Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs

Abstract

Smart meters and dynamic pricing are key factors in implementing a smart grid. Dynamic pricing is one of the demand-side management methods that can shift demand from on-peak to off-peak. Furthermore, dynamic pricing can help utilities reduce the investment cost of a power system by charging different prices at different times according to system load profile. On the other hand, a dynamic pricing strategy that can satisfy residential customers is required from the customer’s perspective. Residential load profiles can be used to comprehend residential customers’ preferences for electricity tariffs. In this study, in order to analyze the preference for time-of-use (TOU) rates of Korean residential customers through residential electricity consumption data, a representative load profile for each customer can be found by utilizing the hourly consumption of median. In the feature extraction stage, six features that can explain the customer’s daily usage patterns are extracted from the representative load profile. Korean residential load profiles are clustered into four groups using a Gaussian mixture model (GMM) with Bayesian information criterion (BIC), which helps find the optimal number of groups, in the clustering stage. Furthermore, a choice experiment (CE) is performed to identify Korean residential customers’ preferences for TOU with selected attributes. A mixed logit model with a Bayesian approach is used to estimate each group’s customer preference for attributes of a time-of-use (TOU) tariff. Finally, a TOU tariff for each group’s load profile is recommended using the estimated part-worth.

Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

“…As an initial stage for grouping, the representative load pattern of each household energy consumption was derived from median value of hourly energy consumption [27]. Normalization is performed using the maximum and minimum normalization of the representative load pattern for each household [28]:…”

Analysis of Residential Consumers’ Attitudes Toward Electricity Tariff and Preferences for Time-of-Use Tariff in Korea

“…The conditional logit model, introduced by McFadden et al [19], is relatively easy to estimate and interpret results but has limitations in that it does not sufficiently account for the heterogeneity of preferences among individual consumers. In this study, a mixed-logit model and a latent class conditional logit model are introduced to solve the limitation of not accounting for preference heterogeneity [20], [21]. As preferences toward electricity vary according to each factors that make up the electricity bill are also different.…”

“…The equations of the mixed-logit method are presented in (5), (6), and (7) as like [5], [20]. If respondent n faces a choice set comprising J alternatives, then the utility of respondent n for alternative j in the choice set is presented as:…”

“…As documented in [20], if the parameter of ( 6) is a discrete random variable with values S), and each probability is S), is the probability of a latent class conditional logit model as follows:…”

Residential Electricity Rate Plans and Their Selections Based on Statistical Learning

“…To ensure electricity consumers have a choice of electricity rate plans, the EU has recommended that electric power sellers should provide consumers with sufficient information regarding the dynamic rate plan [2]. It is important to inform consumers in advance which rate plan will be advantageous to them so that they can choose an appropriate rate plan [21]- [27]. In addition, utility companies also need to select areas where many consumers can choose the TOU rate plan for an effective AMI deployment.…”