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

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