2019/04/30 by Katharina Gruber, Claude Kloeckl, Gruber, Katharina +7
Engineering · Decision Sciences · Social Sciences · #Wind Energy Research and Development #demographic modeling and climate adaptation #Social Acceptance of Renewable Energy
paper · pdf · doi:10.48550/arxiv.1904.13083
NASAs MERRA-2 reanalysis is a widely used dataset in renewable energy\nresource modelling. The Global Wind Atlas (GWA) has been used to bias-correct\nMERRA-2 data before. There is, however, a lack of an analysis of the\nperformance of MERRA-2 with bias correction from GWA on different spatial\nlevels - and for regions outside of Europe, China or the United States. This\nstudy therefore evaluates different methods for wind power simulation on four\nspatial resolution levels from wind park to national level in Brazil. In\nparticular, spatial interpolation methods and spatial as well as spatiotemporal\nwind speed bias correction using local wind speed measurements and mean wind\nspeeds from the GWA are assessed. By validating the resulting timeseries\nagainst observed generation it is assessed at which spatial levels the\ndifferent methods improve results - and whether global information derived from\nthe GWA can compete with locally measured wind speed data as a source of bias\ncorrection. Results show that (i) bias correction with the GWA improves results\non state, sub-system, and national-level, but not on wind park level, that (ii)\nthe GWA improves results comparably to local measurements, and that (iii)\ncomplex spatial interpolation methods do not contribute in improving quality of\nthe simulation.\n