The increasing market penetration of electric vehicles in Germany challenges the low-voltage grid in the upcoming years. Besides conventional grid expansion, emerging congestions can be cured by using demand-side flexibility. One approach is the model of peak smoothing. This paper investigates the effects of a realisation. Therefore, the electricity grid model GridSim is extended. The effect of peak smoothing is analysed based on load flow calculations of 1,206 low-voltage grids. Future developments such as tariff-optimized (dis-)charging of bidirectional electric vehicles, known as vehicle-to-grid (V2G), are considered. The results show that the model of peak smoothing postpones but mostly does not avoid the need for grid expansion. Until the year 2040, 386 out of 1,206 grids must be expanded despite the application of peak smoothing. In most cases, this happens due to transformer overloads and voltage band violations. However, on the other hand, the model results in minor restrictions for customers, as an implementation only leads to grid operator interventions for a few hours a year. In addition, almost all the curtailed energy (> 97,8%) is recharged by the customers later. Due to the infrequent interventions, there is also little impact on the power forecast.

The transition to battery electric vehicles (BEV) is progressing: it is estimated that the share of BEV and plug-in hybrid electric vehicles (PHEV) in Germany will increase from 1.2% in 2020 up to 24.4% in 2030. This rapid growth poses challenges to peak load management, electricity demand as well as grid and charging infrastructure, but might also enable opportunities. Aside from the decarbonization of the transport sector, the transition to BEV offers a variety of new possibilities for load shifting and thus grid stabilizing measures due to their huge electricity storage potential that might reach up to 458 GWh in 2030. To investigate both challenges and opportunities, a multi-storey car park in the German city of Schwäbisch Hall was equipped with 108 BEV and PHEV charging points, while charging data is logged in a cloud-based monitoring system. Based on this data and data from several other car parks, a digital twin of the car park's charging infrastructure is created. In parallel an app is being developed to monitor the charging infrastructure, enabling accurate recording of charging curves and states. For example, an occupancy of a charging point can be detected where the charging process has already ended or where the charging infrastructure is occupied without a charging process. Furthermore, the app enables the utilization of the parking garage to be displayed on a parking space-specific basis. Users of the parking garage thus have the option of reserving a parking space and retrieving the parking space availability with a customer-specific web app. In addition, grid-supporting scenarios based on charging power forecasts and charging load management are investigated. Vehicle-specific technical and quantitative data is used for that. In conjunction with the occupancy of the car park and the parking duration, the car park and thus the charging processes are simulated. The result is a prediction of the power required to charge vehicles as well as the ability to predict the charging capacity of a parking garage. Within this paper we present the ongoing work and the obtained results on those tasks.

The first variable electricity tariffs to charge electric vehicles are offered. Variable electricity tariffs for electric vehicle charging can lead to high simultaneity of charging because each user wants to charge at the times when electricity costs are low. This charging behavior can stress the distribution grid because, for example, peak loads and overloading of grid components can occur. In addition, this effect is enhanced by similar user behavior in terms of electric vehicle usage times and idle times. The shifting of charging processes and the consideration of grid loads in the variable tariffs are suitable methods in order to reduce grid overloading. In this paper, different price models for different transmission zones in Germany are introduced. Based on these prices, the charging times at the lowest cost are calculated. The usage times and idle times of the battery electric vehicle are equal for all calculations. It is assumed that the battery electric vehicle is charged overnight. Subsequently, the different charging times are compared. It is shown that different charging times for the different price models occur. However, the resulting prices for charging do not reflect the real cost of the electricity consumption with a variable electricity tariff. Therefore, the real cost of charging is compared to the lowest possible cost during the considered idle period. A possible solution to reduce these price differences is the reduction or increase of grid fees These pricing models can reduce the simultaneity of charging processes. Furthermore, it is planned to evaluate the impact of these charging times on the grid as part of the field test related to the LISA4CL project.

