IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v314y2025ics0360544224039458.html
   My bibliography  Save this article

The optimal bidding strategy for multi-energy prosumers in the double auction electricity-heat market: A bidding space model

Author

Listed:
  • Fu, Yang
  • Shan, Jie
  • Li, Zhenkun
  • Xie, BoLin
  • Pan, Jeng-Shyang

Abstract

With the rise of multi-energy prosumers (MEPs) in the local energy system, an efficient multi-energy management is increasingly significant. This paper designs a double auction electricity-heat market for simultaneous electricity and heat trading and proposes a bidding space model for MEPs. Firstly, it introduces the bidding rules, clearing mechanism, and market trading execution for the electricity-heat market. Secondly, a MEP's energy supply cost model considering the equipment off-design performance and the market clearing price (MCP) prediction model considering the electricity-heat coupling are established, which together constitute the bidding space. Then, by analyzing the bidding space, it determines the optimal bidding quantity and a reasonable bidding price range, which guides MEPs to participate in the market. After that, considering the new bidding risks in the electricity-heat market compared to the traditional electricity market, it proposes an optimal bidding price model based on the conditional value at risk (CVaR), which helps find the most competitive price within the given range. Finally, case studies show that the proposed electricity-heat market and the bidding space model improve the economic benefits of the multi-energy system by 15.4 %. Moreover, compared to the “cost-plus method” and “MCP prediction method”, the proposed bidding strategy has an increase in revenue of 9.1 % and 12.3 %, respectively.

Suggested Citation

  • Fu, Yang & Shan, Jie & Li, Zhenkun & Xie, BoLin & Pan, Jeng-Shyang, 2025. "The optimal bidding strategy for multi-energy prosumers in the double auction electricity-heat market: A bidding space model," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224039458
    DOI: 10.1016/j.energy.2024.134167
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224039458
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.134167?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. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    2. Tushar, Wayes & Yuen, Chau & Saha, Tapan K. & Morstyn, Thomas & Chapman, Archie C. & Alam, M. Jan E. & Hanif, Sarmad & Poor, H. Vincent, 2021. "Peer-to-peer energy systems for connected communities: A review of recent advances and emerging challenges," Applied Energy, Elsevier, vol. 282(PA).
    3. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    4. Bhandari, Binayak & Lee, Kyung-Tae & Lee, Caroline Sunyong & Song, Chul-Ki & Maskey, Ramesh K. & Ahn, Sung-Hoon, 2014. "A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources," Applied Energy, Elsevier, vol. 133(C), pages 236-242.
    5. Wang, Dan & Hu, Qing'e & Jia, Hongjie & Hou, Kai & Du, Wei & Chen, Ning & Wang, Xudong & Fan, Menghua, 2019. "Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations," Applied Energy, Elsevier, vol. 248(C), pages 656-678.
    6. Guo, Tianyu & Li, Peng & Wang, Zixuan & Shi, Ruyu & Han, Zhonghe & Xia, Hui & Li, Jianyi, 2021. "Integrated modelling and optimal operation analysis of multienergy systems based on Stackelberg game theory," Energy, Elsevier, vol. 236(C).
    7. Couture, Toby & Gagnon, Yves, 2010. "An analysis of feed-in tariff remuneration models: Implications for renewable energy investment," Energy Policy, Elsevier, vol. 38(2), pages 955-965, February.
    8. Zhou, Yue & Wu, Jianzhong & Song, Guanyu & Long, Chao, 2020. "Framework design and optimal bidding strategy for ancillary service provision from a peer-to-peer energy trading community," Applied Energy, Elsevier, vol. 278(C).
    9. Wang, Zibo & Yu, Xiaodan & Mu, Yunfei & Jia, Hongjie, 2020. "A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System," Applied Energy, Elsevier, vol. 260(C).
    10. Wang, Ni & Liu, Ziyi & Heijnen, Petra & Warnier, Martijn, 2022. "A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors," Applied Energy, Elsevier, vol. 311(C).
    11. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    12. Thomas Morstyn & Niall Farrell & Sarah J. Darby & Malcolm D. McCulloch, 2018. "Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants," Nature Energy, Nature, vol. 3(2), pages 94-101, February.
    13. Zhang, XiaoWei & Yu, Xiaoping & Ye, Xinping & Pirouzi, Sasan, 2023. "Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method," Energy, Elsevier, vol. 278(PB).
    14. Liang, Hejun & Pirouzi, Sasan, 2024. "Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources," Energy, Elsevier, vol. 293(C).
    15. Chen, Kaixuan & Lin, Jin & Song, Yonghua, 2019. "Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model," Applied Energy, Elsevier, vol. 242(C), pages 1121-1133.
    16. Jiang, Chengcheng & Zhu, Qunzhi, 2023. "Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer," Applied Energy, Elsevier, vol. 348(C).
    17. Al-Swaiti, Mustafa S. & Al-Awami, Ali T. & Khalid, Mohammad Waqas, 2017. "Co-optimized trading of wind-thermal-pumped storage system in energy and regulation markets," Energy, Elsevier, vol. 138(C), pages 991-1005.
    18. Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    19. Li, Zhenpeng & Ma, Tao, 2020. "Peer-to-peer electricity trading in grid-connected residential communities with household distributed photovoltaic," Applied Energy, Elsevier, vol. 278(C).
    20. Cho, Heejin & Smith, Amanda D. & Mago, Pedro, 2014. "Combined cooling, heating and power: A review of performance improvement and optimization," Applied Energy, Elsevier, vol. 136(C), pages 168-185.
    21. Zhang, Bidan & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Jiang, Lin & Yan, Ke, 2022. "A novel adaptive penalty mechanism for Peer-to-Peer energy trading," Applied Energy, Elsevier, vol. 327(C).
    22. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
    Full references (including those not matched with items on IDEAS)

