Coupling Computational Fluid Dynamics With The High Resolution Rapid Refresh Model For Forecasting Dynamic Line Ratings

Abstract

This study looks at forecasted dynamic line ratings in southern Idaho by using data from the high resolution rapid-refresh (HRRR) model for forecasted weather conditions. The HRRR model can provide accurate 18-h forecasts with a 15-min temporal resolution. Typical static ratings used for overhead transmission lines use overly conservative assumptions for local weather conditions, such as using the maximum solar irradiance and ambient temperature measured during the summer, combined with low wind speed for an entire season. The HRRR forecast model used here has high spatial resolution to provide local forecast conditions along the entire length of a transmission line. The area that is of interest in this study is in southern Idaho, spanning a total of 15,000 square kilometers. The forecasted weather data are coupled with a computational fluid dynamics (CFD) model of the wind in the region for fine-scale resolution of convective cooling rates on the midpoint of each individual transmission-line span. This high-fidelity approach can be used to find the minimum ampacity across all given midpoints of a transmission line to determine the limit to be used for the line rating. The ability to increase the overhead line rating above the conservative static approach with the forecasted weather data provides a large potential to alleviate congestion and provide data for utility-market transactions, as well as benefits for wind energy generation through concurrent cooling. This study shows that for the region of interest, using forecasted weather data coupled with CFD modeling can calculate DLR ampacity rating above static over 90% of the time, with a small relative error in the forecasted ampacity over time.