IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v321y2025ics0360544225011120.html

Internal pricing driven dynamic aggregation of virtual power plant with energy storage systems

Author

Listed:
  • Meng, He
  • Jia, Hongjie
  • Xu, Tao
  • Hatziargyriou, Nikos
  • Wei, Wei
  • Wang, Rujing

Abstract

Virtual power plant (VPP) has emerged as an energy service platform that can monitor, forecast, schedule and trade heterogeneous distributed flexible resources in an aggregated approach across various locations to enhance the flexibility and economics of power systems. One of the major goals of VPP is to maximize revenue for its resources that belong to different stakeholders. Therefore, it can be considered as a game with multiple players, various pricing strategies and payoffs. To incentivize the participation of distributed energy resources (DERs), including energy storage systems (ESSs), an internal pricing driven dynamic aggregation model of VPP is established based on a Stackelberg game. An improved artificial fish swarm algorithm (AFSA) and a mixed integer quadratic programming (MIQP) method are utilized to find the equilibrium solution of the model and to determine the internal transaction prices, dynamic aggregation and dispatching schemes of VPP. Two operation modes, VPP direct control ESS and photovoltaic (PV)-ESS joint operation, are compared to investigate the aggregation approaches and associated economics.

Suggested Citation

  • Meng, He & Jia, Hongjie & Xu, Tao & Hatziargyriou, Nikos & Wei, Wei & Wang, Rujing, 2025. "Internal pricing driven dynamic aggregation of virtual power plant with energy storage systems," Energy, Elsevier, vol. 321(C).
    • Handle: RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011120
    DOI: 10.1016/j.energy.2025.135470
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225011120
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135470?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Xiaoxue & Wang, Chengshan & Xu, Tao & Guo, Lingxu & Li, Peng & Yu, Li & Meng, He, 2018. "Optimal voltage regulation for distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 210(C), pages 1027-1036.
    2. Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    3. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    4. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    5. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).
    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. Zhang, Qingzhu & Mu, Yunfei & Jia, Hongjie & Yu, Xiaodan & Hou, Kai, 2025. "Optimized hybrid hydrogen-battery storage planning for Island microgrids: A TSA-THC approach for addressing multi-time-scale imbalances," Applied Energy, Elsevier, vol. 398(C).
    2. Lei, Xiang & Yu, Hang & Zhong, Jiahao & Shao, Ziyun & Jian, Linni, 2025. "A data-driven robust optimal day-ahead bidding strategy considering V2G operation for distribution-system-side virtual power plant," Energy, Elsevier, vol. 338(C).
    3. Yujie Jin & Ciwei Gao, 2025. "Market Applications and Uncertainty Handling for Virtual Power Plants," Energies, MDPI, vol. 18(14), pages 1-27, July.
    4. Kuo, Wen-Chi & Chen, Chiun-Hsun & Wang, Chi-Chuan, 2026. "Unlocking sub-MW microgrids for ancillary services: A stochastic bilevel VPP framework with weighted Shapley allocation," Energy, Elsevier, vol. 344(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. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    2. Zare, Alireza & Shafie-khah, Miadreza & Siano, Pierluigi & Lazaroiu, George Cristian, 2026. "A systematic review of Virtual Power Plant configurations and their interaction with electricity, carbon, and flexibility markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
    3. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    4. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Jingjing Luo & Yajing Gao & Wenhai Yang & Yongchun Yang & Zheng Zhao & Shiyu Tian, 2018. "Optimal Operation Modes of Virtual Power Plants Based on Typical Scenarios Considering Output Evaluation Criteria," Energies, MDPI, vol. 11(10), pages 1-22, October.
    6. Mak, Davye & Choeum, Daranith & Choi, Dae-Hyun, 2020. "Sensitivity analysis of volt-VAR optimization to data changes in distribution networks with distributed energy resources," Applied Energy, Elsevier, vol. 261(C).
    7. A.S. Jameel Hassan & Umar Marikkar & G.W. Kasun Prabhath & Aranee Balachandran & W.G. Chaminda Bandara & Parakrama B. Ekanayake & Roshan I. Godaliyadda & Janaka B. Ekanayake, 2021. "A Sensitivity Matrix Approach Using Two-Stage Optimization for Voltage Regulation of LV Networks with High PV Penetration," Energies, MDPI, vol. 14(20), pages 1-24, October.
    8. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    9. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    10. 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.
    11. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    12. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    13. Jingpeng Yue & Zhijian Hu & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2019. "A Multi-Market-Driven Approach to Energy Scheduling of Smart Microgrids in Distribution Networks," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    14. Wu, Tianyu & Han, Fengwu & Zhao, Yunlong & Yu, Zishuo, 2025. "A decarbonization-oriented and uncertainty-aware energy management strategy for multi-district integrated energy systems with fair peer-to-peer trading," Energy, Elsevier, vol. 323(C).
    15. Rui Gao & Hongxia Guo & Ruihong Zhang & Tian Mao & Qianyao Xu & Baorong Zhou & Ping Yang, 2019. "A Two-Stage Dispatch Mechanism for Virtual Power Plant Utilizing the CVaR Theory in the Electricity Spot Market," Energies, MDPI, vol. 12(17), pages 1-18, September.
    16. Liu, Wei & Li, Zhuangzhuang & Xing, Xinran & Chen, Xi & Wang, Yufei & Wang, Xuechun, 2025. "Non-cooperative game optimization for virtual power plants considering carbon trading market," Energy, Elsevier, vol. 317(C).
    17. 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).
    18. 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).
    19. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    20. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:321:y:2025:i:c:s0360544225011120. 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.