Validation of an open source high voltage grid model for AC load flow calculations in a delimited region

Peters, Dorothee; Heitkoetter, Wilko; Völker, Rasmus; Gross, Thorsten; Petters, Benjamin; Schuldt, Frank; Maydell, Karsten von

doi:10.1049/iet-gtd.2020.0107

Cited by 8 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following requirements for congestion prediction are addressed by the proposed models of this study: Detailed information of node-injections into the 110 kV grid level [21], [22], consideration of n-1 security (which means that one grid component can fail and grid security is still maintained) [23], [24], and the amount of required RE curtailment should become determinable. As it is aimed to develop a method applicable for grid operation, it should be validated against actual grid data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

Memmel¹,

Steens²,

Schlüters³

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

Memmel¹,

Steens²,

Schlüters³

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…To predict RE curtailment caused by congestions in distribution grids or at connected high voltage/extra high voltage (HV/EHV) transformers requires detailed information, like grid topology, or node specific injections, and consumption for the grid of interest [15], [16]. Furthermore curtailment can occur for just some minutes wherefore its prediction requires a high temporal resolution.…”

mentioning

confidence: 99%

Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties

Memmel¹,

Schlüters²,

Völker³

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract