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dc.contributor.authorHan, Chengyuan
dc.contributor.authorHilger, Hannes
dc.contributor.authorMix, Eva
dc.contributor.authorBöttcher, Philipp
dc.contributor.authorReyers, Mark
dc.contributor.authorBeck, Christian
dc.contributor.authorWitthaut, Dirk
dc.contributor.authorRydin Gorjao, Leonardo
dc.date.accessioned2022-08-31T08:10:55Z
dc.date.available2022-08-31T08:10:55Z
dc.date.created2022-05-11T16:27:13Z
dc.date.issued2022-04-07
dc.identifier.issn2768-5608
dc.identifier.urihttps://hdl.handle.net/11250/3014617
dc.description.abstractThe large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyze electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 h. Fluctuations are strongly correlated and persistent below 12 h, which contributes to extreme price events and a strong multifractal behavior. On longer time scales, they get anticorrelated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 h. The long-term behavior is strongly influenced by the evolution of a large-scale weather pattern with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of q-Gaussian distributions as the best fit to the empiric distribution of electricity prices.en_US
dc.language.isoengen_US
dc.publisherAmerican Physical Societyen_US
dc.relation.ispartofseriesPRX Energy;Vol. 1, Iss. 1 — April - June 2022
dc.relation.urihttps://journals.aps.org/prxenergy/pdf/10.1103/PRXEnergy.1.013002
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectRenewable power sourcesen_US
dc.subjectElectricity marketsen_US
dc.subjectPrice time seriesen_US
dc.subjectElectricity pricesen_US
dc.subjectSpot marketsen_US
dc.titleComplexity and Persistence of Price Time Series of the European Electricity Spot Marketen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.articlenumber013002en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
dc.identifier.doihttps://doi.org/10.1103/PRXEnergy.1.013002
dc.identifier.cristin2023698
dc.source.journalPRX Energyen_US
dc.source.volume1en_US
dc.source.issue1en_US
dc.source.pagenumber1-17en_US


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