This paper presents an overview of the findings of the intraday use case in the Bidirectional Charging Management project’s pilot study. The intraday use case aims to trade energy for the financial profit of the customers. Each day the flexibility of the EV fleet is forecasted for the next day accordingly a load forecast is predetermined, and the forecasted flexibility is used to make trades on the continuous intraday market. The customer behaviour is a key aspect for the success of the intraday use case, as it determines the provided flexibility. Another factor is the performance of the system itself. The optimizations of scheduling improved the execution of trades from 58% for charging and 36% for discharging in April to 79% for charging and 61% for discharging in July. The round-trip efficiency of the intraday use case was determined at 80% which is competitive with storage methods such as pumped storage power plants. To increase the revenues of the use case further optimizations are still possible since for example the trades are limited to the night and thus only hourly spreads in the night time were taken advantage of.

In this research project, an algorithm for a smart FNN control box, the so called SMECON-Box (Smart-Meter-CONtrol-Box), is implemented, which curtails the charging power of an EV in case of grid overloading. To keep the communication needs as low as possible, the SMECON-Box should decide by itself, based on local measurements, if the grid is overloaded. The algorithm is tested in simulation, laboratory tests and field tests. In this paper the general project approach as well as first simulation results are presented.

The growing share of distributed generators and electric vehicles (EVs) in low-voltage (LV) grids is a challenge for distribution system operators (DSOs), as the volatility increases and higher power peaks are expected due to the simultaneity of EV charging processes in particular. To reduce possible necessary grid reinforcement, the grids could be operated closer to their operational limits. To make this possible, more grid transparency in LV grids is required, which is largely nonexistent because there are only very few measurements on MV/LV transformers or access to existing measurements for the DSOs. State monitoring can help to provide more grid transparency and enable the DSOs to implement novel and automatic operation management strategies for LV grids and thus reduce the costs for grid expansion. To monitor the grid state, a method for state estimation (SE) that can handle a low density of direct measurements is needed. In this paper, we use a method based on artificial neural networks (ANN) in [1] developed for medium voltage grids. Preliminary work has shown that even with a low density of measuring points monitoring with ANNs can map network states with small estimation errors [1].

The ANN-based grid SE is divided into the training phase (TP) and the operating phase (OP). In the TP, we generate a broad range of grid scenarios and perform power flow calculations to calculate the desired output labels and the simulated measurements (input features) based on the given type and position of measurements. The ANN is then trained to learn the relationship between the output labels and the input features. In the OP, the actual measurements are used as inputs for the ANN to estimate the desired grid state variables (e.g., voltages and line/transformer loadings) in real-time.

The main contribution of this paper is the application of the proposed method in three SimBench grids and the investigation of the accuracy with different measurement configurations. In addition, based on the beeDIP-Platform, we apply the proposed approach in a field test in the city of Braunschweig. Two suburban low voltage grids (350 households, 27 PV, and 53 EVs) operated by DSO BS|NETZ are included. The corresponding grid state is estimated and displayed in a graphical user interface.

The analysis shows that while high accuracy can be expected for bus voltage and transformer loading, the line loading accuracy is sig-nificantly decreased for lower grid load - if there is no over-loading here, this would also be less of a problem for grid operation. Furthermore, the SimBench case study shows that the measurements on the transformer and the adjacent LV feeders have the most significant impact on the estimation accuracy.

[1] J.-H. Menke, N. Bornhorst, and M. Braun, “Distribu-tion system monitoring for smart power grids with distributed generation using artificial neural networks”, International Journal of Electrical Power & Energy Systems, 2019.

The share of renewable energies in electricity generation in Germany is steadily increasing; in 2020, the share exceeded 50 percent for the first time. At the same time, the share of electric vehicles is also ramping up, and now exceeds one percent of registered passenger cars. In the course of this development, volatile feed-in of renewable energies and the need to integrate new loads due to the electrification of various sectors, the use of load-side flexibility is becoming increasingly important for power grid operators. Electric vehicles offer a high potential due to the built-in storage and the possibility of flexible charging during the entire dwell time. As part of the BMU-funded Netz_eLOG project, various grid-serving charging strategies are being developed and their effects investigated using the examples of an electric logistics fleet and a bus fleet.

