论文标题
在欧洲的行业耦合能量模型中，绿色氢的内源性学习
Endogenous learning for green hydrogen in a sector-coupled energy model for Europe
论文作者
论文摘要
许多研究表明，氢可以在难以利用的部门的能量转变中发挥重要作用，但以前的建模尚未包括评估其作用的必要特征。他们要么忽略了氢需求的重要部门，因此忽略了系统的时间变化，要么忽略了学习效应的动力学。我们解决了这些局限性，并考虑在详细的欧洲部门耦合模型中对具有不同气候目标的全绿色氢生产链学习。在这里，我们表明，在接下来的10年中，与欧盟在Repowereu计划中所设想的电解能力和可再生能力更快，以达到 +1.5°C的目标。这将氢产量的成本降低到2050年。氢的生产从灰色转向绿色氢，省略了蓝色氢的选择。如果对电解成本进行建模而没有通过动态学习进行模型，则电解量表会大大延迟，而总系统成本被高估了13％，并且氢的平整成本被高估了67％。
Many studies have shown that hydrogen could play a large role in the energy transition for hard-to-electrify sectors, but previous modelling has not included the necessary features to assess its role. They have either left out important sectors of hydrogen demand, ignored the temporal variability in the system or neglected the dynamics of learning effects. We address these limitations and consider learning-by-doing for the full green hydrogen production chain with different climate targets in a detailed European sector-coupled model. Here, we show that in the next 10 years a faster scale-up of electrolysis and renewable capacities than envisaged by the EU in the REPowerEU Plan is cost-optimal in order to reach the +1.5°C target. This reduces the costs for hydrogen production to 1.26 Eur/kg by 2050. Hydrogen production switches from grey to green hydrogen, omitting the option of blue hydrogen. If electrolysis costs are modelled without dynamic learning-by-doing, then the electrolysis scale-up is significantly delayed, while total system costs are overestimated by up to 13% and the levelised cost of hydrogen is overestimated by 67%.
