IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipbs030626192301468x.html
   My bibliography  Save this article

Cyber-physical system planning for VPPs supporting frequency regulation considering hierarchical control and multidimensional uncertainties

Author

Listed:
  • Kong, Xiangyu
  • Sun, Yuce
  • Khan, Muhammad Ahmad
  • Zheng, Lin
  • Qin, Junda
  • Ji, Xiaotong

Abstract

Renewable energy resources with uncertain output are being connected to the power grid, which puts great pressure on the grid frequency regulation (FR). Virtual power plants (VPPs), which aggregate numerous flexible resources, have significant potential for participation in FR services. However, the FR capability of VPPs is often unreliable due to uncertain resource potential and unreliable communications, which limits their FR applications. Therefore, a cyber-physical system (CPS) planning method considering multidimensional uncertainties for hierarchical VPPs to provide reliable FR capacity is proposed in this paper. Firstly, a fast and reliable hierarchical architecture for VPPs is presented. Then, the reliable capacity model of VPPs for service planning is established within this architecture. The minimum probability scenario method based on the Pauta criterion and the failure mode and effects analysis (FMEA) method are used to quantify the impact of information-physical uncertainty in the model. Based on this, a CPS planning model for VPPs with the objective of minimizing investment and the constraints of normal capacity, reliable capacity and delay under various scenarios is constructed. By linearizing the model, the optimal solution for coordinating flexible resource pool composition, edge terminal allocation and communication can be solved. Numerical simulations on the improved IEEE 33-node and 123-node systems demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Kong, Xiangyu & Sun, Yuce & Khan, Muhammad Ahmad & Zheng, Lin & Qin, Junda & Ji, Xiaotong, 2024. "Cyber-physical system planning for VPPs supporting frequency regulation considering hierarchical control and multidimensional uncertainties," Applied Energy, Elsevier, vol. 353(PB).
  • Handle: RePEc:eee:appene:v:353:y:2024:i:pb:s030626192301468x
    DOI: 10.1016/j.apenergy.2023.122104
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192301468X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122104?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. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    2. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    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. Reza Nadimi & Masahito Takahashi & Koji Tokimatsu & Mika Goto, 2024. "The Reliability and Profitability of Virtual Power Plant with Short-Term Power Market Trading and Non-Spinning Reserve Diesel Generator," Energies, MDPI, vol. 17(9), pages 1-19, April.
    2. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    3. Lin, Wen-Ting & Chen, Guo & Zhou, Xiaojun, 2022. "Distributed carbon-aware energy trading of virtual power plant under denial of service attacks: A passivity-based neurodynamic approach," Energy, Elsevier, vol. 257(C).
    4. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    5. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    6. Dominika Kaczorowska & Jacek Rezmer & Michal Jasinski & Tomasz Sikorski & Vishnu Suresh & Zbigniew Leonowicz & Pawel Kostyla & Jaroslaw Szymanda & Przemyslaw Janik, 2020. "A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics," Energies, MDPI, vol. 13(24), pages 1-22, December.
    7. Mashlakov, Aleksei & Pournaras, Evangelos & Nardelli, Pedro H.J. & Honkapuro, Samuli, 2021. "Decentralized cooperative scheduling of prosumer flexibility under forecast uncertainties," Applied Energy, Elsevier, vol. 290(C).
    8. Xie, Haonan & Jiang, Meihui & Zhang, Dongdong & Goh, Hui Hwang & Ahmad, Tanveer & Liu, Hui & Liu, Tianhao & Wang, Shuyao & Wu, Thomas, 2023. "IntelliSense technology in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    9. Martin Bichler & Hans Ulrich Buhl & Johannes Knörr & Felipe Maldonado & Paul Schott & Stefan Waldherr & Martin Weibelzahl, 2022. "Electricity Markets in a Time of Change: A Call to Arms for Business Research," Schmalenbach Journal of Business Research, Springer, vol. 74(1), pages 77-102, March.
    10. 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).
    11. Mendicino, Luca & Menniti, Daniele & Pinnarelli, Anna & Sorrentino, Nicola, 2019. "Corporate power purchase agreement: Formulation of the related levelized cost of energy and its application to a real life case study," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.
    13. Feng, Bin & Liu, Zhuping & Huang, Gang & Guo, Chuangxin, 2023. "Robust federated deep reinforcement learning for optimal control in multiple virtual power plants with electric vehicles," Applied Energy, Elsevier, vol. 349(C).
    14. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    15. Hyang-A Park & Gilsung Byeon & Wanbin Son & Jongyul Kim & Sungshin Kim, 2023. "Data-Driven Modeling of HVAC Systems for Operation of Virtual Power Plants Using a Digital Twin," Energies, MDPI, vol. 16(20), pages 1-14, October.
    16. Song, Shaojian & Xiong, Hao & Lin, Yuzhang & Huang, Manyun & Wei, Zhinong & Fang, Zhi, 2022. "Robust three-phase state estimation for PV-Integrated unbalanced distribution systems," Applied Energy, Elsevier, vol. 322(C).
    17. Catra Indra Cahyadi & Suwarno Suwarno & Aminah Asmara Dewi & Musri Kona & Muhammad Arif & Muhammad Caesar Akbar, 2023. "Solar Prediction Strategy for Managing Virtual Power Stations," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 503-512, July.
    18. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    19. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    20. Zahid Ullah & Arshad & Hany Hassanin, 2022. "Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources," Energies, MDPI, vol. 15(14), pages 1-16, July.

    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:353:y:2024:i:pb:s030626192301468x. 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.