Understanding electricity prices beyond the merit order principle using explainable AI

Abstract

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Various market models are based on this principle when attempting to predict electricity prices, yet the principle is fraught with assumptions and simplifications and thus is limited in accurately predicting prices. In this article, we present an explainable machine learning model for the electricity prices on the German day-ahead market which foregoes of the aforementioned assumptions of the merit order principle. Our model is designed for an ex-post analysis of prices and builds on various external features. Using SHapley Additive exPlanation (SHAP) values we disentangle the role of the different features and quantify their importance from empiric data, and therein circumvent the limitations inherent to the merit order principle. We show that load, wind and solar generation are the central external features driving prices, as expected, wherein wind generation affects prices more than solar generation. Similarly, fuel prices also highly affect prices, and do so in a nontrivial manner. Moreover, large generation ramps are correlated with high prices due to the limited flexibility of nuclear and lignite plants. Overall, we offer a model that describes the influence of the main drivers of electricity prices in Germany, taking us a step beyond the limited merit order principle in explaining the drivers of electricity prices and their relation to each other.

Keywords

• Electricity prices
• Merit order principle
• Explainable artificial intelligence
• Machine learning
• Fuel prices
• Energy market