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Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error

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  • Hui, Hongxun
  • Ding, Yi
  • Song, Yonghua
  • Rahman, Saifur

Abstract

Demand response has been widely utilized to provide frequency regulation service for the power systems by adjusting the power consumption of flexible loads. The frequency regulation service is time-sensitive and generally realized by direct load control, due to the quick response requirement (generally a few seconds). Most of the existing studies assume that the control on flexible loads can be implemented immediately without communication latency (CML), and the system frequency deviations can be detected without errors (FDE). However, in reality, the CML and FDE are ever-present during the control process and can influence the effectiveness of regulation significantly. To address this issue, this paper develops the aggregation models of ON-OFF flexible loads and continuously adjustable flexible loads, respectively. The centralized and distributed control methods considering the CML and FDE are developed, respectively. On this basis, a novel hybrid control method is proposed to compensate the CML and FDE, in which the modification method is developed for improving the estimation accuracy of the FDE. The results in the numerical studies show that the maximum system frequency deviation extends from −0.112 Hz to −0.120 Hz and −0.221 Hz due to the FDE and CML, respectively. After the modification by the proposed hybrid control method, the maximum frequency deviation is decreased to −0.110 Hz, which is almost equal to the ideal value when there is no FDE and CML. Therefore, this research can compensate the CML and FDE well, which is useful for guiding demand response projects in smart grid.

Suggested Citation

  • Hui, Hongxun & Ding, Yi & Song, Yonghua & Rahman, Saifur, 2019. "Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error," Applied Energy, Elsevier, vol. 250(C), pages 161-174.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:161-174
    DOI: 10.1016/j.apenergy.2019.04.191
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    References listed on IDEAS

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    10. Deepak Kumar Gupta & Amitkumar V. Jha & Bhargav Appasani & Avireni Srinivasulu & Nicu Bizon & Phatiphat Thounthong, 2021. "Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems," Energies, MDPI, vol. 14(6), pages 1-16, March.
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    13. Hui, Hongxun & Ding, Yi & Shi, Qingxin & Li, Fangxing & Song, Yonghua & Yan, Jinyue, 2020. "5G network-based Internet of Things for demand response in smart grid: A survey on application potential," Applied Energy, Elsevier, vol. 257(C).
    14. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    15. Huang, Zhijia & Wang, Fang & Lu, Yuehong & Chen, Xiaofeng & Wu, Qiqi, 2023. "Optimization model for home energy management system of rural dwellings," Energy, Elsevier, vol. 283(C).
    16. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    17. Yang, Shaohua & Lao, Keng-Weng & Hui, Hongxun & Chen, Yulin, 2023. "A robustness-enhanced frequency regulation scheme for power system against multiple cyber and physical emergency events," Applied Energy, Elsevier, vol. 350(C).
    18. Xiaohong Dong & Yang Ma & Xiaodan Yu & Xiangyu Wei & Yanqi Ren & Xin Zhang, 2023. "Secondary Frequency Regulation Control Strategy with Electric Vehicles Considering User Travel Uncertainty," Energies, MDPI, vol. 16(9), pages 1-18, April.
    19. Oshnoei, Arman & Kheradmandi, Morteza & Blaabjerg, Frede & Hatziargyriou, Nikos D. & Muyeen, S.M. & Anvari-Moghaddam, Amjad, 2022. "Coordinated control scheme for provision of frequency regulation service by virtual power plants," Applied Energy, Elsevier, vol. 325(C).
    20. 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).
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    22. Mohamed El-Hendawi & Zhanle Wang & Xiaoyue Liu, 2022. "Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation," Energies, MDPI, vol. 15(12), pages 1-22, June.

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