Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

被引:8
作者
Memmel, Elena [1 ]
Schlueters, Sunke [1 ]
Voelker, Rasmus [1 ]
Schuldt, Frank [1 ]
Von Maydell, Karsten [1 ]
Agert, Carsten [1 ]
机构
[1] DLR Inst Networked Energy Syst, D-26129 Oldenburg, Germany
关键词
Predictive models; Load modeling; Uncertainty; Context modeling; Brain modeling; Probability density function; Data models; Power system operation; distribution grid; power flow analysis; congestion management; renewable power curtailment; short-term prediction; probabilistic uncertainty quantification; PROBABILISTIC LOAD FLOW; WIND; SYSTEMS;
D O I
10.1109/ACCESS.2021.3073754
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 Sorensen 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.
引用
收藏
页码:60828 / 60840
页数:13
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