A new deep clustering method with application to customer selection for demand response program. (August 2023)

• Record Type: Journal Article
• Authors: Xiao, Jiang-Wen; Xie, Yutao; Fang, Hongliang; Wang, Yan-Wu
• Is Part Of: International journal of electrical power & energy systems. Volume 150(2023)
• Publication Date: 2023-08
• Subjects: Demand response -- Smart meter -- Customer selection -- Deep clustering -- Intra-cluster compactness -- Inter-cluster separation; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2023.109072
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A new deep learning-based clustering method is proposed to deal with the large number of daily load curves. A novel customer selection framework is proposed to identify the potential good candidates for demand response programs. The proposed clustering method outperforms the classical K-means and two state-of-the-art deep clustering methods. The customers selected by the proposed framework contribute to 59 % of total peak reduction with only accounting for 23 % of population. Abstract: Demand response (DR) is regarded as a promising solution to the problem of renewable energy integration, while it remains one of the key barriers for DR to target the right customers, i.e. the customers with high potential in peak reduction. Exploring the electricity consumption behaviors of customers can help DR program practitioners evaluate customers' peak reduction potential. Clustering has been the most common approach to describe customers' consumption behaviors in the literature. However, the existing clustering methods suffer from either unscalability or poor performance when handling a large number of daily load curves. To overcome these issues, a new deep learning-based clustering method is proposed in this paper, which integrates both intra-cluster compactness and inter-cluster separation into the objective function of clustering for consideration. Based on the clustering results, a new customer selection framework is developed to identify the potential good candidates for DR programs, which takes the stability of consumption behaviors and the characteristics at peak hours into account. Case studies on a London dataset demonstrate the superiority of the proposed clustering method over classical K-means and two state-of-the-art deep clustering methods in terms of clustering validity indexes. Furthermore, the simulations performed on an actual price-based pilot show that the customers selected by the proposed customer selection framework contribute to 59 % of total peak reduction with only accounting for 23 % of population.