Renewable Energy Grid Integration Week 2024

The unIT-e² project represents a pioneering endeavor in the realm of electromobility, focusing on the harmonious integration of electric vehicles (EVs) into the energy network. This comprehensive research initiative is structured around four distinct clusters, each addressing unique challenges and opportunities within the EV ecosystem.Harmon-E emphasizes the market-optimized and grid-friendly charging and discharging of EVs, ensuring compatibility with existing network restrictions. Heav-E explores the network impact of widespread EV adoption through extensive field testing, aiming to develop new incentives for grid-supportive charging. Sun-E derives its name from the synergy between electromobility and photovoltaic power generation, prioritizing the creation of attractive, customer-centric solutions that balance grid and market demands. Cit-E-Life extends the project’s scope to urban environments, tackling the complexities of property structures within the automotive and energy sectors.The field trials conducted across these clusters span a diverse range of production and consumption structures, as well as geographical areas, all underpinned by scientific support from accompanying subprojects. The project breaks away from company-specific insular solutions, harmonizing and standardizing the competing requirements of electromobility and the energy system. Key outcomes include the development of secure and interoperable concepts for the grid and market integration of EVs, utilizing the standardized iMSys architecture. The project also enhances public trust in electromobility through customer-oriented incentive mechanisms and product design.In its first project phase, unIT-e² focused on designing, evaluating, and selecting use cases for implementation. Subsequent field tests yielded valuable insights into the potential, interoperability, and incentives of the proposed solutions. The project is concluded by synthesizing the findings into actionable recommendations, contributing to ongoing research, development, and standardization efforts beyond the project’s duration. This synthesis of subprojects and clusters has not only advanced the technical implementation of intelligent control systems in hardware and software but also fostered societal acceptance and trust in electromobility, marking a significant step towards sustainable and integrated energy and transportation systems.

In Scotland, the UK and Internationally, the transition to battery electric HGVs to deliver on net-zero policies poses significant challenges to all enabling stakeholders. While the availability of eHGVs, particularly from major manufacturers, shows promise, there remains a notable gap in research and policy concerning the necessary charging infrastructure network to support eHGV operators nationwide.While it could be argued that ‘obvious’ locations for charging infrastructure already exist at the service station sites across the country, supporting eHGV in "off-trunk-road" environments is particularly difficult as outlined by the Scottish Government Zero Emission Truck Task Force. In Scotland, 90% of the 5600 fleet license holders operate a portfolio of ten vehicles or fewer, with an additional 8% managing fleets with <50 vehicles. Given that most license holders are operating smaller fleets, it becomes crucial to implement just-transition policies and incentives that cater to these businesses' specific needs and constraints. The major challenge associated with the transition relates to identifying areas where there is both a need for eHGV charging and suitable power network capacity to supply high-capacity chargers from both a power and energy perspective. Therefore, a successful transition to eHGVs in Scotland requires a considered approach given the diverse needs of license holders and constraints in power transmission and distribution networks. The WattRoutes project tackled this challenge by designing an eHGV charging infrastructure planning tool. This initiative employs a data-driven approach with the core objectives of identifying optimal charging locations, quantifying energy requirements and assessing power grid readiness & availability. The developed process leverages data analytics to pinpoint key routes and locations of optimised eHGV charging infrastructure. By focusing on areas with high HGV traffic flows, the approach ensured that the charging infrastructure aligns with the diverse needs of operators located both urban and rural communities. The paper presents detailed energy requirements for eHGVs along the identified routes using publically available data traffic counts, governmental transport statistics and industry insight. This evidence-based approach allows for the development of targeted charging solutions, ensuring that the infrastructure is tailored to the specific demands of the Scottish transportation landscape often characterised by small single carriageways/tracks which are often slow to traverse and are fuel inefficient. Utilising datasets provided by the transmission and distribution grid operators, the project mapped future charging infrastructure requirements to current grid capabilities to help support network operators in their ahead-of-need planning laying the groundwork for future developments.

