Title: Day-ahead probability forecasting for redispatch 2.0 measures Authors:  Alla Petukhina - HTW Berlin (Germany) [presenting]
Christina Erlwein-Sayer - University of Applied Sciences HTW Berlin (Germany)
Mai Phan - University of Kaiserslautern-Landau & HTW Berlin (Germany)
Maria Basangova - HTW Berlin (Germany)
Alexandra Conda - The Bucharest University of Economic Studies (Romania)
Vlad Bolovaneanu - Bucharest Academy of Economic Studies (Romania)
Awdesch Melzer - Humboldt University zu Berlin (Germany)
Abstract: The purpose is to advance a data-driven, day-ahead forecasting model for assessing the probability, direction, and scale of electrical congestions within the German complex power grid. Utilizing state-of-the-art machine learning algorithms, the model is specifically designed to operate on an hourly basis, thereby offering timely insights for grid management. The analysis uncovers compelling evidence that key exogenous variables, such as real-time meteorological conditions, electricity supply-demand indicators, and Brent oil price fluctuations, can be harnessed to make highly reliable predictions concerning grid congestion events. The model has the potential to serve as a useful resource for transmission system operators (TSOs) and policymakers interested in grid management and cost mitigation efforts.