Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

Romain Dupin, Laura Cavalcante, Ricardo J. Bessa, Georges Kariniotakis and Andrea Michiorri
Additional contact information
Romain Dupin: MINES ParisTech, PSL University, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), CS 10207 rue Claude Daunesse, Cedex, 06904 Sophia Antipolis, France
Laura Cavalcante: INESC TEC, Centre for Power and Energy Systems, Campus da FEUP, Rua Dr Roberto Frias, 4200-465 Porto, Portugal
Ricardo J. Bessa: INESC TEC, Centre for Power and Energy Systems, Campus da FEUP, Rua Dr Roberto Frias, 4200-465 Porto, Portugal
Georges Kariniotakis: MINES ParisTech, PSL University, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), CS 10207 rue Claude Daunesse, Cedex, 06904 Sophia Antipolis, France
Andrea Michiorri: MINES ParisTech, PSL University, Centre for Processes, Renewable Energies and Energy Systems (PERSEE), CS 10207 rue Claude Daunesse, Cedex, 06904 Sophia Antipolis, France

Energies, 2020, vol. 13, issue 12, 1-21

Abstract: This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors’ ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles’ reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.

Keywords: forecasting; ampacity; overhead lines; electric power systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/12/3090/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/12/3090/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:12:p:3090-:d:371877

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().