The three studied strategies offer different control options for the distribution system operator (DSO) and set economic incentives for the grid users. In the first strategy, the DSO sends energy price signals of the network charges to all grid users in an area, while in the second strategy, time windows are sent for which no consumption of energy is desired. In the third strategy, grid users individually get a charging schedule by the DSO, which contains the power to be drawn in all timesteps for all flexible loads. This schedule is created on the basis of the flexibility potential of the grid user and the expected grid situation.
The modeling is carried out over a six-month simulation period with the open-source simulation model SpiceEV, developed by RLI. All strategies will be investigated in different grid areas, which differ in terms of feed-in, load and curtailment.
Furthermore, the influence of the integration of further components on the flexibility will be investigated. It is expected that the flexibility potential will be permanently increased by stationary battery storage as well as by a vehicle-to-grid functionality of the vehicles. The integration of further consumers such as building loads and local generators will also have an effect on the control potential.

The most important grid-side criteria in the investigated grid areas are the avoidance of curtailment and a high utilization of generation surpluses. For this reason, the grid-serving charging strategies are designed to shift electricity consumption to these times, if possible. This enables the DSO to postpone vehicle charging during times of regionally high generation surplus and at the same time avoid the increase of existing load peaks in the grid. In this context, the charging strategies react with different sensitivities to load changes. The paper will consider the control effort for the DSO as well as the economic benefits. These include savings in commodity and service prices, as well as grid charges and compensation payments in the event of curtailment.

Driving long distances with battery electric vehicles is becoming feasible thanks to increasing battery capacities and a growing network of fast-charging stations. During peak usage hours, multiple users may require recharging and thereby exceeding available charge points resulting in a queue. To avoid such waiting times, an algorithm is required to predict when a charging station is likely to be occupied and how long the waiting times at such a station would be. This paper presents a methodology to cascade two machine-learning models to create such an algorithm. The first of these submodels predicts the likelihood that a current occupant is still at the charge point for any moment in time in the future. It is implemented by training an ensemble learner with past charge events and able to learn station-specific as well as general usage characteristics. The second submodel predicts the likelihood that new visitors come to the station and the occupation probability. Both achieve high accuracies in their respective domains. By mathematically combining both models, it is possible to construct an overarching model able to predict future charging station occupation likelihood based on the current occupation level of the station.

Photovoltaic (PV)-powered electric vehicle charging stations (EVCS) are expected to play a critical role in the carbon neutralizing transport sector as PV power has a great ability to reduce CO2 emission. By integrating PV into an EVCS, solar energy can supply a considerable portion of the electric vehicle (EV) energy demand. Besides a good energy management strategy (EMS) for the system, the efficiency of the system is an important aspect that needs to be discussed. Be it vehicle charging/discharging, energy exchange with the grid or battery use - power losses occur in the system.
Previous studies have mostly only discussed the effects of EMS on energy losses in the system. In this paper we conduct a systematic analysis of the efficiency of different components for an EVCS integrated with PV, stationary battery (BAT), and grid connection and its effect on the energy losses in the system. The analytical study is carried out for a DC coupled system. Converter efficiency for PV, grid, BAT, and EVs, battery efficiency for BAT, and overall system efficiency are considered for our analysis. A comprehensive comparison of system efficiency and energy losses over different seasons and a full year is presented and discussed. Finally, we discuss the financial losses influenced by the efficiency losses in the system over the year.

