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• W2982445103 abstract "In smart power system, effective methods have to be chosen to interact with consumers. Demand response programmes (DRPs) as the most effective way to interact with consumers have a variety of types that should be decided to be chosen and implemented according to consumer's behaviour. Power consumers are classified according to their behaviour and consumption. This classification consists of various clusters including residential, commercial, agricultural, industrial and public consumption. The consumers’ behaviour approach is classified by weighed fuzzy average K-means clustering method. An accurate DRPs non-linear model considering coefficient of participation is presented and applied to each cluster. The decision-making indicators are determined and weighed using the entropy method. The aim is to prioritise the DRPs implementation in each cluster, which is done by the technique for order preference by similarity to ideal-solution method. Finally, a case study is investigated on a real network and the impact of variables and factors on the priority of DRPs is discussed." @default.
• W2982445103 created "2019-11-08" @default.
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• W2982445103 date "2019-11-13" @default.
• W2982445103 modified "2023-10-01" @default.
• W2982445103 title "Optimal DRPs selection using a non‐linear model based on load profile clustering" @default.
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• W2982445103 doi "https://doi.org/10.1049/iet-gtd.2019.1085" @default.
• W2982445103 hasPublicationYear "2019" @default.
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