Clustered Wind Resource Domains for the South African Renewable Energy Development Zones

This paper presents the results of an investigation to cluster the temporal wind speed profiles associated with the South African renewable energy development zones. The study makes use of a renewable energy resource dataset produced by the Council for Scientific and Industrial Research. The clustering large applications algorithm, which is based on the partitioning around mediods algorithm, is used in the clustering exercise. Results are presented for each of the eight South African renewable energy zones. These results include clustered mean daily temporal profiles of the wind speed obtained for the high demand and low demand season, as well as the corresponding geographical cluster maps. Clustering performance metrics, including the average within cluster distance, the Dunn index and the average silhouette width are presented.The clustering results yield optimal output of three to five clusters for each of the individual renewable energy development zones. This implies that the wind speed profiles associated with each of these zones can be reduced to three to five archetypal mean daily profiles, which greatly reduces the computational cost of high-level capacity allocation optimization studies.