Stochastic Optimization of Hybrid Wind-Solar-Hydro System Configuration Under Spatiotemporal Heterogeneity and Climate Uncertainty：Datasets and Supplementary Materials
Description
This dataset comprises comprehensive data to support research in WSH-OPT modeling. It primarily includes the following components: *************************************************************************************************************** 1、ERA5 Hourly Meteorological Data (1940-2022): Format: NetCDF (.nc) Content: This file contains ERA5 hourly reanalysis meteorological data spanning from 1940 to 2022. The data is associated with the specific locations of planned wind and photovoltaic (PV) power plants, providing crucial meteorological variable inputs for wind and solar resource assessment and power generation modeling. 2、Clustering Analysis of Power Plants within the River Basin: Script: clustering_fuzzyCmeans.py Description: This Python script implements the Fuzzy C-means clustering algorithm. It is used to perform spatial clustering of all planned wind and PV power plants within the river basin. Method: The optimal number of clusters is determined by calculating the Xie-Beni index, ensuring an optimal balance between cluster compactness and separation. 3、Clustering Results :pv_clustering_details.txt &wind_clustering_details.txt Description: This text file details the results of the Fuzzy C-means clustering performed on the wind and PV power plants. Content: It includes the central coordinates (longitude and latitude) of each cluster and the number of power stations within that cluster. Based on the file content, the PV power plants were grouped into 6 clusters and the wind power plants were grouped into 7 clusters. 4、Wind and Solar Power Generation Scenario Generation Model:Script: CVAE.py Description: This Python script implements a Conditional Variational Autoencoder (CVAE) model. Purpose: The model is designed to learn the time-series characteristics of wind and solar power generation. It can generate diverse and statistically representative power output scenarios conditioned on specific factors (e.g., month). This is crucial for studies such as power system planning and reliability assessment. 5、Hydropower Runoff Data: Please note that hydropower runoff data is not included in this dataset due to confidentiality restrictions.