Clustering Spatiotemporal Wind Power for Resource Data Characterization and Potential Assessment in South Africa

Abstract

Integrating wind energy into an established electrical grid presents significant challenges due to its variability, necessitating robust grid integration strategies. Effective planning for integrating wind resources requires a thorough evaluation of resource characteristics and energy potential. This study utilizes clustering techniques on spatiotemporal wind power data in South Africa to support the country's efforts in incorporating wind power into its energy mix. The electricity grid in South Africa is considerably constrained due to inadequate infrastructure, limiting grid connection capacity. The research is focused on geographic regions within South Africa where connection capacity exists. Utilizing wind power data, which has been translated from speed data provided by the Wind Atlas for South Africa, the study aims to identify distinct clustered regions with varying characteristics that align with the national energy demand profile. K-means clustering, based on statistical features extracted from demand profiles, facilitates the identification of optimal sites and capacities for wind power plants. The research estimates the potential capacity for new wind generation, considering geographic, socioeconomic, and ecological factors, including grid capacity constraints. This method offers critical insights into resource adequacy and risk mitigation in energy planning,