High-resolution gridded dataset of China's offshore wind potential and costs under technical change.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-14 DOI:10.1038/s41597-025-04428-8
Kangxin An, Wenjia Cai, Xi Lu, Can Wang
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Abstract

Assessing the dynamics of offshore wind potential and costs is essential for low-carbon energy policy decision-making and energy modeling, but no open-source, spatial explicit and technologically detailed dataset is available. This study addresses this gap by employing a consistent assessment framework that integrates GIS analysis, a wind reanalysis model, a component-based cost model and scenario analysis. It identifies suitable space for offshore wind deployment considering 12 technical and policy constraints, estimates hourly output curves, capacity factors, and technology cost dynamics by components across 5058 grid points with a 10 km resolution from 2020 to 2035 under three technical change scenarios. The dataset has been validated through comparisons with existing offshore wind projects and datasets, and is stored in two formats (GeoTIFF and NetCDF4). This dataset offers extensive potential for use as an input in climate policy and energy system research.

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技术变革下中国海上风电潜力和成本的高分辨率网格数据集。
评估海上风电潜力和成本的动态对低碳能源政策决策和能源建模至关重要,但目前还没有开源的、空间明确的、技术详细的数据集。本研究通过采用统一的评估框架解决了这一差距,该框架集成了GIS分析、风再分析模型、基于组件的成本模型和情景分析。该报告考虑了12项技术和政策限制因素,确定了适合海上风电部署的空间,并在2020年至2035年三种技术变革情景下,估算了5058个10公里分辨率电网点组件的每小时输出曲线、容量因素和技术成本动态。该数据集已通过与现有海上风电项目和数据集的比较进行了验证,并以两种格式(GeoTIFF和NetCDF4)存储。该数据集具有广泛的潜力,可作为气候政策和能源系统研究的输入。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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