Battery Electric Buses often require daytime charging. Their unavailability during non-negligible charging window significantly increases the fleet size. Here, we show that we can reduce the fleet size by modifying the Trip Assignment Criteria and reduce the total idle time between the trips. The proposed urban-scaled model can also find the optimal number of charging stations. We used complex networks approach and mathematical programming to develop two adjoined graph-based algorithms. The Restricted Minimum Weight Matching algorithm minimises the fleet size considering the electrification constraints. The Smooth Load Distributing algorithm maximises the total energy delivered to the batteries with a minimum load applied to the electrical grid. Both algorithms are P-class and can solve the problem. We applied the model to Sydney in Australia and Birmingham in UK and showed that it can significantly reduce the fleet size and the number of charging stations for these metropolises.

Electric mobility comprises the interactions between transportation and power grid infrastructures. Knowledge integration in such interdisciplinary contexts comes with important challenges. Researchers dealing with this subject often face data coming from diverse sources. Even when data exchange standards are followed strictly, bringing them together can lead to costly data integration tasks and the risk of misinterpretation. The FAIR guiding principles for scientific data management and stewardship are guidelines intended to aid researchers and organizations in enabling the computer actionability of their data. As part of our research, we explore ways in which their implementation can be used to manage data in interdisciplinary contexts. In our work related to charging infrastructure, we performed three main tasks. First, we evaluate the syntax and semantics of existing open-data publications associated with charging infrastructure. Secondly, using this knowledge we develop demonstrative data models and ontologies from our understanding of the analysed data and publications. We show how the same information can be expressed in different data models and how an ontology can be used to bring together these representations. Lastly, based on our interpretation of the FAIR principles we propose some concrete solutions on how to avoid problems of reproducibility and interoperability. In future applications, we intend to use ontologies in microservice architectures that can be coupled with other methodologies such as the ones coming from disciplines like machine learning, operations research, and agent-based modelling.

Interoperability is crucial in the electromobility ecosystem as it ensures seamless integration and operation of electric vehicles (EVs) across energy grids, charging infrastructures, and communication protocols. In this context, interoperability is vital to prevent disruptions in EV charging and support the scaling of e-mobility solutions. The unIT-e² project highlights the importance of interoperability through its standard based smart charging solutions and comprehensive testing efforts, especially during the unIT-e² Plugfest. This event was a key platform for evaluating interoperability across various systems and components involved in the so called clusters of the project. Interoperability was measured in terms of semantic, pragmatic, and dynamic aspects, with each layer addressing different communication and operational needs. The findings from the Plugfest indicated that while pragmatic interoperability was generally achieved, dynamic interoperability—related to the interchangeability of components—presented challenges. Based on these insights, the project recommends increased standardization of communication protocols alongside continuous and rigorous testing of system integration in real-world scenarios. These recommendations aim to enhance the reliability and efficiency of the electromobility ecosystem, paving the way for broader adoption and smoother operation of EVs in the future.

With the rise in frequency and severity of power grid disruptions, there is a pressing need for innovative methods to improve power supply resilience. Electric vehicles (EVs), acting as mobile storage units, offer a unique opportunity to establish an EV-based virtual electricity network (EVEN), facilitating electricity transfer from stable regions to those facing outages. This approach is particularly valuable during extended disruptions, such as those caused by crises. However, existing research typically treats EVs as uncoordinated, individual backup sources, missing the broader potential of a coordinated system. This study introduces a comprehensive model for the EVEN solution, focusing on the coordination of electricity distribution via EVs. The model incorporates a central emergency microgrid (MG) that can operate independently when the main grid fails, along with multiple EV-equipped households. Evaluated using a real-world scenario in Sweden, the study measures performance through metrics like energy deficit days, electricity delivery, and battery degradation. The findings demonstrate that the EVEN solution significantly boosts grid resilience, especially for smaller energy users, with minimal impact on battery health. The solution is most efficient when households are close to the central MG, minimizing energy loss. This research provides key insights into enhancing grid resilience using EVs.

The paper analyzed the network tariff regulation at the EU level with a special focus on its role in supporting the transformation to e-mobility. Using traditional legal interpretation methods it shows how the complex and unsystematic legal framework can be made comprehensible and operable. The principle of cost-reflective non-discrimination is identified as the central guideline for network tariff regulation. Although there are many possible exceptions to this principle, they are not unlimited and are subject to a strict proportionality test. The paper points out that the expansion of the objectives resulting from the most recent reform of the legal text entails the risk that the legal framework will become dysfunctional due to its far-reaching mandate. Finally, the possibilities of incentivizing e-mobility through grid charges are evaluated, and the potential benefits of limiting the legal framework for network tariff regulation to its core are pointed out.

