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Energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning systems

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  • Zhang, Zi-Yang
  • Zhang, Chun-Lu
  • Xiao, Fu

Abstract

Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller.

Suggested Citation

  • Zhang, Zi-Yang & Zhang, Chun-Lu & Xiao, Fu, 2020. "Energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning systems," Applied Energy, Elsevier, vol. 279(C).
  • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s030626192031223x
    DOI: 10.1016/j.apenergy.2020.115732
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    References listed on IDEAS

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    1. Insung Kang & Kwang Ho Lee & Je Hyeon Lee & Jin Woo Moon, 2018. "Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season," Energies, MDPI, vol. 11(7), pages 1-15, June.
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    4. Chen, Yi & Yan, Huaxia & Luo, Yimo & Yang, Hongxing, 2019. "A proportional–integral (PI) law based variable speed technology for temperature control in indirect evaporative cooling system," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    5. Yan, Huaxia & Xia, Yudong & Deng, Shiming, 2017. "Simulation study on a three-evaporator air conditioning system for simultaneous indoor air temperature and humidity control," Applied Energy, Elsevier, vol. 207(C), pages 294-304.
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