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Experimental study on the dynamics, quality and impacts of using variable-speed pumps in buildings for frequency regulation of smart power grids

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  • Wang, Huilong
  • Wang, Shengwei
  • Shan, Kui

Abstract

The increased generation of renewable power is challenging in terms of the power balance and reliability of power grids due to the intermittent nature of renewable sources. Heating, ventilation, and air-conditioning (HVAC) systems in buildings, at demand side, are promising candidates for providing frequency regulation. In this study, we systematically assessed the use of variable-speed pumps in HVAC systems for frequency regulation. The dynamic characteristics of the pump were investigated. The results show that the power of the pump can response to the frequency change rapidly (returning stable within 1s), while the response of flow rate is slightly slower. A frequency regulation control strategy is proposed and implemented on a test rig. From the viewpoint of power grids, the experimental results show that the pump can provide high-quality frequency regulation (performance scores of 0.989 and 0.968). From the viewpoint of buildings, the experimental results indicate that the fluctuation magnitude of the air-handling unit outlet air temperature increases (from 3.3 K to 7.1 K) with increasing automatic generation control (AGC) signal frequency, whereas the fluctuation magnitude of the indoor air temperature increases to some extent and then decreases when the frequency of the AGC signal is above a certain level.

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  • Wang, Huilong & Wang, Shengwei & Shan, Kui, 2020. "Experimental study on the dynamics, quality and impacts of using variable-speed pumps in buildings for frequency regulation of smart power grids," Energy, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:energy:v:199:y:2020:i:c:s0360544220305132
    DOI: 10.1016/j.energy.2020.117406
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    References listed on IDEAS

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    1. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    2. Wang, Huilong & Wang, Shengwei & Tang, Rui, 2019. "Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses," Applied Energy, Elsevier, vol. 250(C), pages 697-712.
    3. Li, Weilin & Xu, Peng & Lu, Xing & Wang, Huilong & Pang, Zhihong, 2016. "Electricity demand response in China: Status, feasible market schemes and pilots," Energy, Elsevier, vol. 114(C), pages 981-994.
    4. Taibi, Emanuele & Fernández del Valle, Carlos & Howells, Mark, 2018. "Strategies for solar and wind integration by leveraging flexibility from electric vehicles: The Barbados case study," Energy, Elsevier, vol. 164(C), pages 65-78.
    5. Wang, Huilong & Xu, Peng & Lu, Xing & Yuan, Dengkuo, 2016. "Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels," Applied Energy, Elsevier, vol. 169(C), pages 14-27.
    6. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    7. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    8. 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.
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    Cited by:

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    5. Lankeshwara, Gayan & Sharma, Rahul & Yan, Ruifeng & Saha, Tapan K., 2022. "A hierarchical control scheme for residential air-conditioning loads to provide real-time market services under uncertainties," Energy, Elsevier, vol. 250(C).
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    8. Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    9. Wang, Huilong & Wang, Shengwei, 2021. "A disturbance compensation enhanced control strategy of HVAC systems for improved building indoor environment control when providing power grid frequency regulation," Renewable Energy, Elsevier, vol. 169(C), pages 1330-1342.
    10. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
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