Electric buses are generally considered as zero-emission vehicles and have thus become common in city transport to reduce pollution. However, electric buses in cold regions often utilise diesel-powered auxiliary heaters for cabin thermal management, which contradicts the zero-emission status. Studies have shown auxiliary heaters can emit significant amounts of gaseous and particulate emissions, although the extend of this issue has not been investigated. This paper introduces a method for estimating the annual emissions stemming from electric bus auxiliary heater use. The emission estimate was derived by combining empirical emission data with bus operation schedules and historical weather data. The findings reveal that a fleet of 1 254 electric buses with auxiliary heaters could annually emit up to 5.3 million kilograms of tailpipe CO2 along with notable quantities of local pollutants. Nevertheless, greenhouse gas emissions from the auxiliary heaters were found to be relatively low compared to diesel engines, with CO2 emissions per unit of work used being approximately 10% to 30% of those produced by Euro VI buses. The local pollutants (CO, NOx and particulates) from auxiliary heaters can be significantly higher. For instance, auxiliary heater CO emissions per unit of work were found to be 1 to 19 times higher than those of diesel engines.

To achieve the goal of having 15 million electric vehicles (EVs) on its roads by 2030, the German government has implemented subsidies for EV purchases. However, the effectiveness of these financial incentives heavily relies on end-user acceptance of this innovative technology. While there exists a substantial body of research on user acceptance of EVs, our understanding of the customer journey and potential pain points during this journey for consumers’ purchase decision-making and post-purchase experiences remains limited. Even if consumers have a positive attitude towards EVs, they may refrain from purchasing if they encounter obstacles in this new and different “path to purchase” which encompasses not only the EV itself but also complementary components, such as a wallbox or a Home Energy Management System (HEMS). Moreover, negative post-purchase experiences of customers may deter other potential buyers.This research is part of the “unIT-e²” project, dedicated to implementing smart charging methods on a broad scale. A nine-month field trial conducted in 2023 within the “Harmon-E Cluster” offered the unique opportunity to acquire valuable insights within a real-user context and helped us address the following questions to enhance user acceptance of connected e-mobility:

• What pain points do users encounter throughout their customer journey, both during the field trial and in their broader experiences outside of it?
• How do participants evaluate their user experience with the charging technology, EVs, complementary components, and the overall system during the field trial and what insights can be derived from it?

To understand the user perspective on the customer journey and the field trial, we employed a mixed method approach, including qualitative in-depth interviews and standardized online questionnaires.Participants pinpointed significant pain points especially during the pre- and post-purchase phases related to cost, technical performance, integration, and consumer education and assess their user experiences. Some of the most notable issues included:

• Lack of consumer knowledge about the components
• Inadequate information, e.g. about the compatibility of different products
• Lack of transparency during usage, resulting in uncertainty about which components can be retrofitted and which will integrate with existing systems
• A prevailing sense of isolation experienced by users in these situations

Nevertheless, throughout the field trial, the assessment of the overall system (EV, wallbox and HEMS) showed a positive trend. Both its average usefulness and satisfaction scores remained high, with minor fluctuations over time. The technology's suitability for everyday use, while positive on average, experienced a decline during the trial as pain points and problems (e.g. too few charging stations or limited range) were identified.By the conclusion of the field trial, a majority of test users expressed high satisfaction with the trial. Around half of the participants indicated that they had not noticed whether a use case had been tested or not. Moreover, 94% of participants expressed a strong willingness to participate in the field trial again retrospectively.In summary, while users generally expressed overall satisfaction with the charging technology and EVs, there are areas for improvement, especially in addressing pre- and post-purchase pain points. Despite challenges, participants expressed satisfaction and evaluated the field trial positively.

