A statistical Time-Of-Use tariff based wind resource clustering approach using Self-Organizing Maps

The optimized siting of grid-scale renewable generation is a viable technique to minimize the variable component of the electricity generation portfolio. This process, however, requires simulated meteorological datasets, and consequently, significant computational power to perform detailed studies. This is particularly true for countries with large geographic areas. Clustering is a viable data reduction technique that can be utilized to reduce the computational burden. This work proposes the use of Self-Organizing Maps to partition high-dimensional wind speed data using statistical features derived from Time-Of-Use tariff periods. This approach is undertaken with the view towards the optimization of wind farm siting for grid-support objectives where tariff incentivization is the main driver. The proposed approach is compared with clusters derived using Self-Organizing Maps with the temporal wind speed data for the input feature set. The results show increased cluster granularity, superior validation results and decreased execution time when compared with the temporal clustering approach.