**1. Introduction**

Recently, 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. Because of this trend, the power demand characteristics have been more complicated. In addition, various business models and policies as resources application increased have been created. However, the DER expansion leads to load fluctuation on the distribution system locally. The DR program has been regarded as one of the solutions to mitigate imbalance. For this reason, DR programs have recently received significant attention. Under a DR program, electricity consumers change their electricity consumption patterns in response to a time-based rate or incentive payments for the periods

when needed [1]. Utilities and/or independent system operators (ISO) manage DR programs to avoid peak demand, high prices, and variable generation of renewables.

DR programs can be divided into two types: price- and incentive-based. Price-based DR programs vary the electricity price depending on certain time conditions being met [1]. Time of use (TOU), critical peak pricing (CPP), and real time pricing (RTP) are examples of this type of DR. Meanwhile, incentive-based DR programs encourage customers to shed their load or sell back to the electricity market. In the case of incentive-based DR programs, targeting suitable customers takes priority before DR implementation [2]. 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]. Before DR program introduction, the customer demand characteristics analysis is significant because of heterogeneous characteristics. Especially, the costs of recruiting DR customers may be considerable, as the process involves several activities such as marketing, education, and DR system support and operation. If utility companies or ISO do not select suitable customers for enrollment in the DR program, the losses caused by enrollment of inappropriate customers could be substantial. Therefore, to minimize losses, it is essential to secure a large DR capacity with a relatively small number of customers.

Before choosing suitable customers with potential in electricity consumption and similarity between peak time and event, analyzing the load profiles of customers is essential. We considered the customer targeting concept through analyzing several typical load profiles as a result of load profile segmentation. Therefore, load profile segmentation analysis should be conducted for selecting adequate customers. Various clustering methods are normally employed to perform electricity consumer segmentation. Residential electricity consumption is uncertain and variable due to various factors affecting demand, such as home appliance usage patterns, the number of family members, lifestyle patterns, customer occupations, and income levels. These factors cause residential demand to have far more variability than commercial and industrial demand [4], thus making the residential load profile segmentation problem relatively more difficult. When analyzing load profile clusters, their load patterns or characteristics are commonly applied as variables. However, in residential load profile clustering, only considering load patterns poses a number of problems such as an excessively broad spectrum of hourly consumption rates and different peak occurrence times within the same group, whereas the drawback of only considering load characteristics is that consumer patterns are not reflected accurately. To determine suitable DR participant groups, residential customers should therefore be segmented by both pattern and consumption scales.

This paper proposes a two-stage k-means model to address pattern and consumption scales. In the first stage, k-means clustering is conducted based on load characteristics, such as daily consumption and peak occurrence time. In the second stage, k-means clustering is performed based on hourly load profile of residential customers. This methodology is applied to over 800 Korean residential DR participants, for whom hourly electricity use data is available. The results reveal an appropriate segmentation methodology for DR participants. This paper contributes to the literature on load profile segmentation for targeting customers by:


The remainder of this paper is organized as follows. In Section 2, we illustrate the current state-of-the-art clustering methodology. In Section 3, we present the proposed two-stage k-means model, which ensures effective household load profile segmentation for targeting residential customers. In Section 4, we show the effect of targeting residential customers in the DR program and compare this

effect to the effect of opt-in enrollment in Korea. Section 5 concludes and outlines ideas for further research in this area.

#### **2. Literature Review**

This section presents a review of the current state-of-the-art methodology for load profile segmentation. Many studies have been performed to segment load profile accurately by applying various clustering methods. K-means, self-organizing maps (SOM), mixture models, expectation maximization (EM), and spectral clustering have been widely used as clustering methods. Among the several methods available for clustering to address load pattern segmentation, the most commonly employed are standard k-means [5–10], adaptive k-means [11,12], fuzzy k-means [13,14], and g-means [15], which is an alternative clustering model to k-means. SOM [16,17] is commonly employed by itself but has also been combined with other clustering methods such as k-means and hierarchical clustering as a hybrid model [18]. Mixture models [19,20] and EM [21] are also popular as statistical clustering methods. For DR program operation, DR customer segmentation is commonly conducted for many reasons. Spectral clustering applying information entropy based piecewise aggregate approximation is proposed for commercial demand response application being able to reflect multiscale similarities [22]. Recently, deep learning based clustering such as deep embedded clustering has become a trend for use in residential baseline estimation [23]. Each of the existing clustering methods normally used for electricity consumer segmentation has its own characteristics and is summarized in Table 1 for each characteristic. As explained in Table 1, each clustering method has its advantages in terms of data type or separation process. Although there are a lot of existing clustering methods, k-means has great strength in that it is easier than other existing models and shows good performance in various problem solving cases.


**Table 1.** Characteristics of previous clustering methods.

It also can be used to increase accuracy of customer baseline and select appropriate customers for DR. Zhang et al. [7] proposed clustering by k-means before baseline estimation, and it demonstrated improved results. In regard to addressing clustering structure issues, some studies have employed two-stage clustering methods [14,15] which are similar with the proposed methodology in this study, showing that this structure could reflect all the load factors (i.e., voltage, residential type, consumption, and pattern) better than the structures prevalent in the literature. However, load profile segmentation

for DR targeting enrollment was not performed in these studies, and they were just focused on similar patterns in groups, which has the limitation of large variation in customer daily consumption. It is hard to use the existing models as it is in this study. Therefore, we considered the two-stage methodology to reflect load characteristics affecting DR at the first stage.

Commonly, optimization methods are utilized for customer targeting in DR program, and there are some studies on this without load profile segmentation [25,26]. Kwac et al. [25] proposed solving the stochastic knapsack problem (SKP) as a means to recruit optimal customers for DR programs. Zhou et al. [26] designed an adaptive targeting method to estimate DR effects.

This paper describes a customer targeting and DR analysis model through a two-stage clustering analysis. The proposed methodology will enable the effective selection of customers for DR programs and illustrate a better DR effect than in opt-in enrollment.
