  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. Many market models implement this principle to
predict electricity prices but typically require certain assumptions and
simplifications. In this article, we present an explainable machine learning
model for the prices on the German day-ahead market, which substantially
outperforms a benchmark model based on the merit order principle. Our model is
designed for the ex-post analysis of prices and thus builds on various external
features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle
the role of the different features and quantify their importance from empiric
data. Load, wind and solar generation are most important, as expected, but wind
power appears to affect prices stronger than solar power does. Fuel prices also
rank highly and show nontrivial dependencies, including strong interactions
with other features revealed by a SHAP interaction analysis. Large generation
ramps are correlated with high prices, again with strong feature interactions,
due to the limited flexibility of nuclear and lignite plants. Our results
further contribute to model development by providing quantitative insights
directly from data.