Electric vehicles can be charged on many occasions which are embedded in different charging use cases in the user's daily routine as complementarily as possible. Longer charging times are therefore not perceived as a loss of comfort for the user. A large number of different stakeholders are involved in a charging process -- from energy generation to energy supply and energy distribution to energy consumption. The user's goal of obtaining as much energy as necessary for the next stage of the journey without additional waiting time can be achieved with different combinations of stakeholders and charging use cases. The charging use case determines where and on which occasions the vehicle is charged and thus also the need for dedicated charging power in order to meet the range requirements of the EV driver. The aim of this research is the development of an model in which the stakeholders involved can be represented and related to the charging use cases. In this three-dimensional model, essential charging scenarios can be mapped and simulated with regard to the inherent preferences of the heterogeneous stakeholder structure in the form of an application preference, operational preference and timetable preference in order to achieve an overall energy and techno-economic optimum.

Energy storage systems (ESS) are used to improve the feasibility and economics of adding loads to the grid, or in some cases for running totally islanded operations. Many of the analysis methods for looking at energy storage and microgrids assume a fixed load profile. EV smart charging, such as for a depot of electric buses or electric trucks, can adjust the load profile to the grid needs, providing similar values such as peak shaving and reduced energy costs as an ESS without needing any further hardware. This paper will look at a few of the key value drivers for energy storage combined with EV charging and presents simulation results comparing the solutions with and without EV smart charging. Peak Shaving / Capacity limiting, Load Shifting and Solar cases are discussed. Peak Shaving seeks to reduce the impact of peak tariffs, but EV charging can often be scheduled to avoid creating peaks it the first place, reducing the value. Keeping under an existing power limit is often necessary due to grid capacity constraints and smart charging can reduce the need for energy storage in this case, however, may not eliminate it. Load shifting reduces energy costs by moving energy consumption from expensive time periods to cheaper periods. However, EV smart charging can defer considerable energy consumption, relying on the existing vehicle batteries instead of supplementary energy storage. Storing solar energy can be accomplished with an energy storage system, but depending on the EV fleet load profile, much of the solar energy can potentially be directed immediately in to the EVs without needing further storage.
The evaluation for a given project is always site specific because of the diversity of tariffs, incentives, and other considerations. This presentation will provide an overview of some of the energy storage value propositions and simulation results demonstrating how the flexibility of smart charging can impact them.

According to the U.S. Department of Energy, Light-Duty Plug-in Electric Vehicles (EV) sales in the United States nearly doubled from 308,000 in 2020 to 608,000 in 2021. Furthermore, EV sales accounted for 73% of all plug-in electric vehicles in 2021, with EV sales growing by 85% from 2020 to 2021. With this enormous growth, electric utilities can play a role in the mass transition to EV adoption and potentially capitalize on an expected $10 billion opportunity poised to grow by 2025.

Here are a couple of ways in which utilities can take advantage of this:

To support the continued rate of adoption of electric vehicles, private investors can work together to build out an electric vehicle charging infrastructure. By doing so, utilities in distribution networks can leverage their existing distribution lines and make improvements where needed. By identifying circuits with available capacity for early-stage EV adoption, utilities will be able to better plan for substation and line upgrades on fully loaded circuits

Furthermore, utilities and stakeholders alike can also leverage other partnership opportunities. Public-private partnership (P3) can also allow for more private sector participation. P3 involvement will give more private sector companies to engineer, procure, and construct a facility or systems. Usually in P3, the private sector will perform all or some of the scopes of the function normally undertaken by the government, but the public sector will retain legal ownership. Potential P3 benefits in P3 will include monetization of existing assets, risk transfer, and public control and accountability. In Michigan, USA, a charging station company that manages DC Fast Charging (DCFC) projects which usually cost $150 – 180k for 2-3 charging stations at 120 kW, using rebates and Volkswagen settlement funds and matching grants funds, allows for them to bring this cost down to $30k – 40k.

Another related potential partnership is in rural/low-income communities, especially where there is a lot of tourism, but not necessarily a lot of seasonal travel. As EV drivers have to stop to charge their vehicles, which can take from 30 minutes to 2 hours on average, adding charging stations in downtown areas, will encourage EV drivers to shop and do business at local restaurants, further driving economic development in such areas. In this way, utilities can help plant that seed in making local investment opportunities by creating partnerships with the state and local communities.

