DYNAMIC LINE RATING DAY-AHEAD FORECASTS-COST BENEFIT BASED SELECTION OF THE OPTIMAL QUANTILE

Dynamic Line Rating (DLR) is a promising field of research aiming to help network operators face challenges, such as increased penetration of renewable energies and peak electricity demand. Research on real-time overhead line ampacity estimation is currently advanced and, in the last few years, research has started to address medium-term DLR forecasting. The focus is on probabilistic forecasts, in order to select ratings associated with very low probability of occurrence. For this reason, 1%-quantiles are usually selected. In this paper, the authors propose a methodology for selecting the most appropriate quantiles based on a cost-benefit analysis, considering both the economic benefits of an increased line ampacity and the costs associated with a DLR forecast that is higher than its observed value. The proposed methodology is evaluated using realistic weather data on a virtual line connecting Belgium and France.