IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9377-d1000161.html
   My bibliography  Save this article

Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control

Author

Listed:
  • Xuehua Wu

    (School of Electrical Engineering, Nanjing Vocational University of Industry Technology, Nanjing 210023, China)

  • Qianqian Qian

    (School of Electrical Engineering and Automation, Nanjing Normal University, Nanjing 210023, China)

  • Yuqing Bao

    (School of Electrical Engineering and Automation, Nanjing Normal University, Nanjing 210023, China)

Abstract

Demand response (DR) has a great potential for stabilizing the frequency of power systems. However, the performance is limited by the accuracy of the frequency detection, which is affected by measurement disturbances. To overcome this problem, this paper proposes a disturbance estimation-based Kalman filtering method, which is utilized for the frequency control. By using the rate of change of frequency (RoCoF), the Kalman filtering method can estimate the state of the ON/OFF loads well. In this way, the influence of detection error can be reduced, and the DR performance can be improved. Test results show that the proposed disturbance estimation-based Kalman filtering method has a higher accuracy of frequency detection than existing methods (such as the low-pass filter method) and therefore improves the frequency control performance of DR.

Suggested Citation

  • Xuehua Wu & Qianqian Qian & Yuqing Bao, 2022. "Demand Response Using Disturbance Estimation-Based Kalman Filtering for the Frequency Control," Energies, MDPI, vol. 15(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9377-:d:1000161
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9377/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9377/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming Li & Jin Ye, 2022. "Design and Implementation of Demand Side Response Based on Binomial Distribution," Energies, MDPI, vol. 15(22), pages 1-15, November.
    2. Yajing Hu & Jing Liu & Xiandong Xu, 2022. "Dynamic Interactions between Local Energy Systems Coupled by Power and Gas Distribution Networks," Energies, MDPI, vol. 15(22), pages 1-15, November.
    3. Muhammad Saeed Uz Zaman & Syed Basit Ali Bukhari & Khalid Mousa Hazazi & Zunaib Maqsood Haider & Raza Haider & Chul-Hwan Kim, 2018. "Frequency Response Analysis of a Single-Area Power System with a Modified LFC Model Considering Demand Response and Virtual Inertia," Energies, MDPI, vol. 11(4), pages 1-20, March.
    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. Xiaoyu Deng & Ruo Mo & Pengliang Wang & Junru Chen & Dongliang Nan & Muyang Liu, 2023. "Review of RoCoF Estimation Techniques for Low-Inertia Power Systems," Energies, MDPI, vol. 16(9), pages 1-19, April.

    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. Muhammad Kashif Rafique & Zunaib Maqsood Haider & Khawaja Khalid Mehmood & Muhammad Saeed Uz Zaman & Muhammad Irfan & Saad Ullah Khan & Chul-Hwan Kim, 2018. "Optimal Scheduling of Hybrid Energy Resources for a Smart Home," Energies, MDPI, vol. 11(11), pages 1-19, November.
    2. Marian Kampik & Marcin Fice & Adam Pilśniak & Krzysztof Bodzek & Anna Piaskowy, 2023. "An Analysis of Energy Consumption in Small- and Medium-Sized Buildings," Energies, MDPI, vol. 16(3), pages 1-21, February.
    3. Ana Fernández-Guillamón & Antonio Vigueras-Rodríguez & Emilio Gómez-Lázaro & Ángel Molina-García, 2018. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    4. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, vol. 11(10), pages 1-19, September.
    5. Muhammad Saeed Uz Zaman & Muhammad Irfan & Muhammad Ahmad & Manuel Mazzara & Chul-Hwan Kim, 2020. "Modeling the Impact of Modified Inertia Coefficient (H) due to ESS in Power System Frequency Response Analysis," Energies, MDPI, vol. 13(4), pages 1-18, February.
    6. Min-Sung Kim & Raza Haider & Gyu-Jung Cho & Chul-Hwan Kim & Chung-Yuen Won & Jong-Seo Chai, 2019. "Comprehensive Review of Islanding Detection Methods for Distributed Generation Systems," Energies, MDPI, vol. 12(5), pages 1-21, March.

    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:15:y:2022:i:24:p:9377-:d:1000161. 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.