With so much potential in eMobility, utilities should act now to capitalize on these opportunities. This paper takes a deep dive into how utilities can thrive in a growing market with its rules and regulations.

Electric vehicle load patterns are important inputs for power system integration studies of electric vehicle fleets. While model input usually comprises profiles on the temporal domain, the adequacy of connection and charging is better assessed by variable distributions that can easily be measured and interpreted. The heterogeneity of socio-technical characteristics of EV charging in general and lack of representative data for the German context specifically pose challenges to charging validation and consequently to pattern representation.
We suggest a hybrid approach consisting of bottom-up trip, park and charge modeling as well as multi-level validation. The former is carried out with the open source tool VencoPy building on representative trip data for Germany to model charging based on today’s vehicle mobility behavior. The latter is a collection of literature studies complemented by charging session data.
Our two-level validation approach differentiates between scope-adequacy and charging session representation. Recent electric vehicle measurements in Germany provide input with high scope-adequacy but low detail of charging session data since full data is usually not disclosed. Additionally, we use charging session measurements with lower scope-adequacy but high detail of charging session representation. This data is gathered from charging station operators to which social background information of electric vehicle users is unknown.
Analyzed variables comprise arrival hour, arrival weekday, plugging-out hour, charging volume, and charging time. Due to the varying scopes of reported German pilot studies, the modeling tool is applied to respective sub-sets of the trip data entirety, corresponding to the scope of respective empirical measurement studies, in this case public AC charging.
The analysis provides a valuable status update on real-world measurement summary data as a benchmark for a travel-survey based simulation tool, both for the German case.
By accessing the detailed variables of the German national travel survey, we overcome two persisting challenges of modeling electric vehicle charging: (1) Current tools are limited to reported mobility pattern distributions which leads to (2) an unsuitability of validation because the scopes of both empirical evidence and reported mobility patterns have to coincide for this purpose. With our approach, we qualify state-of-the-art validity of electric vehicle charging and in doing so identify yet to better understand socio-technical scopes.

Bidirectional charging of electric vehicles enables the implementation of various new use cases, which provide additional value to the user or to the electricity grid by charging and discharging according to external signals. These applications require the installation and operation of measurement equipment in order to collect e. g. power and energy in high temporal resolution. These data can also be utilized to provide additional value to the customer by implementing services such as warranties based on these data. Therefore, transparent and tamper-resilient data storage is important to create a reliable data basis for implementation of these services. A blockchain-based data logging platform can provide a solution for this challenge since these features are inherent to the technology.
In this research project, the described platform was developed, which allows automated recording and notarization of data. Subsequently, data which is notarized once can be verified by the platform, which prevents manipulation and also proves the chronological sequence. For data minimization reasons, the collected data from EVs is not directly notarized, but in hashed form, which also improves scalability of the solution. In the current research implementation, the data is handled through the OEM’s backend system and only afterwards transferred to the notarization platform. This is to be improved in a real implementation, since the backend system might be seen as being prone to manipulation.
The services which can be implemented based on the acquired and notarized data include e. g. warranties on the battery dependent on defined usage (for instance energy throughput or charging/discharging cycles), reliable and verifiable data about the battery for reselling or warranties on the performance of charging equipment. Since the platform provides the central infrastructure, synergy effects between these services are possible. Therefore, the developed system enables to provide addional value to the customer at little additional expense, and thus contributes to an accepted integration of bidirectional EVs in the energy system.

