ReEDS: Offshore wind profiles

This record provides modeled hourly offshore wind capacity factor profiles for the contiguous United States (U.S.), generated using the Wind Integration National Dataset Toolkit (WTK), High-Resolution Rapid Refresh (HRRR) dataset, System Advisor Model (SAM), and Renewable Energy Potential (reV) model. These profiles are used in the Regional Energy Deployment System (ReEDS) model. Additional details are provided in the ReEDS documentation. Technology assumptions Offshore wind capacity factor profiles use assumptions consistent with the 2024 Annual Technology Baseline (ATB) and Lopez et al. (2025) assumptions for 2035, namely: Turbine rating: 12 MW Rotor diameter: 216 m Hub height: 137 m Specific power: 327 $W/m^2$ Losses: Time-resolved and site-specific; described by Lopez et al. (2025). Average losses are approximately 15% and range from 7% to 25%. Temporal resolution All capacity factor profiles are at hourly resolution in U.S. Central Standard Time (UTC–06:00) and represent instantaneous values on the hour. The profiles span 15 weather years (2007–2013 + 2016–2023); profiles for 2007–2013 use WTK data and profiles for 2016–2023 use bias-corrected HRRR data, with further information available in the Wind Resource Database (WRDB). For leap years, the final day of the year (December 31) is dropped, such that each year contains 8760 hours. Spatial resolution Capacity factor profiles are provided for three siting access assumptions described by Lopez et al. (2025) ("limited", "reference", and "open"), and three spatial resolutions: wind-ofs_radial_{access scenario}_ba: Offshore sites radially connected to a subset of the 134 ReEDS onshore model zones wind-ofs_radial_{access scenario}_county: Offshore sites radially connected to a subset of U.S. counties wind-ofs_meshed_{access scenario}_ba: Offshore sites grouped into 27 ReEDS offshore model zones Profiles are further differentiated into ten resource classes: classes 1–5 represent fixed-bottom installations and 6–10 represent floating installations, with higher class numbers within each category indicating higher average capacity factor. File structure county2zone.csv: U.S. counties comprising each of the 134 ReEDS onshore model zones offshore_zones.gpkg: GeoPackage map of the 27 ReEDS offshore model zones cf_wind-ofs_{radial or meshed}_{access scenario}_{ba or county}.h5: Hourly capacity factor profiles. Columns are labeled as {resource class}|{zone name or p{5-digit county FIPS code}}. sc_wind-ofs_{radial or meshed}_{access scenario}_{ba or county}.csv: Available capacity [MW] associated with each class/region capacity factor profile Capacity factor profiles are saved as hierarchical Data Format (HDF5) files. The following Python function can be used to read a capacity factor .h5 file into a pandas dataframe: import h5py import pandas as pd def read_cf_profile(filepath): """ Read a CF profile from `filepath` and return a pandas dataframe. Usage: `df = read_profile('/path/to/filename.h5')` """ encoding = 'utf-8' with h5py.File(filepath, 'r') as f: df = pd.DataFrame( f['data'][:], columns=pd.Series(f['columns']).str.decode(encoding), index=f['index_0'], ) df.index = pd.to_datetime( pd.Series(df.index, name='datetime').str.decode(encoding) ) return df本记录提供美国本土（contiguous United States）的逐小时海上风电容量系数（capacity factor）模拟剖面，其生成依托风能集成国家数据集工具包（Wind Integration National Dataset Toolkit, WTK）、高分辨率快速刷新数据集（High-Resolution Rapid Refresh, HRRR）、系统顾问模型（System Advisor Model, SAM）以及可再生能源潜力模型（Renewable Energy Potential, reV）。此类剖面已应用于区域能源部署系统（Regional Energy Deployment System, ReEDS）模型。 更多详细信息可参见ReEDS官方文档。 ### 技术假设 海上风电容量系数剖面采用与2024年度技术基线（Annual Technology Baseline, ATB）以及Lopez等人（2025）针对2035年设定的假设一致的参数，具体如下： - 机组额定功率：12 MW - 叶轮直径：216 m - 轮毂高度：137 m - 比功率：327 W/m² - 损耗：具备时间分辨率与场地特异性，详细说明参见Lopez等人（2025）。平均损耗约为15%，波动区间为7%至25%。 ### 时间分辨率 所有容量系数剖面均采用美国中部标准时间（UTC-06:00）的逐小时分辨率，代表整点时刻的瞬时值。数据集涵盖15个气象年（2007–2013年及2016–2023年）：2007–2013年的剖面采用WTK数据生成，2016–2023年的剖面则基于经过偏差校正的HRRR数据生成，更多信息可参见风能资源数据库（Wind Resource Database, WRDB）。对于闰年，将移除当年最后一日（12月31日），确保每年均包含8760小时。 ### 空间分辨率 容量系数剖面基于Lopez等人（2025）提出的三种场址接入场景（分别为“受限”“基准”与“开放”），并提供三种空间分辨率格式： 1. `wind-ofs_radial_{接入场景}_分区`：径向连接至134个ReEDS陆上模型分区子集的海上场址 2. `wind-ofs_radial_{接入场景}_郡县`：径向连接至美国部分郡县的海上场址 3. `wind-ofs_网格化_{接入场景}_分区`：被划分为27个ReEDS海上模型分区的海上场址 此外，剖面还进一步分为10个资源等级：1至5类为固定式基础装机，6至10类为漂浮式装机，同一类别中等级数值越高代表平均容量系数越高。 ### 文件结构 - `county2zone.csv`：包含组成134个ReEDS陆上模型分区的美国郡县信息 - `offshore_zones.gpkg`：包含27个ReEDS海上模型分区的地理包（GeoPackage）地图 - `cf_wind-ofs_{径向或网格化}_{接入场景}_{分区或郡县}.h5`：逐小时容量系数剖面文件。列标签格式为`{资源等级}|{分区名称或5位数字FIPS郡县代码}` - `sc_wind-ofs_{径向或网格化}_{接入场景}_{分区或郡县}.csv`：与每类/区域容量系数剖面关联的可用装机容量[MW] 容量系数剖面以分层数据格式（Hierarchical Data Format, HDF5）文件存储。以下Python函数可用于将容量系数.h5文件读取为pandas数据框： python import h5py import pandas as pd def read_cf_profile(filepath): """ 从指定路径读取容量系数剖面并返回pandas数据框。 使用方法： `df = read_profile('/path/to/filename.h5')` """ encoding = 'utf-8' with h5py.File(filepath, 'r') as f: df = pd.DataFrame( f['data'][:], columns=pd.Series(f['columns']).str.decode(encoding), index=f['index_0'], ) df.index = pd.to_datetime( pd.Series(df.index, name='datetime').str.decode(encoding) ) return df