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Ein Teil des Helmholtz-Inkubators Information und Data Science ist die Helmholtz Metadata Collaboration (HMC). HMC soll die Beschreibung von Forschungsdaten durch Metadaten zu deren besseren Auffindbarkeit vorantreiben sowie organisatorisch und technisch umsetzen. Metadaten sind essentielle Infor¬mationen über Forschungsdaten, die für deren Auffinden und Verstehen sowie für deren Vernetzung und Nachnut¬zung im Sinne der FAIR-Prinzipien erforderlich sind. Zur Umsetzung wird die wissenschaftliche Expertise zum Thema Metadaten aus einzelnen Fachdomainen in sogenannten Metadata Hubs der einzelnen Forschungsbereiche zusammengefasst, auf übergeordneter Ebene harmonisiert und, mit Hilfe zentral entwickelter Methoden und Werkzeugen, Metadatenplattformen bereitgestellt. Für den Forschungsbereich Energie ist der HMC Hub Energie verantwortlich. Aufgabe ist hierbei die vorhandenen Standards zur Energiedaten- und Metadatenbeschreibung, etablierte Beschreibungs- und Erfassungsprozesse sowie zugehörige Softwarewerkzeuge zu erfassen, Lücken zu identifizieren und Szenarien zur Ergänzung und Weiterentwicklung in der Domäne Energie zu entwerfen. Einheitliche Ziele von HMC sind die einfache und FAIRe Erschließung und Nutzung vorhandener und zukünftiger Datensammlungen der Forschungsbereiche sowie die Befähigung der Forschenden FAIRe Daten (semi-) automatisch zu erstellen. Das Poster beschreibt die Struktur von HMC allgemein und dem Hub Energie im speziellen, die entwickelten Methoden und Werkzeuge und gibt anhand von Anwendungsbeispielen Impulse für die Umsetzung der Methoden und Werkzeuge hin zu FAIRen Metadaten. Weiterhin werden Verknüpfungen zu Trainings- und Schulungsunterlagen von HMC hergestellt. Das Poster soll dazu einladen mit dem HMC Hub Energie Kontakt aufzunehmen um von den Arbeiten von HMC profitieren zu können.

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Modeling in energy systems analysis needs a lot of data from a variety of different sources: Meteorological time series for renewable power generation, electricity demand from the transparency platforms, technology data, socio economic data, etc. The first step in modeling is usually a longer research exercise in data gathering. There are a lot different sources available, but they are sometimes hard to find and each has different ways of accessing the data and different ways how data fields are defined. Similar problems arose in earth observation about two decades ago. The answer was the development of a distributed data infrastructure for Earth Observation data, the Global Earth Observation Systems of Systems (GEOSS). Data could be found through data catalogs, where providers registered their metadata and ways of data access could be discovered though standardized interface descriptions as e.g. WSDL (Webservice Description Language). For energy systems analysis we want to learn from this experience and started a project for the development of a distributed data infrastructure for energy systems analysis. The project is about the development of networked data base concept based on the ideas of linked open data and the semantic web for input and output data of energy system models in energy systems analsis

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

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The expected increase in battery electric vehicles poses both, opportunities and risks for the decarbonisation of future power systems. While electric mobility may technically serve as a source of flexibility, it increases the overall power demand and may as well increase the demand for power in peak load hours. While electric mobility in national and continental scale energy system optimization is often treated in an aggregated fleet, user behaviour and charging decisions play a crucial role in the availability of vehicles' batteries for balancing electric load and renewable energy feed-in. Michaelis, Gnann & Klingler (2018) find that reduction of peak load and RE surplus electricity stemming from plug-in vehicles in Germany may be as high as 2.2 GW and 1.8 TWh respectively for 2030. However, they assume constant charging stations' power of 3.7 kW that seem conservative. Also, the authors don't explicitly describe to what effect user behaviour limits vehicle charging. By linking the modular energy system optimization model REMix with the vehicle stock model VECTOR21 and the charging decision simulation tool CURRENT, we aim to determine a more realistic demand response potential for future electric vehicles compared to previous studies. All three models are developed at the German Aerospace Center (DLR) and have been applied and validated in different project contexts. We for the first time integrate hourly profiles determined by individual charging decisions as input for an energy system optimization model. We determine the reduction potential of peak load, renewable energy (RE) curtailment as well as RE capacity expansion for the different cases of controlled charging versus uncontrolled charging for Germany for 2030. Thus, we provide both methodological and quantitative insights into the effects of coupling power and transport sector that are of value to the scientific discussion.

Despite ambitious climate goals, the German transportation sector has failed to reduce emissions. As these emissions are dominated by personal vehicles, electric vehicles are central for achieving environmental objectives. To determine potential emission reductions from electric vehicles, a detailed analysis of the transportation and energy sectors is necessary. Thus we present a methodology to calculate charging demand of electric vehicles using a time and location specific microsimulation and probability estimation based on a utility function for charging behavior. The transportation model is coupled with a detailed energy model for Germany, which provides electricity generation per energy source on an hourly basis over a year. We apply the methodology and models to the case study of Germany in 2030 for five scenarios. The scenarios represent difference pricing schemes reflecting policy options for electric vehicles. The results show that charging demand can be shifted using market incentives. We find that charging subsidies can shift charging demand to or away from peaks. We then combine charging demand with the energy model to quantify the CO2 emissions. The results show that shifting charging demand can reduce emissions, albeit at a minimal level. For the entire year, shifting charging to the daytime can reduce emissions by 2%. New areas of research including bidirectional charging and hourly pricing are needed to ensure maximum emission reductions from electric vehicles.

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Integrating self-consumption and prosumer behavior into an agent-based model of the German electricity system (AMIRIS). Assessing the prospective uptake of PV batteries in an overall system context. Studying different rate designs (capacity based tariffs, realtime pricing, timevariable feed-in remuneration) to regulate deployment.

Data on the potential generation of electricity from wind is crucial information for analysing the future role of this renewable energy source. In this report, a description is presented of the methodologies used for the derivation of a dataset. The dataset consists of an estimation of (1) wind speeds accounting for high-resolution effects, (2) power production accounting for a wide range of turbine types, (3) suitable areas and (4) associated cost estimates. Wind speed information is systematically derived from years of meteorological data based on the MERRA reanalysis dataset, and from the high resolution geo-spatial data based on the Global Wind Atlas. Within this project, the wind potentials and techno-economic parameters are gathered and processed into input datasets for the JRC-EU-TIMES model. This allows improved modelling of the competition and the complementarity of wind with other technologies, the key functionality of the JRC-EU-TIMES model. Moreover the datasets can also be used for the analysis of policy questions relating to the availability of wind energy., JRC.C.7-Knowledge for the Energy Union