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.

翻译：电力在不同时间尺度和监管规则的市场中进行交易。随着可再生能源渗透率的提高，短期日内交易日益重要。在德国，日内电价通常以明显的小时模式围绕欧洲电力交易所（EPEX）现货市场的日前电价波动。本文提出一种概率建模方法，对日内电价与日前合约的价差进行建模。该模型将每个日前电价区间内的四个15分钟间隔视为四维联合概率分布，从而捕捉涌现的小时模式。通过归一化流（一种结合条件多元密度估计与概率回归的深度生成模型）学习得到的非平凡多元价差分布。此外，本文基于文献见解及可解释人工智能（XAI）影响分析，探讨了不同外部影响因素的效应。将归一化流与基于高斯连接函数和高斯回归模型的历史数据优选及概率预测方法进行对比。在多种模型中，归一化流以最高精度识别趋势且预测区间最窄。XAI分析与实证实验均表明，价差实时实现的历史数据以及日前价格增量对价差影响最为显著。