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Operating reserve evaluation of aggregated air conditioners

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  • Hui, Hongxun
  • Ding, Yi
  • Liu, Weidong
  • Lin, You
  • Song, Yonghua

Abstract

The penetration of renewable energy sources (RES) in power system is increasing around the world. However, the severe intermittency and variability characteristics of RES make the operating reserve become more and more important for the electric power system to maintain balance between supply and demand. Moreover, the flexible loads, especially for air conditioners (AC), are growing so rapidly that they account for an increasingly large share in power consumption. With the development of information and communication technologies (ICT), ACs can be monitored and controlled remotely to provide operating reserve and respond actively when needed by the electric power system operation. In this paper, a novel control strategy for the aggregation model of ACs based on the thermal model of the room is proposed. By resetting the temperature of each AC, the operation state is adjusted temporarily without affecting customers’ satisfaction. The operation characteristics of both individual AC and the aggregation model of ACs are analysed. Furthermore, several indexes are put forward to evaluate the operating reserve performance, including reserve capacity, response time, duration time and ramp rate. The effectiveness of the proposed control strategy is illustrated in the numerical studies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:196:y:2017:i:c:p:218-228
    DOI: 10.1016/j.apenergy.2016.12.004
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    14. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    15. Wang, Jixiang & Chen, Xingying & Xie, Jun & Xu, Shuyang & Yu, Kun & Gan, Lei, 2019. "Dynamic control strategy of residential air conditionings considering environmental and behavioral uncertainties," Applied Energy, Elsevier, vol. 250(C), pages 1312-1320.
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    17. 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).
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    19. 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.
    20. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    21. 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.
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