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  • Featured in Physics
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Complexity and Persistence of Price Time Series of the European Electricity Spot Market

Chengyuan Han1,2, Hannes Hilger2, Eva Mix2, Philipp C. Böttcher1, Mark Reyers3, Christian Beck4,5, Dirk Witthaut1,2, and Leonardo Rydin Gorjão1,2,6,7,*

  • 1Forschungszentrum Jülich, Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE), Jülich 52428, Germany
  • 2Institute for Theoretical Physics, University of Cologne, Köln 50937, Germany
  • 3Institute for Geophysics and Meteorology, University of Cologne, Köln 50937, Germany
  • 4School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
  • 5Alan Turing Institute, London NW1 2DB, United Kingdom
  • 6German Aerospace Center (DLR), Institute of Networked Energy Systems, Oldenburg, Germany
  • 7Department of Computer Science, OsloMet – Oslo Metropolitan University, Oslo N-0130, Norway

  • *Corresponding author: leonardo.rydin@gmail.com
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PRX Energy 1, 013002 – Published 7 April, 2022

DOI: https://doi.org/10.1103/PRXEnergy.1.013002

Abstract

The 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.

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synopsis

Predicting Fickle Electricity Markets

Published 7 April, 2022

Identifying and explaining patterns in volatile electricity prices could help small-scale wind and solar producers to integrate with the power grid.

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Popular Summary

Wind and solar power are essential elements of the transition to a sustainable energy supply. Their large temporal variability constitutes a main challenge for the organization and operation of energy and power systems. The coordination of power generation is mainly achieved via trading on electricity markets, which is heavily affected by fluctuations in renewable energy. Electricity prices are getting much more volatile and even become negative occasionally. Here, the authors provide an in-depth statistical analysis of the prices on the main European electricity exchanges, elucidating the role of extremes and correlations and the impacts of weather. Large-scale weather conditions have a pronounced footprint in both the averages and the fluctuations of electricity prices, which persist for approximately four days. Price fluctuations show an intriguing pattern with persistent plateaus and occasional extreme jumps, limiting the possibility of hedging. Extreme price events occur if these jumps and strong weather conditions coincide.

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PRX Energy 1, 013002– Published 7 April, 2022
  • Received 6 December 2021
  • Revised 21 February 2022
  • Accepted 28 February 2022
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