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Open AccessArticle

Life-Cycle Optimization of a Chiller Plant with Quantified Analysis of Uncertainty and Reliability in Commercial Buildings

by Chengchu Yan 1,*, Qi Cheng 2,* and Hao Cai 1
1
College of Urban Construction, Nanjing Tech University, Nanjing 210009, China
2
Shenzhen Secom Technology Co., Ltd., Shenzhen 518055, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(8), 1548; https://doi.org/10.3390/app9081548
Received: 5 March 2019 / Revised: 11 April 2019 / Accepted: 11 April 2019 / Published: 14 April 2019
Conventional and most optimal design methods for chiller plants often address the annual cooling load distribution of buildings and their peak cooling loads based on typical meteorological year (TMY) data, while the peak cooling load only appears a few times during the life-cycle and the sized chiller plant usually operates within its low efficient region. In this paper, a robust optimal design method based on life-cycle total cost was employed to optimize the design of a chiller plant with quantified analysis of uncertainty and reliability. By using the proposed design method, the optimized chiller plant can operate at its highly efficient region under various cooling load conditions, and provide sufficient cooling capacity even alongside some equipment/systems with failures. The minimum life-cycle total cost, which consists of the capital cost, operation, and availability-risk cost, can be achieved through optimizing the total cooling capacity and the numbers/sizes of chillers. A case study was conducted to illustrate the detailed implementation process of the proposed method. The performance of this design method was evaluated by comparing with that of other design methods. View Full-Text
Keywords: life-cycle; building energy efficiency; optimal design; uncertainty; reliability; chiller plant life-cycle; building energy efficiency; optimal design; uncertainty; reliability; chiller plant
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MDPI and ACS Style

Yan, C.; Cheng, Q.; Cai, H. Life-Cycle Optimization of a Chiller Plant with Quantified Analysis of Uncertainty and Reliability in Commercial Buildings. Appl. Sci. 2019, 9, 1548.

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