Spatio-temporal weather model-based probabilistic forecasting of dynamic thermal rating for overhead transmission lines

Abstract

Transfer capability of an overhead transmission line is limited by its thermal rating. The static thermal rating based on the worst-case weather condition does not utilize dynamic cooling and heating effects on conductors from ambient weather. The dynamic thermal rating (DTR) adapts the thermal capacity based on measured and predicted weather and typically results in a higher rating without reducing system security. Appropriate hourly DTR forecasts could be integrated with day-ahead market to provide more economic benefits. Employing DTR forecasting, however, is difficult in view of many relevant weather factors involved in the thermal model, weather data availability, inherent uncertainties as well as the spatial topology of transmission lines. In this paper, a probabilistic DTR forecasting approach is established to resolve the above difficulties. Major weather factors are selected based on impact analysis, and a spatio-temporal regression model is developed for weather forecast with available weather sources. Spatial topology of transmission lines and weather uncertainties are captured by modeling the line rating as a random variable, which is the minimum value of selected span thermal capacities. Distribution and the corresponding percentiles of the DTR are then obtained with low computational requirements and high modeling accuracy. Numerical testing results for a short line segment case and a long line case demonstrate that our method provides secure and high ratings for overhead transmission lines.