The topic of this master thesis will be renewables power feed-in management (Einspeisemanagement) forecasting applying statistical and machine learning tools.
Germany took a leading role in terms of installing solar and wind power plants. This transition, from thermal to renewable power plants, is a huge challenge for all market participants. A particular concern is the limited grid capacity. To avoid outages in the grid the grid operators can take several measurements such as Redispatch, Netzreserve or Einspeisemanagement. The latter means that grid operators can switch-off wind or solar power plants to avoid overload in specific grid points. Regardless, the renewable power operator, even though switched off, has to deliver the planned electricity. In this case the wind operator faces a high price risk since buying power just before delivery can be significantly more expansive.
Therefore, the operators of renewable power plants are interested in a prognosis on when these switch offs (Einspeisemanagement) will happen. The task will be to generate short-term forecasts for Einspeisemanagement interventions considering renewable feed-in and grid topologies. As a methodology we suggest to apply appropriate statistical and machine learning tools such as neuronal networks.
The thesis will be written in cooperation with our industry partner ICIS Analytics. The target group are Master students. Bachelor students with an outstanding academic record are also encouraged to apply.
The thesis is supposed to be written in English. Interest in economic research questions as well as statistical analysis is required. Programming knowledge is desired but not necessary.
Schermeyer, H., Vergara, C., & Fichtner, W. (2018). Renewable energy curtailment: A case study on today's and tomorrow's congestion management. Energy Policy, 112, 427-436.
Joos, M., & Staffell, I. (2018). Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany. Renewable and Sustainable Energy Reviews, 86, 45-65.