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

1
Department of Electrical Engineering, Dong-A University, 37, Nakdong-daero 550beon-gil, Saha-gu, Busan 49315, Korea
2
Department of Energy Systems Research, Ajou University, 206, Worldcup-ro, Yeongtong-gu, Suwon, Gyeonggi-do 16499, Korea
*
Author to whom correspondence should be addressed.
Energies 2020, 13(7), 1723; https://doi.org/10.3390/en13071723
Received: 12 March 2020 / Revised: 29 March 2020 / Accepted: 2 April 2020 / Published: 4 April 2020
(This article belongs to the Section Smart Grids and Microgrids)
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. View Full-Text
Keywords: energy storage system (ESS); time-of-use (TOU) tariff; energy management in smart grid; peak shaving operation; arbitrage operation; customer load clustering energy storage system (ESS); time-of-use (TOU) tariff; energy management in smart grid; peak shaving operation; arbitrage operation; customer load clustering
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MDPI and ACS Style

Jeong, H.C.; Jung, J.; Kang, B.O. Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea. Energies 2020, 13, 1723.

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