Abstract
This study investigated the impact of global warming on Japanese wind energy resources and their short-term variations using the large ensemble d4PDF dataset, which consists of dynamically downscaled historical and +4K future climate projections. The capacity factor under the future and present climate was estimated from an idealized power curve based on hourly near-surface wind speeds. The +4K warming future climate projections showed significant changes in wind energy resources that varied both regionally and seasonally. The wind energy potential was projected to slightly increase (decrease) from winter to spring over northern (southern) Japan and decrease from summer to autumn over most of Japan. The projected annual production decreased by about ~5% over Japan in response to climate change. The frequency of wind ramp events also decreased in the latter seasons. The relationship to synoptic weather was investigated using self-organizing maps, whereby weather patterns (WPs) over the region in the present and future +4K climate were classified for a two-dimensional lattice. Future probabilistic projections of WPs under the global warming scenario showed both increases and decreases in the frequency of different WPs, with corresponding advantages and disadvantages for wind power generation with regard to future changes in capacity factors in Japan. The importance of these frequency changes on the total change was further assessed by separating the dynamical and thermodynamic contributions.
Highlights
Observed and projected climate changes have raised the need to increase renewable energy production in future decades
To better understand the meteorological cause of these changes, we applied weather classification using machine learning to examine the impact of climate change on the relationships between synoptic-scale weather patterns (WPs) and local-scale wind speeds in Japan
We found large decreases in offshore wind energy resources in the southern part of the Japanese Exclusive Economic Zone (EEZ)
Summary
Observed and projected climate changes have raised the need to increase renewable energy production in future decades. Future projections from climate models show wind speeds changing heterogeneously, with wind resource potential significantly increasing or decreasing in some areas [3,9,10] Such studies have focused primarily on changing wind speed or density and estimated energy output using idealized power curves to discuss climate change’s impact on energy potential. Sudden large increases and decreases in wind energy output over a short period, caused by wind speed fluctuations, are known as wind ramp events and are one of the dominant problems in wind energy [11,12,13,14,15] These occur often in Japan because of geographic/climatological conditions that affect the load generation balance of the electricity supply. To better understand the meteorological cause of these changes, we applied weather classification using machine learning to examine the impact of climate change on the relationships between synoptic-scale weather patterns (WPs) and local-scale wind speeds in Japan
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