Energetica India Magazine December 2020

WIND POWER Mesoscale and Microscale Modeling for Wind Energy Applications Because wind turbines operate in the natural atmosphere, high-resolution weather modeling is a powerful tool to help wind-energy projects succeed throughout the project lifecycle. When properly applied, such modeling helps optimize wind farms by reducing the uncertainty associated with spatial variability of wind flow across the site. Studies have shown that the uncertainty of mesoscale modeling for wind energy, when run at microscales, is lower than that of other modeling techniques such as linear flow models and computational fluid dynamics (CFD). Key features in underlying physics and technique of application of mesoscale models compared to the other modeling methods result in higher accuracy for wind energy assessment. Those differences are described herein. Additionally, we present validation statistics for a large dataset of wind projects, to explore relationships between model error and other model and site parameters. What are Mesoscale Models? Run on supercomputers, mesoscale models are a class of flu - id dynamics models used to predict the time series of natu- ral weather over a limited area. Because they were originally designed to represent weather phenomena at scales between “global” (>2000 km, e.g., a jet stream that spans a continent) and “micro” (<2 km, e.g., a tornado), they are known as “meso- scale models” (Orlanski, 1975). They solve the physical equa- tions that govern the time-evolution of wind and weather within a 3-dimensional fluid. Mesoscale models were originally developed by scientists at universities and national labs in the 1960s and 70s, and today nearly every nation relies on them for accurate daily weather forecasting. In the 1990s, meteorologists first employed me - soscale modeling for short-term operational wind energy fore- casting. It was then recognized that mesoscale models could be used not just to predict the short-term future but to recreate the long-term past. Consultants started using retrospective mesoscale modeling at high spatial resolution (100-400 meter, or microscale, grid spacing) for project-scale wind flow model - ing in support of financeable wind energy assessment studies. The reasons for doing so are compelling, as described in the next section. Key to the ability to perform these retrospective simulations was the advent of global reanalysis datasets such as the NCAR/ NCEP Reanalysis Project, ERA40, ERA-Interim, CFSR, MERRA, MERRA2, and ERA5. These research datasets provided glob- al gridded weather variables at hourly time resolution, going back 40+ years. Although they are too coarse (grid spacing of several 10s of km) to be used directly for project-scale wind flow mapping, they can be used to drive retrospective meso - scale model simulations at high resolution, by providing the necessary initial and boundary conditions the mesoscale mod- els require to simulate the real weather of the past. Why Does Mesoscale Modeling Work for Wind Energy As- sessment? Linear/CFD models are applied with an assumed uniformity of wind flow on the horizontal edges and vertical top of the model domain, and the model’s job is to adjust the hub-height wind flow across the project due only to the effects of fine- scale topography and land use across the site. If there are 38 energetica INDIA- December_2020 Gregory S. Poulos PhD - CEO, Principal Atmospheric Scientist, ArcVera Renewables Mark Stoelinga PhD - Lead, Atmospheric Science Innovation and Applications, ArcVera Renewables Next-Generation Numerical Weather Prediction for Wind Flow Modeling