The Influence of Frequency Containment Reserve Flexibilization on the Economics of Electric Vehicle Fleet Operation

Simultaneously with the transformation in the energy system, the spot and ancillary service markets for electricity have become increasingly flexible with shorter service periods and lower minimum powers. This flexibility has made the fastest form of frequency regulation - the frequency containment reserve (FCR) - particularly attractive for large-scale battery storage systems (BSSs) and led to a market growth of these systems. However, this growth resulted in high competition and consequently falling FCR prices, making the FCR market increasingly unattractive to large-scale BSSs. In the context of multi-use concepts, this market may be interesting especially for a pool of electric vehicles (EVs), which can generate additional revenue during their idle times. In this paper, multi-year measurement data of 22 commercial EVs are used for the development of a simulation model for marketing FCR. In addition, logbooks of more than 460 vehicles of different economic sectors are evaluated. Based on the simulations, the effects of flexibilization on the marketing of a pool of EVs are analyzed for the example of the German FCR market design, which is valid for many countries in Europe. It is shown that depending on the sector, especially the recently made changes of service periods from one week to one day and from one day to four hours generate the largest increase in available pool power. Further reductions in service periods, on the other hand, offer only a small advantage, as the idle times are often longer than the short service periods. In principle, increasing flexibility overcompensates for falling FCR prices and leads to higher revenues, even if this does not apply across all sectors examined. A pool of 1,000 EVs could theoretically generate revenues of about 5,000 EUR - 8,000 EUR per week on the German FCR market in 2020.Comment: Preprint, 23 pages, 21 figures, 10 table

Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”

Simultaneity Factors of Public Electric Vehicle Charging Stations Based on Real-World Occupation Data

Charging of electric vehicles may cause stress on the electricity grid. Grid planners need clarity regarding likely grid loading when creating extensions. In this paper, we analyse the simultaneity factor (SF) or peak power of public electric vehicle charging stations with different recharging strategies. This contribution is the first of its kind in terms of data quantity and, therefore, representativeness. We found that the choice of charging strategy had a massive impact on the electricity grid. The current “naive” charging strategy of plugging in at full power and recharging until the battery is full cause limited stress. Price-optimised recharging strategies, in turn, create high power peaks. The SFs varied by strategy, particularly when using several connectors at once. Compared to the SF of a single connector in naive charging, the SF decreased by approximately 50% for groups of 10 connectors. For a set of 1000 connectors, the SF was between 10% and 20%. Price-optimised strategies showed a much slower decay where, in some cases, groups of 10 connectors still had an SF of 100%. For sets of 1000 connectors, the SF of price-optimised strategies was twice that of the naive strategy. Overall, we found that price optimisation did not reduce electricity purchase costs by much, especially compared to peak-related network expansion costs

Simultaneity Factors of Public Electric Vehicle Charging Stations Based on Real-World Occupation Data

Charging of electric vehicles may cause stress on the electricity grid. Grid plannersneed clarity regarding likely grid loading when creating extensions. In this paper, we analyse thesimultaneity factor (SF) or peak power of public electric vehicle charging stations with differentrecharging strategies. This contribution is the first of its kind in terms of data quantity and, therefore,representativeness. We found that the choice of charging strategy had a massive impact on theelectricity grid. The current “naive” charging strategy of plugging in at full power and recharginguntil the battery is full cause limited stress. Price-optimised recharging strategies, in turn, createhigh power peaks. The SFs varied by strategy, particularly when using several connectors at once.Compared to the SF of a single connector in naive charging, the SF decreased by approximately50% for groups of 10 connectors. For a set of 1000 connectors, the SF was between 10% and 20%.Price-optimised strategies showed a much slower decay where, in some cases, groups of 10 connectorsstill had an SF of 100%. For sets of 1000 connectors, the SF of price-optimised strategies was twice thatof the naive strategy. Overall, we found that price optimisation did not reduce electricity purchasecosts by much, especially compared to peak-related network expansion costs

The Influence of Frequency Containment Reserve on the Operational Data and the State of Health of the Hybrid Stationary Large-Scale Storage System

The expansion of renewable energy with its volatile feed-in character places higher demandson the power grid of the future. Large-scale storage systems (LSS) are a promising option forsupporting the electricity grid and have been gaining importance in the last years, both on the marketfor frequency containment reserve (FCR) and in research. The majority of publications investigatingthe interaction between storage and FCR are based on simulations rather than on field measurements.This paper presents the analyses of multi-year, high-resolution field measurements of the hybrid6 MW/7.5 MWh battery storage “M5BAT” to address this issue. The influence of FCR operationon the operation and degradation of the hybrid LSS and the individual battery technologies isinvestigated via a statistical evaluation of the historical operating data between 2017 and 2021. Thedata-based analysis of the LSS and the individual battery technologies reveals a high availability ofthe LSS of over 96.5%. Furthermore, the FCR operation results in an average SOC of the LSS of 50.5%and an average C-rate of the battery units of 0.081 C. A capacity test after four years of operationexposes that the lead-acid batteries have experienced a loss of energy capacity of up to 36%, whereasthe lithium batteries have only experienced a loss of up to 5%. The calendar ageing predominates inthis context. The presented results can be used to investigate and model the influence of FCR on theoperation and battery degradation of the LSS and its different battery technologies