In the context to alleviate the existing bottleneck of charging infrastructure for electric vehicles and to optimize the entire charging process, the automation of the charging process is a current field of research. Automated charging not only promises maximum convenience for the user, but also potentially offers the opportunity to implement new and innovative smart charging functionalities at the same time, thus reducing the load on the power grid. The broad range of potential application scenarios for charging robots is described in the introduction to this paper. The variety of applications results in a multitude of requirements in the development of charging robots, which arise from the respective application scenarios and the differently complex environment in which charging robots are to be used. Additionally, various stakeholders have to be considered, who bring a wide variety of goals and requirements into the development process. This is also reflected in the variety of different prototypes and studies with different system architectures shown in the reported state of the art.
The complex environment and the application-specific and requirement-oriented conceptual design of charging robots requires a comprehensive methodical approach. A respective approach which has not been described in a dedicated way so far is presented in this paper. The methodology, which is based on and adapts the V-model for the development of mechatronic systems, uses the so-called charging scenario to consider the superordinate system of all systems involved in the process of automated charging and derives an ideal solution from it. By deploying this methodology, which was developed and initially applied in a research project with BMW, charging robots can then be developed that are optimized for an application scenario and implement the automated charging process most efficiently.

International politics (Glasgow Climate Agreement) and German politics (Climate Protection Act) recently tightened their targets for reducing greenhouse gas (GHG) emissions. The German target of reducing GHG emissions by 65% until 2030 particularly affects the transport sector, which contributed 19% of total emissions in 2021. Battery-electric mobility represents the most promising post-fossil mobility approach as the number of electric vehicles (EV) worldwide has grown exponentially in recent years. Parallel to the increase in vehicle counts, the number of charging points and the corresponding charging infrastructure must to grow as well. The increased load from these charging processes was unknown while planning and building the electric grid of existing districts and nowadays may cause violations of operational boundaries

The goal of this research is to analyze effects and impacts of an increasing EV penetration rate on the low-voltage grid in an existing district in Lower Saxony and identify the maximum possible grid capacity for EV charging. Identified limiting factors are then considered in multiple scenarios. Opportunities for different levels of cooperative energy generation, storage and smart charging strategies are applied to enhance the grid’s capacity for EV. The simulation scenarios, the used models (self-developed and modified existing ones) will be accessible under open-source license enabling a transparent research process and improving research quality and accessibility. Researchers therefore will be able to extend the co-simulation with their own models or implement and examine various other districts and communities.

Due to the multidisciplinary nature of the components involved in the simulation of the district, a co-simulation framework is beneficial to conduct the power system analyses. The co-simulation framework mosaik allows coupling different simulation tools and models enabling the orchestration and communication of parameters between components, so that a systems-wide perspective is achieved. The components of the districts’ energy system are modeled object-orientated in Python, allowing setting individual properties for each component. Control methods exist as separate models (e.g. district energy management system) and as part of components (e.g. smart charging in charging station model). The grid electricity demand of EV is calculated from empirical data using the Emobpy tool. Mosaik orchestrates all data flows and initializes a Pandapower grid model to perform power flow calculations in each timestep forming quasi-dynamic load flow calculations. The results are processed in a self-developed grid observer and validated in accordance with applicable standards to determine the grid capacity for EV. In multiple scenarios different combinations of renewable energy system models and control models are simulated to increase grid capacity and prevent critical grid situations for high EV penetration rates.

