Presentations
Comparing Mesoscale and Measurement-based Energy Production Assessments for Wind Farms: Uncertainty Analysis and Implications for Early-stage Development
Javier Esteras, Wind Analyst, DNV
Abstract
Indicative Energy Production Assessments (iEPAs) are essential in the early stages of wind farm development, providing preliminary estimates of a site's energy potential when onsite measurements are unavailable. By leveraging mesoscale-based wind speed data, developers can efficiently identify promising locations for wind farms. This study benchmarks the energy production estimates of indicative (mesoscale-based) EPAs against full (measurement-based) EPAs, which involve comprehensive manual analyses of mast data, including data cleaning, long-term adjustment, and vertical profile estimation. The iEPAs were conducted through an automated process, utilizing the same turbine layout and model as those used in standard EPAs. Automated iEPAs were generated by calibrating mesoscale wind speed maps from the Global Wind Atlas and Vortex with measured data from nearby meteorological masts. Frequency distributions and wind roses derived from ERA5 reanalysis data were adjusted to align with the predicted wind speeds from the calibrated maps. Turbine interaction losses were accounted for using a generic turbulence intensity assumption. This comparative analysis quantifies the uncertainty range of mesoscale-based energy assessments and identifies key factors contributing to deviations from mast measurement-based assessments. Specifically, we examine the effects of using calibrated mesoscale maps versus onsite mast data, reanalysis-derived versus measured frequency distributions, wind flow extrapolations via mast data and WAsP versus downscaled mesoscale data, and differences in shear profiles derived from measurements versus mesoscale models. Our findings provide wind farm developers with actionable insights into the reliability and limitations of mast-less energy assessments. Furthermore, the study highlights critical areas for improving accuracy and reducing uncertainty, empowering developers to make better-informed decisions during the early planning stages and increasing the likelihood of project success.
