YoungSeok Hwang

In both the private and public sectors, green hydrogen is treated as a promising alternative to fossil energy commodities. However, building up production capacities involves significant carbon production, especially when considering secondary infrastructure, e.g., renewable power sources. The amount of required capacity as well as the carbon production involved is calculated in this article. Using Germany as an example we show that the switch to purely green hydrogen involves significant bow waves in terms of carbon production as well as financial and resource demand. An economic model for an optimal decision is derived and—based on empirical estimates—calibrated. It shows that, even if green hydrogen is a competitive technology in the future, using alternatives like turquoise hydrogen or carbon capture and storage is necessary to significantly reduce or even avoid the mentioned bow waves.

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO₂ emissions. It represents the CO₂ emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO₂ flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO₂ flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO₂ flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.