Multi Power-Market Bidding: Stochastic Optimization and Reinforcement Learning

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dc.contributor.authorMiskiw, Kim
dc.contributor.authorHarder, Nick
dc.contributor.authorStaudt, Philipp
dc.date.accessioned2023-12-26T18:39:40Z
dc.date.available2023-12-26T18:39:40Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.370
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other3700814e-a995-4fd5-a812-e2d5c6df5890
dc.identifier.urihttps://hdl.handle.net/10125/106753
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPolicy, Markets, and Analytics
dc.subjectmulti-market bidding
dc.subjectreinforcement learning
dc.subjectsequential decision problems
dc.subjectstochastic optimisation
dc.titleMulti Power-Market Bidding: Stochastic Optimization and Reinforcement Learning
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThe growing importance of short-term electricity trading over independent subsequent markets in Europe presents market participants with intricate decision challenges. Established solutions based on stochastic programs are often used but suffer from shortcomings such as the curse of dimensionality in multi-stage decision processes. Reinforcement learning is a promising alternative. However, best practices for the comparison of the two approaches and the ex-post evaluation of reinforcement learning are not yet established. In this paper, we offer a comparison of stochastic programs and reinforcement learning and propose measures for a comparative performance evaluation between the two approaches. We demonstrate them on an empirical case study over subsequent market stages of the German market zone within the coupled European power market.
dcterms.extent10 pages
prism.startingpage3062

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