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Development of energy storage system scheduling algorithm for simultaneous self-consumption and demand response program participation in South Korea

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  • Lee, Wonjun
  • Kang, Byung O
  • Jung, Jaesung

Abstract

This paper presents an Energy Storage System (ESS) scheduling algorithm for the simultaneous self-consumption and Demand Response (DR) program participation in South Korea. To do this, two ESS scheduling algorithms are developed to participate in the DR program in South Korea. The first algorithm is designed to maximize the customer's profit using energy arbitrage, after which the remaining ESS capacity is used to participate in the DR program. The second algorithm is designed to maintain its self-consumption at a minimum so that the ESS can participate in the DR program as much as possible. In addition, both algorithms have the same objectives of minimizing the peak demand in order to reduce the contract power as well as to minimize the charge/discharge cycles in order to lengthen the life of ESS. The developed algorithms are validated using the actual industrial load profiles with ESS. Furthermore, the economic analysis is performed when ESS that applies two different algorithms participates in the DR program. The simulation results indicate that the proposed algorithm prioritizing the self-consumption shows a relatively higher profit than that obtained by prioritizing the DR participation owing to uncompetitive compensation of the current South Korea DR program.

Suggested Citation

  • Lee, Wonjun & Kang, Byung O & Jung, Jaesung, 2018. "Development of energy storage system scheduling algorithm for simultaneous self-consumption and demand response program participation in South Korea," Energy, Elsevier, vol. 161(C), pages 963-973.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:963-973
    DOI: 10.1016/j.energy.2018.07.190
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    References listed on IDEAS

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    Cited by:

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    2. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    3. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
    4. 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.
    5. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    6. Jeseok Ryu & Jinho Kim, 2020. "Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty," Energies, MDPI, vol. 13(4), pages 1-14, February.

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