Before the end of this decade, battery electric (BE) trucks are expected to achieve significant shares in the road transport sector. An adequate charging infrastructure is a precondition for successful operation of BE trucks, though. Currently, uncertainty exists regarding the appropriate density and size of charging stations along motorways. This uncertainty also concerns the required connection capacity to the electricity networks. This is even more complicated by the fact that motorway service stations have to serve both, passenger cars and trucks, simultaneously. Their mobility patterns and technology requirements differ substantially.
Lead times for planning, permitting and construction of electricity networks may exceed 10 years, in particular if connections to high voltage (HV) networks are required. In fact, planning procedures for charging stations which should be available before 2030 have to be started now. Likewise, the suitability of potential sites must also be assessed from a point of view of network access.
In a scenario study we assessed the functional requirements of prototypical public charging hubs along the motorway network during the take-off period of BE trucks (2027 to 2035). We distinguished three cases with different traffic intensity. For truck charging, the prototypes offer Megawatt chargers with a capacity of 900 kW each and overnight chargers with a capacity of 100 kW, while chargers for passenger BE vehicles are rated at 360 kW.
Based on monitored public traffic data and a specific queuing model, within predefined performance levels, we designed the local charging infrastructure. The simulation results showed that – during the considered period – in many cases medium voltage (MV) connections are viable, as long as only one direction is connected to the network. If both sides have the same connection point or in case of intense traffic, a connection to HV networks is inevitable from 2030 latest.
Interviews with distribution system operators (DSO) revealed that the scale of the challenge urgently needs to be integrated in development plans and procedures as the timing is already ambitious. DSOs emphasised that coordinated planning is crucial. Parallel developments and connection applications by different mobility service providers at the same location will be inefficient and cause additional delay.
Assumptions, like existing regulation on drivers’ daily routines, have a strong impact on the modelling results. The choice, whether trucks release a charger after being sufficiently charged or whether they stay for the complete break makes a significant difference in required capacity. Also the introduction of autonomous driving and the performance of the charging infrastructure at depots and logistic hubs will affect the charging requirements at motorways.
The paper will summarise key assumptions, methodology and results as well as derived conclusions and recommendations.

Both the solar photovoltaic (PV) installation and electric vehicles (EVs) deployment are increasing significantly worldwide. With the large-scale integration of PV and EVs, problems such as the voltage deviations and overloading of components can arise, since the existing distribution grids are not designed to host the large shares of new EV loads and the intermittent PV power feed-in. This thesis investigates a C2V2C (i.e., Building Community to EV to Community) energy flow concept and evaluates how it can improve the power balance performances in communities with both PV and EV integrated in Sweden. Community refers to a group of buildings (i.e., two or more) connected within the same microgrid. It aims to develop a C2V2C model, which utilizes smart charging of electric vehicles to deliver electricity between different communities, for improving the performances at multiple-community-level. A coordinated control of EV smart charging is developed using the genetic algorithm, and its performance is compared with an existing individual control. Two control strategies are considered: (i) minimizing the peak energy exchanges with the grid and (ii) minimizing the electricity costs. Case studies are conducted considering a residential community and workplace community, as well as one EV commuting between them. The study results show that the advanced control achieves a cost reduction of up to 282 % in a summer week compared to the individual control. In a winter week, a performance improvement of up to 13.3% can be achieved using advanced control. The advanced control can also reduce the energy exchange peaks with the power grid of the multiple communities. This study has proven the effectiveness of the C2V2C2 model in enhancing the local power balance at multiple-community-level. It will enhance the resilience and grid-friendliness of building communities, thus paving way for the large PV and EV penetration in the future.

Germany is moving towards a sustainable future with 100% of renewable energy supply and 100% target of achieving electric mobility by 2050.
The increasing number of electric vehicles poses new challenges to the power grid. Their charging process stresses the power system, as additional energy must 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.
However, the energy transition will also bring its challenges to the stability of energy system. Solar and wind will provide a fluctuated supply to the daily energy system. This will create difficulties to balance the daily demand. A flexible demand and a fast-reacting storage will be needed for the future German power system.
The combination of these two changes causes problems but can also create interesting synergies. Because to be able to decarbonise the transport sector, electric vehicles should only be charged with electricity from renewable sources. To be able to integrate renewable energy into the system, more flexibility in the grid network is required. Electric vehicles could absorb surpluses from renewable energies and thus solve both problems. However, e-cars could put additional strain on the grid elsewhere if charging quantities and times are not intelligently controlled.
The integration of electric vehicle and renewable can be regarded as one of the solutions in solving this problem. This requires understanding of three factors, firstly where and when the vehicles are parked during the day, secondly if these locations have enough capacity to deliver the required energy, and thirdly if there is enough excess renewable energy available in the overall grid. Currently, the smart charging is not being implemented in most of the existing infrastructures. The necessity of smart charging (V1G) will need to be evaluated for the future renewable system.
In this research, aim is to try to analyse that in future 2050 scenario, where almost all the electricity demand for driving electric cars can be utilized from overproduction of renewable energy, especially solar. The process starts with locating all the parking types and then analyses the grid network infrastructure. With the excess energy that is available in the grid between 10:00 hrs and 16:00 hrs , analysis has been done if each of the vehicle in its specific grid network is able to fulfil the daily energy requirement.
This forms the base for formulating the mechanism for smart charging and helps us to understand the various datasets that would be required to implement smart charging in an efficient manner.
The purpose of this paper is to realise on city grid level what changes needs to be done in order to integrate 100% renewable energy and 100% electric Vehicles in the future (2050).

