Posters

Abstract

The development of wind energy projects in complex environments requires rigorous site assessment workflows to integrate terrain analysis with measurement uncertainty and aerodynamic interaction modeling. This work presents an automated open-source Python framework designed to streamline the assessment of terrain complexity and Annual Energy Production estimation. The framework operates on a modular architecture that ingests site metadata, including turbine coordinates and optional LiDAR positions. Upon initialization, the system automatically retrieves Digital Elevation Model data from the Terrarium repository to generate hypsometric profiles and execute a terrain complexity assessment in accordance with IEC 61400-1 and IEC 61400-12-1 standards. Algorithms evaluate slope and sector variations to classify site complexity for each turbine location and identify specific wind sectors that require exclusion from power performance analysis due to the orographic features. For campaigns utilizing ground remote sensing, the framework incorporates a module for estimating Type B uncertainty based on IEC 61400-50-2 by correlating the specific LiDAR location with the surrounding terrain morphology to quantify flow distortion risks.  The final module couples site conditions with the PyWake simulation engine using wind rose distributions retrieved from Open-Meteo and user-defined turbine specifications. Given the limitations of global reanalysis data for microscale resolution, the framework performs a screening-level analysis of wake effects to compute the preliminary energy losses attributable to wake interactions for each turbine. This integration of terrain compliance and wake dynamics into a unified pipeline reduces computational overhead for due diligence, site screening, and campaign planning design by providing standardized reports that identify site constraints. The tool will be shared via GitHub and included.