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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

    as
    1. Zhang, Xiangyu & Pipattanasomporn, Manisa & Rahman, Saifur, 2017. "A self-learning algorithm for coordinated control of rooftop units in small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 205(C), pages 1034-1049.
    2. Wang, Yi & Gan, Dahua & Sun, Mingyang & Zhang, Ning & Lu, Zongxiang & Kang, Chongqing, 2019. "Probabilistic individual load forecasting using pinball loss guided LSTM," Applied Energy, Elsevier, vol. 235(C), pages 10-20.
    3. Xie, Dunjian & Hui, Hongxun & Ding, Yi & Lin, Zhenzhi, 2018. "Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals," Applied Energy, Elsevier, vol. 216(C), pages 338-347.
    4. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    5. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    6. Behrangrad, Mahdi, 2015. "A review of demand side management business models in the electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 270-283.
    7. Ding, Yi & Cui, Wenqi & Zhang, Shujun & Hui, Hongxun & Qiu, Yiwei & Song, Yonghua, 2019. "Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation," Applied Energy, Elsevier, vol. 241(C), pages 46-58.
    8. Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
    9. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    10. Hui, Hongxun & Ding, Yi & Liu, Weidong & Lin, You & Song, Yonghua, 2017. "Operating reserve evaluation of aggregated air conditioners," Applied Energy, Elsevier, vol. 196(C), pages 218-228.
    11. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
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