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

TL;DR

This paper analyzes the complex, multi-scale dynamics of European electricity prices, revealing persistent short-term fluctuations, mean-reverting long-term behavior, and the influence of weather patterns, with implications for market stability and modeling.

Contribution

It provides a detailed multi-scale analysis of electricity price fluctuations, identifying distinct behaviors below and above 12 hours and linking long-term trends to weather patterns.

Findings

Short-term fluctuations are persistent and correlated below 12 hours.

Long-term price behavior is anti-correlated and mean-reverting.

Electricity prices follow q-Gaussian distributions fitting empirical data.

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 analyse electricity price time series from the European EPEX 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 hours. Fluctuations are strongly correlated and persistent below 12 hours, which contributes to extreme price events and a strong multifractal behaviour. On longer time scales, they get anti-correlated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 hours. The long-term behaviour is strongly influenced by the evolution of a large-scale weather patterns 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.