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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. • Probabilistic forecasting of intraday electricity prices by normalizing flows. • Multivariate forecasts capture correlations between consecutive time steps. • Explainable artificial intelligence for selecting informative input features. • Normalizing flows outperform Gaussian regression and copula models. [ABSTRACT FROM AUTHOR]

• Fuel costs were of highest decision-relevance for German car drivers. • The impact of pollutant emission reductions on fuel preferences was irrelevant. • Currently low market success for alternative fuels without incentives in Germany. • Policy interventions should target costs and availability to stimulate market demand. In the search for sustainable transport solutions, fuel production from renewable resources has received significant attention. Some proposed synthetic fuels have favorable combustion properties compared to existing fuels, e.g., significant reductions in pollutant formation. However, penetration of such fuels requires a favorable social acceptance, as demonstrated by the consumer boycott of the ethanol-blend fuel with 10% ethanol and 90% gasoline (E10) in Germany. Therefore, the consumer perspective and their preferences regarding alternative fuels should be considered in the fuel design. We use conjoint methodology to analyze the preferences of German car drivers for alternative fuels. This aims at understanding which criteria determine consumer preferences and usage decisions. Among the five considered fuel attributes (fuel availability, driving range, pollutant emissions, fuel costs, and usage requirements to enable the use of alternative fuels), fuel costs had the highest decision impact for alternative fuel preferences, followed by fuel availability and usage requirements. Pollutant emissions had the lowest impact on alternative fuel choices. A market simulation of conventional diesel and alternative fuels (dimethyl ether (DME) and a blend of diesel with oxymethylene dimethyl ethers (OME)) revealed that currently a large majority of car drivers would prefer conventional fossil fuel options, indicating a currently low consumer demand for alternative fuels. Thus, the findings demonstrate the importance of integrating social acceptance as an objective function in the design of novel fuels and production processes. [ABSTRACT FROM AUTHOR]