H2貿易モデル

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:水素貿易の部分均衡モデル手法と、一般均衡モデルとの差異を理解する上で有用。

🏢実務担当者:水素・合成燃料の国際調達戦略やサプライチェーンリスク評価に活用できる。

🏛政策担当者:水素輸入国としての調達先多様化や関税・金利政策の影響を検討する際の参考になる。

Based on exogenous target scenarios, the model simulates international trade in red and green hydrogen, as well as synthetic fuels derived from them, which represent an alternative to domestic or European production. Unlike conventional approaches, this model does not optimise for the lowest costs across all economies, as is the case, for example, in general equilibrium models. Rather, the model’s microeconomic, partial-equilibrium approach assumes competition between globally active firms seeking to maximise individual profits, and examines the robustness of trade relations as well as the positioning of ‘first movers’ (regions and nations) in the global market over time. The model determines the offer prices to third countries and thus the options available compared with national production. Potential-related electricity generation costs, generation costs for hydrogen and its derivatives, power storage system costs, transport costs, customs duties and country-specific interest rates on debt and equity [1], all influence the offer prices in the respective sales countries. As electrolysers play a key role in determining the cost of hydrogen production, renewable energy sources and electrolysers were combined in such a way as to ensure that hydrogen is produced at the lowest possible cost. The demand for hydrogen and its derivatives is specified exogenously. The scenarios from the Global Energy and Climate Outlook 2022 (GECO) [3] were best suited to the model and the research questions. The scenario does not break down the EU Member States into individual nations, which is why these countries were taken from the TYNDP 2022 – Scenario Report [4]. Electricity demand, with the exception of the electrolysers in the conversion sector, was met in line with the installed capacity of the scenarios based on the most favourable potentials. It is therefore assumed that additional renewable energy generation plants will be built for green hydrogen. Electricity transmission costs are not taken into account. Given the differing stances taken by nations on nuclear energy, this is also considered as an option for producing hydrogen. The trading algorithm assesses importing countries’ willingness to pay – and thus potential selling prices within an interconnected global trade system – rather than merely production costs. The model assumes that all market participants act simultaneously, which raises the question of which participants will be the first to conclude a trade and thereby alter supply and demand. With the focus on optimisation from an individual economic perspective, each trade aims to maximise its own profit. Products are offered taking into account transport costs and customs duties in all countries, including one’s own. The trade relationship with the highest margin per unit of energy is given priority, and demand in the importing country as well as supply from the H2 supply region is adjusted across all countries. This process is repeated until all demand for the year under consideration has been met. [1]          J. Terrapon-Pfaff et al., „Risikobewertung und Risikokostenanalyse der MENA-Region. MENA-Fuels: Teilbericht 8,“ Wuppertal, Stuttgart, Köln, Saarbrücken, 2022. [2]          DLR, „EnDAT – Energy Data Analysis Tool,“ [Online]. Available: https://www.dlr.de/de/ve/forschung-und-transfer/infrastruktur/modelle/endat. [3]          K. Keramidas et al., „Global Energy and Climate Outlook 2022: Energy trade in a decarbonised world,“ European Commission, Luxembourg, 2022. [4]          A. Kättliz et al., „TYNDP 2022 - Scenario Report,“ ENTSO-E; ENTSOG, Brüssel, 2022.

• Zenodo https://zenodo.org/records/23083837first seen 2026-10-02 04:13:11