IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipas030626192401763x.html
   My bibliography  Save this article

A novel risk-averse multi-energy Management for Effective Offering Strategy of integrated energy production units in a real-time electricity market

Author

Listed:
  • Li, Yixin
  • Li, Zhengshuo

Abstract

Transforming a coal-fired power generation unit into a so-called integrated energy production unit (IEPU) is considered a promising low-carbon technology. An IEPU produces not only “greener” electricity but also hydrogen and natural gas by capturing carbon emissions from its coal-fired unit. Moreover, an IEPU has an enhanced capacity to adjust its electricity output, i.e., it has better flexibility, and can play a significant role in real-time electricity market (RTM) by trading its flexibility to recover its initial outlay. However, because of the complicated production process and temporal coupling constraint, it is challenging to effectively manage the multi-energy production of IEPU to reach its RTM offering strategy. To solve this problem, this work proposes a novel risk-averse multi-energy management method for effective offering strategy of the IEPU in the RTM. This multi-energy management process comprises three phases. In the first phase, a novel distributionally robust optimal self-scheduling IEPU model is established to determine its desired operating point, which has the best expected profit. This model accurately captures the complicated multi-vector energy production process and leverages the discounted mean entropic value-at-risk to improve the IEPU's ability to withstand risks with an acceptable economic compromise. A novel uncertainty decoupling algorithm is also proposed to improve computational efficiency. In the second phase, an offering strategy making method is proposed to maximize the probability of IEPU winning bids at the desired point. In the third phase, a post-clearing IEPU scheduling method is proposed to ensure that the IEPU operates following the winning bid power. Case studies demonstrate that the proposed method enables the IEPU operator to participate in the RTM at its desired operating point at the maximum probability, with properly balanced profit and risk. Additionally, the proposed solution algorithm is verified to significantly accelerate the computation process, thus enhancing the practicality of this multi-energy management method for the RTM participant, who often faces a strict time constraint in decision-making.

