IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v276y2020ics0306261920309466.html
   My bibliography  Save this article

Spectrum-domain stability assessment and intrinsic oscillation for aggregated mobile energy storage in grid frequency regulation

Author

Listed:
  • Dong, Chaoyu
  • Gao, Qingbin
  • Xiao, Qiao
  • Chu, Ronghe
  • Jia, Hongjie

Abstract

This paper assesses the aggregation stability of mobile energy storage for the grid frequency regulation, which employs distributed electric-vehicle capacities. To reveal the aggregation dynamics, a multiple-aggregator model is established in the state space, which introduces aggregation factors coupled with the time for distributed vehicles. Interlinking the aggregated model of mobile energy storage system with the electrical grid model, an integration model is developed for the stability assessment. According to the transcendental model feature, an efficient stability region extraction method is then proposed to disclose the unstable risk, which consists of four sequential stages: quasi-polynomial formulation, Dixon-resultant based spectrum estimation, oscillation spectrum extraction, and stability map determination. In the first stage, Rekasius substitution is deployed converting the notoriously complicated model to a quasi-polynomial form. Since the real and imaginary parts of the quasi-polynomial have to vanish simultaneously, an elimination method called Dixon resultant is utilized, which constructs the necessary and sufficient assessment condition. After that, the discriminant is carried out to generate the imaginary spectra bound, which is scanned to finally determine the oscillation spectrum. It is revealed that the aggregation of mobile energy storage system might induce an oscillation spectrum due to asynchronous processes. Besides, the bound of the oscillation caused by the aggregation process is disclosed. Moreover, the established mapping relationship between the kernel curve and their offspring curves visualizes the complete stability map. The whole model and stability analysis are theoretically derived and verified through a typical grid frequency regulation system with mobile energy storage.