Rapid market growth and ambitious climate goals to increase adoption of all types of electric vehicles necessitates that decarbonization, resilience, and energy equity and justice strategies are simultaneously employed to keep pace with the evolving social and policy climate. This is even more imperative now that electric vehicles can be considered a grid storage asset with the implementation of vehicle-to-grid bidirectional charging strategies. This study aims to characterize the energy equity and community benefits of mobile energy storage solutions (MESS) via a storage adequacy analysis of energy access for the following three use-cases—utility-scale networks of MESS assets that are operated within the distribution system; community public transit MESS assets; and behind-the-meter personal vehicle MESS assets. These different use-cases correspond to different battery capacities, charging schedules, and distribution within the grid for which the relevant equity co-benefits must be understood. The results of the resource adequacy analysis will inform a discussion of additional energy equity metrics to establish a prioritization framework matching community and system needs to better inform the distribution of electric vehicles and charging infrastructure, utility planning processes, and the wider network of transportation and energy system stakeholders.

Rising cost of grid fees and increasing population of electric vehicles (EVs) hold a huge potential for reducing electricity costs through peak shaving for companies in Germany. The project bidirectional charging management (BCM) tests bidirectional charging management applications including peak shaving. In total, the use case peak shaving will be demonstrated at five company sites, two of which are examined in more detail in this work. The results show that the peak loads can be successfully reduced and that the system is operable. Three different system behaviours are examined extensively. The availability of EVs to participate in the peak load shaving has the greatest influence on the success of the use case. In addition to the acquisition of more vehicles, decreasing the limiting state of charge can have a positive influence on the reduction potential.

Renewable energy sources like wind and solar, being fluctuating of nature, introduce new challenges for a reliable power supply. To prevent resulting power shortages in times of zero or low renewable electricty and heat generation, a flexible local power source is required to fill this gap. This work investigates whether fuel cell electric vehicles as mobile coupled power and heat sources can be a flexibility to solve to this problem. For this, a scenario analysis is performed using the open energy modeling framework (OEMOF) for fuel cell electric vehicles providing both, electricity and heat, to a neighbourhood compiled of wellinsulated all electric buildings. Scenarios with and without storages and the influence of an increasing number of battery electric vehicles to be charged are analyzed. The additional heat used from the vehicles can save around 14.5 % electrical energy. Only 1.6 % of the adult residents need to provide a vehicle to fully supply the neighbourhood. Hydrogen supply via the internal tanks would be possible, but more vehicles are needed then. A stationary supply would be beneficial for different technical aspects. The focus of the investigation is on the technical aspects, not on cost issues.

Electric road systems provide an exciting decarbonization solution, particularly for long-distance heavy goods road transport. The vehicles are thus supplied with electric traction energy directly while driving. And the battery is charged at the same time, so that electric driving away from electric roads is also possible. In this way, energy can be used very efficiently. And at the same time, the voltage peaks in the energy grid that would occur if many vehicles were charged quickly at the same time, e.g. during rest periods, and could pose a major challenge to energy supply networks, are avoided.

The eHighway system is an electric road system specifically designed for heavy-duty traffic on expressways. The technology is presented using the example of eHighway Hessen "ELISA", a pilot project on the German highway A5 between Frankfurt am Main and Darmstadt.