Suggested Citation

  • Li, Yixin & Li, Zhengshuo, 2025. "A novel risk-averse multi-energy Management for Effective Offering Strategy of integrated energy production units in a real-time electricity market," Applied Energy, Elsevier, vol. 377(PA).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pa:s030626192401763x
    DOI: 10.1016/j.apenergy.2024.124380
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192401763X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124380?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. Wang, Jun & Xu, Jian & Wang, Jingjing & Ke, Deping & Yao, Liangzhong & Zhou, Yue & Liao, Siyang, 2024. "Two-stage distributionally robust offering and pricing strategy for a price-maker virtual power plant," Applied Energy, Elsevier, vol. 363(C).
    2. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    3. Varkani, Ali Karimi & Daraeepour, Ali & Monsef, Hassan, 2011. "A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets," Applied Energy, Elsevier, vol. 88(12), pages 5002-5012.
    4. Zhao, Jing & Yang, Zilan & Shi, Linyu & Liu, Dehan & Li, Haonan & Mi, Yumiao & Wang, Hongbin & Feng, Meili & Hutagaol, Timothy Joseph, 2024. "Photovoltaic capacity dynamic tracking model predictive control strategy of air-conditioning systems with consideration of flexible loads," Applied Energy, Elsevier, vol. 356(C).
    5. Liu, Yangyang & Zhou, Jiangxin & Zhou, Qihui & Liu, Chuanquan & Yu, Feng, 2023. "Bidding strategy of integrated energy system considering decision maker’s subjective risk aversion," Applied Energy, Elsevier, vol. 341(C).
    6. Dai, Xuemei & Li, Yaping & Zhang, Kaifeng & Feng, Wei, 2020. "A robust offering strategy for wind producers considering uncertainties of demand response and wind power," Applied Energy, Elsevier, vol. 279(C).
    7. repec:inm:orijoo:v:5:y:2023:i:1:p:68-91 is not listed on IDEAS
    8. Dirin, Sepehr & Rahimiyan, Morteza & Baringo, Luis, 2023. "Optimal offering strategy for wind-storage systems under correlated wind production," Applied Energy, Elsevier, vol. 333(C).
    9. Huang, Yi & Gordon, Dan & Scott, Paul, 2023. "Receding horizon dispatch of multi-period look-ahead market for energy storage integration," Applied Energy, Elsevier, vol. 352(C).
    10. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    11. Lee, Boreum & Lee, Hyunjun & Lim, Dongjun & Brigljević, Boris & Cho, Wonchul & Cho, Hyun-Seok & Kim, Chang-Hee & Lim, Hankwon, 2020. "Renewable methanol synthesis from renewable H2 and captured CO2: How can power-to-liquid technology be economically feasible?," Applied Energy, Elsevier, vol. 279(C).
    12. Sun, Shitong & Kazemi-Razi, S. Mahdi & Kaigutha, Lisa G. & Marzband, Mousa & Nafisi, Hamed & Al-Sumaiti, Ameena Saad, 2022. "Day-ahead offering strategy in the market for concentrating solar power considering thermoelectric decoupling by a compressed air energy storage," Applied Energy, Elsevier, vol. 305(C).
    13. Dominguez, R. & Baringo, L. & Conejo, A.J., 2012. "Optimal offering strategy for a concentrating solar power plant," Applied Energy, Elsevier, vol. 98(C), pages 316-325.
    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. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    2. Xiao, Xiangsheng & Wang, JianXiao & Hill, David J., 2022. "Impact of Large-scale concentrated solar power on energy and auxiliary markets," Applied Energy, Elsevier, vol. 318(C).
    3. Domínguez, R. & Conejo, A.J. & Carrión, M., 2014. "Operation of a fully renewable electric energy system with CSP plants," Applied Energy, Elsevier, vol. 119(C), pages 417-430.
    4. Khalili, Reza & Khaledi, Arian & Marzband, Mousa & Nematollahi, Amin Foroughi & Vahidi, Behrooz & Siano, Pierluigi, 2023. "Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs," Applied Energy, Elsevier, vol. 334(C).
    5. Yang, Peiwen & Dong, Jun & Lin, Jin & Liu, Yao & Fang, Debin, 2021. "Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach," Energy, Elsevier, vol. 228(C).
    6. Pousinho, H.M.I. & Silva, H. & Mendes, V.M.F. & Collares-Pereira, M. & Pereira Cabrita, C., 2014. "Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach," Energy, Elsevier, vol. 78(C), pages 524-534.
    7. Rodríguez, I. & Pérez-Segarra, C.D. & Lehmkuhl, O. & Oliva, A., 2013. "Modular object-oriented methodology for the resolution of molten salt storage tanks for CSP plants," Applied Energy, Elsevier, vol. 109(C), pages 402-414.
    8. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    9. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    10. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    11. Jiao, P.H. & Chen, J.J. & Cai, X. & Wang, L.L. & Zhao, Y.L. & Zhang, X.H. & Chen, W.G., 2021. "Joint active and reactive for allocation of renewable energy and energy storage under uncertain coupling," Applied Energy, Elsevier, vol. 302(C).
    12. Huan Guo & Haoyuan Kang & Yujie Xu & Mingzhi Zhao & Yilin Zhu & Hualiang Zhang & Haisheng Chen, 2023. "Review of Coupling Methods of Compressed Air Energy Storage Systems and Renewable Energy Resources," Energies, MDPI, vol. 16(12), pages 1-22, June.
    13. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    14. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
    15. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    16. Dominik Latoń & Jakub Grela & Andrzej Ożadowicz, 2024. "Applications of Deep Reinforcement Learning for Home Energy Management Systems: A Review," Energies, MDPI, vol. 17(24), pages 1-30, December.
    17. Liu, Mingzhe & Guo, Mingyue & Fu, Yangyang & O’Neill, Zheng & Gao, Yuan, 2024. "Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications," Applied Energy, Elsevier, vol. 372(C).
    18. He, Jiawei & Mu, Rui & Li, Bin & Li, Ye & Zhou, Bohao & Xie, Zhongrun & Wang, Wenbo, 2024. "Applicability boundary calculation for directional current protection in distribution networks with accessed PV power sources," Applied Energy, Elsevier, vol. 370(C).
    19. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    20. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2014. "Technical feasibility study on a standalone hybrid solar-wind system with pumped hydro storage for a remote island in Hong Kong," Renewable Energy, Elsevier, vol. 69(C), pages 7-15.

    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:appene:v:377:y:2025:i:pa:s030626192401763x. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.