Ensemble Bayesian Model Averaging Projections of Wind-Speed Extremes for Wind Energy Applications Over China Under Climate Change

Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100-m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2-4.5 and SSP5-8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031-2060) and the end (2071-2100) of the 21st century relative to the baseline period (1985-2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations. Wind energy is one of the key renewable energy sources and has been widely developed worldwide to mitigate global warming. Extreme wind can damage wind turbines and jeopardize the reliability of wind power generation, disturbing the stability of regional power supplies. Therefore, understanding the spatial and temporal variability of extreme wind speeds at wind turbine hub height under climate change is essential for the development of local wind power. This study examines the spatial and temporal patterns of 100-m extreme wind speeds in China using empirical downscaling and Bayesian model averaging ensemble methods driven by several global climate models and reanalysis data. The results show that Inner Mongolia, northeastern China, western Tibet, and eastern coastal areas are the areas in China where high extreme wind speeds are most prevalent. This spatial pattern will typically persist in the future, but extreme wind speeds in Tibet, Qinghai, and Sichuan will drop dramatically by the end of the 21st century. Extreme wind speeds are considerably higher in winter and autumn than in the other two seasons. Local governments should take measures to prevent regional energy disparities due to the deterioration of wind turbines because of the anticipated wind extremes under climate change. An empirical downscaling Bayesian ensemble projection model is constructed to investigate extreme wind speed variationsExtreme wind speeds in central and southeastern China are projected to increase in future periodsExtreme wind speeds are considerably higher in winter and autumn than in the other two seasons