Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America
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This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022). Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input. Folder (3) provides the corresponding shapefiles. Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size. Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening). Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022). Countries/territories included in the dataset: ArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaCubaDominican RepublicEcuadorEl SalvadorFrench GuianaGuatemalaGuyanaHaitiHondurasJamaicaNicaraguaPanamaParaguayPeruSurinameUruguayVenezuela References Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5 Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14870967
本数据集提供适配能源模型的就绪数据，可在中南美洲各国层面的能源模型（主要为容量扩展模型与调度模型）中纳入太阳能与风电投资方案的空间差异化考量。 本数据集的构建方法综合考量了资源品质、土地使用限制、距基础设施距离等多项因素。该方法此前被用于构建全非洲数据集，相关细节见于Sterl等人（2022）的研究，并由Sterl、Hussain与Elabbas（2023）正式发布。 文件夹（1）包含按上述限制条件与Sterl等人（2022）所述标准筛选得到的、中南美洲各国太阳能与风电项目开发的整体可行区域矢量形状文件（shapefile）。 文件夹（2）包含中南美洲各国太阳能与风电开发潜力排名前5%的区域（“最优”以预期平准化度电成本（Levelized Cost of Energy, LCOE）从低到高排序，包含电网与道路扩建成本；前5%以国家国土覆盖范围占比计），并附带可供模型输入的逐小时时间序列数据。 文件夹（3）包含配套的矢量形状文件（shapefile）。 文件夹（4）包含基于MSR集群的简化/聚合结果（详细说明参见Sterl等人2022年研究），并附带以2018年气象年为基准的逐小时时间序列数据。集群数量根据国家规模设定为3、5或10个。 文件夹（5）包含各国层面的PDF格式地图，展示了经筛选后的资源禀赋强度与MSR集群划分结果。 所有电子表格文件的表头说明详见Sterl等人（2022）的补充材料。 本数据集涵盖的国家/地区如下： 阿根廷、伯利兹、玻利维亚、巴西、智利、哥伦比亚、哥斯达黎加、古巴、多米尼加共和国、厄瓜多尔、萨尔瓦多、法属圭亚那、危地马拉、圭亚那、海地、洪都拉斯、牙买加、尼加拉瓜、巴拿马、巴拉圭、秘鲁、苏里南、乌拉圭、委内瑞拉。 参考文献 Sterl, S.、Hussain, B.、Miketa, A. 等. 面向太阳能光伏与风电的能源模型“供应区域”全非洲数据集[J]. 科学数据, 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5 Sterl, S.、Hussain, B.、Elabbas, M. (2023). 论文《面向太阳能光伏与风电的能源模型“供应区域”全非洲数据集》配套数据（版本1.2.0）[数据集]. Zenodo. https://doi.org/10.5281/zenodo.14870967