    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. Arnob Das & Susmita Datta Peu & Md. Abdul Mannan Akanda & Abu Reza Md. Towfiqul Islam, 2023. "RETRACTED: Peer-to-Peer Energy Trading Pricing Mechanisms: Towards a Comprehensive Analysis of Energy and Network Service Pricing (NSP) Mechanisms to Get Sustainable Enviro-Economical Energy Sector," Energies, MDPI, vol. 16(5), pages 1-27, February.
    2. Zhang, Bidan & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Jiang, Lin & Yan, Ke, 2022. "A novel adaptive penalty mechanism for Peer-to-Peer energy trading," Applied Energy, Elsevier, vol. 327(C).
    3. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K. & Yuen, Chau & Smith, David, 2022. "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    4. Qiu, Dawei & Ye, Yujian & Papadaskalopoulos, Dimitrios & Strbac, Goran, 2021. "Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach," Applied Energy, Elsevier, vol. 292(C).
    5. Meng, Yuan & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints," Applied Energy, Elsevier, vol. 356(C).
    6. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
    7. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Ge, Shaoyun & Li, Jifeng & He, Xingtang & Liu, Hong, 2021. "Joint energy market design for local integrated energy system service procurement considering demand flexibility," Applied Energy, Elsevier, vol. 297(C).
    9. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
    10. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    11. Xia, Yuanxing & Xu, Qingshan & Li, Fangxing, 2023. "Grid-friendly pricing mechanism for peer-to-peer energy sharing market diffusion in communities," Applied Energy, Elsevier, vol. 334(C).
    12. Wang, Longze & Liu, Jinxin & Yuan, Rongfang & Wu, Jing & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2020. "Adaptive bidding strategy for real-time energy management in multi-energy market enhanced by blockchain," Applied Energy, Elsevier, vol. 279(C).
    13. Schwidtal, J.M. & Piccini, P. & Troncia, M. & Chitchyan, R. & Montakhabi, M. & Francis, C. & Gorbatcheva, A. & Capper, T. & Mustafa, M.A. & Andoni, M. & Robu, V. & Bahloul, M. & Scott, I.J. & Mbavarir, 2023. "Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    14. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer energy trading of net-zero energy communities with renewable energy systems integrating hydrogen vehicle storage," Applied Energy, Elsevier, vol. 298(C).
    15. Wang, Ni & Liu, Ziyi & Heijnen, Petra & Warnier, Martijn, 2022. "A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors," Applied Energy, Elsevier, vol. 311(C).
    16. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Markovska, Natasa & Georghiou, George E., 2022. "Virtual net-billing: A fair energy sharing method for collective self-consumption," Energy, Elsevier, vol. 254(PB).
    17. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    18. Wang, Xiaoyu & Jia, Hongjie & Jin, Xiaolong & Mu, Yunfei & Wei, Wei & Yu, Xiaodan & Liang, Shuo, 2024. "Bi-level optimal operations for grid operator and low-carbon building prosumers with peer-to-peer energy sharing," Applied Energy, Elsevier, vol. 359(C).
    19. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    20. Deniz Kenan Kılıç & Peter Nielsen & Amila Thibbotuwawa, 2024. "Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region," Energies, MDPI, vol. 17(12), pages 1-15, June.

    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:314:y:2025:i:c:s0360544224039458. 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.