Understanding the European energy crisis through structural causal models

TL;DR

This paper uses causal models to analyze the French electricity market during Europe's energy crisis, revealing how nuclear unavailability and gas prices influenced electricity prices and exports.

Contribution

It introduces a novel combination of linear causal models and machine learning with Shapley flows to understand complex causal relationships in energy markets.

Findings

Nuclear unavailability increased France’s dependence on imports.

Gas prices significantly affected electricity prices.

Causal models clarified indirect effects and resolved correlation paradoxes.

Abstract

Natural gas supplies in Europe were disrupted and energy prices soared in the context of Russia's invasion of Ukraine. Electricity prices in France experienced the largest relative increase among European countries, even though natural gas plays a negligible role in the French electricity system. In this article, we demonstrate the importance of causal statistical methods and propose causal graphs to investigate the French electricity market and pinpoint key influencing factors on electricity prices and net exports. We demonstrate that a causal approach resolves paradoxical results of simple correlation studies and enables a quantitative analysis of indirect causal effects. We introduce a linear structural causal model as well as non-linear tree-based machine learning combined with Shapley flows. The models elucidate the interplay of gas prices and the unavailability of nuclear power plants during the energy crisis: The high unavailability made France dependent on imports and linked prices to neighbouring countries.