The objectives of the R & D activities in this project are to design, develop, and evaluate innovative internet technology-based data processing and information management methods for the design, analysis, and provision of energy information system components. These should serve as planning and visualization tools for the control of innovative smart grid solutions as well as a basis for the interaction and dialogue with citizens on issues related to the energy system.
The basic research approach is hereby founded on a distributed IT architecture based on micro services. This is where data and information management services (e.g., for the management of measurement and weather data, of geo-information, technical meta data, or textual and binary content) will be implemented through modular micro services. Here, the respective information can also be consumed through modular, intelligent web components in web applications or mobile applications. The use of the services and web components hereby also allows for larger, web-based information portals or mobile applications. The developed concepts and modules will be implemented in and evaluated on specific application projects for the development of smart grid solutions for the energy system or for the provision of information for the public dialogue in web-based information portals. Some of the projects include:
• the environmental portal and “Meine Umwelt” environmental app developed under the INOVUM project,
• the research-oriented control center infrastructure of Energy Lab 2.0,
• the Baden-Württemberg energy atlas,
• and the HGF project – Energy systems 2050.
Furthermore, basic research on the use and instrumentation of Cloud and Big-Data technologies (e.g., Kubernetes, Docker, Mesos, Hadoop-Stack, Google Big Data Cloud Infrastructure) will also be performed as a runtime platform of micro service-based applications. Here, the goal is to automate and optimize the micro services and associated applications through different runtime infrastructures (cloud runtime infrastructures, such as in Google or Amazon Cloud, on local computer clusters that are instrumented for container automation, etc.).