A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation

Renewable energy resources have garnered considerable attention owing to concerns regarding climate change mitigation and sustainability. The performance of renewable energy resources varies based on weather conditions, which is an important consideration in power system planning as renewable energy penetration increases. In this study, a probabilistic approach for the potential estimation of renewable energy resources based on augmented spatial interpolation was proposed. The proposed algorithm was verified using empirical data obtained from wind farms in Jeju Island. Wind power output scenarios were modeled through ordinary kriging and Monte Carlo simulations. Moreover, the point and cause of line overload according to the seasonal wind power output and power demand were analyzed through transmission security analysis, and the frequency and scale of the curtailments were estimated. This can be used to estimate potential renewable energy resources and establish a power system operation plan. Further study includes the development of stable power system operation plans for large-scale renewable energy resource-integrated power systems.