Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties
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 o...
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doaj-e751446d48f645419943de45379785622021-04-26T23:00:55ZengIEEEIEEE Access2169-35362021-01-019608286084010.1109/ACCESS.2021.30737549406009Forecast of Renewable Curtailment in Distribution Grids Considering UncertaintiesElena Memmel0https://orcid.org/0000-0003-0619-5905Sunke Schluters1https://orcid.org/0000-0002-2186-812XRasmus Volker2https://orcid.org/0000-0001-8291-9907Frank Schuldt3https://orcid.org/0000-0002-4196-2025Karsten Von Maydell4Carsten Agert5DLR Institute of Networked Energy Systems, Oldenburg, GermanyDLR Institute of Networked Energy Systems, Oldenburg, GermanyDLR Institute of Networked Energy Systems, Oldenburg, GermanyDLR Institute of Networked Energy Systems, Oldenburg, GermanyDLR Institute of Networked Energy Systems, Oldenburg, GermanyDLR Institute of Networked Energy Systems, Oldenburg, GermanyRenewable 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.https://ieeexplore.ieee.org/document/9406009/Power system operationdistribution gridpower flow analysiscongestion managementrenewable power curtailmentshort-term prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elena Memmel Sunke Schluters Rasmus Volker Frank Schuldt Karsten Von Maydell Carsten Agert |
spellingShingle |
Elena Memmel Sunke Schluters Rasmus Volker Frank Schuldt Karsten Von Maydell Carsten Agert Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties IEEE Access Power system operation distribution grid power flow analysis congestion management renewable power curtailment short-term prediction |
author_facet |
Elena Memmel Sunke Schluters Rasmus Volker Frank Schuldt Karsten Von Maydell Carsten Agert |
author_sort |
Elena Memmel |
title |
Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties |
title_short |
Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties |
title_full |
Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties |
title_fullStr |
Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties |
title_full_unstemmed |
Forecast of Renewable Curtailment in Distribution Grids Considering Uncertainties |
title_sort |
forecast of renewable curtailment in distribution grids considering uncertainties |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Power system operation distribution grid power flow analysis congestion management renewable power curtailment short-term prediction |
url |
https://ieeexplore.ieee.org/document/9406009/ |
work_keys_str_mv |
AT elenamemmel forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties AT sunkeschluters forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties AT rasmusvolker forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties AT frankschuldt forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties AT karstenvonmaydell forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties AT carstenagert forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties |
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1721507414959718400 |