Abstract
Renewable energies curtailment induced by grid congestions increase due to grown renewable energies integration and the resulting mismatch of grid expansion. Short-term predictions for curtailment can help to increase the efficiency of its management. This paper proposes a novel, holistic approach of a short-term curtailment prediction for distribution grids. The load flow calculations for congestion detection are realized by taking different operational security criteria into account, whereas the models for the node-injections are adjusted to the characteristic of each grid node specifically. The determination of required curtailment based on the resulting congestions considers uncertainties of component loading and its corresponding probability. The forecast model is validated using an actual 110 kV distribution grid located in Germany. In order to meet the requirements of a forecast model designed for operational business, prediction accuracy, and its greatest source of error are analyzed. Furthermore, a suitable length of training data is investigated. Results indicate that a six month time period for maintenance gains the highest accuracy. Curtailment prediction accuracy is better for transmission system operator components than for distribution system operator components, but the Sørensen Dice factor for the aggregated grid shows a high match of historic and predicted curtailment with a value of 0.84 and a low error for curtailed energy, which makes 2.23% of the historic curtailed energy. The model is a promising approach, which can contribute to improvement of curtailment strategies and enable valuable insight into distribution grids.
Highlights
Distributions are selected using the Kolmogorov-Smirnov test [33] and fitted to the data of ε for each interval. In this way the error of predicted wind power can be sampled for each node, connecting the wind parks with the high voltage (HV) grid level considering the forecasted power, and denoted as i in εi| WPpi red ∼ fε(WPpi red ) Peaki
The results indicate that a time period for maintenance of every 6 month for the medium voltage/high voltage (MV/HV)-model gets the highest precision
RE curtailment due to grid congestions means a spillage of renewable energy and it requires optimization
Summary
A challenge of the energy transition are grid overloadings resulting from increasing injections of renewables (RE) into the grid whereby, among other things, the maximum. Using power flow calculations to detect grid congestions contingencies, dynamic line rating, and uncertainties of component loading are considered. A novel approach for short-term curtailment prediction in distribution grids considering uncertainties is proposed, meeting the requirements for predictions with a high temporal and spatial resolution defined by upcoming changes like ‘‘Redispatch 2.0’’. In cases of congested components, the generation units, that need to be curtailed, and the required power reduction have to be determined. The model can be separated into three parts illustrated in Fig. 1: the calculation of the node-injections, the detection of congestions in the 110 kV distribution grid and the determination of required curtailment. For the selected contingency case the load flow is calculated again and all the reductions of real power P, that are required to rectify occurring congestions, are determined. If the highest level is reached and Pj is still not zero, the smaller sensitive unit is chosen to be curtailed
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