IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipbs0306261924018713.html
   My bibliography  Save this article

Optimal energy management for prosumers and power plants considering transmission congestion based on carbon emission flow

Author

Listed:
  • Wu, Chun
  • Chen, Xingying
  • Hua, Haochen
  • Yu, Kun
  • Gan, Lei
  • Wang, Bo

Abstract

With the rapid development of high-efficiency, long-distance, and large-capacity power interaction in multiple communities, prosumers in each community who can participate in three markets, i.e., green power market, electricity market and carbon market, may make decisions based on incomplete rational behaviors. The behaviors, e.g., purchasing plenty of power from the power plants through the independent system operator (ISO) at a certain time slot, may cause the problem that a certain power line cannot transmit the power since the amount of power intended to transmit via the power line is beyond the constraint of the physical network, which is regarded as the transmission congestion. How to realize the optimization of energy management for the prosumers and power plants in three markets considering transmission congestion arouse the public concern. In this paper, an optimal energy management method is proposed for the power plants and prosumers with community energy storage considering transmission congestion based on carbon emission flow. It is constructed with a three-level structure, i.e., prosumer level, ISO level and power plant level. At the first level, i.e., prosumer level, based on the cumulative prospect theory, an incomplete rational behavior model is developed for the prosumers who can store the excess power in community energy storage for backup. Due to the existing prosumer peer-to-peer energy trading approach, all prosumers in the same community can be aggregated into a community agent to participate in the three markets, which can deliver the power demand from the prosumers to the ISO at the second level. At the third level, power plant level, two energy trading models of power plants are established, which can deliver the power supply from the power plants to the ISO at the second level, i.e., ISO level, as well. One is presented for the coal-fired power plants according to the cost-benefit function theory, the other one is constructed for the renewable power plants considering the uncertainty of renewable output power. Then, at the second level, an energy management method considering transmission congestion is developed in respect of the power demand from the first-level behavior and the power supply from the third level. Finally, the optimization of energy management is solved under the Lagrange multiplier method with the improved differential evolution algorithm, which is verified in numerical simulations with the effectiveness of the proposed method.

