Optimization in "Smart Grids": Dealing with Uncertainty

Bachelorarbeit, Diplomarbeit, Masterarbeit

Themen-Schwerpunkt: Elektromobilität, Energie, Energiemanagement
Studiengänge: Elektrotechnik, Informatik, Informationstechnik, Informationswirtschaft, Mathematik, Wirtschaftsinformatik, Wirtschaftsingenieurwesen, Wirtschaftsmathematik

Umfeld

The fluctuating energy supply based on distributed and renewable energy resources results in a major change of the energy system since electrical energy demand and supply always have to be balanced. In order to enable the future "Smart Grid", the interconnection of smaller, self-controlling energy grids has been recognized as a promising way. Scenarios for such energy grids are manifold and range from small commercial sites up to grids covering whole cities. Optimizations in this context allow to realize local benefits such as decreasing energy costs, while at the same time increasing the overall grid stability by provisioning of system services.

Aufgaben

Optimizations in Smart Grids are characterized by uncertainty on various levels. When offering electrical flexibility as balancing power, e.g., the provider can never be certain about when exactly and for how long his flexibility offer is called. This thesis aims at an optimization scheme that is able to manage such uncertainties. At this, the detailed focus of the thesis can be suited to your individual background and interests.

Wir bieten

  • An intensive supervision
  • A pleasant working atmosphere and a workplace in the FZI House of Living Labs
  • Interesting insights (e.g., FZI Living Lab smartEnergy) and contacts to industry through cooperation with major players in the area of energy
  • A student seminar for exchange of ideas and viewpoints
  • Possibility to continue your work as working student

Wir erwarten

  • High interest in optimizations that solve real-world problems in the context of Smart Grids
  • Background in computer science, mathematics, industrial engineering, or a related area
  • Very good knowledge in optimization techniques, preferably first experiences with meta-heuristics
  • Fundamental understanding of the electrical energy system
  • Fluent in English or German

Ihre Bewerbung

Please attach your CV and a current transcript of records to your cover letter.

Weitere Informationen

The supervising institute at KIT is the Institute of Applied Informatics and Formal Description Methods (AIFB), Prof. Dr. Hartmut Schmeck.