Integrating hydrogen in single-price electricity systems: The effects of spatial economic signals

Abstract

Hydrogen can contribute substantially to the reduction of carbon emissions in industry and transportation. However, the production of hydrogen through electrolysis creates interdependencies between hydrogen supply chains and electricity systems. Therefore, as governments worldwide are planning considerable financial subsidies and new regulation to promote hydrogen infrastructure investments in the next years, energy policy research is needed to guide such policies with holistic analyses. In this study, we link a electrolytic hydrogen supply chain model with an electricity system dispatch model. We use this methodology for a cross-sectoral case study of Germany in 2030. We find that hydrogen infrastructure investments and their effects on the electricity system are strongly influenced by electricity prices. Given current uniform zonal prices, hydrogen production increases congestion costs in the electricity grid by 11%. In contrast, passing spatially resolved electricity price signals leads to electrolyzers being placed at low-cost grid nodes and further away from consumption centers. This causes lower end-use costs for hydrogen. Moreover, congestion management costs decrease substantially, by 24% compared to the benchmark case without hydrogen. These savings could be transferred into according subsidies for hydrogen production. Thus, our study demonstrates the benefits of differentiating subsidies for hydrogen production based on spatial criteria.

Power sector effects of alternative production and storage options for green hydrogen

“…Moreover, we do not analyze the spatial aspects of domestic hydrogen provision in this study. Studies such as [15] suggest that green hydrogen production could enhance bottlenecks in the German electricity grid if there are no adequate spatial price signals. This may impede the flexible use of electrolysis and limit the system benefits of flexible green hydrogen identified in this study.…”

“…Such models are also applied to explore the spatial aspects of hydrogen production and its interactions with electricity grids. For example, vom Scheidt et al focus on the differences between uniform and zonal electricity market pricing on hydrogen infrastructure investments in Germany and find lower hydrogen costs in case of spatially electricity price signals [15]. Their study is particularly detailed with respect to the types and geographic locations of hydrogen demand locations, but again uses a pure dispatch model, i.e., does not investigate the repercussions of green hydrogen on optimal power plant portfolio, and abstracts from temporal flexibility aspects and hydrogen storage.…”

Green hydrogen – How grey can it be?

“…While for lower shares of renewables the energy efficiency in the hydrogen supply structure is more important, for higher shares of renewables the temporal flexibility of electrolysers gains in importance. In the study of vom Scheidt et al (2022) the production of electrolytic hydrogen is assessed from a different angle. The authors analyse the effect of spatial price signals for the integration of electrolyser systems in single-price markets.…”

“…Therefore, within this study we focus on the captive production and supply of electrolytic hydrogen to the German industry. 13 Derived hydrogen demand figures for 2030 were taken from vom Scheidt et al (2022). They include the hydrogen demand of four industrial sectors: the production of methanol, ammonia, and steel as well as its use in refineries.…”

“…This is in particular relevant for the extreme case 1, the off-grid case, where the renewable generation has to be near the hydrogen production and demand location. However, instead of modelling all of the 23 industrial hydrogen demand locations listed in (vom Scheidt et al 2022) individually, we split the country in three parts (north, south and centre) and aggregate the corresponding demand per zone. This approach enables to capture regional renewable resource availabilities while reducing computational complexity.…”