  Electricity is traded on various markets with different time horizons and
regulations. Short-term intraday trading becomes increasingly important due to
the higher penetration of renewables. In Germany, the intraday electricity
price typically fluctuates around the day-ahead price of the European Power
EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a
probabilistic modeling approach that models the intraday price difference to
the day-ahead contracts. The model captures the emerging hourly pattern by
considering the four 15 min intervals in each day-ahead price interval as a
four-dimensional joint probability distribution. The resulting nontrivial,
multivariate price difference distribution is learned using a normalizing flow,
i.e., a deep generative model that combines conditional multivariate density
estimation and probabilistic regression. Furthermore, this work discusses the
influence of different external impact factors based on literature insights and
impact analysis using explainable artificial intelligence (XAI). The
normalizing flow is compared to an informed selection of historical data and
probabilistic forecasts using a Gaussian copula and a Gaussian regression
model. Among the different models, the normalizing flow identifies the trends
with the highest accuracy and has the narrowest prediction intervals. Both the
XAI analysis and the empirical experiments highlight that the immediate history
of the price difference realization and the increments of the day-ahead price
have the most substantial impact on the price difference.