Market Review and Technical Properties of Electric Vehicles in Germany

Electromobility has grown rapidly, and especially in China, Europe, and the United States.Within Europe, Germany is the largest market. Our goal in this paper is to provide a data-drivenoverview of the key data, including the number of vehicles sold, place of registration, battery capacity,and charging power, in Germany. The results were generated by linking car-registration data with thetechnical details for each car model. We identified more than 84% of the battery electric vehicles inthe fleet, but the uncertainty is larger for plug-in hybrid electric vehicles. The number of sold electricvehicles doubled annually over the last two years. Simultaneously, the battery capacity and chargingpower per vehicle are rising. Combined, the two effects cause the cumulative battery capacity andcharging power of the fleet to grow at an even faster pace. The battery energy built into electricvehicles in Germany registered on 1 August 2022 was 50.5 GWh, of which 9.5 GWh belonged toplug-in hybrids. The combined charging system became the dominant charger type for fast chargingin Germany, and only 2% of the vehicle fleet used the competing CHAdeMO standard. To allowfellow researchers to work with the data, we published them free of charge on our data platformmobility charts, and we update the data monthly

Market Review and Technical Properties of Electric Vehicles in Germany

Electromobility has grown rapidly, and especially in China, Europe, and the United States. Within Europe, Germany is the largest market. Our goal in this paper is to provide a data-driven overview of the key data, including the number of vehicles sold, place of registration, battery capacity, and charging power, in Germany. The results were generated by linking car-registration data with the technical details for each car model. We identified more than 84% of the battery electric vehicles in the fleet, but the uncertainty is larger for plug-in hybrid electric vehicles. The number of sold electric vehicles doubled annually over the last two years. Simultaneously, the battery capacity and charging power per vehicle are rising. Combined, the two effects cause the cumulative battery capacity and charging power of the fleet to grow at an even faster pace. The battery energy built into electric vehicles in Germany registered on 1 August 2022 was 50.5 GWh, of which 9.5 GWh belonged to plug-in hybrids. The combined charging system became the dominant charger type for fast charging in Germany, and only 2% of the vehicle fleet used the competing CHAdeMO standard. To allow fellow researchers to work with the data, we published them free of charge on our data platform mobility charts, and we update the data monthly

Balancing group deviation & balancing energy costs due to the provision of frequency containment reserve with a battery storage system in Germany

renewable energy sources in the power grid. Thus, control reserves such as frequency containment reserve aregaining in importance and need further investigation. In Germany, the power grid is divided into balancinggroups, in which supply and demand must be balanced out. The provision of frequency containment reserve,creates an imbalance in the respective balancing group depending on the grid condition. However, this energeticimbalance and the resulting costs for the balancing group manager are further to be quantified. This workprovides a simulation model that examines the energetic imbalances resulting from the provision of frequencycontainment reserve. We validate the simulation results with field-data from the operation of a 6 MW batterystorage system and derive the resulting cost for the energy imbalances. In addition, flexibility options for batteriesgiven by the regulatory framework in form of the degrees of freedom are evaluated. The results show, thatthe degrees of freedom enable a battery storage operator to additionally charge up to 8.68 MWh/MW frequencycontainment reserve per month or dis-charge up to 9 MWh/MW frequency containment reserve per month onaverage. The additional profits from the German imbalance settlement price vary on average between 302 € and1,068 € per MW frequency containment reserve per month. In Conclusion, the field-data confirm the simulationdata in terms of energy deviations in the balancing group due to the provision of FCR. Over the period of onemonth, the deviation usually leads to a cost-related advantage for the balancing group manager. The provision offrequency containment reserve as a grid service can therefore be seen as a positive gain for a balancing group

The Influence of Frequency Containment Reserve on the Operational Data and the State of Health of the Hybrid Stationary Large-Scale Storage System

The expansion of renewable energy with its volatile feed-in character places higher demands on the power grid of the future. Large-scale storage systems (LSS) are a promising option for supporting the electricity grid and have been gaining importance in the last years, both on the market for frequency containment reserve (FCR) and in research. The majority of publications investigating the interaction between storage and FCR are based on simulations rather than on field measurements. This paper presents the analyses of multi-year, high-resolution field measurements of the hybrid 6 MW/7.5 MWh battery storage “M5BAT” to address this issue. The influence of FCR operation on the operation and degradation of the hybrid LSS and the individual battery technologies is investigated via a statistical evaluation of the historical operating data between 2017 and 2021. The data-based analysis of the LSS and the individual battery technologies reveals a high availability of the LSS of over 96.5%. Furthermore, the FCR operation results in an average SOC of the LSS of 50.5% and an average C-rate of the battery units of 0.081 C. A capacity test after four years of operation exposes that the lead-acid batteries have experienced a loss of energy capacity of up to 36%, whereas the lithium batteries have only experienced a loss of up to 5%. The calendar ageing predominates in this context. The presented results can be used to investigate and model the influence of FCR on the operation and battery degradation of the LSS and its different battery technologies