On the determination of coherent solar microclimates for utility planning and operations

• We conduct cluster analysis on a satellite gridded GHI dataset of Southern California.
• A stable initialization method of the seed centers and a variability index are proposed.
• The appropriate range of spatially coherent regions is determined.
• This work contributes to the planning, siting and monitoring of solar resources.

This work presents a cluster analysis for the determination of coherent zones of Global Horizontal Irradiance (GHI) for a utility scale territory in California, which is serviced by San Diego Gas & Electric. Knowledge of these coherent zones, or clusters, would allow utilities and power plants to realize cost savings through regional planning and operation activities such as the mitigation of solar power variability through the intelligent placement of solar farms and the optimal placement of radiometric stations. In order to determine such clusters, two years of gridded satellite data were used to describe the evolution of GHI over a portion of Southern California. Step changes of the average daily clear-sky index at each location are used to characterize the fluctuation of GHI. The k-means clustering algorithm is applied in conjunction with a stable initialization method to diminish its dependency to random initial conditions. Two validity indices are then used to define the quality of the cluster partitions as well as the appropriate number of clusters. The clustering algorithm determined an optimal number of 14 coherent spatial clusters of similar GHI variability as the most appropriate segmentation of the service territory map. In addition, 14 cluster centers are selected whose radiometric observations may serve as a proxy for the rest of the cluster. A correlation analysis, within and between the proposed clusters, based both on single-point ground-based and satellite-derived measurements evaluates positively the coherence of the conducted clustering. This method could easily be applied to any other utility scale region and is not dependent on GHI data which shows promise for the application of such clustering methods to load data and/or other renewable resources such as wind.