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      Clustering-based method for the feeder selection to improve the characteristics of load shedding

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          Abstract

          Under-frequency load shedding (UFLS) schemes are designed by specifying a given amount of load to shed at various frequency thresholds to prevent the collapse of the electrical power system in the event of a large generation-load imbalance. An UFLS step is constituted of a group of medium-voltage feeders that trip when a given frequency threshold is reached. This study focuses on the method to be used when allocating a given feeder to a given step. First, the authors introduce performance metrics to quantify the accuracy level with which the UFLS target is met. Second, they model: the allocation method currently used in France; a variant of that method; and a new method introduced in this study, based on an automated clustering technique. Third, based on real consumption patterns measured from a vast area in France, and using the introduced performance metrics, they compare the efficiency of the three described methods. This study is conducted for the current state of loading of the considered distribution network and for a hypothetical situation with an increased share of distribution-side photovoltaic generation. For the chosen performance metrics, they demonstrate that the first two methods provide similar results while the clustering-based method performs remarkably better.

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          Most cited references 18

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          Regression Quantiles

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            Comparisons Among Clustering Techniques for Electricity Customer Classification

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              Overview and performance assessment of the clustering methods for electrical load pattern grouping

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                Author and article information

                Contributors
                Journal
                IET-STG
                IET Smart Grid
                IET Smart Grid
                The Institution of Engineering and Technology
                2515-2947
                19 August 2019
                30 September 2019
                December 2019
                : 2
                : 4
                : 659-668
                Affiliations
                [1 ] University of Grenoble Alpes , CNRS, Grenoble INP, G2Elab, 38000 Grenoble, France
                [2 ] Enedis , Paris, France
                Article
                IET-STG.2019.0064 STG.2019.0064.R2
                10.1049/iet-stg.2019.0064

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                Page count
                Pages: 0
                Product
                Funding
                Funded by: Enedis Industrial Research Chair on Smart Grids
                Award ID: -
                Categories
                Research Article

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