An augmented spatial model for estimating the potential capacity factors of wind generating resources

Wind Generating Resources (WGRs) are variable, uncontrollable, and uncertain compared to traditional generating resources. As Wind Generating Resources (WGRs) have the intermittent nature of WGRs and uncertain characteristics according to the weather condition, the accurate prediction of WGRs' capacity factor is an essential factor associated with integrating a large amount of wind generating resources into the grid. As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is also needed to estimate power outputs of wind generation resources. In addition, geographic information such as latitude and longitude is considered for estimating the capacity factors of spatially distributed wind farms. The potential capacity factor of wind generating resources plays a key role to study a long-term transmission planning by optimal spatial modelling analysis. In this paper, we propose the optimal weighting factors to estimate the potential capacity factors of Wind Generating Resources (WGRs) using the augmented spatial analysis and modelling of power outputs produced by wind farms that are geographically distributed in windy areas. To validate the proposed spatial prediction model, we use the empirical data from the main and Jeju Island's wind farms in South Korea.