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Clustering and forecasting of day-ahead electricity supply curves using a market-based distance

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dc.affiliation.dptoUC3M. Departamento de Estadística
dc.contributor.authorLi, Zehang
dc.contributor.authorAlonso Fernández, Andrés Modesto
dc.contributor.authorElías, Antonio
dc.contributor.authorMorales, Juan M.
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.contributor.funderEuropean Commission
dc.contributor.funderMinisterio de Ciencia e Innovación (España)
dc.contributor.funderEuropean Commission
dc.date.accessioned2024-04-05T15:23:36Z
dc.date.available2024-04-05T15:23:36Z
dc.date.issued2024
dc.description.abstractGathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features. Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.en
dc.description.sponsorshipThe authors gratefully acknowledge financial support from the Spanish government through the Ministry of Science and Innovation projects PID2019-108311GB-I00, PID2020-115460GB-I00, and PID2022-138114NB-I00. Andrés M. Alonso has been a beneficiary of the Google Cloud Research Credits Program carrying out part of the calculations on this platform. Antonio Elías was supported under PAIDI 2020 funded by the Junta de Andalucía and the European Social Fund. The work of Juan M. Morales has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement No 755705).en
dc.format.extent43
dc.identifier.issn2387-0303
dc.identifier.urihttps://hdl.handle.net/10016/43805
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries2024-02
dc.relation.projectIDGobierno de España. PID2019-108311GB-I00
dc.relation.projectIDGobierno de España. PID2020-115460GB-I00
dc.relation.projectIDGobierno de España. PID2022-138114NB-I00
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/755705
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ecienciaEstadística
dc.subject.otherClusteringen
dc.subject.otherForecastingen
dc.subject.otherSupply curveen
dc.subject.otherElectricity marketen
dc.titleClustering and forecasting of day-ahead electricity supply curves using a market-based distance
dc.typeworking paper*
dc.type.hasVersionSMUR
dspace.entity.typePublication
relation.isAuthorOfPublication2952b725-aa30-476c-abee-0a7a0f7a7b96
relation.isAuthorOfPublication.latestForDiscovery2952b725-aa30-476c-abee-0a7a0f7a7b96
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