IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p5979-d1217282.html
   My bibliography  Save this article

Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter

Author

Listed:
  • Xinlei Cai

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Naixiao Wang

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Qinqin Cai

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Hengzhen Wang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Zhangying Cheng

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Zhijun Wang

    (Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510220, China)

  • Tingxiang Zhang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Ying Xu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations.

Suggested Citation

  • Xinlei Cai & Naixiao Wang & Qinqin Cai & Hengzhen Wang & Zhangying Cheng & Zhijun Wang & Tingxiang Zhang & Ying Xu, 2023. "Day-Ahead Dynamic Assessment of Consumption Service Reserve Based on Morphological Filter," Energies, MDPI, vol. 16(16), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5979-:d:1217282
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/5979/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/5979/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zahid Ullah & Arshad & Hany Hassanin & James Cugley & Mohammed Al Alawi, 2022. "Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty," Energies, MDPI, vol. 15(19), pages 1-16, October.
    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. Mohammad Shalby & Mohamed R. Gomaa & Ahmad Salah & Abdullah Marashli & Talal Yusaf & Mohamd Laimon, 2023. "Impact of the Air Filtration in the Nacelle on the Wind Turbine Performance," Energies, MDPI, vol. 16(9), pages 1-12, April.
    2. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    3. Zahid Ullah & Arshad & Jawad Ahmad, 2022. "The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts," Energies, MDPI, vol. 15(21), pages 1-16, November.
    4. Łukasz Mazur & Sławomir Cieślik & Stanislaw Czapp, 2023. "Trends in Locally Balanced Energy Systems without the Use of Fossil Fuels: A Review," Energies, MDPI, vol. 16(12), pages 1-31, June.
    5. Ali Ahmadian & Kumaraswamy Ponnambalam & Ali Almansoori & Ali Elkamel, 2023. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning," Energies, MDPI, vol. 16(2), pages 1-17, January.
    6. Yang, Shenbo & Fang, Jiangpeng & Zhang, Zheyu & Lv, ShuoShuo & Lin, Hongyu & Ju, Liwei, 2024. "Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid," Energy, Elsevier, vol. 292(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:16:y:2023:i:16:p:5979-:d:1217282. 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.