Traditional methods for obtaining point-estimates of hub-height wind speeds at individual wind turbines in complex terrain rely on computationally-intensive fluid-dynamics modeling algorithms or expensive ground measurement campaigns (or both). While these are adequate for individual wind project investment diligence exercises, the complexity and duration of these methods prevent them from being applied at scale, typically relegating them to use as additional in-depth modeling tools after a wind farm site has already been chosen for development.
As renewable energy deployment increases, investors and planners increasingly need access to on-demand high-quality local estimates of wind resource variability–e.g. for updated risk assessments of existing resources or early stage selections of optimal wind farm sites from large sets of prospective locations.
In this presentation, we evaluate a wind energy modeling framework that combines a parameterized turbine wake model with WindNinja, an open-source momentum-conserving solver of spatially varying wind fields over complex terrain. Originally developed to be run quickly on laptops for wildland fire prediction, WindNinja is computationally efficient and capable of resolving wind fields at 100m resolution–and when combined with a turbine wake model allows us to model turbine-specific historical hourly wind speeds at all 1500+ operational wind farms (74,000+ turbines) in the United States.
We validate our modeling framework by comparing hourly generation values derived from WindNinja-computed hub-height historical wind fields against actual hourly energy production (removing hours subject to availability and curtailment losses) at all utility-scale wind farms in the Texas energy grid (ERCOT) as reported in Security Constrained Economic Dispatch (SCED) data. For a sample of wind farms, we also compare WindNinja-derived monthly generation levels against monthly energy production reported by the EIA.
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