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As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6. [ABSTRACT FROM AUTHOR]