Performance improvement of a large chilled-water plant by using simple heat rejection control strategies

Abstract Energy efficient cooling systems are urgently required in order to face the increasing cooling demand of the building and industry sectors and comply with the ambitious EU target of greenhouse gas emission reductions. The present work aims to show that simple heat rejection control strategies can lead to significant energy savings. Different approaches of sequencing the operation of several cooling towers and a method to adapt the cooling water temperature set-point to the actual ambient conditions are proposed. These strategies are first investigated theoretically for an existing chilled-water plant of 13.7 MW installed cooling capacity. Up to 20% energy savings are theoretically possible depending on the used control strategy and the weather conditions. A variable cooling water set-point as a function of the wet bulb temperature allows for the most significant savings. After implementation of this measure in the existing cooling plant, 2 years monitoring results show that the overall system efficiency of the chilled water plant could be increased by 15% compared to the initial status confirming the results of the theoretical study.

[1]  J. Mitchell,et al.  Optimal control development for chilled water plants using a quadratic representation , 2001 .

[2]  Sanford Klein,et al.  Methodologies for optimal control of chilled water systems without storage , 1989 .

[3]  Ursula Eicker,et al.  Heat rejection and primary energy efficiency of solar driven absorption cooling systems , 2012 .

[4]  Ursula Eicker,et al.  Approaches for the optimized control of solar thermally driven cooling systems , 2017 .

[5]  Zhenjun Ma,et al.  Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm , 2011 .

[6]  Dietrich Schneider,et al.  Control Optimization of Solar Thermally Driven Chillers , 2016 .

[7]  Bo Fan,et al.  Optimal control strategies for multi-chiller system based on probability density distribution of coo , 2011 .

[8]  Douglas T. Reindl,et al.  Evaporative condenser control in industrial refrigeration systems , 2001 .

[9]  H. Kretzschmar,et al.  The IAPWS Industrial Formulation 1997 for the Thermodynamic Properties of Water and Steam , 2000 .

[10]  Zhaohui Liu,et al.  Optimal chiller sequencing control in an office building considering the variation Of chiller maximum cooling capacity , 2017 .

[11]  Sen Huang,et al.  Improved cooling tower control of legacy chiller plants by optimizing the condenser water set point , 2017 .

[12]  J. E. Braun,et al.  Near-optimal control of cooling towers for chilled-water systems , 1990 .

[13]  Yaoyu Li,et al.  Real-time optimization of a chilled water plant with parallel chillers based on extremum seeking control , 2017 .

[14]  Xing Fang,et al.  Evaluation of the design of chilled water system based on the optimal operation performance of equipments , 2017 .

[15]  Yongjun Sun,et al.  Robustness analysis of chiller sequencing control , 2015 .