four ways, including a novel multi-objective (MO) dispatch to combine self-consumption (SC) enhancement and peak power shaving. Each model scenario is compared for peak power shaving ability, SC rate and pay-back-period (PBP). The BESS controls are also evaluated for annual degradation and associated cost. The results show that the novel MO dispatch performs well for peak shaving and SC, effectively reducing the BESS’s idle periods. The MO dispatch also results in the battery controls’ lowest PBP (6.9 years) using the nominal economic parameters. Furthermore, a sensitivity analysis for the PBP shows that the peak power tariff significantly influences the PBP for BESS investment.

Electric Vehicle Trip Information Inference Based on Time-Series Residential Electricity Consumption

Pure electric vehicles (EVs) become more and more popular in current automotive markets. With wide application of EVs in the market, more EVs will be charged in residential homes. An advanced metering infrastructure measures real-time electricity consumption of individual residential homes. However, the advanced metering infrastructure is exposed to attacks given the fact that its components (e.g., smart meters) are located at public places. Under this situation, electricity consumption information of an individual residential home with an EV can be easily stolen. In this paper, we propose an EV Trip Information Inference System (TIIS) to infer EV trip information (origin-destination of each driving trip in a day) of a residential home based on its real-time electricity consumption measurements in a day. To the best of our knowledge, this is the first driving privacy relevant work caused only by residential electricity consumption data. For a residential home with an EV, TIIS derives the relationship between trip numbers in a day and charging-starting time, and the relationship between total driving distance and total charging energy. To derive the EV trip information of a residential home, TIIS firstly extracts an EV charging profile from its electricity consumption data and identifies EV model type and trip numbers as well as total driving distance based on EV charging profiles. Then, TIIS infers EV trip information based on the identified information. We used electricity consumption data and EV driving data to evaluate inference performance of TIIS. The experimental results demonstrate TIIS has as high as 81% trip inference accuracy and works on residential homes with different EV model types.

An efficient data-driven optimal sizing framework for photovoltaics-battery-based electric vehicle charging microgrid

The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids (MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging stations. Traditional sizing methods cannot efficiently evaluate large-scale scenarios through nonlinear optimization models to ensure the economy and reliability of the design. This paper combines the physical-economical model (PEM) and data-driven model (DDM) for fast-speed and accurate determination of MG sizes, considering nonlinear battery degradation and optimal power dispatch under variant EV charging profiles. The DDM predicts a truncated initial solution subspace where the optimal result is likely to reside, which performs 90 % time savings while maintaining over 95 % optimality compared with the benchmark PEM. The artificial neural network (ANN) is adopted for DDM with the input of load features and economic parameters, and the accuracy can be continuously improved with more data input. The EV load training set is extracted from charging piles and enlarged two orders of magnitude larger than the original based on reversed Monte Carlo (RMC) and feature space expansion. Through sensitivity analysis of over 40 scenarios, we show that the optimal results are mainly related to load features, correlation with solar irradiation, PV and ESS prices. Most adequately designed stations will pay back around five years. Projects with poor revenue will become profitable when the ESS price and PV price are below 154 $/kWh and 615 $/kW, and all the studied cases are worth the investment with the ESS price lower than 77 $/kWh.

Probability density function forecasting of residential electric vehicles charging profile

Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical forecasting method to provide accurate information of the charging profile in the look-ahead hours. In power system, in order to optimize the production and consumption as much as possible, in addition to accurately predicting the amount of electricity consumption, it is necessary for the stability of the grid to take into account the imminent probabilities. This paper presents the main principle of the probability density function forecasting approach in residential electric vehicle (REV) charging profile. To this end, an end-to-end deep learning structure is designed and integrated with kernel density estimation (KDE). The designed network is composed of four major blocks, i.e., convolutional layers to extract full spatial features, gated recurrent unit (GRU) to fully understand the temporal features as a time-efficient version of the gated deep network, an autoregressive (AR) to model the long patterns including battery type, REV type, and number of REVs and kernel density estimator block. Furthermore, to improve the learning ability of the designed network, an attention mechanism is integrated into the design network. The numerical results on the actual REVs (about 348 REVs) demonstrate the effectiveness and superiority of the proposed network through several cases and comparison with several well-known deep and shallow-based methods.

Electric Vehicle Charging Model in the Urban Residential Sector

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.

Assessing the influence of electric vehicle charging profile modelling methods

In the scientific literature, it has been a common assumption that electric vehicles (EVs) draw a constant current during the whole charging session. In reality, EV charging profiles are not linear, and the non-linearities have recently gained more attention. However, a thorough analysis of the influences of different charging profile modelling methods is not yet carried out. This paper aims to fill this gap by comparing experimental measurements of four commercial EVs and results of a developed simulation model that considers different charging profile modelling methods. According to the results, the use of linear charging profiles may lead to notable modelling inaccuracies (error > 30%) whereas the use of measurement-based non-linear charging profile models yields relatively accurate results (error mostly ≤ 3.5%). The results also demonstrate that the use of a simple, but justified, bilinear charging profile model is likely to be sufficiently accurate in most scenarios.

Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability

Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-afternoon and early-evening hours, that can lead to great challenges that electricity grids need to face. Therefore, accurate estimation of Electric Vehicle (EV) charging loads and time of use is of utmost importance for different participants in the electricity markets. In this paper, a scalable methodology for detecting, from smart meter data, household EV charging events and their load consumption with robust evaluation, is proposed. This is achieved via a classifier based on Random Decision Forests (RF) with load reconstruction via novel post-processing and a regression approach based on sequence-to-subsequence Deep Neural Network (DNN) with conditional Generative Adversarial Network (GAN). Emphasis is placed on the generalisability of the approaches over similar houses and cross-domain transferability to different geographical regions and different EV charging profiles, as this is a requirement of any real-case scenario. Lastly, the effectiveness of different performance and generalisation loss metrics is discussed. Both the RF classifier with load reconstruction and the DNN, based on the sequence-to-subsequence model, can accurately estimate the energy consumption of EV charging events in unseen houses at scale solely from household aggregate smart meter measurements at 1–15 min resolutions.

Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies

Electric vehicle (EV) grid integration presents significant challenges and opportunities for electricity system operation and planning. Proper assessment of the costs and benefits involved in EV integration hinges on correctly modeling and evaluating EV-user driving and charging patterns. Recent studies have evidenced that EV users do not plug in their vehicle every day (here called non-systematic plug-in behavior), which can alter the impacts of EV charging and the flexibility that EV fleets can provide to the system. This work set out to evaluate the effect of considering non-systematic plug-in behavior in EV grid integration studies. To do so, an open-access agent-based EV simulation model that includes a probabilistic plug-in decision module was developed and calibrated to match the charging behavior observed in the Electric Nation project, a large-scale smart charging trial. Analysis shows that users tend to plug-in their EV between 2 and 3 times per week, with a lower plug-in frequency for large-battery EVs and large heterogeneity in user charging preferences. Results computed using our model show that non-systematic plug-in behavior effects reduce the impact of EV charging, especially for price-responsive charging, as fewer EVs charge simultaneously. On the other hand, non-systematic plug-in can reduce available flexibility, particularly when considering current trends towards larger battery sizes. Counter-intuitively, large-battery fleets can have reduced flexibility compared to small-battery fleets, both in power and stored energy, due to lower plug-in frequency and higher energy requirements per charging session. Improving plug-in ratios of EV users appears as key enabler for flexibility. In comparison, augmenting charging power can increase the flexibility provided by EV fleets but at the expense of larger impacts on distribution grids. ● Agent-based model to generate EV charging profiles considering non-systematic plug-in behavior. ● Plug-in model calibrated with real-world data from a large-scale EV trial. ● Evaluation of non-systematic plug in behavior on EV-grid integration studies. ● Non-systematic plug-in behavior is crucial to assess the impact of EV charging correctly. ● Counter-intuitively, large-battery EVs may provide less flexibility to electricity systems.

Integration Analysis of Electric Vehicle Charging Station Equipped with Solar Power Plant to Distribution Network and Protection System Design

The improvements and implementation of new generation equipment to the conventional grids are increasing along with the increase of the number of renewable energy power plants and smart grid components. The existing grid infrastructure is required to be modified and upgraded as a result of the increasing integration of the renewables and new generation consumers where the availability of the existing networks has to be analyzed for integration as well. In order to keep the grid stable and integrate the renewables to the grid optimally, necessary analyses have to be realized including the investigation of the optimal integration criteria. During the integration phase of the new generation consumers and producers, two ways power flows and variable power production of renewables have to be considered for system design. In this study, analysis for optimal sizing and integration studies are performed for electric vehicle parking lot and solar power plants located on the campus distribution network considering optimal sizing criteria and the aim of stabilization of voltage regulation during day time operation of solar power plant and random charging profile of electric vehicles. The proposed network is equipped with adaptive protection system to ensure stability and the reliability of the distribution network in the concept of smart grid.

Evaluating the role of behavior and social class in electric vehicle adoption and charging demands

SummaryUnderstanding electric vehicle (EV) adoption rates and charging patterns is critical in enabling grid operators to maintain quality of supply and offers the potential to procure network services and avoid or postpone capital investments. Agent-based models have separately been shown to be useful in modeling EV adoption, policy options, behavioral influences, and grid impacts. In this work, we bring together these threads with real world travel data to present a multi-scale, behaviour-based EV adoption and use model able to replicate historical changes in vehicle fleets and match the most recent real world EV charging profile data. We have shown how our model can be used to simulate the impact of policies and consumer behavior on the rate of EV adoption across socio-economic groups and the locational grid impacts of EV charging, and as such we believe it to be of value to policy makers, grid operators, and demand response aggregators.

Analysis and Prediction of Electromobility and Energy Supply by the Example of Stuttgart

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.