**5. Conclusions**

We presented an appropriate DR customer selection methodology for a Korean residential DR program to maximize the DR effect with lower customer enrollment. The proposed method showed better performance than other methods. Our method is divided into two parts. The first is customer segmentation according to load profile and consumption, and the second is targeted group selection based on two standards for DR participation. When we conducted customer segmentation, a two-stage clustering method was introduced. Customers were clustered by demand characteristics as variables in the first stage, and then segmented based on load patterns in the second stage. It can reflect more features of residential demand data than existing clustering methods, that makes better result in customer segmentation. Customer groups were classified as having higher DR potential by peak time and consumption patterns to select adequate groups having large potential in PTR program. As a result, the targeted groups were 1, 2, 3, 10, and 11 in our sample of residential customers in Korea, and their average demand reduction was 0.3496 (kWh), for an improvement of approximately 0.0876 (kWh), which increased savings by 33.44% compared to demand reduction due to opt-in enrollment. The proposed method allowed identifying enhanced DR effects. After the DR targeting demand reduction, we also conducted the cost-effective analysis of the PTR program from the perspective of the DR operator.

As a result, we observed that targeted DR capacity may be achieved with a small number of customers if targeted enrollment is implemented, which can use infrastructure and operation costs effectively. These results provide insights into the efficient use of DR in Korea. The number of customers and total DR capacity of targeted enrollment decreased compared with opt-in enrollment. However, if the number of customers who would like to participate in the DR program is high enough when the official full-scale program starts, selecting optimal customers among them would be more highly important. Therefore, the proposed method would be of great help in ensuring an efficient and economically sensible DR program in Korea.

We considered the residential customer targeting based on customer segmentation in demand response in this paper. Customer segmentation focus on the model structure to reflect features affecting demand response well. Some researches consider clustering model with heuristic algorithm in other areas, so we will apply this concept in further study.

**Author Contributions:** Conceptualization, E.L. and J.K.; data analysis, simulation, and methodology framework development, E.L.; writing, review, and editing, J.K.; supporting data collection and comments for improving the article, D.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by GIST Research Institute(GRI) grant funded by the GIST in 2020, and this work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20191210301930).

**Conflicts of Interest:** The authors declare no conflict of interest.
