DFG Graduate School 2153 Energy Status Data – Informatics Methods for its Collection, Analysis and Exploitation

Thomas Dengiz, Hasan Ümitcan Yilmaz, Patrick Jochem

The design of future energy systems which can cope with fluctuating supply and flexible demand is an important societal concern. An essential aspect is the consumption of energy, particularly of complex systems such as factories or IT infrastructures. Important points are the flexibilization of energy consumption, so that the share of locally generated 'green' energy increases, robustness of energy provisioning, or the efficient design of new energy systems serving these purposes. To accomplish this, a core prerequisite is a structured collection, storage and analysis of energy status data. Energy status data describes the provisioning of energy, its storage, transmission and consumption, be it the outcomes of measurements, be it metadata such as the extent of fatigue of batteries, be it other relevant data such as electricity rates.

A distinctive feature of the research agenda graduates have to deal with as part of their education with us is the comprehensive treatment of the life cycle of energy status data, which consists of the phases 'collection', 'analysis' and 'deployment'. It yields a significant added value, compared to stand alone Ph.D. work that otherwise would have to cover that entire life cycle by itself: For instance, Ph.D. topics falling into an early phase of the life cycle might tailor specific methods of collecting energy status data if it is known how it will be used. Topics from the phase 'deployment' in turn, which want to design better energy systems in a data-driven fashion, can work with data of exactly the right quality. (Source: http://www.energystatusdata.kit.edu)

The PhD students of the IIP focus on the following topics:

• Quantification and utilization of load flexibility potentials in German households focusing on Power-To-Heat
• Modelling Intermittent Renewable Power Generation in the European Energy System Considering Model Complexity Challenges