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Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

Author

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  • Jeseok Ryu

    (Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea)

  • Jinho Kim

    (Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea)

Abstract

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:883-:d:321575
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    References listed on IDEAS

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    1. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
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    3. Jimyung Kang & Soonwoo Lee, 2018. "Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System," Energies, MDPI, vol. 11(11), pages 1-14, October.
    4. Wang, Ge & Zhang, Qi & Li, Hailong & McLellan, Benjamin C. & Chen, Siyuan & Li, Yan & Tian, Yulu, 2017. "Study on the promotion impact of demand response on distributed PV penetration by using non-cooperative game theoretical analysis," Applied Energy, Elsevier, vol. 185(P2), pages 1869-1878.
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    Cited by:

    1. Jeseok Ryu & Jinho Kim, 2020. "Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference," Energies, MDPI, vol. 13(23), pages 1-14, December.

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