Suggested Citation

  • Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Wang, Bo, 2025. "Optimal energy management for prosumers and power plants considering transmission congestion based on carbon emission flow," Applied Energy, Elsevier, vol. 377(PB).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924018713
    DOI: 10.1016/j.apenergy.2024.124488
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924018713
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124488?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. Hafiz, Faeza & Rodrigo de Queiroz, Anderson & Fajri, Poria & Husain, Iqbal, 2019. "Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach," Applied Energy, Elsevier, vol. 236(C), pages 42-54.
    2. Zeng, Bo & Zhou, Yinyu & Xu, Xinzhu & Cai, Danting, 2024. "Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets," Applied Energy, Elsevier, vol. 357(C).
    3. Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Shen, Jun & Ding, Yi, 2024. "Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade," Applied Energy, Elsevier, vol. 358(C).
    4. Wu, Yunna & Xu, Chuanbo & Zhang, Ting, 2018. "Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China," Energy, Elsevier, vol. 147(C), pages 1227-1239.
    5. 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).
    6. Liu, Di & Qin, Zhaoming & Hua, Haochen & Ding, Yi & Cao, Junwei, 2023. "Incremental incentive mechanism design for diversified consumers in demand response," Applied Energy, Elsevier, vol. 329(C).
    7. Attar, Mehdi & Repo, Sami & Mutanen, Antti & Rinta-Luoma, Jukka & Väre, Teemu & Kukk, Kalle, 2024. "Market integration and TSO-DSO coordination for viable Market-based congestion management in power systems," Applied Energy, Elsevier, vol. 353(PB).
    8. Babagheibi, Mahsa & Jadid, Shahram & Kazemi, Ahad, 2023. "An Incentive-based robust flexibility market for congestion management of an active distribution system to use the free capacity of Microgrids," Applied Energy, Elsevier, vol. 336(C).
    9. Gan, Lei & Yang, Tianyu & Wang, Bo & Chen, Xingying & Hua, Haochen & Dong, Zhao Yang, 2023. "Three-stage coordinated operation of steel plant-based multi-energy microgrids considering carbon reduction," Energy, Elsevier, vol. 278(C).
    10. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    11. Aghdam, Farid Hamzeh & Mudiyanselage, Manthila Wijesooriya & Mohammadi-Ivatloo, Behnam & Marzband, Mousa, 2023. "Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management," Applied Energy, Elsevier, vol. 333(C).
    12. Lee, Won-Poong & Han, Dongjun & Won, Dongjun, 2022. "Grid-Oriented Coordination Strategy of Prosumers Using Game-theoretic Peer-to-Peer Trading Framework in Energy Community," Applied Energy, Elsevier, vol. 326(C).
    13. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
    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. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    2. Hui Wang & Yao Xu, 2024. "Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study," Energies, MDPI, vol. 17(11), pages 1-15, May.
    3. Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Shen, Jun & Ding, Yi, 2024. "Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade," Applied Energy, Elsevier, vol. 358(C).
    4. 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).
    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. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Feng, Jianghong & Guo, Ping & Xu, Guangyi & Xu, Gangyan & Ning, Yu, 2024. "An integrated decision framework for resilient sustainable waste electric vehicle battery recycling transfer station site selection," Applied Energy, Elsevier, vol. 373(C).
    8. Jiang, Yihuo & Ni, Hongliang & Ni, Yihan & Guo, Xiaomei, 2023. "Assessing environmental, social, and governance performance and natural resource management policies in China's dual carbon era for a green economy," Resources Policy, Elsevier, vol. 85(PB).
    9. 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).
    10. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Integrated prosumers–DSO approach applied in peer-to-peer energy and reserve tradings considering network constraints," Applied Energy, Elsevier, vol. 317(C).
    11. Mingshan Mo & Xinrui Xiong & Yunlong Wu & Zuyao Yu, 2023. "Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties," Energies, MDPI, vol. 16(22), pages 1-18, November.
    12. Moradi, Amir & Salehi, Javad & Shafie-khah, Miadreza, 2024. "An interactive framework for strategic participation of a price-maker energy hub in the local gas and power markets based on the MPEC method," Energy, Elsevier, vol. 307(C).
    13. Yinhe Bu & Xingping Zhang, 2021. "On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    14. Zhao, Meng & Xu, Chang & Zhao, Wenxian & Lin, Mingwei, 2023. "New energy vehicle online selection method considering attribute compensation relationship and aspiration strength," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    15. Zhu, Jianquan & Xia, Yunrui & Mo, Xiemin & Guo, Ye & Chen, Jiajun, 2021. "A bilevel bidding and clearing model incorporated with a pricing strategy for the trading of energy storage use rights," Energy, Elsevier, vol. 235(C).
    16. Mostafayi Darmian, Sobhan & Tavana, Madjid & Ribeiro-Navarrete, Samuel, 2024. "An investment evaluation and incentive allocation model for public-private partnerships in renewable energy development projects," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    17. Abdul, Daud & Wenqi, Jiang & Tanveer, Arsalan, 2022. "Prioritization of renewable energy source for electricity generation through AHP-VIKOR integrated methodology," Renewable Energy, Elsevier, vol. 184(C), pages 1018-1032.
    18. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    19. Man Yiu Tsang & Tony Sit & Hoi Ying Wong, 2022. "Adaptive Robust Online Portfolio Selection," Papers 2206.01064, arXiv.org.
    20. Tong, Ziqiang & Mansouri, Seyed Amir & Huang, Shoujun & Rezaee Jordehi, Ahmad & Tostado-Véliz, Marcos, 2023. "The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism," Applied Energy, Elsevier, vol. 351(C).

    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:pb:s0306261924018713. 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.