Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems

Nicola Viafora, Jakob Glarbo Møller, Rasmus A. Olsen, Anders S. Kristensen, Joachim Holbøll

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Dynamic Line Rating (DLR) consists in an innovative way to operate power systems, which allows for higher power flows on transmission lines depending on weather conditions. Extending the application of DLR technology from one to numerous lines across a larger transmission power system presents challenges with respect to the scalability due to the large amount of data required. Firstly, a modified overhead line thermal model and the use of historical weather data are considered in this paper to preliminary assess the margin for increased rating of transmission lines. Secondly, spatial correlation of line ratings are analyzed and a comparison of various rating approaches, which rely on different combinations of weather variables, is presented. The resulting probability distributions of line ratings are compared with constant seasonal ratings highlighting the trade-off between those solutions that yield a large increase in rating at a cost of high volatility, against simpler approaches which are more conservative and require less information. The results reported are based on actual data of the western section of the Danish power transmission system.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Number of pages6
PublisherIEEE
Publication date2018
Pages1-6
ISBN (Electronic)978-1-5386-3596-4
DOIs
Publication statusPublished - 2018
EventIEEE International Conference on Probabilistic Methods Applied to Power Systems - Boise, United States
Duration: 24 Jun 201828 Jun 2018

Conference

ConferenceIEEE International Conference on Probabilistic Methods Applied to Power Systems
CountryUnited States
CityBoise
Period24/06/201828/06/2018

Keywords

  • Dynamic line rating
  • Historical weather data
  • Correlation
  • Overhead lines
  • Thermal model

Cite this

Viafora, N., Møller, J. G., Olsen, R. A., Kristensen, A. S., & Holbøll, J. (2018). Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (pp. 1-6). IEEE. https://doi.org/10.1109/PMAPS.2018.8440449
Viafora, Nicola ; Møller, Jakob Glarbo ; Olsen, Rasmus A. ; Kristensen, Anders S. ; Holbøll, Joachim. / Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems. 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. pp. 1-6
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title = "Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems",
abstract = "Dynamic Line Rating (DLR) consists in an innovative way to operate power systems, which allows for higher power flows on transmission lines depending on weather conditions. Extending the application of DLR technology from one to numerous lines across a larger transmission power system presents challenges with respect to the scalability due to the large amount of data required. Firstly, a modified overhead line thermal model and the use of historical weather data are considered in this paper to preliminary assess the margin for increased rating of transmission lines. Secondly, spatial correlation of line ratings are analyzed and a comparison of various rating approaches, which rely on different combinations of weather variables, is presented. The resulting probability distributions of line ratings are compared with constant seasonal ratings highlighting the trade-off between those solutions that yield a large increase in rating at a cost of high volatility, against simpler approaches which are more conservative and require less information. The results reported are based on actual data of the western section of the Danish power transmission system.",
keywords = "Dynamic line rating, Historical weather data, Correlation, Overhead lines, Thermal model",
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language = "English",
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booktitle = "2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)",
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Viafora, N, Møller, JG, Olsen, RA, Kristensen, AS & Holbøll, J 2018, Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems. in 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, pp. 1-6, IEEE International Conference on Probabilistic Methods Applied to Power Systems, Boise, United States, 24/06/2018. https://doi.org/10.1109/PMAPS.2018.8440449

Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems. / Viafora, Nicola; Møller, Jakob Glarbo; Olsen, Rasmus A.; Kristensen, Anders S.; Holbøll, Joachim.

2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems

AU - Viafora, Nicola

AU - Møller, Jakob Glarbo

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AU - Holbøll, Joachim

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N2 - Dynamic Line Rating (DLR) consists in an innovative way to operate power systems, which allows for higher power flows on transmission lines depending on weather conditions. Extending the application of DLR technology from one to numerous lines across a larger transmission power system presents challenges with respect to the scalability due to the large amount of data required. Firstly, a modified overhead line thermal model and the use of historical weather data are considered in this paper to preliminary assess the margin for increased rating of transmission lines. Secondly, spatial correlation of line ratings are analyzed and a comparison of various rating approaches, which rely on different combinations of weather variables, is presented. The resulting probability distributions of line ratings are compared with constant seasonal ratings highlighting the trade-off between those solutions that yield a large increase in rating at a cost of high volatility, against simpler approaches which are more conservative and require less information. The results reported are based on actual data of the western section of the Danish power transmission system.

AB - Dynamic Line Rating (DLR) consists in an innovative way to operate power systems, which allows for higher power flows on transmission lines depending on weather conditions. Extending the application of DLR technology from one to numerous lines across a larger transmission power system presents challenges with respect to the scalability due to the large amount of data required. Firstly, a modified overhead line thermal model and the use of historical weather data are considered in this paper to preliminary assess the margin for increased rating of transmission lines. Secondly, spatial correlation of line ratings are analyzed and a comparison of various rating approaches, which rely on different combinations of weather variables, is presented. The resulting probability distributions of line ratings are compared with constant seasonal ratings highlighting the trade-off between those solutions that yield a large increase in rating at a cost of high volatility, against simpler approaches which are more conservative and require less information. The results reported are based on actual data of the western section of the Danish power transmission system.

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KW - Thermal model

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Viafora N, Møller JG, Olsen RA, Kristensen AS, Holbøll J. Historical Data Analysis for Extending Dynamic Line Ratings Across Power Transmission Systems. In 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE. 2018. p. 1-6 https://doi.org/10.1109/PMAPS.2018.8440449