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Modeling and Control of Air Conditioning Loads for Consuming Distributed Energy Sources

Author

Listed:
  • Dongsheng Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Xinyi Zhang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

This paper aims to tap the potential of air conditioning loads (ACLs) for consuming photovoltaic power (PV) and wind power (WP). By fully considering different thermal comfort of different users, an ACL twice-clustered model based on different ACL parameters and users tolerance values (UTV) is built. Then, a two-stage ACL control method based on both temperature control (TC) and switch control (SC) is proposed, which achieves rapid control of ACLs as well as diminishing users’ discomfort. Widely existent communication time delay in ACL control network causes obvious control error, which leads to ACL consumption deviation from the target. Therefore, on the basis of analyzing errors and impacts of ACLs caused by communication time delay, this paper proposes a time delay compensation method based on a network predictive control system. Applying the ACLs clustered model and the control method into consuming PV and WP, a dual-stage consumption model considering communication time delay is established. Simulations of the PV and WP consumption effects based on ACLs clusters are conducted, and the influence of SC cycle and outdoor temperature on the simulation results are analyzed. The simulation results demonstrate the validity of the model and the methods proposed in this paper, showing a strong adaptability in different circumstances.

Suggested Citation

  • Dongsheng Yang & Xinyi Zhang & Bowen Zhou, 2017. "Modeling and Control of Air Conditioning Loads for Consuming Distributed Energy Sources," Energies, MDPI, vol. 10(10), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1630-:d:115234
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    Citations

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    Cited by:

    1. Zhang, Lidong & Li, Jiao & Xu, Xiandong & Liu, Fengrui & Guo, Yuanjun & Yang, Zhile & Hu, Tianyu, 2023. "High spatial granularity residential heating load forecast based on Dendrite net model," Energy, Elsevier, vol. 269(C).
    2. da Fonseca, André L.A. & Chvatal, Karin M.S. & Fernandes, Ricardo A.S., 2021. "Thermal comfort maintenance in demand response programs: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).

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