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