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A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications

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  • Ran, Fengming
  • Gao, Dian-ce
  • Zhang, Xu
  • Chen, Shuyue

Abstract

The power grid is facing the critical issue concerning the power imbalance. To address the issue, demand response programs are increasingly deployed to encourage the end-users to change their load profiles. For buildings, the existing fast demand response strategy has been demonstrated effective in performing quick response to the grid request by reducing the power demand. However, the existing fast demand response methods require a physical flowmeter to be installed in each air handling unit. While in most of the existing commercial buildings, flowmeters are rarely installed in individual air handling units due to the high initial cost. As a result, the existing fast demand response method may not be applicable in these commercial buildings. Thus, this paper presents a virtual sensor based self-adjusting control strategy for fast demand response of building heating, ventilation and air-conditioning system. A virtual flowmeter model is first developed to estimate the water flow rate in each air handling unit. Based on the virtual flowmeter model, a self-adjusting water flow supervisor, in which the online self-adjusting method is integrated to reduce efforts in parameter identification, is then developed to achieve a balanced water flow distribution among different air handling units. The performances of the proposed control strategy have been tested and evaluated on a simulated system. The results show that the virtual flowmeter model has good accuracy for estimating the water flow rate in AHUs. The proposed control strategy can achieve significant and quick power reduction and meanwhile address the hydraulic imbalance problem.

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  • Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920306152
    DOI: 10.1016/j.apenergy.2020.115103
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    9. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
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