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Dynamic line rating day-ahead forecasts - cost benefit based selection of the optimal quantile

Abstract : 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.
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Submitted on : Thursday, November 17, 2016 - 3:36:57 PM
Last modification on : Thursday, September 24, 2020 - 5:20:17 PM
Long-term archiving on: : Thursday, March 16, 2017 - 5:56:38 PM

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  • HAL Id : hal-01398440, version 1

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Romain Dupin, Andrea Michiorri, Georges Kariniotakis. Dynamic line rating day-ahead forecasts - cost benefit based selection of the optimal quantile. CIRED 2016 workshop - Electrical networks for society and people , CIREC - Centre International de Recherche sur l’Environnement et le Développement, Jun 2016, Helsinki, Finland. ⟨hal-01398440⟩

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