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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

Author

Listed:
  • Hyun Cheol Jeong

    (Department of Electrical Engineering, Dong-A University, 37, Nakdong-daero 550beon-gil, Saha-gu, Busan 49315, Korea)

  • Jaesung Jung

    (Department of Energy Systems Research, Ajou University, 206, Worldcup-ro, Yeongtong-gu, Suwon, Gyeonggi-do 16499, Korea)

  • Byung O Kang

    (Department of Electrical Engineering, Dong-A University, 37, Nakdong-daero 550beon-gil, Saha-gu, Busan 49315, 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.

Suggested Citation

  • Hyun Cheol Jeong & Jaesung Jung & Byung O Kang, 2020. "Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea," Energies, MDPI, vol. 13(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1723-:d:341558
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    References listed on IDEAS

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    1. Reza Arghandeh & Jeremy Woyak & Ahmet Onen & Jaesung Jung & Robert P. Broadwater, 2014. "Economic Optimal Operation of Community Energy Storage Systems in Competitive Energy Markets," Papers 1407.0433, arXiv.org, revised Sep 2014.
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    5. Kang, Byung O. & Lee, Munsu & Kim, Youngil & Jung, Jaesung, 2018. "Economic analysis of a customer-installed energy storage system for both self-saving operation and demand response program participation in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 69-83.
    6. Reihani, Ehsan & Motalleb, Mahdi & Ghorbani, Reza & Saad Saoud, Lyes, 2016. "Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration," Renewable Energy, Elsevier, vol. 86(C), pages 1372-1379.
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    8. Maher AbuBaker, 2019. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine," Energies, MDPI, vol. 12(22), pages 1-29, November.
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    Cited by:

    1. Zhiyuan Zeng & Tianyou Li & Jun Su & Longyi Sun, 2023. "Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers," Energies, MDPI, vol. 16(20), pages 1-21, October.
    2. Hongli Liu & Luoqi Wang & Ji Li & Lei Shao & Delong Zhang, 2023. "Research on Smart Power Sales Strategy Considering Load Forecasting and Optimal Allocation of Energy Storage System in China," Energies, MDPI, vol. 16(8), pages 1-18, April.
    3. Kyo Beom Han & Jaesung Jung & Byung O Kang, 2021. "Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea," Energies, MDPI, vol. 14(19), pages 1-17, October.

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