Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Renewable energies curtailment induced by grid congestions increase due to grown renewable integration and the resulting mismatch of expansion. Short-term predictions for can help efficiency its management. This paper proposes a novel, holistic approach short-term prediction distribution grids. The load flow calculations congestion detection are realized taking different operational security criteria into account, whereas models node-injections adjusted characteristic each node specifically. determination required based on considers uncertainties component loading corresponding probability. forecast model is validated using an actual 110 kV located in Germany. In order meet requirements designed business, accuracy, greatest source error analyzed. Furthermore, suitable length training data investigated. Results indicate that six month time period maintenance gains highest accuracy. Curtailment accuracy better transmission system operator components than components, but Sørensen Dice factor aggregated shows high match historic predicted with value 0.84 low curtailed energy, which makes 2.23% energy. promising approach, contribute improvement strategies enable valuable insight