IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p236-d1562004.html
   My bibliography  Save this article

Robust Trading Decision-Making Model for Demand-Side Resource Aggregators Considering Multi-Objective Cluster Aggregation Optimization

Author

Listed:
  • Fei Liu

    (Economic and Technical Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China)

  • Shaokang Qi

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shibin Wang

    (Economic and Technical Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China)

  • Xu Tian

    (Economic and Technical Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China)

  • Liantao Liu

    (Economic and Technical Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China)

  • Xue Zhao

    (Economic and Technical Research Institute of State Grid Qinghai Electric Power Company, Xining 810001, China)

Abstract

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics.

Suggested Citation

  • Fei Liu & Shaokang Qi & Shibin Wang & Xu Tian & Liantao Liu & Xue Zhao, 2025. "Robust Trading Decision-Making Model for Demand-Side Resource Aggregators Considering Multi-Objective Cluster Aggregation Optimization," Energies, MDPI, vol. 18(2), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:236-:d:1562004
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/236/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/236/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qunru Zheng & Ping Yang & Yuhang Wu & Zhen Xu & Peng Zhang, 2023. "Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market," Sustainability, MDPI, vol. 15(20), pages 1-25, October.
    2. Liu, Xin & Huang, Tao & Qiu, Haifeng & Li, Yang & Lin, Xueshan & Shi, Jianxiong, 2024. "Optimal aggregation of a virtual power plant based on a distribution-level market with the participation of bounded rational agents," Applied Energy, Elsevier, vol. 364(C).
    3. Cai, Sinan & Matsuhashi, Ryuji, 2022. "Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization," Applied Energy, Elsevier, vol. 319(C).
    4. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    5. Ju, Liwei & Bai, Xiping & Li, Gen & Gan, Wei & Qi, Xin & Ye, Fan, 2024. "Two-stage robust transaction optimization model and benefit allocation strategy for new energy power stations with shared energy storage considering green certificate and virtual energy storage mode," Applied Energy, Elsevier, vol. 362(C).
    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. Liu, Xin & Lin, Xueshan & Qiu, Haifeng & Li, Yang & Huang, Tao, 2024. "Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator," Applied Energy, Elsevier, vol. 376(PA).
    2. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
    3. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions," Energies, MDPI, vol. 17(16), pages 1-23, August.
    4. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    5. Tang, Qinghu & Guo, Hongye & Zheng, Kedi & Chen, Qixin, 2024. "Forecasting individual bids in real electricity markets through machine learning framework," Applied Energy, Elsevier, vol. 363(C).
    6. Hou, Langbo & Tong, Xi & Chen, Heng & Fan, Lanxin & Liu, Tao & Liu, Wenyi & Liu, Tong, 2024. "Optimized scheduling of smart community energy systems considering demand response and shared energy storage," Energy, Elsevier, vol. 295(C).
    7. Luwen Pan & Jiajia Chen, 2024. "Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
    8. Li, Jinchao & Sun, Zihao & Niu, Xiaoxuan & Li, Shiwei, 2024. "Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price," Energy, Elsevier, vol. 305(C).
    9. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    10. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    11. Lu, Xin & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach," Applied Energy, Elsevier, vol. 358(C).
    12. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    13. Dong, Min & Su, Juan & Zhao, Jing & Dong, Yanjun & Du, Songhuai, 2024. "Fraudulent balancing operation strategy for multi-agent P2P electricity trading considering neighborhood scene public energy storage," Applied Energy, Elsevier, vol. 375(C).
    14. Ebrahimi, Mahoor & Ebrahimi, Mahan & Shafie-khah, Miadreza & Laaksonen, Hannu, 2024. "EV-observing distribution system management considering strategic VPPs and active & reactive power markets," Applied Energy, Elsevier, vol. 364(C).
    15. Jiang, Yuzheng & Dong, Jun & Huang, Hexiang, 2024. "Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm," Energy, Elsevier, vol. 306(C).
    16. Jia, Dongqing & Li, Xingmei & Gong, Xu & Lv, Xiaoyan & Shen, Zhong, 2024. "Bi-level strategic bidding model of novel virtual power plant aggregating waste gasification in integrated electricity and hydrogen markets," Applied Energy, Elsevier, vol. 357(C).
    17. Uda Bala & Wei Li & Wenguo Wang & Yuying Gong & Yaheng Su & Yingshu Liu & Yi Zhang & Wei Wang, 2024. "The Sharing Energy Storage Mechanism for Demand Side Energy Communities," Energies, MDPI, vol. 17(21), pages 1-19, October.
    18. Suwei Zhai & Wenyun Li & Chao Zheng & Weixin Wang, 2024. "Distributed Optimization Strategy for New Energy Stations and Energy Storage Stations Considering Multiple Time Scales," Energies, MDPI, vol. 17(19), pages 1-13, October.
    19. Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
    20. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(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:gam:jeners:v:18:y:2025:i:2:p:236-:d:1562004. 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.