Reducing energy storage demand by spatial-temporal coordination of multienergy systems：Datasets and Supplementary Materials

This dataset include the the method and relating code, as well as the long-term power generation process of wind, PV and hydropower stations. ****************************************************************************************************************************************************************************** The data relating to wind and PV power modelling in the basin were derived from the dataset of ERA5-Land monthly averaged data from 1981 to the present and the dataset of high-resolution (3 hour, 10 km) global surface solar radiation (1983-2017) , which are used to simulate long-term power generation. Both datasets are grid-point data. We obtained the construction or planning locations of power stations (most of them have not yet been built). The temperature, radiation, and wind speed at the planned power station locations were used to calculate the long-term power generation of the corresponding energy sources using the wind and PV output models . The installed capacity of the wind and PV power stations was taken as the initial input based on the planned installed capacity of the geographical location. Here are the long-term power generation of102 wind power stations and 70 PV power stations obtained by simulation. Considering that all the power stations in the basin have with regulation ability, the long-term power generation process of hydropower was simulated using the historical runoff in the basin (1953 to 2019) as input and the maximum power generation as the objective. The Strengthen Elitist GA templet (SEGA) method in Geatpy in Python was used for optimization.本数据集涵盖相关方法与配套代码，以及风电、光伏（PV）与水电站的长期发电过程数据。 本数据集流域内风电与光伏（PV）电站建模所用的数据，源自1981年至今的ERA5-Land逐月平均数据集，以及1983-2017年的高分辨率（3小时、10公里）全球地表太阳辐射数据集，两类数据集均为格点数据，用于模拟长期发电过程。我们收集了流域内各电站的建设或规划点位（多数尚未建成），结合规划电站点位处的气温、辐射与风速数据，通过风电与光伏出力模型计算对应能源的长期发电量。风电与光伏电站的装机容量以该地理区位的规划装机容量作为初始输入。经模拟，最终得到102座风电站与70座光伏电站的长期发电数据。 考虑到流域内所有电站均具备调节能力，我们以流域1953-2019年的历史径流数据作为输入，以最大发电量为优化目标，模拟了水电站的长期发电过程。本次优化采用Python开源进化计算库Geatpy中的增强精英遗传算法（Strengthen Elitist GA，SEGA）模板完成。