This paper investigates the impact of the 2023 framework of rules to § 14a of the Energiewirtschaftsgesetz - German Energy Industry Act, EnWG - (§ 14a framework) on electric vehicle (EV) users. The § 14a framework requires distribution grid operators (DSOs) to connect new heat pumps (HPs) and EV charging infrastructure (EVSE) while allowing them to reduce power drawn from HPs and EVSEs in case of grid overload. The DSOs are mandated to provide remuneration for these components. The study develops remuneration mechanisms in the form of variable grid fee tariffs together with DSOs, which are then implemented into an optimization model for the flexible marketing of EVs to analyse their effect on the EV charging behaviour and the total costs. The analysis reveals that the § 14a framework gives high flexibility in designing variable grid fee tariffs to meet the needs of the different distribution grids. These different tariffs enable flexible EV users to achieve substantial cost savings, thereby providing a strong incentive for the load shifting required by DSOs. This research fills a gap in understanding how the incentives outlined in the § 14a framework affect EV users.

IntroductionElectric vehicles (EVs) are a key component to decarbonize the transport sector. In the conflicting area of economic and systemic concerns, use cases of smart charging and bidirectional charging offer an attractive solution. A use case is defined as the optimization of an EV’s charging strategy to achieve an objective, typically to minimize electricity costs or create additional revenues. Such an optimization of an EV’s charging strategy is referred to as smart charging. If the EV can also be discharged, the term bidirectional charging is used. This work aims to evaluate the prospects of smart and bidirectional charging from the user’s perspective in Germany between 2024 and 2030 via a multi-criteria analysis, as benefits for users are the gateway for large-scale implementation of EV use cases.MethodologyWe introduce three different aspects to analyse and discuss use case prospects in Germany: the technical readiness of the respective use case (using the calculated implementation effort), the number of potential EV users that could apply a use case, and the net cost savings/ profits resulting from use cases. The three aspects result from different, previously published analyses. In this work, these analyses are inter-connected to obtain a coherent, differentiated picture of the prospects of smart and bidirectional charging today (2024) and in the future (2030).The technical implementation effort is used to estimate at which time a use case will become applicable with manageable effort. The actual availability of technical components is not investigated, but an expert survey is included to estimate the date of technical maturity. The number of potential EV users is based on detailed calculations of the respective market volumes or locations suited for a specific use case. It is used to display the user potential in contrast to the total number of available EVs. Profits per use case result from simulations conducted with the optimization model eFlame. The model optimizes charging strategies for different use cases using various input parameters as sensitivities. Averaged simulation results are displayed in a colour rating.ResultsResults are obtained for the use cases photovoltaic (PV) self-consumption optimization, peak shaving, dynamic electricity tariffs (based on day-ahead market prices), and balancing services (frequency containment reserve, FCR). In addition, two multi-use cases are also analysed: sequential spot market trading (day-ahead and intraday markets) and PV self-consumption optimization plus intraday market trading. Our results show that none of the use cases is technically mature today. For smart charging, the first use cases to become technically scalable and profitable from the user’s perspective are PV self-consumption optimization and peak shaving. Bidirectional charging is found to become technically mature later in time. Large-scale implementation of the first bidirectional charging cases could start around the end of 2025. All of the investigated use cases are projected to become profitable around 2030 at the latest with profits ranging from less than 100 € up to more than 2,000 € per EV and year. Thus, the prospects of all use cases are positive, with most cases becoming generally beneficial from a user’s perspective between today and 2030.

The base for an energy management system (EMS) in the low-voltage grid is a control for grid coupling on this level. Grid coupling has the advantage that conventional expansion can be reduced. The infrastructure can be protected from overload and the voltage limit will be maintained. This reduces the conventional expansion. This paper presents a fuzzy based control for dynamical grid coupling and his simulation model. To build up the fuzzy, the input parameters must be defined. In case of different digital measurement expansion levels, the parameters are calculated by the minimum of needed input, the voltage and the current of the feeder in the substation. The simulation results are presented and discussed.

Smart charging use cases for bidirectional electric vehicles in vehicle-to-home or vehicle-to-grid can significantly accelerate sector coupling between the transport and energy sectors. Building on the tried and tested use case methodology and existing use case descriptions, we include additional tools from the unified modelling language to provide a deeper understanding of the underlying processes and interactions. With this paper, we try to structure a large variety of complex use case combinations into a few use case combinations (releases) that, in their entirety, cover the field of possible V2X applications. Applying these tools to the use cases of BDL Next, we discuss how different use cases complement each other and map their interaction, indicating synergies, constraints, conflicts, and transition conditions. To give end customers insight into the processes and benefits of the use cases, we also prepare the customer journey during the usage phase.

