Optimising quantile-based trading strategies in electricity arbitrage

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dc.contributor.authorO'Connor, Ciaranen
dc.contributor.authorCollins, Josephen
dc.contributor.authorPrestwich, Steven D.en
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-10-17T14:59:24Z
dc.date.available2025-10-17T14:59:24Z
dc.date.issued2025-01-27en
dc.description.abstractEfficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while mitigating revenue losses caused by curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants face numerous options, each presenting unique challenges and opportunities, with trading strategies fundamental towards maximising profits. This study explores the optimisation of day-ahead and balancing market trading in the Irish electricity market from 2019 to 2022, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research evaluates trading strategies, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, with the implementation of dynamic dual-market strategies playing a significant role in maximising profits and addressing market challenges. Finally, we evaluate the economic viability of four commercial battery storage systems through scenario analysis, showing that larger batteries achieve shorter returns on investment.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid100476en
dc.identifier.citationO'Connor, C., Collins, J., Prestwich, S. and Visentin, A. (2025) 'Optimising quantile-based trading strategies in electricity arbitrage', Energy and AI, 20,100476 (15pp). https://doi.org/10.1016/j.egyai.2025.100476en
dc.identifier.doi10.1016/j.egyai.2025.100476en
dc.identifier.eissn2666-5468en
dc.identifier.endpage15en
dc.identifier.journaltitleEnergy and AIen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18066
dc.identifier.volume20en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.rights© 2025, the Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ ).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectElectricity price forecastingen
dc.subjectEnergy storage systemsen
dc.subjectArbitrage tradingen
dc.subjectMachine learningen
dc.subject~Computer Science - Journal Articles~en
dc.subject~Mathematical Sciences- Journal Articles~en
dc.titleOptimising quantile-based trading strategies in electricity arbitrageen
dc.typeArticle (peer-reviewed)en
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