Optimal DRPs selection using a non‐linear model based on load profile clustering. Issue 24 (13th November 2019)

• Record Type: Journal Article
• Authors: Moazzen, Farid; Alikhani, Majid; Parsa Moghaddam, Mohsen; Gitizadeh, Mohsen
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 24(2019)
• Page Start: 5495
• Page End: 5503
• Publication Date: 2019-11-13
• Subjects: pattern clustering -- demand side management -- fuzzy set theory -- decision making -- entropy -- pattern classification -- smart power grids -- consumer behaviour -- power consumption; load profile clustering -- smart power system -- demand response programmes -- power consumers -- residential consumption -- commercial consumption -- agricultural consumption -- industrial consumption -- public consumption -- decision‐making indicators -- entropy method -- DRP implementation -- ideal‐solution method -- accurate DRP nonlinear model -- optimal DRP selection -- weighed fuzzy average K‐means clustering method -- consumer behaviour; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.1085
• Languages: English
• ISSNs: 1751-8687

Abstract

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.