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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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    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.

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