Clustering Techniques for Improved Solar Forecasting and Utility Operations

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Tuesday, 6 January 2015: 9:15 AM
224B (Phoenix Convention Center - West and North Buildings)
Carlos F.M. Coimbra, University of California, La Jolla, CA

We provide a new tool to select candidate locations for solar systems that take into account solar variability and geographical smoothing effects. This tool takes the form of maps created by a clustering technique that determines regions of coherent solar quality attributes as defined by a feature which considers both solar clearness and solar variability. An efficient combination of two well-known clustering algorithms, the affinity propagation and the k-means, is introduced to find stable partitions of the data to a variety of number of clusters in a computationally fast and reliable manner. We use multiple years worth of the 30-min GHI gridded data for different utility regions, including SDG&E in Southern California, SMUD in Central California, and MECO on the island of Lanai (HI) to produce, validate and reproduce clustering maps. A family of appropriate number of clusters is obtained by evaluating the performance of three internal validity indices. We apply a correlation analysis to the family of solutions to determine the map segmentation that maximizes a definite interpretation of the distinction between and within the emerged clusters. Having selected single clustering solutions we validate the clustering algorithm by using an independent dataset to demonstrate that the degree of similarity between the two partitions remains high at 90.91\%. In the end we show how the clustering map can be used for solar planning and operations. We explore the effects of geographical smoothing in terms of the clustering maps, by determining the average ramp ratio for two location within and without the same cluster and identify the pair of clusters that shows the highest smoothing potential. Then, we demonstrate how the maps can be used to select locations for GHI measurements to improve solar forecasting for PV plants, by showing that additional measurements from within the cluster where the PV plant is located can lead to improvements of the order of 10\% in the forecasts.