GODEEEP-hydro: Historical and projected power system ready hydropower data for the United States.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-05-28 DOI:10.1038/s41597-025-05097-3

Cameron Bracken, Youngjun Son, Daniel Broman, Nathalie Voisin

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Abstract

Hydropower is a critical electricity resource in the United States which, in addition to low-cost electricity generation, provides valuable ancillary grid services, and supports the integration of nondispatchable weather-dependent resources (e.g., wind and solar). Despite its value to the grid, there are very few comprehensive datasets available from which to study both historical and future impacts of climate, weather driven energy droughts, and integration of other weather driven generation. In this paper, we present a hydropower generation dataset covering 1,452 hydroelectric plants in the contiguous U.S. The dataset contains monthly and weekly hydropower generation estimates for both historical (1982-2019) and future (2020-2099) periods which includes 4 future climate scenarios. In addition, this dataset provides weekly and monthly constraints such as minimum and maximum power which are particularly useful in power system models which are used to study grid reliability, transmission planning and capacity expansion.

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godeep -hydro：美国历史和预测的电力系统水电数据。

水电在美国是一种重要的电力资源，除了低成本发电外，还提供有价值的辅助电网服务，并支持不可调度的天气依赖资源（例如风能和太阳能）的整合。尽管它对电网有价值，但很少有全面的数据集可以用来研究气候、天气驱动的能源干旱以及其他天气驱动发电的历史和未来影响。在本文中，我们提供了一个水力发电数据集，涵盖了美国相邻的1452座水力发电厂，该数据集包含了历史（1982-2019）和未来（2020-2099）时期的每月和每周水力发电量估算，其中包括4种未来气候情景。此外，该数据集还提供了每周和每月的约束，例如最小和最大功率，这在用于研究电网可靠性，输电规划和容量扩展的电力系统模型中特别有用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.