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Predicting Renewable Curtailment in Distribution Grids Using Neural Networks

Memmel, Elena and Steens, Thomas and Schlüters, Sunke and Völker, Rasmus and Schuldt, Frank and von Maydell, Karsten (2023) Predicting Renewable Curtailment in Distribution Grids Using Neural Networks. IEEE Access. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/ACCESS.2023.3249459. ISSN 2169-3536.

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Official URL: https://ieeexplore.ieee.org/document/10054042/authors#full-text-header


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.

Item URL in elib:https://elib.dlr.de/194148/
Document Type:Article
Title:Predicting Renewable Curtailment in Distribution Grids Using Neural Networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Memmel, ElenaUNSPECIFIEDhttps://orcid.org/0000-0003-0619-5905UNSPECIFIED
Steens, ThomasUNSPECIFIEDhttps://orcid.org/0000-0002-4218-3015UNSPECIFIED
Schlüters, SunkeUNSPECIFIEDhttps://orcid.org/0000-0002-2186-812XUNSPECIFIED
Schuldt, FrankUNSPECIFIEDhttps://orcid.org/0000-0002-4196-2025UNSPECIFIED
von Maydell, KarstenUNSPECIFIEDhttps://orcid.org/0000-0003-0966-5810UNSPECIFIED
Date:27 February 2023
Journal or Publication Title:IEEE Access
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Power system operation, distribution grid, congestion management, renewable power curtailment, artificial neural network, short-term prediction, vertical power flow.
HGF - Research field:Energy
HGF - Program:Energy System Design
HGF - Program Themes:Digitalization and System Technology
DLR - Research area:Energy
DLR - Program:E SY - Energy System Technology and Analysis
DLR - Research theme (Project):E - Energy System Technology
Location: Oldenburg
Institutes and Institutions:Institute of Networked Energy Systems > Energy System Technology
Deposited By: Memmel, Elena
Deposited On:06 Mar 2023 14:13
Last Modified:07 Mar 2023 15:51

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