At the EECON Lab, we support the transformation of the energy system using energy economic analysis with three research focuses:

1. Research focus: Quantitative research concerning the social acceptance of renewable energy (infrastructure), participation, as well as flexibility of energy use

  • We utilize lab experiments in which individuals make payout-dependent decisions to better understand individual behavior concerning energy infrastructure measures, participation formats, or energy use. We invite you to participate in our lab experiments. Please follow this link for detailed information.
  • Additionally, we use virtual reality (VR) in which study participants can experience, for example, the impacts of energy infrastructure measures on the environment. Besides VR headsets, we utilize a VR Cave, which offers a unique, close-to-reality 3D experience for our participants.

2. Research focus: Monitoring and evaluating the energy economic transformation in Lusatia

  • Due to the structural change in the energy economy new business models arise. Market and competition analysis provides insights into the prospects of success of such business models.
  • Surveys of relevant stakeholders are expected to provide qualitative insights into the supply chain of the energy economy’s structural change in Lusatia. A key aspect is the energy economy’s transition from central to decentral organization.
  • For that purpose, the existing range of methods (especially the Input-Output analysis) will be tested on its suitability to illustrate the transitioning energy economy’s supply chain in Lusatia. If necessary, methodical extensions will be carried out.
  • Ultimately, effects on employment and the supply chain will be identified empirically.

3. Research focus: Modeling of energy systems and market design as well as improvement of forecasts concerning energy system demand

  • We conduct fundamental and applied research relevant to sector coupling and collaborate with our partner 50Hertz GmbH on possibilities to decompose and reduce the complexity of large energy system models.
  • In the field of energy system modeling, we specifically research the effects of uncertainty using, for instance, robust system planning or implementation of risk aversion.
  • Employing economic models and machine learning, we research improved forecasts in the energy system, which are subject to uncertainties associated with the increased supply of electric energy from photovoltaics and wind power plants