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、光伏与水电站的长期发电过程数据。本研究中用于流域风电与光伏功率建模的基础数据，取自1981年至今的ERA5-Land逐月平均数据集，以及1983-2017年高分辨率（3小时、10公里）全球地表太阳辐射数据集，上述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选址信息（多数尚未建成），并以对应地理位置的规划装机容量为依据，将风电、光伏电站的装机容量作为初始输入参数。通过风电与光伏出力模型，利用规划电站点位处的气温、辐射强度与风速数据，计算对应能源类型的长期发电出力。本次模拟共得到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，本研究以流域1953-2019年的历史径流量作为输入，以最大发电量为优化目标，对水电站的长期发电过程进行模拟。本次优化采用Python环境下Geatpy工具库中的强化精英遗传算法模板（Strengthen Elitist GA templet，SEGA）完成。