Abstract
Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.
Highlights
Distributed energy resources (DER) such as photovoltaic (PV), wind turbine (WT), energy storage system (ESS), and demand response (DR) have been rapidly expanded on the distribution system
Load profile including information such as peak time, duration, and electricity consumption can estimate approximately how much customers can reduce their capacity, so this information could be an important factor for determining which customers can reduce the most demand during the implementation of the DR program
We presented an appropriate DR customer selection methodology for a Korean residential DR
Summary
Distributed energy resources (DER) such as photovoltaic (PV), wind turbine (WT), energy storage system (ESS), and demand response (DR) have been rapidly expanded on the distribution system. According to the peak time rebate program implemented by San. Diego Gas & Electric (SDG&E), targeted enrollment, which selects suitable customers to participate in incentive-based DR programs, is essential for efficient DR operation [3]. Extending the k-means clustering method to reflect all load patterns and characteristics, resulting in outstanding performance; Deriving home appliances and usage pattern data using only electricity consumption data and not any additional data such as customer information, making the analysis more efficient; Presenting load profile segmentation of Korean household electricity demand data; and Conducting data analysis to suitable select groups for DR.
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