Hyun Cheol Jeong’s research while affiliated with Korea University and other places

In recent years, a Time-of-Use (TOU) tariff for residential consumers has received much more attention with the increasing deployment of smart meters for residential consumers in Korea. The introduction of the TOU tariff for residential consumers is expected to allow residential consumers to have a choice of electricity rate plans in addition to load management. An analysis of residential consumers’ preferences for the TOU tariff is needed to identify TOU attributes’ levels. This paper analyzes Korean consumers’ attitudes toward current residential electricity tariff and preferences for TOU tariff using demographic characteristics collected by a face-to-face survey. Consumers’ preferences on key attributes of TOU tariff are analyzed using the conjoint analysis, and attitudes toward current residential electricity tariff are estimated by the multiple indicators multiple causes (MIMIC) model. The analysis results of this study show that based on their attitude toward electricity tariff, consumers’ attitudes can be divided into three latent variables, and preferences for TOU tariff are discrete by group rather than continuous by individual consumer.

Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.

In contrast to city-level and larger aggregate-level load forecasting, load forecasting for residential customers is a much more challenging problem because residential loads are much more volatile. In order to forecast the residential load at one-hour interval 24-h loads the day before, a 2-Step SARIMAX method for residential load forecasting is proposed in this study. The forecasting performance of the proposed method is compared with the existing forecasting methods including SARIMA.

In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers.

This study proposes a methodology to develop adaptive operational strategies of customer-installed Energy Storage Systems (ESS) based on the classification of customer load profiles. In addition, this study proposes a methodology to characterize and classify customer load profiles based on newly proposed Time-of-Use (TOU) indices. The TOU indices effectively distribute daily customer load profiles on multi-dimensional domains, indicating customer energy consumption patterns under the TOU tariff. The K-means and Self-Organizing Map (SOM) sophisticated clustering methods were applied for classification. Furthermore, this study demonstrates peak shaving and arbitrage operations of ESS with current supporting polices in South Korea. Actual load profiles accumulated from customers under the TOU rate were used to validate the proposed methodologies. The simulation results show that the TOU index-based clustering effectively classifies load patterns into ‘M-shaped’ and ‘square wave-shaped’ load patterns. In addition, the feasibility analysis results suggest different ESS operational strategies for different load patterns: the ‘M-shaped’ pattern fixes a 2-cycle operation per day due to battery life, while the ‘square wave-shaped’ pattern maximizes its operational cycle (a 3-cycle operation during the winter) for the highest profits.