Posters

Abstract

Introduction As wind energy development expands into complex terrains, achieving reliable and finance-grade wind resource assessments has become increasingly critical. This paper presents a comprehensive proof book consolidating global methodologies, validations, and results from Computational Fluid Dynamics (CFD)-based conversions of Lidar data. The focus is on validating the overall soundness of CFD-based conversion approaches, demonstrating their applicability to remote sensing devices such as wind Lidar. The inherent accuracy of Continuous Wave wind Lidars as standalone measurement devices, both pre- and post-conversion, in simple and complex terrain will be evidenced. Methodology The proof book aggregates findings from over 140 global validation campaigns, spanning a wide range of terrain complexities, wind regimes, and environmental conditions. The ZX 300's volumetric measurement capabilities were assessed pre- and post-CFD conversion to determine their alignment with industry standards for Energy Yield Analysis (EYA). CFD conversions, including widely used methods such as Deutsche WindGuard’s Complex Flow Solver (CFS) and DNV’s Flow Curvature Correction (FCC), were evaluated for their ability to resolve terrain-induced wind flow distortions. Performance metrics such as alignment with cup anemometer data, and uncertainty levels were analysed across diverse terrains. Innovative Contributions * Global Validation Evidence: This work consolidates findings from extensive validation studies to establish CFD-based conversions as a robust and universally applicable methodology for Lidar in complex terrain. * Post-Conversion Bankability: Applying CFD conversions to Lidar data delivers results that align with finance-grade requirements for standalone EYAs, ensuring transparency and reliability. * Standardised Uncertainty Management: The use of standards such as MEASNET guidelines for uncertainty estimation, offers a transparent framework for uncertainty management within energy yield prediction. Results Aggregated validation data highlighted the following key outcomes: * Pre-Conversion Accuracy: ZX 300 data consistently achieved minimal bias (typically