Posters

Abstract

Climate change presents both challenges and opportunities for renewable energy investments, particularly in wind energy, due to its potential to alter wind patterns and intensities over time. This study develops an integrated methodology to assess the long-term impacts of climate change on wind energy generation, combining scientific rigor with practical insights for informed investment decision-making. High-resolution regional climate models (RCMs), reanalysis datasets, and advanced statistical correction techniques were utilized to ensure the reliability of projections. The analysis focuses on six different measurement stations across six Polat Energy Wind Farms. A total of approximately 26 years of ten-minute interval wind measurement data, with an average recovery rate of 94.2%, collected from six meteorological masts, were utilized in this study. Long-term wind datasets were constructed using ERA5 reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF), covering the period 1971–2024 with hourly resolution at a 100-meter height. Python scripts were used to preprocess the data, including horizontal interpolation to site-specific coordinates and calculation of hourly wind speeds and directions. Measure-Correlate-Predict (MCP) correction of each mast data has been done using wake-cleaned measurement data to construct long-term corrected datasets for wind speed and direction. To account for climate change impacts, seven RCMs from the EURO-CORDEX initiative were employed, offering high-resolution projections (12.5 km grid) for Europe, including Türkiye. These projections covered historical data for 1976–2005 and climate scenarios for 2006–2045 under two Representative Concentration Pathways (one RCP2.6 and six RCP8.5 scenarios). Biases inherent in the model outputs were corrected using the Quantile Mapping method via the “qmap” package in R, aligning the corrected projections with observed historical data. The corrected climate model outputs were used to estimate capacity factors—minimum, maximum, and average—for each wind farm. To calculate the measured power curve for each wind farm, energy production data obtained from 10-minute mean SCADA measurements were grouped by turbine model and weighted averages were applied. The capacity factor for each wind farm was then derived using these measured power curves. Findings indicate significant variability in future wind energy performance across sites, influenced by local conditions and deviations between observed and modeled capacity factors. Sites with historically higher capacity factors are projected to maintain robust performance, while others may experience slight declines, emphasizing the critical need for site-specific evaluations in investment planning. This study underscores the importance of integrating climate projections into renewable energy planning to mitigate risks and identify opportunities in a changing climate. The proposed methodology provides a replicable framework for assessing climate impacts on wind power plants production, enabling investors to make strategic decisions that align with long-term sustainability goals. By leveraging advanced data processing tools, bias correction methods, and high-resolution climate models, this research bridges the gap between climate science and practical investment strategies, fostering resilience in renewable energy portfolios.