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DLCL Energy

The Data Life Cycle Lab Energy (DLCL Energy) focuses its research on data life cycles of energy-related projects at the Karlsruhe Institute of Technology and at other Helmholtz Centers. Research in our partners’ projects comprises many different energy-related topics which are driven by public interest. They reflect the prominent international role that Germany plays in the field of renewable energy. The projects which we are collaborating with focus on the area of energy generation, intelligent distribution of energy, energy storage, and intelligent energy management.

The projects or parts of them which are in the main focus of our data life cycle analysis can be divided into several major areas (alphabetical order).

Battery research

Efficient and safe storage of electrical energy in a smart grid scenario creates the demand for large amounts of batteries. Their properties and the efficient incorporation in an existing grid are subject to research. Cooperation partners in this area are Competence E and Battery-Electric Storage System.

Chemical Processes

Not all details of the chemical processes in batteries are well understood so far. Multi-scale models of the respective processes and chemical reactions aim at combining models at an atomic scale with macro models of batteries. A partner is the project Complexity of Electro-chemical Systems.

E-Mobility

Electric vehicles EVs will play a more important rule in the future. Charging many electric vehicles at the same time results in a massive increase of load on the power grid. Research is looking into efficient charging methods as well as using electric vehicles as mobile storage of electrical energy. Partners in this area are the projects CROME and iZEUS.

Smart Buildings

The research in this area aims at incorporating intelligent generation, distribution, storage, and usage of electrical energy in either smart homes or smart (industrial) buildings. Cooperation partners are iZEUS and KASTEL.

Smart Grids

The term smart grid refers to an electrical power grid in which profiling and analysis of the creation and consumption of electrical energy is used to optimize the construction and use of the electrical power grid. Both physical structure of the grid and information on user data play a crucial role. Cooperation partners are iZEUS, the SimLab Energy, and SISKA.

The main goal of the DLCL Energy is the analysis of energy-specific demands in the respective data life cycles of its collaboration partners.

When compared to other DLCLs it is evident that the main challenges of the DLCL Energy do not lie in big file sizes or an extremely large number of files. Datasets which are considered large from an energy-data point of view might not be considered very big from another science’s point of view. They could still be handled relatively well compared to very data-intensive tasks such as image processing for example.

However, energy data has one property that distinguishes it from many other big data applications: very often, energy data is personal data. Often a connection to a person can either be made directly or indirectly. Therefore, privacy and security of the stored data must be guaranteed not only by demands of the users but also by law.

In many cases, the availability of energy data for research depends crucially on the explicit consent of the persons whose privacy is concerned. Usage agreements have to explicitly specify the intended use of the data. Hence, these agreements will strongly influence all phases of the data life cycle, from generation of data by measuring energy-relevant processes over management and analysis options to the final safe disposal of data.

Big files and a large number of files complicate things even further. The results from our data life cycle analysis in various projects show that privacy concerns are a very common issue. Energy data such as measurements of energy consumption in a smart building, or tracking data from an electric vehicle almost always yield information that can be linked to a person. Standard anonymization or pseudonymization techniques often cannot be applied properly and there is always a trade-off between usability of data and anonymization.

To complicate things further data life cycles differ greatly. Different types of data, different tools, and different methods make defining a prototype of a data life cycle for energy projects difficult. Nonetheless, the design of methods for authentication, access, and usage control as well as different anonymization and pseudonymization techniques is a crucial task when creating an optimal data life cycle for energy applications. Several explicit use cases have been identified across different projects for which generic services will be developed in cooperation with DSIT.