Optimizing Pricing Strategies to Maximize Profits for Balancing Service Providers in FCR-N and FCR-D Reserve Markets
annif.suggestions | prices|pricing|marketing|simulation|markets (systems)|optimisation|electricity market|profitability|batteries|modelling (representation)|en | en |
annif.suggestions.links | http://www.yso.fi/onto/yso/p750|http://www.yso.fi/onto/yso/p10773|http://www.yso.fi/onto/yso/p5878|http://www.yso.fi/onto/yso/p4787|http://www.yso.fi/onto/yso/p1865|http://www.yso.fi/onto/yso/p13477|http://www.yso.fi/onto/yso/p16837|http://www.yso.fi/onto/yso/p4257|http://www.yso.fi/onto/yso/p2307|http://www.yso.fi/onto/yso/p3533 | en |
dc.contributor.author | Hossain, Amjad | |
dc.contributor.faculty | fi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations| | - |
dc.contributor.organization | fi=Vaasan yliopisto|en=University of Vaasa| | |
dc.date.accessioned | 2025-06-19T10:18:05Z | |
dc.date.accessioned | 2025-06-25T17:54:12Z | |
dc.date.available | 2025-06-19T10:18:05Z | |
dc.date.issued | 2025-06-07 | |
dc.description.abstract | The goal of this thesis is to determine how a 1 MW battery can profit in Finland’s frequency reserve markets with smart pricing strategies. It consists of two markets: FCR-N, run continuously and FCR-D which is brought in when there are unusual fluctuations in frequency. This work looks at how different pricing methods can change the profit a battery system earns. Using real data from Fingrid's 2024 frequency reserve markets, a battery operating model was run to provide the findings reported in this master's thesis. For FCR-N, two control methods were examined: a straightforward rule based one and an advanced approach using practical logic that responds to changes in prices and frequency. The easy-to-use model earned more profit, yet the market rules were better followed with the improved logic. A review of FCR-D was included, though a lack of activation data results in imprecise and less trustworthy findings. This study demonstrates that using a good strategy and precise information, battery systems may do well in FCR-N. Future work on the model could add battery aging as a factor, make the controlling system more complex and include the complete set of FCR-D activation data. | - |
dc.format.bitstream | true | |
dc.format.content | fi=kokoteksti|en=fulltext| | - |
dc.format.extent | 56 | - |
dc.identifier.olddbid | 24012 | |
dc.identifier.oldhandle | 10024/19871 | |
dc.identifier.uri | https://osuva.uwasa.fi/handle/11111/12220 | |
dc.identifier.urn | URN:NBN:fi-fe2025060762730 | - |
dc.language.iso | eng | - |
dc.rights | CC BY-NC 4.0 | - |
dc.source.identifier | https://osuva.uwasa.fi/handle/10024/19871 | |
dc.subject.degreeprogramme | Master´s Programme in Smart Energy | - |
dc.subject.discipline | fi=Sähkö- ja energiatekniikka|en=Electrical Engineering and Energy Technology| | - |
dc.subject.yso | pricing | - |
dc.subject.yso | simulation | - |
dc.subject.yso | optimisation | - |
dc.subject.yso | electricity market | - |
dc.subject.yso | profitability | - |
dc.subject.yso | batteries | - |
dc.title | Optimizing Pricing Strategies to Maximize Profits for Balancing Service Providers in FCR-N and FCR-D Reserve Markets | - |
dc.type.ontasot | fi=Pro gradu -tutkielma|en=Master's thesis|sv=Pro gradu -avhandling| | - |
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