Motivation:Advancing anthropogenic climate change requires a rapid transition away from fossil fuels. The increasing sector coupling and the resulting growing share of electric vehicles and heat pumps will lead to a substantial increase in decentral loads in electrical distribution networks. Particularly in urban areas with high load density, this can pose considerable challenges for distribution system operators, which is why a reliable planning basis is required.Background and objectives:Next to electromobility, decentralized electrical heat generation plays a significant role when forecasting the future grid load. Therefore, the electrical load profiles of heat pump systems are to be assessed in more detail in this work. The objective is to analyse the effects of different sensitivities on the electrical power demand of heat pump systems for typical German buildings in order to estimate the resulting additional grid load caused by different configurations. This work is part of the Grid for Electrification research project, in which the Stadtwerke München, Technical University of Munich, and Technical University of Applied Sciences Augsburg work together.Methodology:A previously developed heating system model that enables a building-specific assessment in a one-minute resolution is used to conduct the analyses. This paper focuses on the influence of different heat pump system configurations (e.g., different control concepts and dimensioning strategies) on the daily electrical power demand. Modelling assumptions for relevant technical design and control variants are based on a survey of several heat pump manufacturers as well as expert interviews and detailed literature research and are then examined as part of a sensitivity analysis. The results are evaluated using the example of typical German buildings.Results:As a key result, standard load profiles are created for different heat pump system configurations. It is further presented which parameters have a relevant effect on the electrical power demand of the respective systems. Both unfavourable system configurations, which can lead to a high grid load, and favourable system configurations, which enable a reduction in power demand, are highlighted. For example, it is shown that water-source heat pumps in monovalent design cause the lowest grid load and that over-dimensioning the heat pump can lead to a reduction in electrical power demand in certain system configurations.Preview of full version and significance of results:In the full version of the paper, these and further sensitivities and their influence on the electrical load profile of different heat pump systems are evaluated in detail, and standard load profiles are created for typical buildings. Grid operators can utilize the findings to estimate future grid loads caused by the heat transition. The results also provide information about desirable configurations from an economic and system operator's point of view and thus show recommendations for action that could, e.g., be incentivized as part of governmental funding programs.

The rapid expansion of electric vehicles (EVs) presents formidable challenges for power systems, especially regarding the scaling of EV charging infrastructure to meet the growing demands of EV fleets. These demands are influenced by complex interdependencies between spatial and temporal factors, such as transport work, weather conditions, traffic density, route and charging infrastructure, leading to imprecise charging demand predictions by existing models that do not fully address all factors. This study tackles this problem by introducing an innovative predictive model, named Weather Traffic Routes and Chargers (WeTRaC), which offers high-precision forecasts of spatio-temporal charging demands for EV fleets of e-buses and e-trucks, managing various operational conditions and ensuring efficient use of charging infrastructure. The model combines graph neural networks with detailed physics-based vehicle simulations using real-world inputs collected from cities around the world to produce state-of-charge (SOC) predictions. By pinpointing critical areas and peak times for charging demand, the model can optimise the placement of charging stations, thereby preventing grid overload and facilitating a green transition. It significantly accelerates prediction times with only a minimal trade-off in accuracy, as demonstrated in our simulated studies, making it an ideal tool for analysing vehicle fleet charging demand.

The electrification of the mobility sector, particularly in the truck segment, requires a comprehensive charging infrastructure. Significant challenges remain in implementing a nationwide charging network. Key obstacles include the high costs and long implementation times associated with medium- or high-voltage grid connections, compared to low-voltage alternatives. Integrating electricity storage systems can alleviate these challenges by enabling grid connections at lower levels, either temporarily or long-term, thus improving the overall system's economic efficiency. Lower grid connections can reduce investment and operating costs, lowering grid usage fees by reducing peak loads. Additionally, electricity costs can be minimized by purchasing power during periods of lower prices, and participating in the electricity market can generate additional income. This article examines how electricity storage systems affect the economic feasibility of truck charging infrastructure, focusing on various business models through a case study in Germany. It considers factors like price-controlled charging, storage size, grid connection, and energy demands. The findings provide insights into how storage systems can enhance the economic viability of high-power charging infrastructure.