Suggested Citation

  • Dong, Chaoyu & Gao, Qingbin & Xiao, Qiao & Chu, Ronghe & Jia, Hongjie, 2020. "Spectrum-domain stability assessment and intrinsic oscillation for aggregated mobile energy storage in grid frequency regulation," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309466
    DOI: 10.1016/j.apenergy.2020.115434
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920309466
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115434?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. Jia, Hongjie & Li, Xiaomeng & Mu, Yunfei & Xu, Chen & Jiang, Yilang & Yu, Xiaodan & Wu, Jianzhong & Dong, Chaoyu, 2018. "Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays," Applied Energy, Elsevier, vol. 210(C), pages 1363-1376.
    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. Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
    2. Jeon, Soi & Choi, Dae-Hyun, 2022. "Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty," Applied Energy, Elsevier, vol. 310(C).
    3. Dong, Chaoyu & Li, Xiangke & Jiang, Wentao & Mu, Yunfei & Zhao, Jun & Jia, Hongjie, 2021. "Cyber-physical modelling operator and multimodal vibration in the integrated local vehicle-grid electrical system," Applied Energy, Elsevier, vol. 286(C).
    4. Zhuoxin Lu & Xiaoyuan Xu & Zheng Yan & Dong Han & Shiwei Xia, 2024. "Mobile Energy-Storage Technology in Power Grid: A Review of Models and Applications," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    5. Mehrjerdi, Hasan & Mahdavi, Sajad & Hemmati, Reza, 2021. "Resilience maximization through mobile battery storage and diesel DG in integrated electrical and heating networks," Energy, Elsevier, vol. 237(C).
    6. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(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. Roberto Germanà & Francesco Liberati & Emanuele De Santis & Alessandro Giuseppi & Francesco Delli Priscoli & Alessandro Di Giorgio, 2021. "Optimal Control of Plug-In Electric Vehicles Charging for Composition of Frequency Regulation Services," Energies, MDPI, vol. 14(23), pages 1-17, November.
    2. Mokhtar Aly & Emad A. Mohamed & Abdullah M. Noman & Emad M. Ahmed & Fayez F. M. El-Sousy & Masayuki Watanabe, 2023. "Optimized Non-Integer Load Frequency Control Scheme for Interconnected Microgrids in Remote Areas with High Renewable Energy and Electric Vehicle Penetrations," Mathematics, MDPI, vol. 11(9), pages 1-31, April.
    3. Tang, Yi & Li, Feng & Chen, Qian & Li, Mengya & Wang, Qi & Ni, Ming & Chen, Gang, 2018. "Frequency prediction method considering demand response aggregate characteristics and control effects," Applied Energy, Elsevier, vol. 229(C), pages 936-944.
    4. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    5. Alaperä, Ilari & Honkapuro, Samuli & Paananen, Janne, 2018. "Data centers as a source of dynamic flexibility in smart girds," Applied Energy, Elsevier, vol. 229(C), pages 69-79.
    6. Ashraf Khalil & Ang Swee Peng, 2018. "A New Method for Computing the Delay Margin for the Stability of Load Frequency Control Systems," Energies, MDPI, vol. 11(12), pages 1-18, December.
    7. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Muhammad Omer Khan & Chul-Hwan Kim, 2021. "Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders," Energies, MDPI, vol. 14(2), pages 1-16, January.
    8. Neofytos Neofytou & Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2019. "Modeling Vehicles to Grid as a Source of Distributed Frequency Regulation in Isolated Grids with Significant RES Penetration," Energies, MDPI, vol. 12(4), pages 1-23, February.
    9. Dong, Chaoyu & Gao, Qingbin & Xiao, Qian & Yu, Xiaodan & Pekař, Libor & Jia, Hongjie, 2018. "Time-delay stability switching boundary determination for DC microgrid clusters with the distributed control framework," Applied Energy, Elsevier, vol. 228(C), pages 189-204.
    10. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    11. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    12. Nie, S. & Huang, Z.C. & Huang, G.H. & Yu, L. & Liu, J., 2018. "Optimization of electric power systems with cost minimization and environmental-impact mitigation under multiple uncertainties," Applied Energy, Elsevier, vol. 221(C), pages 249-267.
    13. Rao, Yingqing & Yang, Jun & Xiao, Jinxing & Xu, Bingyan & Liu, Wenjing & Li, Yonghui, 2021. "A frequency control strategy for multimicrogrids with V2G based on the improved robust model predictive control," Energy, Elsevier, vol. 222(C).
    14. Hamad, Basil R. & Al-Durra, Ahmed & Al-Jaafari, Khaled Ali & Zeineldin, Hatem & Mohamed, Yasser Abdel-Rady I. & El-Saadany, Ehab, 2024. "Improving the robustness of distributed secondary control in autonomous microgrids to mitigate the effects of communication delays," Applied Energy, Elsevier, vol. 364(C).
    15. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Chul-Hwan Kim, 2018. "A Bi-Level EV Aggregator Coordination Scheme for Load Variance Minimization with Renewable Energy Penetration Adaptability," Energies, MDPI, vol. 11(10), pages 1-28, October.
    16. Shang-Guan, Xingchen & He, Yong & Zhang, Chuanke & Jiang, Lin & Spencer, Joseph William & Wu, Min, 2020. "Sampled-data based discrete and fast load frequency control for power systems with wind power," Applied Energy, Elsevier, vol. 259(C).
    17. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    18. Badesa, L. & Teng, F. & Strbac, G., 2020. "Pricing inertia and Frequency Response with diverse dynamics in a Mixed-Integer Second-Order Cone Programming formulation," Applied Energy, Elsevier, vol. 260(C).
    19. Zhang, Wenjie & Gandhi, Oktoviano & Quan, Hao & Rodríguez-Gallegos, Carlos D. & Srinivasan, Dipti, 2018. "A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination," Applied Energy, Elsevier, vol. 229(C), pages 96-110.

    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:276:y:2020:i:c:s0306261920309466. 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.