Posters

Abstract

Precise knowledge of the wind climate at a target site is crucial for estimating the wind potential not only in the planning phase of wind farms, but also during the projects lifetime of 20 years and more. For this purpose, measurement campaigns are carried out typically covering a full yearly cycle which are then extended with a wind time series based on numerical data (long-term data) covering at least 10 years, in order to investigate a representative period. This is typically done by linking the short-term and long-term data by statistical methods based on the overlapping time period of both data sets, commonly known as Measure-Correlate-Predict (MCP) methods. In current site assessment industry standards (e.g., the German Technical Guideline (TR6) for Wind Turbines) a wind climate persistence is assumed, i.e. there is no relevant climate trend. In view of climate change, however, it is questionable whether projecting wind speed into the future solely based on historical data is still a reliable assumption.Therefore, this study shows how climate projections and advanced MCP methods can be usedto obtain a more representative view on future wind speed. A consistent methodology from short-term over long-term to future climate projection is presented. Our analysis focuses on several sites in Europe and North America with long-term measurements in varying terrain complexity from simple over heterogeneous to (very) complex terrain. The historical wind climate was predicted based on measurement and ERA5- reanalysis data. To predict the future wind climate, an ensemble of several CMIP6 model data provide additional long-term data in the MCP-method. Based on the resulting ensemble of future predictions an uncertainty estimation for the future wind climate is obtained. First results using the global climate models MPI-ESM-1-2-HR and IPSL-CM6A-LR indicate that implementing climate model data into the process of determining the future wind resource is promising. In the historical overlap period, the predicted wind speeds based on climate model data reasonably agree with the measurement data and the historical prediction based on classical reanalysis data. This emphasizes that using the MCP method to correct climate model data to a target site, is a great benefit in wind energy site assessment. Analyzing the near future (2041 - 2070) and far future (2071 - 2100) wind speed predictions reveal seasonal changes. In the summer months July to September a decrease in wind speed is detected while in winter the models show no clear tendency. A more detailed analysis of the summer months showsa less frequent occurrence of higher wind speeds while the frequency of occurrence rises in the range of 3 - 5 m/s. This study highlights the significance of climate projections in assessing future wind speeds at specific locations. By incorporating climate model data into the MCP method, more precise predictions of the future wind climate can be generated, enabling the analysis of seasonal variations. These findings offer valuable insights for evaluating wind energy site suitability and facilitate the incorporation of climate-related changes and uncertainties into long-term planning.