Solar Resource Zoning Method Considering Spatial and Temporal Characteristics

In the context of climate change, the share of global renewable energy generation continues to grow. The flexible supply and demand balance of the system has become a central planning and operational concern, which places greater demands on the integrated management of solar resources. In planning, modelling only individual plants is computationally complex because of the large number of photovoltaic (PV) plants and their widely distributed locations. A model for zoning solar resources considering spatial and temporal characteristics is established in this paper. The annual irradiance eigenvectors are used as clustering indicators in each spatial area grid. K-means clustering is performed after determining the optimal number of clusters using the Davis-Boldin criterion. Using satellite data collected from 1987 to 2016 in a province of China as the research object, generation density was used to verify the effectiveness of the method and process.