This work assesses the potential of electric vehicles participating in frequency services in the Nordics. For this, data from aworkspace parking lot is used to create artificial load profiles to take the perspective from an aggregator. The study is then dividedinto two parts: Firstly, a machine learning model is developed to forecast the parking lot load. In a second step, the predictions aregiven to a rolling-horizon mixed integer linear program that optimally allocates the capacities to Frequency Containment Reserveservices. It is found that the machine learning approach almost doubles the profitability compared to offering bids just based onhistorical values. Finally, a hypothetical market structure is considered, where the FCR-D late auction is moved to an hour-aheadintra-day auction. The analysis shows that the opportunity to correct bids intra-day improves participation in frequency servicesand triples profits compared to the day-ahead auction.

The energy demand of future electromobility poses new challenges for distribution grids due to critical load peaks. Integrating electric vehicles (EVs) into the energy system by controlled charging could offer additional flexibility for the overall system yet may increase the peak loading in the distribution grid. We therefore developed and implemented a modular optimization model representing the simultaneous controlled charging of multiple EVs in one defined branch of a distribution grid from a combined aggregator and grid perspective. The objective consists of minimizing the costs to provide electrical energy to fully recharge all EVs connected to that grid branch during a full year, either in perfect foresight or by applying a rolling horizon with a 36h/24h scheme. The time intervals of connection to the power grid are explicitly modelled as well as the grid limitations. Using Monte Carlo simulations, a multiplicity of different single grid branches is evaluated. The number of households connected to these grid branches with a defined probability of EV ownership is applied as predictor for the load burden of the future German EV fleet. In the model, a classification of grid branches into fifteen distinct types is used, each defined by specific parameter settings, allowing to capture the diversity of grid branch configurations, e.g. regarding the degree of urbanization. Randomization is employed to create a representative sample of grid configurations based on the predefined clusters and corresponding ranges of grid parameters. This enhances the model's adaptability to reflect the variability of grid configurations. Compared to uncontrolled charging, which is considered as a reference in the model, “smart” optimised controlled charging of multiple EVs is concentrated during periods of the lowest spot prices, which do not coincide with the existing peak load hours in the evening. The impact of grid constraints on the controlled charging patterns is found to depend both on the degree of urbanization and the specificities of the considered grid branches.

For a seamless integration of Electric Vehicles (EVs) and associated charging infrastructure into the power grid, interoperability within the Electromobility (E-mobility) sector is highly essential. The goal is to ensure the harmonious functionality of all components involved, regardless of the manufacturer or regional differences. This is achieved by standardizing system designs, technologies and communication protocols in order to minimize technical barriers. The Vehicle-to-Grid (V2G) concept allows bidirectional flow of electrical energy between EVs and the power grid, enabling EVs to function as mobile energy storage units. This supports grid services and the optimal utilization of Renewable Energy Sources (RES). Despite the extensive research and small-scale implementations, there is however, limited experience with scaling V2G technologies and integrating them into comprehensive energy systems. This work aims on researching and analysing the current communication protocols and regulatory frameworks used in E-mobility ecosystems to ensure interoperability. The focus here lies on Austria, where within the framework of the project Car2Flex, diverse testbeds were implemented at country-wide locations including elements like RES and battery energy storage. By addressing the technical and regulatory challenges, this work aims to pave the way for a more resilient and efficient energy system leveraging the full potential of E-mobility and V2G technologies.

Transmission system operators (TSOs) are facing the challenge of integrating renewable generation and increasing electrification across sectors, while running the electricity system efficiently. Therefore, unlocking flexibility plays a crucial role: It aims at flattening consumption peaks and balancing out volatile supply of renewable energy via market-oriented consumption. E-mobility promises to be one of the main flexible solutions on a residential level, as electric vehicles (EVs) can act as a decentralized energy resource contributing to load balancing.With a prediction of 31 million EVs in Germany until 2037 as per network development plan, there is a need to understand the implications of integrating these assets into the market. For that purpose, on the one hand, the customer perspective is analyzed: We assess different market price signals and according revenue potentials EV owners can expect, when steering their asset in accordance to those signals. On the other hand, the systemic perspective poses the question if and to which extent price response is measurable. In our analysis, we develop a methodology to identify price response in consumption time series. This methodology is then applied to consumer data to gain valuable insights on system effects and the question how flexibility can be unlocked.

