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Transmission Line Loss Prediction Based on Linear Regression and Exchange Flow Modelling
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.ORCID iD: 0000-0002-6431-9104
2017 (English)In: 2017 IEEE Manchester PowerTech, Powertech 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7980810Conference paper, Published paper (Refereed)
Abstract [en]

Inaccurate line loss predictions leads to additional regulation costs for Transmission System Operators (TSOs) that place energy bids at the day-ahead market to account for these losses. This paper presents a line loss prediction model design, applicable with the TSOs forecast conditions, that can reduce additional expenditure due to inaccurate predictions. The model predicts line losses for the next day per bidding area in relation to prognosis data on electrical demand, supply, renewable energy generation and regional exchange flows. Linear regression analysis can extract these relation factors, known as line loss rates, and derive a line loss prediction with increased accuracy and precision. Required input data is available at the power exchange markets apart from future exchange flows, which instead have been modelled as an optimisation problem and predicted by linear programming. Simulations performed on the Swedish National Grid for 2015 demonstrate the models performance and adequacy for TSO application.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. article id 7980810
Keywords [en]
Exchange Flow Modelling, Line Loss Prediction, Linear Programming, Line Loss Rate, Regression Analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215854DOI: 10.1109/PTC.2017.7980810ISI: 000411142500024Scopus ID: 2-s2.0-85034743331ISBN: 978-1-5090-4237-1 OAI: oai:DiVA.org:kth-215854DiVA, id: diva2:1149758
Conference
2017 IEEE Manchester PowerTech, Powertech 2017, Manchester, United Kingdom, 18 June 2017 through 22 June 2017
Note

QC 20171017

Available from: 2017-10-17 Created: 2017-10-17 Last updated: 2018-02-15Bibliographically approved

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Ghandari, Mehrdad

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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