IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i4p883-d321575.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/4/883/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/4/883/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    2. Shigetomi, Yosuke & Matsumoto, Ken'ichi & Ogawa, Yuki & Shiraki, Hiroto & Yamamoto, Yuki & Ochi, Yuki & Ehara, Tomoki, 2018. "Driving forces underlying sub-national carbon dioxide emissions within the household sector and implications for the Paris Agreement targets in Japan," Applied Energy, Elsevier, vol. 228(C), pages 2321-2332.
    3. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    4. Homa Rashidizadeh-Kermani & Hamid Reza Najafi & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2018. "Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets," Energies, MDPI, vol. 11(9), pages 1-20, September.
    5. Wu, Xiaohua & Hu, Xiaosong & Yin, Xiaofeng & Zhang, Caiping & Qian, Shide, 2017. "Optimal battery sizing of smart home via convex programming," Energy, Elsevier, vol. 140(P1), pages 444-453.
    6. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    7. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
    8. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    9. 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.
    10. Pedro Faria & João Spínola & Zita Vale, 2018. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs," Energies, MDPI, vol. 11(8), pages 1-17, July.
    11. Wu, Qiong & Xie, Zhun & Ren, Hongbo & Li, Qifen & Yang, Yongwen, 2022. "Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study," Energy, Elsevier, vol. 254(PC).
    12. Jinseok Kim & Hyungseop Hong & Ki-Il Kim, 2018. "Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index," Energies, MDPI, vol. 11(7), pages 1-23, July.
    13. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    14. Viana, Matheus Sabino & Manassero, Giovanni & Udaeta, Miguel E.M., 2018. "Analysis of demand response and photovoltaic distributed generation as resources for power utility planning," Applied Energy, Elsevier, vol. 217(C), pages 456-466.
    15. Feijoo, Felipe & Kundu, Abhishake & Flores, Francisco & Matamala, Yolanda, 2024. "Photovoltaic sizing assessment for microgrid communities under load shifting constraints and endogenous electricity prices: A Stackelberg approach," Energy, Elsevier, vol. 307(C).
    16. Mahmoud Elkazaz & Mark Sumner & Seksak Pholboon & Richard Davies & David Thomas, 2020. "Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation," Energies, MDPI, vol. 13(13), pages 1-23, July.
    17. Sobhani, Seyed Omid & Sheykhha, Siamak & Madlener, Reinhard, 2020. "An integrated two-level demand-side management game applied to smart energy hubs with storage," Energy, Elsevier, vol. 206(C).
    18. Elkazaz, Mahmoud & Sumner, Mark & Thomas, David, 2021. "A hierarchical and decentralized energy management system for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 291(C).
    19. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    20. Jiang, Bo & Muzhikyan, Aramazd & Farid, Amro M. & Youcef-Toumi, Kamal, 2017. "Demand side management in power grid enterprise control: A comparison of industrial & social welfare approaches," Applied Energy, Elsevier, vol. 187(C), pages 833-846.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:883-:d:321575. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.