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On the Optimal Coordinated Hydropower Bidding Strategy in Day-Ahead Energy and Manual Frequency Restoration Reserve Markets
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-4791-8380
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-8189-2420
2021 (English)In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 1-6, article id 9494813Conference paper, Published paper (Refereed)
Abstract [en]

Hydropower plants (HPs) is an important source of flexibility in the electricity market and need operation and planning strategies to participate in various market setups. Due to lacking enough bids in the balancing market, manual frequency restoration reserve (mFRR) capacity market is going to be announced in the near future in Nordic electricity market. This paper develops a two-stage stochastic optimization framework that maximizes the revenue of the HP owners participating in mFRR capacity market beside their bidding in day-ahead and mFRR energy market. The impact of various mFRR capacity and future electricity prices have been considered. Furthermore, we show the influence of active time of mFFR energy bids on the revenue obtained from different markets. The results show a moderate increase in total revenue when participating in mFRR capacity market and considerable impacts from variable active time on the revenue obtained from each market setups.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 1-6, article id 9494813
Keywords [en]
capacity market, coordinated bidding, Hydropower plant, manual frequency restoration reserve (mFRR), Nordic electricity market
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-306696DOI: 10.1109/PowerTech46648.2021.9494813ISI: 000848778000064Scopus ID: 2-s2.0-85112372937OAI: oai:DiVA.org:kth-306696DiVA, id: diva2:1621671
Conference
2021 IEEE Madrid PowerTech, PowerTech 2021, Madrid, 28 June 2021through 2 July 2021
Note

QC 20220927

Part of proceedings: ISBN 978-1-6654-3597-0

Available from: 2021-12-20 Created: 2021-12-20 Last updated: 2022-09-27Bibliographically approved

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Khodadadi, AbolfazlSöder, Lennart

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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Output format
  • html
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  • asciidoc
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