Electricity Consumption Behaviors and Clustering of Distribution Grids in Terms of Demand Response

Cetinkaya U., Avci E., BAYINDIR R.

Electric Power Components and Systems, vol.50, no.9-10, pp.498-515, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 9-10
  • Publication Date: 2022
  • Doi Number: 10.1080/15325008.2022.2136787
  • Journal Name: Electric Power Components and Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.498-515
  • Keywords: clustering analysis, demand response, distribution grid, electricity consumption behaviors
  • TED University Affiliated: No


Knowing of energy consumption behaviors and similarities between consumers becomes extremely important for electric power systems operators to apply green energy trends by ensuring the stability and flexibility of the grid. Especially in the modern power systems and the energy markets, consumer-based market structures must be implemented. Consumer types, energy needs, and demand behaviors of the distribution grid can affect cumulatively the demand behavior of the full electricity system. Thus, understanding the consumption behaviors and similarities among different distribution grid operator can provide a significant advantage in determining the types and applications of demand response activities in terms of the transmission grid. In this study, the consumption of distribution grids analyzes statistically according to the consumer types (like residential, commercial, industrial), and to identify the consumption similarities making clustering analysis. For this reason, first, the total consumption reflected on the transmission grid is examined periodically concerning the distribution grids. Second, we group distribution grids by consumption similarity using past real consumption data using a MATLAB application developed. A data-driven regional demand aggregation approach is shown together with the cluster analysis. Finally, the demand response potential got from distribution loads has been evaluated.