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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Main Authors: Elena Memmel, Sunke Schluters, Rasmus Volker, Frank Schuldt, Karsten Von Maydell, Carsten Agert
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9406009/
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spelling 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/
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AT rasmusvolker forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties
AT frankschuldt forecastofrenewablecurtailmentindistributiongridsconsideringuncertainties
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