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Probabilistic optimal design concerning uncertainties and on-site adaptive commissioning of air-conditioning water pump systems in buildings

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  • Li, Hangxin
  • Wang, Shengwei

Abstract

Sizing of air-conditioning water pump systems in buildings is a critical issue in design practice concerning the pump energy consumption in operation and risk of being undersized. As a result, significant energy is often wasted in operation due to oversizing to avoid the risk of being undersized. In current practice, throttling of commissioning valves are commonly adopted to push water flowrate (and pressure head) back to the design point no matter how much oversizing exists in a system. That partly mitigates the oversizing problem. This paper presents a novel approach consisting of probabilistic optimal design concerning uncertainties and on-site adaptive commissioning to further maximize energy savings of constant water flow pump systems. Minimized throttling is achieved by on-site adaptive commissioning, which reduces unnecessary pressure head and significant energy consumption. Pumps selected by the probabilistic optimal design can operate under both conventional design conditions and the projected possible off-design (oversized) conditions. The projection is based on the probability distribution of actual pressure head, which is estimated using Monte Carlo simulation by quantifying uncertainties in pressure loss calculation and system construction. Three case studies are conducted to test and validate this new design and commissioning approach. Results show that about 20% energy saving could be achieved, when the system is oversized by 20%, compared to conventional design and commissioning methods. The proposed approach also offers better energy performance in general compared to the designs all using variable speed pumps.

Suggested Citation

  • Li, Hangxin & Wang, Shengwei, 2017. "Probabilistic optimal design concerning uncertainties and on-site adaptive commissioning of air-conditioning water pump systems in buildings," Applied Energy, Elsevier, vol. 202(C), pages 53-65.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:53-65
    DOI: 10.1016/j.apenergy.2017.05.131
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    References listed on IDEAS

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    2. Jia, Zhiyang & Jin, Xinqiao & Lyu, Yuan & Xue, Qi & Du, Zhimin, 2023. "A robust capacity configuration selection method of multiple-chiller system concerned with the uncertainty of annual hourly load profile," Energy, Elsevier, vol. 282(C).
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    4. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang & Lan, Bo, 2019. "A robust optimization model for designing the building cooling source under cooling load uncertainty," Applied Energy, Elsevier, vol. 241(C), pages 390-403.
    5. Cheung, Howard & Wang, Shengwei, 2019. "Optimal design of data center cooling systems concerning multi-chiller system configuration and component selection for energy-efficient operation and maximized free-cooling," Renewable Energy, Elsevier, vol. 143(C), pages 1717-1731.

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