Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea

Abstract

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. Energies 2020, 13, 1723 2 of 17 algorithms [6]. Lee et al. classified the electric power consumption characteristics of industrial customers using the standard industry classification code in South Korea [7]. Bidoki et al. applied different clustering algorithms based on K-means, weighted fuzzy mean K-means, modified follow leader (MFTL), self-organizing map (SOM), and layer algorithm to classify load curves of different types of customers, and the results from comparing the clustering performances utilized to determine the adaptive clustering algorithms [8]. Zhou et al. proposed the five-stage process model based on K-means, SOM, Fuzzy c-average (FCM), and hierarchical clustering algorithms to analyze the impact of electric power suppliers and their consumers in smart grid circumstances [9]. Abubaker classified load profiles achieved from electricity consumers in the Tulkarm district based on the K-means algorithm [10].These clustering-based approaches for analyzing customer load profiles can be applied to the development of effective strategies for energy management in smart grid circumstances. The representative strategy utilizes the Energy Storage System (ESS) installed for electricity consumers [11]. The ESS is a device that enables the storage of electrical energy during off-peak times and supplies the stored energy at the requested time to reduce electricity costs for the customer. The South Korean government had established a long-term roadmap and supporting policies to increase the penetration level of ESS, and, consequently, South Korea has been positioned as the second largest nameplate capacity of ESS since 2016 [12].Several studies have proposed methodologies to optimize and deve...

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