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绿色的内生学习在欧洲的部门合能源模式中
Elisabeth Zeyen1,2, Marta Victoria3,4, Tom Brown5,6
1Department of Digital Transformation in Energy Systems, Faculty of Process Engineering, TU Berlin, Einsteinufer 25 (TA 8), Berlin, 10587, Berlin, Germany. e.zeyen@tu-berlin.de.
Nature communications
|June 23, 2023
概括
加快可再生能源和电解能力对于成本最佳的绿色气生产至关重要,使1.5°C气候目标成为可能. 在建模中忽视学习效应会大大延迟扩展,并高估成本.
科学领域:
- 能源系统分析 能源系统分析
- 气候变化缓解缓解 气候变化缓解
- 绿色经济 绿色经济
背景情况:
- 对于难以电气化的部门的脱碳至关重要.
- 之前的能源模型缺乏关键特征,如需求部门,时间变化和的学习效应.
- 需要精确的建模来评估在能源转型中的作用.
研究的目的:
- 通过纳入绿色生产链的实践学习来解决以前模型中的局限性.
- 评估绿色在实现严格的气候目标 (+1.5°C) 中的作用.
- 分析学习效应对电解扩展和整体系统成本的影响.
主要方法:
- 开发一个详细的欧洲部门合模型.
- 包括整个绿色生产链的学习动态.
- 模拟不同的气候目标,包括最严格的+1.5°C场景.
主要成果:
- 电解和可再生产能比欧盟目前计划的更快的扩张是实现+1.5°C目标的最佳成本.
- 绿色的生产成为主导,取代了灰色,排除了蓝色.
- 通过实践学习,到2050年将气生产成本大幅降低至1.26欧元/公斤.
结论:
- 动态学习效应对于准确的模型和实现气候目标至关重要.
- 没有考虑通过实践学习导致电解扩展延迟,并高估了系统和成本.
- 政策和投资应优先考虑绿色基础设施的快速扩展,以实现雄心勃勃的气候目标.
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