ETHOS.REFLOW:一个开源的工作流程,用于对可重复可再生能源潜力的评估
Tristan Pelser1,2, Jann Michael Weinand1, Patrick Kuckertz1
1Institute of Climate and Energy Systems, Jülich Systems Analysis, Forschungszentrum Jülich, Jülich, Germany.
Patterns (New York, N.Y.)
|March 5, 2025
概括
准确的可再生能源评估对于气候目标至关重要. 一个新的Python工具ETHOS.REFLOW确保了可重复的能源潜力研究,提高了调查结果的透明度和信任.
科学领域:
- 可再生能源资源评估可再生能源资源评估
- 能源系统建模能源系统建模
- 减缓气候变化减缓气候变化减缓
背景情况:
- 可再生能源评估中的不一致的方法和数据导致了显著的结果差异.
- 准确的评估对于实现气候目标的能源系统规划至关重要.
研究的目的:
- 介绍ETHOS.REFLOW,一个基于Python的工作流程管理器,用于透明和可重复的能源潜力评估.
- 自动化从数据采集到报告的整个工作流程,以最大限度地减少可重复分析的努力.
主要方法:
- 通过估计北海海上风电潜力 (固定基础和混合技术) 来证明ETHOS.REFLOW.
- 比较两种轮机安置方法 (明确的安置与均功率密度) 和不同的风力数据集.
- 使用基于Python的工作流程管理器进行自动化数据处理和分析.
主要成果:
- 估计最大可安装的海上风电容量:768-861吉瓦.
- 预计每年能源产量:2,961-3,047 TWh.
- 计算的容量因子:41%-46%,具有显著的时间变化.
结论:
- ETHOS.REFLOW为可再生能源潜力研究提供了一个强大的框架.
- 该工具增强了对研究结果的信任,并使能源系统建模人员能够在现有工作基础上进行构建.
- 标准化工作流程提高了可再生能源资源评估的可靠性.
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