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Stochastic adaptive robust approach for day-ahead energy market bidding strategies in hydro dominated sequential electricity 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
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0001-6000-9363
2022 (English)In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 32, article id 100827Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel methodological approach for the optimal day-ahead energy market bidding behavior of a cascaded hydropower plants (HPPs) portfolio in the sequential electricity markets. The understudy markets are day-ahead energy market and manual frequency restoration reserve (mFRR) markets in both capacity and energy setups. The introduction of the mFRR capacity market ensures transmission system operators (TSOs) about the availability of energy bids in the real-time market, which acts as binding constraints in the mFRR energy markets. As a determining factor, the active-time duration of mFRR energy bids is uncertain at the time of day-ahead bidding, which is modeled as the intervals in our robust optimization, while the electricity prices are considered as the scenarios in the stochastic optimization. Hence, we have proposed a novel stochastic adaptive robust optimization to address the bidding problem in the face of uncertainties accurately. The results show a considerable improvement compared to the conventional fully-stochastic approach in the case study of Swedish cascaded hydropower plants.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 32, article id 100827
Keywords [en]
Hydropower planning and operation, Stochastic adaptive robust optimization, Day-ahead energy market, Manual frequency restoration reserve (mFRR)
National Category
Computer Sciences Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-316028DOI: 10.1016/j.segan.2022.100827ISI: 000830902800001Scopus ID: 2-s2.0-85134316335OAI: oai:DiVA.org:kth-316028DiVA, id: diva2:1686282
Note

QC 20220809

Available from: 2022-08-09 Created: 2022-08-09 Last updated: 2022-08-09Bibliographically approved

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Khodadadi, AbolfazlSöder, LennartAmelin, Mikael

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