The transition to low carbon renewable energy is changing the way consumers use energy and interact with electricity networks, particularly the low voltage electricity networks to which they directly connect. To aid the transition to low carbon energy sources and use, and support electricity distribution network operators (DNOs), ANSA has developed technology to model and visualise LV networks. Recently, ANSA has extended its technology from future hosting capacity to understanding the risk of constraints in each LV network from PV and EVs, or more generally, any generation or change in demand. This allows DNO’s to also consider demand changes such as gas-to-electricity transition and urban infill.Because ANSA’s technology models every element of an LV network, constraint risk can be considered: (1) for the network as a whole; and (2) by each individual element. By assessing each element, it is possible to consider the constraint risk and upgrade risk of each element in an LV network with certain input assumptions. When combined with asset upgrade or replacement costs using ANSA’s LV CAPEX model, an indication of future capital expenditure is calculated. This allows DNOs to establish capital expenditure requirements for each year in the future and to understand how these requirements might change with changing inputs. In turn, this allows them to value demand side interventions and inform network pricing design. It also allows them to consider CAPEX deferral or avoidance by demand flexibility, such as smart charging and load control to maximise local PV consumption.This paper reviews ANSA’s models, drawing on results and images from ANSA’s LV Visibility Dashboard application and ANSA’s LV CAPEX Model.

The increasing penetration of electric vehicles (EVs) is causing uneven loading in low voltage distribution networks, primarily due to variations in smart charging rates and simultaneous charging of single-phase and three-phase EVs. This study experimentally analyzes three mainstream EVs (Renault Zoe R90, Peugeot e-2008, Nissan Leaf e+) charging concurrently, with the Zoe and e-2008 using three-phase power and the Leaf using single-phase power. Smart charging capabilities are used to gradually increase charging current from minimum to maximum power for each vehicle. Harmonic, current, and voltage measurements were taken at the point of common coupling using a power quality harmonic analyzer. The results reveal significant harmonic imbalances in three-phase networks, particularly when different types of EVs are charged simultaneously. This highlights the necessity for active load management to ensure EV charging adheres to industry standards and recommendations, such as IEC 61000-3-12 for harmonic emissions and CIGRE C4.07. Such active management strategies can mitigate the negative impacts of uneven loading and ensure the stability and reliability of power distribution networks as EV adoption continues to grow.

This paper describes results of the research project “SMECON-Box” (Smart-MEter-CONtrol Box). In this project, an algorithm for a smart FNN control box, the so called SMECON-Box, is implemented and tested in laboratory and field tests. The algorithms objective is to curtail the charging power of an electric vehicle (EV) in case of grid overloading. To keep the communication needs as low as possible, the SMECON-Box should decide whether the grid is overloaded, solely based on local measurements.The first part of the paper describes the practical implementation of the field tests, that have been carried out since the previous publications and associated implementation difficulties. In the second part of the paper the field tests are evaluated and the algorithm outcome will be evaluated and discussed.

Due to the ongoing electrification of vehicle fleets in Germany and the planned restriction on the registration of new internal combustion engine vehicles from 2035, the pressure on the electricity grid is increasing. An accurate estimation of the current and future load as well as the storage potential of electric vehicles is essential. In addition, enabling bi-directional charging will support grid services, increase the use of renewable energy, provide ancillary services such as frequency regulation and reactive power, and provide emergency power from electric vehicles. To achieve this, standard profiles are needed for different charge point operators. These profiles are crucial for modelling and evaluating the charging behaviour of different operator types. Focusing primarily on fleet operators, this paper presents a method for creating standard profiles using real data from a charge point management system. The method involves analyzing charging profiles from different operators over several weeks and clustering them based on their charging behavior. The clusters are then evaluated to identify and extract common characteristics and patterns shared by these charge point operators, resulting in a standard profile that describes a group of operator types. The developed profiles can then be parameterized and used for detailed modeling and further analysis. This method provides a robust basis for better estimating both the load and storage capacity potential of electric fleets. It supports informed decision-making in an increasingly electrified transport sector.