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Dynamic control strategy of residential air conditionings considering environmental and behavioral uncertainties

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  • Wang, Jixiang
  • Chen, Xingying
  • Xie, Jun
  • Xu, Shuyang
  • Yu, Kun
  • Gan, Lei

Abstract

The residential air conditionings (RACs) are widely considered as one of the most important demand response (DR) resources due to the thermal storage characteristics. However, due to the uncertainties of the outdoor environment and the customers’ behaviors, the RACs’ operation states and power consumption are difficult to predicate. Facing this issue, this paper proposes a dynamic control strategy for the RACs to participate in DR program considering these uncertainties. Firstly, a single dynamic RAC model considering the uncertain environment and customer behaviors is developed. On this basis, a dynamic aggregate model of RACs is established with different number of RACs. Then, the dynamic aggregate model is identified by actual operation data. A dynamic rolling control strategy-based temperature set-points for large-scale RACs to participate in DR program is formulated. Moreover, the DR provided by RACs is divided into three levels according to the power reduction, where the corresponding control strategies at each level are proposed. Finally, the proposed models and methods are verified by employing the actual data of the urban residential communities in Changzhou City, China. The simulation results show that the proposed control strategy is accurate and effective.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1312-1320
    DOI: 10.1016/j.apenergy.2019.04.184
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    References listed on IDEAS

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    1. Rieger, Alexander & Thummert, Robert & Fridgen, Gilbert & Kahlen, Micha & Ketter, Wolfgang, 2016. "Estimating the benefits of cooperation in a residential microgrid: A data-driven approach," Applied Energy, Elsevier, vol. 180(C), pages 130-141.
    2. 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.
    3. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    4. Chao, Hung-po, 2010. "Price-Responsive Demand Management for a Smart Grid World," The Electricity Journal, Elsevier, vol. 23(1), pages 7-20, January.
    5. Montuori, Lina & Alcázar-Ortega, Manuel & Álvarez-Bel, Carlos & Domijan, Alex, 2014. "Integration of renewable energy in microgrids coordinated with demand response resources: Economic evaluation of a biomass gasification plant by Homer Simulator," Applied Energy, Elsevier, vol. 132(C), pages 15-22.
    6. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    7. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    8. 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.
    9. Liu, Chao & Akintayo, Adedotun & Jiang, Zhanhong & Henze, Gregor P. & Sarkar, Soumik, 2018. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network," Applied Energy, Elsevier, vol. 211(C), pages 1106-1122.
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    Cited by:

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    3. Jeon, Yongseok & Kim, Sunjae & Lee, Sang Hun & Chung, Hyun Joon & Kim, Yongchan, 2020. "Seasonal energy performance characteristics of novel ejector-expansion air conditioners with low-GWP refrigerants," Applied Energy, Elsevier, vol. 278(C).
    4. Yang, Hongxing & Shi, Wenchao & Chen, Yi & Min, Yunran, 2021. "Research development of indirect evaporative cooling technology: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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