Time-of-Use feature based clustering of spatiotemporal wind power profiles. (1st December 2021)

• Record Type: Journal Article
• Authors: van Staden, Chantelle Y.; Vermeulen, Hendrik J.; Groch, Matthew
• Is Part Of: Energy. Volume 236(2021)
• Publication Date: 2021-12-01
• Subjects: Wind power -- Clustering -- Feature classification -- Time-of-Use; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.121474
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The global drive towards a sustainable energy future is giving rise to rapidly increasing penetration of variable renewable energy into modern power grids. This creates a need for the assessment, characterization and classification of renewable energy resources in the context of the operational challenges posed by large-scale grid integration of renewable energy. This investigation explores a methodology for classifying wind energy resources, using feature vectors defined in terms of the statistical properties of the wind resource for Time-of-Use energy demand periods. Results are presented for the geographic areas associated with the South African Renewable Energy Development Zones, using a mesoscale wind atlas dataset as the resource input. The cluster formations obtained with the Time-of-Use feature vector approach are compared with results obtained by clustering the temporal power profiles using the k-means algorithm. It is shown that cluster formations obtained with the respective inputs exhibit distinct differences, especially with reference to the spatial granularity and geographical dispersion of the clusters. It is concluded that the proposed Time-of-Use feature vector approach offers advantages for application as a classification and data partitioning methodology for spatiotemporal wind profiles. Highlights: Translated wind power profiles to statistical energy Time-of-Use feature vectors. Reducion of a high dimensionality temporal wind power dataset. Statistically reduced dataset retaining critical underlying characteristics. Comparison of temporospatial inputs with statistical clustering methodology.