清洁技术成本预测:太阳能,风能,电池和气的投资和平衡成本
Hadi Vatankhah Ghadim1, Jannik Haas2, Christian Breyer3
1Sustainable Energy Research Group (SERG), Department of Civil and Natural Resources Engineering, University of Canterbury, Ōtautahi Christchurch, Aotearoa, New Zealand. hadi.vatankhahghadim@pg.canterbury.ac.nz.
Scientific data
|October 22, 2025
概括
本研究提出了一套统一的清洁能源成本预测数据集,包括主要技术到2050年的资本支出 (CAPEX) 和水平化成本 (LCOE/LCOH). 该资源有助于能源系统建模和投资决策,用于全球能源转型.
科学领域:
- 能源系统分析 能源系统分析
- 技术经济评估 技术经济评估
- 气候变化缓解技术的技术
背景情况:
- 可靠的成本预测对于能源系统建模,政策制定和全球能源转型中的投资决策至关重要.
- 现有的数据往往是零碎的,缺乏时空细分性和标准化元数据.
研究的目的:
- 编制和标准化主要清洁能源技术成本预测的综合数据集.
- 为能源系统建模和技术经济评估提供一个时空细粒度资源.
- 支持对脱碳路径的风险和不确定性评估.
主要方法:
- 从110多项区域和全球研究中系统地收集和协调数据.
- 资本支出标准化 (CAPEX) 和水平化成本 (LCOE/LCOH) 数据.
- 包含关于源类型,区域和截至2050年的年度细分情况的元数据.
主要成果:
- 一个结构化,全面的数据集,涵盖公用事业规模和屋顶光伏,陆上/海上风电,离子电池,集中太阳能和电解剂 (性和PEM).
- 在国家,大陆和全球范围内提供数据,每年预测到2050年.
- 包括各种场景下的数据,以方便不确定性分析.
结论:
- 统一的数据集为稳健的能源系统建模,投资规划和政策设计提供了宝贵的资源.
- 结构化数据增强了技术经济评估,并支持对脱碳的知情决策.
- 新奇之处在于结构化协调,元数据处理和清洁能源成本预测的全面覆盖.
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