IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v225y2021ics0360544221004862.html
   My bibliography  Save this article

Energy sharing platform based on call auction method with the maximum transaction volume

Author

Listed:
  • Sun, Lingling
  • Qiu, Jing
  • Han, Xiao
  • Dong, Zhao Yang

Abstract

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia’s electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high.

Suggested Citation

  • Sun, Lingling & Qiu, Jing & Han, Xiao & Dong, Zhao Yang, 2021. "Energy sharing platform based on call auction method with the maximum transaction volume," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s0360544221004862
    DOI: 10.1016/j.energy.2021.120237
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221004862
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120237?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    2. Mena, Rodrigo & Hennebel, Martin & Li, Yan-Fu & Zio, Enrico, 2016. "A multi-objective optimization framework for risk-controlled integration of renewable generation into electric power systems," Energy, Elsevier, vol. 106(C), pages 712-727.
    3. Fazlalipour, Pary & Ehsan, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets," Energy, Elsevier, vol. 171(C), pages 689-700.
    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. Wu, Chuantao & Zhou, Dezhi & Lin, Xiangning & Sui, Quan & Wei, Fanrong & Li, Zhengtian, 2022. "A novel energy cooperation framework for multi-island microgrids based on marine mobile energy storage systems," Energy, Elsevier, vol. 252(C).
    2. Nykyri, Mikko & Kärkkäinen, Tommi J. & Levikari, Saku & Honkapuro, Samuli & Annala, Salla & Silventoinen, Pertti, 2022. "Blockchain-based balance settlement ledger for energy communities in open electricity markets," Energy, Elsevier, vol. 253(C).

    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. Jun Dong & Yuanyuan Wang & Xihao Dou & Zhengpeng Chen & Yaoyu Zhang & Yao Liu, 2021. "Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    2. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
    3. Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources," Energy, Elsevier, vol. 227(C).
    4. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Day-ahead bidding strategy of regional integrated energy systems considering multiple uncertainties in electricity markets," Applied Energy, Elsevier, vol. 348(C).
    5. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets," Energy, Elsevier, vol. 278(PA).
    6. Guo, Hongye & Chen, Qixin & Shahidehpour, Mohammad & Xia, Qing & Kang, Chongqing, 2022. "Bidding behaviors of GENCOs under bounded rationality with renewable energy," Energy, Elsevier, vol. 250(C).
    7. Yu, Min Gyung & Pavlak, Gregory S., 2023. "Risk-aware sizing and transactive control of building portfolios with thermal energy storage," Applied Energy, Elsevier, vol. 332(C).
    8. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    9. Vinel, Alexander & Mortaz, Ebrahim, 2019. "Optimal pooling of renewable energy sources with a risk-averse approach: Implications for US energy portfolio," Energy Policy, Elsevier, vol. 132(C), pages 928-939.
    10. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    11. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    12. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    13. Peng, Feixiang & Hu, Shubo & Fan, Xuanxuan & Sun, Hui & Zhou, Wei & Guo, Furan & Song, Wenzhuo, 2021. "Sequential coalition formation for wind-thermal combined bidding," Energy, Elsevier, vol. 236(C).
    14. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    15. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    16. Wang, Wei & Cova, Gregorio & Zio, Enrico, 2022. "A clustering-based framework for searching vulnerabilities in the operation dynamics of Cyber-Physical Energy Systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    17. Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    18. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    19. Wang, Jiangjiang & Zhao, Lei & Lu, Hao & Wei, Changqi, 2024. "Multi-objective stochastic-robust based selection-allocation-operation cooperative optimization of rural integrated energy systems considering supply-demand multiple uncertainties," Renewable Energy, Elsevier, vol. 233(C).
    20. Hojnik, Jana & Ruzzier, Mitja & Fabri, Stephanie & Klopčič, Alenka Lena, 2021. "What you give is what you get: Willingness to pay for green energy," Renewable Energy, Elsevier, vol. 174(C), pages 733-746.

    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:eee:energy:v:225:y:2021:i:c:s0360544221004862. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.