English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The large variability of renewable power sources is a central challenge inthe transition to a sustainable energy system. Electricity markets are centralfor the coordination of electric power generation. These markets rely evermoreon short-term trading to facilitate the balancing of power generation anddemand and to enable systems integration of small producers. Electricity pricesin these spot markets show pronounced fluctuations, featuring extreme peaks aswell as occasional negative prices. In this article, we analyse electricityprice time series from the European EPEX market, in particular the hourlyday-ahead, hourly intraday, and 15-min intraday market prices. We quantify thefluctuations, correlations, and extreme events and reveal different time scalesin the dynamics of the market. The short-term fluctuations show remarkablydifferent characteristics for time scales below and above 12 hours.Fluctuations are strongly correlated and persistent below 12 hours, whichcontributes to extreme price events and a strong multifractal behaviour. Onlonger time scales, they get anti-correlated and price time series revert totheir mean, witnessed by a stark decrease of the Hurst coefficient after 12hours. The long-term behaviour is strongly influenced by the evolution of alarge-scale weather patterns with a typical time scale of four days. Weelucidate this dependence in detail using a classification into circulationweather types. The separation in time scales enables a superstatisticaltreatment, which confirms the characteristic time scale of four days, andmotivates the use of $q$-Gaussian distributions as the best fit to the empiricdistribution of electricity prices.

Complexity and Persistence of Price Time Series of the European Electricity Spot Market