Dataset for "Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment"

These are data accompanying "Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment". They consist of pre-solved PyPSA network files and additionally needed resources, and processing files needed for the analysis. In order to reproduce the results, clone the GitHub repository https://github.com/aleks-g/stressed-system (as descriped in README.md) and then download the data provided here and move the corresponding subfolders to the right location within the stressed-system directory: Download folder/archive Target directory Description resources workflow/pypsa-eur/resources/ GeoJSON files needed for the plots in the analysis (would be recreated if re-run from scratch) results workflow/pypsa-eur/results/ `means/` contains dataframes needed to compute anomalies `periods/` contains system-defining events (SDEs), in particular `sde_new_store_1941-2021_100bn_12-336h_90_elec_lc1.25_Co2L.csv` which is used for the main analysis. The remaining periods are used for sensitivity analyses (coming from stressful-weather-sensitivities.yaml) `stressful-weather/weather_year_{year}/networks` contains the pre-solved PyPSA networks in `networks/` `stressful-weather-sensitivities/weather_year_{year}` contains the pre-solved PyPSA networks without transmission expansion in `networks/` that are needed for the validation (dispatch optimisation) where unserved energy is saved for every operational year in .csv files in `validation/` clustering workflow/notebooks/clustering/ Clustering data with the system-defining events (SDEs). processing_data workflow/notebooks/processing_data/ Processing data to generate plots. Need to run `_generate_data_for_analysis.py` in stressed-system/notebooks to re-generate these data. sensitivity_analysis workflow/notebooks/sensitivity_analysis/ Contains the remaining data needed for the plots and supplementary material. `_generate_data_for_analysis.py` with small changes to the configuration options needs to be run to regenerate these data. For more details on how these files were generated and further questions see https://github.com/aleks-g/stressed-system.本数据集配套于论文《最坏情景准备：资源充裕性评估中的长短期极端天气事件》（Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment）。数据集包含预求解后的PyPSA（PyPSA）网络文件、额外所需的配套资源，以及分析所需的处理脚本文件。 若要复现研究结果，请先按照README.md中的说明克隆GitHub仓库https://github.com/aleks-g/stressed-system，随后下载本数据集提供的文件，并将对应子文件夹移动至stressed-system目录下的指定位置： - 配套资源（resources）：目标目录为workflow/pypsa-eur/resources/，包含本分析绘图所需的GeoJSON（GeoJSON）文件（若从头重新运行，可自动生成该类文件）。 - 结果集（results）：目标目录为workflow/pypsa-eur/results/。 `means/` 文件夹内包含计算异常值所需的数据框。 `periods/` 文件夹内包含系统定义事件（System-Defining Events, SDEs），其中尤以`sde_new_store_1941-2021_100bn_12-336h_90_elec_lc1.25_Co2L.csv`为主分析所用文件。其余周期数据则用于敏感性分析（源自stressful-weather-sensitivities.yaml配置文件）。 `stressful-weather/weather_year_{year}/networks` 路径下的`networks/`文件夹内含预求解后的PyPSA（PyPSA）网络文件。 `stressful-weather-sensitivities/weather_year_{year}` 路径下的`networks/`文件夹内含未进行输电扩建的预求解PyPSA（PyPSA）网络文件，这类文件用于验证环节（即调度优化）；验证环节中，各运行年度的未供电能量数据会保存至`validation/`文件夹下的CSV（CSV）文件中。 - 聚类分析（clustering）：目标目录为workflow/notebooks/clustering/，用于基于系统定义事件（SDEs）进行数据聚类。 - 数据预处理（processing_data）：目标目录为workflow/notebooks/processing_data/，用于处理数据以生成绘图结果。若需重新生成该类数据，需在stressed-system项目的notebooks目录下运行`_generate_data_for_analysis.py`脚本。 - 敏感性分析（sensitivity_analysis）：目标目录为workflow/notebooks/sensitivity_analysis/，包含绘图及补充材料所需的其余数据。若需重新生成该类数据，需修改配置选项后运行`_generate_data_for_analysis.py`脚本。 若需了解文件生成的更多细节或有其他疑问，请访问https://github.com/aleks-g/stressed-system。